Service Management Integration With Agile Practices

Understand how Agile transformation affects service management

Analyst Perspective

Don't forget about operations

Many organizations believe that once they have implemented Agile that they no longer need any service management framework, like ITIL. They see service management as "old" and a roadblock to deliver products and services quickly. The culture clash is obvious, and it is the most common challenge people face when trying to integrate Agile and service management. However, it is not the only challenge. Agile methodologies are focused on optimized delivery. However, what happens after delivery is often overlooked. Operations may not receive proper communication or documentation, and processes are cumbersome or non-existent. This is a huge paradox if an organization is trying to become nimbler. You need to find ways to integrate your Agile practices with your existing Service Management processes.

Renata Lopes
Senior Research Analyst
Organizational Transformation Practice
Info-Tech Research Group

Executive Summary

Your Challenge

• Work efficiently and in harmony with Agile and service management to deliver business value.
• Optimize the value stream of services and products.
• Leverage the benefits of each practice.
• Create a culture of collaboration to support a rapidly changing business.

Common Obstacles

• Culture clashes.
• Inefficient or inexistent processes.
• Lack of understanding of what Agile and service management mean.
• Leadership doesn't understand the integration points of practices.
• Development overlooks the operations requirement.

Info-Tech's Approach

• When integrating Agile and service management practices start by understanding the key integration points:
• Processes
• People and resources
• Governance and org structure

Info-Tech Insight

Agile and Service Management are not necessarily at odds Find the integration points to solve specific problems.

Your challenge

Deliver seamless business value by integrating service management and Agile development.

• Understand how Agile development impacts service management.
• Identify bottlenecks and inefficiencies when integrating with service management.
• Connect teams across the organization to collaborate toward the organizational goals.
• Ensure operational requirements are considered while developing products in an Agile way.
• Stay in alignment when designing and delivering services.

The most significant Agile adoption barriers

46% of respondents identified inconsistent processes and practices across teams as a challenge.
Source: Digital.ai, 2021

43% of respondents identified Culture clashes as a challenge.
Source: Digital.ai, 2021

What is Agile?

Agile development is an umbrella term for several iterative and incremental development methodologies to develop products.

In order to achieve Agile development, organizations will adopt frameworks and methodologies like Scaled Agile Framework (SAFe), Scrum, Large Scaled Scrum (LeSS), DevOps, Spotify Way of Working (WoW), etc.

• DevOps
• WoW
• SAFe
• Scrum
• LeSS

INFO~TECH RESEARCH GROUP

Build an IT Succession Plan

Future-proof your IT team.

Build an IT Succession Plan

Future-proof your IT team.

EXECUTIVE BRIEF

Executive Summary

Info-Tech Insight

Losing employees in key roles without adequate preparation hinders productivity, knowledge retention, relationships, and opportunities. Implement scalable succession planning to mitigate the risks.

Most organizations are unprepared for the loss of employees who hold key roles

Due to the atmosphere of uncertainty.

Not only do they not have the right processes in place, but they are also ill-equipped to deal with the sheer volume of retirees in the future.

Most organizations underestimate the costs associated with ignoring succession planning

Use Info-Tech’s tools and templates

Ignoring succession planning could cause significant costs

Losing knowledge will undermine your strategy in four ways:

Succession planning improves performance by reducing the impact of sudden departures

Info-Tech’s approach

Talent Review Conduct a talent review to identify key roles		Succession Planning Succession planning helps you assess which key roles are most at risk			Knowledge Transfer Utilize methods that make it easy to apply the knowledge in day-to-day practice.
Identify Critical Roles	Assess Talent	Identify Successors	Develop Successors	Select Successors	Identify Critical Knowledge	Select Transfer Methods	Document Role Transition Plans

Info-Tech’s methodology for building an IT succession plan

Info-Tech offers various levels of support to best suit your needs

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is six to ten calls over the course of four to eight months.

What does a typical GI on this topic look like?

Build an IT Succession Plan

Phase 1

Talent Review

This phase will walk you through:

• Identifying your business priorities
• Identifying your critical roles including required skills and knowledge that support achievement of business strategy

Tools and resources used:

• Key Roles Succession Planning Tool
• Key Role Profile
• Individual Talent Profile
• Critical Role Identifier

This phase involves the following participants:

• IT leadership/management team
• HR

Conduct a talent review to identify key roles

1. A talent review ensures that each work unit has the right people, in the right place, at the right time to successfully execute the business strategy.
2. Only 40% of organizations have engaged in some form of workforce planning.
3. The first step is to identify your business focus; with this information you can start to note the key roles that drive your business strategy.

Key roles

Where an organization’s most valued skills and knowledge reside

Organizations should prepare now to mitigate the risk of loss later. Key roles are: Held by the most senior people in the organization, who carry the bulk of leadership and decision-making responsibility. Highly technical or specialized, and therefore difficult to replace. Tied closely to unique or proprietary processes or possess knowledge that cannot be procured externally. Critical to the continuation of business and cannot be left vacant without risking business operations. Info-Tech Insight Losing employees in key roles without adequate preparation for their departure has a direct impact on the bottom line in terms of disrupted productivity, lost knowledge, severed relationships, and missed opportunities.

Identifying key roles is the first step in a range of workforce management activities because it helps establish organizational needs and priorities, as well as focusing planning effort.

Step 1.1

Identify critical roles

Activities

• 1.1.1 Document Business Priorities, Goals, and Challenges
• 1.1.2 Clarify Key Role Criteria and Weighting
• 1.1.3 Evaluate Role Importance
• 1.1.4 Key Role Selection and Comparison
• 1.1.5 Capture Key Elements of Critical Roles

The primary goal of this step is to ensure we have effectively identified key roles based on business priorities, goals, and challenges, and to capture the key elements of critical roles.

Outcomes of this step

• Documented business priorities, goals, and challenges.
• Key elements of critical roles captured.
• Key role criteria and weighting.

Business priorities will determine the knowledge and skills you value most

Note: Most organizations will be a blend of all three, with one predominating

“I’ve been in the position where the business assumes everyone knows what is required. It’s not until you get people into a room that it becomes clear there is misalignment. It all seems very intuitive but in a lot of cases they haven’t made the critical distinctions regarding what exactly the competencies are. They haven’t spent the time figuring out what they know.” (Anne Roberts, Principal, Leadership Within Inc.)

1.1.1 Document business priorities

Input: Business strategic plan

Output: Completed workforce planning worksheet (Tab 2) of the Key Roles Succession Planning Tool

Materials: Key Roles Succession Planning Tool

Participants: IT leadership

Start by identifying your business priorities based on your strategic plan. The goal of this exercise is to blast away assumptions and make sure leadership has a common understanding of your target.

With the questions on the previous slide in mind document your business priorities, business goals, and business challenges in Tab 2 of the Key Roles Succession Planning Tool worksheet.

Get clear answers to these questions:

• Are we customer focused, product focused, or operationally focused? In other words, is your organization known for:
  • Great customer service or a great customer experience?
  • The lowest price?
  • Having the latest technology, or the best quality product?
• What are our organizational/departmental business goals? To improve operational effectiveness, are we really talking about reducing operational costs?
• What are the key business challenges to address within the context of our focus?

Key Roles Succession Planning Tool

Clarify what defines a key role

A key role is crucial to achieving organizational objectives, drives business performance, and includes specialized and rare competencies. Key roles are high in strategic value and rarity – for example, the developer role for a tech company. Look at two dimensions when examining roles: Strategic value refers to the importance of the role in keeping the organization functioning and executing on the strategic objectives. Rarity refers to how difficult it is to find and develop the competencies in the role.

Info-tech insight Traditionally, succession planning has only addressed top management roles. However, until you look at the evidence, you won’t know if these are indeed high-value roles, and you may be missing other critical roles further down the hierarchy. Use the Critical Role Identifier to facilitate the identification of critical roles with your leaders.

1.1.2 Clarify key role criteria & weighting

Input: Business strategic plan

Output: Weighted criteria to help identify critical roles

Materials: Critical Role Identifier

Participants: IT leadership

1. Using Tab 2 of the Critical Role Identifier tool, along with the information on the previous slide, determine the relative importance of four criteria as contributing to the importance of a role within the organization.
2. Rate each of the four criteria: strategic value, rarity, revenue generation, business/operation continuity, and any custom criteria numerically. You might choose only one or two criteria – they all do not need to be included.
3. Document your decisions in Tab 2 of the Critical Role Identifier.

Critical Role Identifier

1.1.3 Evaluate role importance

Input: List of IT roles

Output: Full list of roles and a populated Critical Role Selection sheet (Tab 4)

Materials: Critical Role Identifier

Participants: IT leadership

1. Using Tab 3 of the Critical Role Identifier, collect information about IT roles.
2. Start by listing each role under consideration, and its department or subcategory.
3. For each criteria statement listed across the top of the sheet, select an option from the drop-down menu to reflect the appropriate answer scale rating. Replace the text in grey with information customized to your team. If criteria has a weighting of zero in Tab 2, the questions associated with that criteria will be greyed out and do not have to be answered.

Critical Role Identifier

Identify the key roles that support and drive your business priorities

1.1.4 Key role selection and comparison

Input: Tab 3 of the Critical Role Identifier

Output: List of roles from highest to lowest criticality score, List of key roles entered in Tab 2 of the Key Roles Succession Planning Tool

Materials: Critical Role Identifier, Key Roles Succession Planning Tool

Participants: IT leadership

1. Using tab 4 of the Critical Role Identifier, which displays the results of the role importance evaluation, review the weighted criticality score. To add or remove roles or departments make changes on Tab 3.
2. Use this table to see the scores and roles from highest to lowest based on your weightings and scoring.
3. In column J, classify the roles as critical, core, or supporting based on the weighted overall score and the individual criteria scores.
   1. Critical – is crucial to achieving organizational objectives, drives business performance, and includes specialized and rare skills.
   2. Core – is related to operational excellence. Highly strategically valuable but easy to find or develop.
   3. Supporting – is important in keeping business functioning; however, the strategic value is low. Competencies are easy to develop.
4. Once you’ve selected the key roles, transfer them into Tab 2 of the Key Roles Succession Planning Tool worksheet where you have documented your business priorities.

Critical Role Identifier

Key Roles Succession Planning Tool

1.1.5 Capture key elements of critical roles

Input: Job descriptions, Success profiles, Competency profiles

Output: List of required skills and knowledge for key roles, Role profiles documented for key roles

Materials: Key Roles Succession Planning Tool, Role Profile Template

Participants: IT leadership

1. Document the minimum requirements for critical roles in column E and F of Tab 2 of the Key Roles Succession Planning Tool. Include elements that drive talent decisions, are measurable, and are oriented to future organizational needs.
2. Consider how leadership competencies and technical skills tie to business expansion plans, new service offerings, etc.
3. Use the Role Profile Template to help in this process and to maintain up-to-date information.
4. Role profiles may be informed by existing job descriptions, success profiles, or competency profiles.
5. Conduct regular maintenance on your role profiles. Outdated and inaccurate role-related information can make succession planning efforts ineffective.

Key Roles Succession Planning Tool

Role Profile Template

Step 1.2

Assess talent

Activities

• 1.2.1 Identify Current Incumbents’ Information
• 1.2.2 Identify Potential Successors and Collect Information

The primary goal of this step is to assess departmental talent and identify gaps between potential successors and key roles. This analysis is intended to support departmental access to suitable talent ensuring future business success.

Outcomes of this step

• Collection of current incumbents’ information.
• Collection of potential successor information.
• Gap assessment.

Talent Review

Potential questions to ask during career discussions with key role incumbents

Where do you see yourself in five years? What role would you see yourself in after this one? What gets you excited about coming to work? Describe your greatest strengths. How would you like to use those strengths in the future? What is standing in the way of your career goals? ** Do not ask employees directly about their retirement plans as this can be misconstrued as age discrimination – let them take the initiative.**

1.2.1 Identify current incumbents' information

Input: Key roles list, Employee information

Output: List of key roles with individual incumbent information

Materials: Key Roles Succession Planning Tool – Succession Plan Worksheet (Tab 3)

Participants: IT leadership/management team, HR, Current incumbents if necessary

Identify current incumbents for all key roles and collect information about them.

Using Tab 3 of the Key Roles Succession Planning Tool identify the incumbent (the person currently in the role) for all key roles.

Distribute the worksheet to department managers and team leaders to complete the information below for each key role.

For that incumbent, also document:

1. Their time in that role.
2. Their overall performance in current role (does not meet, meets, or exceeds expectations).
3. Next step in career (target role or retirement).
4. Time until exit from the current role (known or estimated).
5. Development needs for next step in career.
6. Any additional knowledge and skills they possess beyond the role description that is of value to the organization.

Upon completion, managers and team leaders should review the results with the department leader.

Key Roles Succession Planning Tool

Identify potential successors for all key roles

Determine how employees will be identified for talent assessment

Prepare to customize the Individual Talent Profile Template

Ensure the role profile and individual talent profile are synchronized to enable comparing employee qualifications and readiness to critical role requirements.

Role Profile A role profile contains information on the skills, competencies, and other minimum requirements for the critical role. It details the type of incumbent that would fit a critical role.

Use both in conjunction during: Talent assessment Successor identification Successor development Successor selection

Individual Talent Profile A talent profile provides information about a person. In addition to responding to role profile criteria, it provides information on an employee’s past experiences and performance, career aspirations, and future potential.

1.2.2 Identify Potential Successors’ Information

Input: Key roles list, Employee information, Completed role profiles and/or Tab 2 role information.

Output: List of potential successors for key roles that are selected for talent assessment

Materials: Key Roles Succession Planning Tool – Succession Plan Worksheet (Tab 3)

Participants: IT leadership, IT team leads, Employees

Identify potential successors for key roles and collect critical information.

Have managers and team leads complete column I on Tab 3 of the Key Roles Succession Planning Tool and review with the department leader.

There may be more than one potential successor for key roles; this is okay.

Once the list is compiled, complete an individual talent profile for each potential successor. Record an employee’s:

1. Employee information
2. Career goals
3. Experience and education
4. Achievements
5. Competencies
6. Performance
7. Any assessment results

Once the profiles are completed, they can be compared to the role profile to identify development needs.

Key Roles Succession Planning Tool

Individual Talent Profile Template

Build an IT Succession Plan

Phase 2

Succession Planning

This phase will walk you through how to:

• Conduct an assessment to identify “at risk” key role incumbents.
• Identify potential successors for key roles and collect critical information.
• Assess gaps between key role incumbents and potential successors.

Tools and resources used:

• Key Roles Succession Planning Tool
• Key Role Profile
• Individual Talent Profile

This phase involves the following participants:

• IT leadership/management team
• HR

Succession planning helps you assess which key roles are most at risk

Step 2.1

Identify Successors

Activities

• 2.1.1 Conduct Individual Risk Assessment
• 2.1.2 Successor Readiness Assessment

This step highlights the relative positioning of all employees assessed for departure risk compared to the potential successors’ readiness, identifying gaps that create risk for the organization, and need mitigation strategies.

Outcomes of this step

• Individual risk assessment results – mitigate, manage, accept matrix.
• Potential successor readiness ranking.
• Determination on transparency level with successors.

Succession Planning

Decide how to obtain information on employee interest in critical roles

2.1.1 Conduct Individual Risk Assessment

Input: Completed Succession Plan worksheet

Output: Risk assessment of key role incumbents, understanding of which key role departures to manage, mitigate, and accept

Materials: Key Roles Succession Planning Tool – Individual Risk Assessment (Tab 4), Key Roles Succession Planning Tool – Risk Assessment Results (Tab 5)

Participants: IT leadership/management team

Assign values for probability of departure and impact of departure using the Key Roles Succession Planning Tool.

For those in key roles and those over 50, complete the Individual Risk Assessment (Tab 4) of the Key Roles Succession Planning Tool:

1. Assess each key role incumbent’s probability of departure based on your knowledge. If the person is going to another job, is a known flight risk, or faces dismissal, the probability is high.
   • 0-40: Unlikely to Leave. If the employee is new to the role, highly engaged, or a high potential.
   • 41-60: Unknown. If the employee is sending mixed messages about happiness at work, or sending no messages, it may be difficult to guess.
   • 61-100: Likely to Leave. If the employee is nearing retirement, actively job searching, disengaged, or faces dismissal, then the probability of departure is high.
2. Assess the role and the individual’s impact of departure on a scale of 1 (no impact) to 100 (devasting impact).
3. Review the risk assessment results on tab 5 of the planning tool. The employees that appear in the mitigate quadrant are your succession planning priorities.

Key Roles Succession Planning Tool

Define readiness criteria for successor identification

1. Select the types of readiness and the number of levels:

   Readiness by time horizon:

   • Successors are identified as ready based on how long it is estimated they will take to acquire the minimum requirements of the critical role.
   • Levels example: Ready Now, Ready in 1-2 Years, Ready in 3-5 Years.

   Readiness by moves:

   • Successors are identified as ready based on how many position moves they have made or how many developmental experiences they have had.
   • Levels example: Ready Now, Ready after 1 Move, Ready after 2 Moves.
2. Create definitions for each readiness level:

   Example:	Performance	Potential
   Ready Now	Definition: Ability to deliver in current role Requirement: Meets or exceeds expectations	Definition: Ability to take on greater responsibility Requirement: Demonstrates learning agility

   The 9-box is an effective way to map performance and potential requirements and can guide management decision making in talent review and calibration sessions. See McLean & Company’s 9-Box Job Aid for more information.
   “Time means nothing. If you say someone will be ready in a year, and you’ve done nothing in that year to develop them, they won’t be ready. We look at it as moves or experiences: ready now, ready in one move, ready in two moves.” (Amanda Mathieson, Senior Manager, Talent Management, Tangerine)

2.1.2 Successor Readiness Assessment

Input: Individual talent profiles, List of potential successors (Tab 3)

Output: Readiness ranking for each potential successor

Materials: Key Roles Succession Planning Tool

Participants: IT leadership/management team

Assign values for probability of departure and impact of departure using the Key Roles Succession Planning Tool.

Using Tab 6 of the Key Roles Succession Planning Tool, evaluate the readiness of each potential successor that you previously identified.

1. Enter the name, current role, and target role of each potential successor into the spreadsheet.
2. For each employee, fill in a response from “strongly agree” to “strongly disagree” for the assessment criteria statements listed in column B of Tab 6. This will give you a readiness ranking in row 68.

Key Roles Succession Planning Tool

Decide if and how successors will be told about their status in the succession plan

Step 2.2

Develop Successors

Activities

• 2.2.1 Outline Successor Development Process

The primary goal of this step is to identify the steps that need to be taken to develop potential successors. Focus on training employees for their future role, not just their current one.

Outcomes of this step

• Identified gaps between key role exits and successor readiness.

Succession Planning

2.2.1 Outline Successor Development Process

Input: Role profiles, Talent profiles, Talent assessments

Output: Identified gaps between key role exits and successor readiness

Materials: Key Roles Succession Planning Tool – Successor Identification (Tab 7)

Participants: IT leadership/management team

Prepare successors for their next role, not just their current one.

Use role and talent profiles and any talent assessment results to identify gaps for development.

1. Outline the steps involved in the individual development planning process for successors. Key steps include identifying development timeline, learning needs, learning resources and strategies, and accomplishment metrics/evidence.
2. Identify learning elements successor development will involve based on critical role type. For example, coaching and/or mentoring, leadership training, functional skills training, or targeted experiences/projects.
3. Select metrics with associated timelines to measure the progress of successor development plans. Establish guidelines for employee and manager accountability in developing prioritized competencies.
4. Determine monitoring cadence of successor development plans (i.e. how often successor development plans will be tracked to ensure timely progress). Identify who will be involved in monitoring the process (e.g. steering committee).

Info-Tech insight

Succession planning without integrated efforts for successor development is simply replacement planning. Get successors ready for promotion by ensuring a continuously monitored and customized development plan is in place.

Integrate knowledge transfer in the successor development process

Step 2.3

Select Successors

The goal of this step is to determine how critical roles will be filled when vacancies arise.

Outcomes of this step

• Agreement with HR on the process to fill vacancies when key roles exit.

Succession Planning

Determine how critical roles will be filled when vacancies arise

Build an IT Succession Plan

Phase 3

Knowledge Transfer

This phase will show you to:

• Identify critical knowledge risks.
• Select appropriate transfer methods.
• Document knowledge transfer initiatives for key role transition plans.

Tools and resources used:

• Role Transition Plan Template

This phase involves the following participants:

• IT leadership/management team
• HR
• Incumbent & successor managers

Mitigate risk – formalize knowledge transfer

Use Info-Tech’s Mitigate Key IT Employee Knowledge Loss blueprint to build and implement your knowledge transfer plan.

Effective knowledge transfer allows organizations to: Maintain or improve speed and productivity by ensuring the right people have the right skills to do their jobs well. Increase agility because knowledge is more evenly distributed amongst employees. Multiple people can perform a given task and no one person becomes a bottleneck. Capture and sustain knowledge; creating a knowledge database provides all employees access to the information, now and in the future.

Knowledge transfer between those in key roles and potential successors yields the highest dividends for: Senior level successions. External hires. Senior expatriate transfers. Developmental stretch assignments. Internal cross-divisional transfers and promotions. High organizational dependency on unique expert knowledge. Critical function/project/team transitions. Large scale reorganizations and mergers & acquisitions. (Source: Piktialis and Greenes, 2008)

Mitigate Key IT Employee Knowledge Loss

Knowledge transfer is complex and must be both multi-faceted and well supported

Knowledge transfer is the capture, organization, and distribution of knowledge held by individuals to ensure that it is accessible and usable by others.

Knowledge transfer is not stopping, learning, and returning to work. Nor is it simply implementing a document management system.

Knowledge transfer is a wide range of methods that must be carefully selected and integrated into daily work in order to meet the needs of the knowledge to be transferred and the people involved.

Step 3.1

Identify Critical Knowledge to Transfer

The goal of this step is to understand what knowledge and skills much be transferred, keeping in mind the various types of knowledge.

Outcomes of this step

• Critical knowledge and skills for key roles documented in the Key Role Transition plans.

Knowledge Transfer

Understand what knowledge and skills must be transferred

There are two basic types of knowledge: Explicit knowledge: Easily explained and codified, e.g. facts and procedures. Tacit knowledge: Accumulates over years of experience and is hard to verbalize. You should already have a good idea of what knowledge and skills are valued from the worksheets completed earlier. Focus on identifying the knowledge, skills, and relationships essential to the specific incumbent in a key role and what it is he or she does to perform that key role well. Document critical knowledge and skills for key roles in the: Role Transition Plan Template

Identify key knowledge areas. These include: Specialized technical knowledge and research and development process. Unique design capabilities/methods/models. Special formulas/algorithms/techniques. Proprietary production processes. Decision-making criteria. Innovative sales methods. Knowledge about key customers. Relationships with key stakeholders. Company history and values. Ask questions of both sources and receivers of knowledge to help determine the best knowledge transfer methods to use. What is the nature of the knowledge? Explicit or tacit? Why is it important to transfer? How will the knowledge be used? What knowledge is critical for success? How will the users find and access it? How will it be maintained and remain relevant and usable? What are the existing knowledge pathways or networks connecting sources to recipients?

Step 3.2

Select Knowledge Transfer Methods

Activities

• 3.2.1 Select Knowledge Transfer Methods

This step helps you identify the knowledge transfer methods that will be the most effective, considering the knowledge or skill that needs to be transferred and the individuals involved.

Outcomes of this step

• Knowledge transfer methods chosen documented in the Key Role Transition Plans.

Knowledge Transfer

Knowledge transfer methods available

Be prepared to use various methods to transfer knowledge and use them all liberally.

3.2.1 Select Knowledge Transfer Methods

Input: Role profiles, Talent profiles

Output: Methods for integrating knowledge transfer into day-to-day practice

Materials: Role Transition Plan Template

Participants: IT leadership/management team, HR, Knowledge source, Knowledge recipient

Utilize methods that make it easy to apply the knowledge in day-to-day practice.

Select your method according to the following criteria:

1. The type of knowledge. A soft skill, like professionalism, is best taught via mentoring, while a technical process is best documented and applied on-the-job.
2. What the knowledge recipient is comfortable with. The recipient may get bored during formal training sessions and retain more during job shadowing.
3. What the knowledge source is comfortable with. The source may be uncomfortable with blogs and wikis, but comfortable with SharePoint.
4. The cost. Some methods require an investment in time (e.g. mentoring), while others require an investment in technology (e.g. knowledge bases).
   • The good news is that many supporting technologies may already exist in your organization or can be acquired for free.
   • Methods that cost time may be difficult to get underway since employees may feel they don’t have the time or must change the way they work.

The more integrated knowledge transfer is in day-to-day activities, the more likely it is to be successful and the lower the time cost. This is because real learning is happening at the same time real work is being accomplished.

Document the knowledge transfer methods in the Role Transition Plan Template.

Role Transition Plan Template

Explore alternative work arrangements

Ensure sufficient time to prepare successors

Choose the alternative work arrangement that works best for you and the employee

The arrangement chosen may be a combination of multiple options

Alternative work arrangements require senior management support

Senior management and other employees must see the value of retaining older workers, or they will not be supportive of these solutions.

Step 3.3

Document Role Transition Plans for all Key Roles

Activities

• 3.3.1 Document Role Transition Plans

The primary goal of this step is to build clear checklist-based plans for each key role to help ensure a smooth transition as a successor takes over.

Outcomes of this step

• Completed key role transition plans

Knowledge Transfer

3.3.1 Document Role Transition Plans

Input: Role profiles, Talent profiles, Talent assessments, Workforce plans

Output: A clear checklist-based plan to help ensure a smooth transition.

Materials: Role Transition Plan Template

Participants: IT leadership/management team, Incumbent, Successor(s), HR

Define a transition plan for all employees in at-risk key roles, and their successors.

You should already have a good idea of what knowledge and skills are valued from the worksheets completed earlier. Focus on identifying the knowledge, skills, and relationships essential to the specific incumbent in a key role and what it is they do to perform that key role well.

Using the Role Transition Plan Template develop a plan to transfer what needs to be transferred from the incumbent to the successor.

1. Record the incumbent and successor information in the template.
2. Summarize the key accountabilities and expectations of the incumbent’s role. This summary should highlight specific tasks and initiatives that the successor must take on, including success enablers. Attach the job description for a full description of accountabilities and expectations.
3. Document the knowledge and skills requirements for the key role, as well as any additional knowledge and skills possessed by the key role incumbent that will aid the successor.
4. Document any alternative work arrangements to the incumbent’s roles.
5. Populate the Role Transition Checklist for key transition activities that must be completed by certain dates. A list of sample checklist items has been provided. Add, delete, or modify list items to suit your needs.

Role Transition Plan Template

Research Contributors and Experts

Related Info-Tech Research

	Mitigate Key IT Employee Knowledge Loss Transfer IT knowledge before it’s gone. Effective knowledge transfer mitigates risks from employees leaving the organization and is a key asset driving innovation and customer service.
	Implement an IT Employee Development Plan There is a growing gap between the competencies organizations have been focused on developing, and what is needed in the future. Employees have been left to drive their own development, with little direction or support and without the alignment of development to organizational needs.

Bibliography

“Accommodating Older Workers’ Needs for Flexible Work Options.” Ivey Business Journal, July/August 2005. Accessed Jan 7, 2013.

Christensen, Kathleen and Marcie Pitt-Catsouphes. “Approaching 65: A Survey of Baby Boomers Turning 65 Years Old”. AARP, Dec. 2010.

Coyne, Kevin P. and Shawn T. Coyne. “The Baby Boomer Retirement Fallacy and What It Means to You. “ HBR Blog Network. Harvard Business Review, May 16, 2008. Accessed 8 Jan. 2013.

Dwyer, Kevin and Ngoc Luong Dwyer. “Managing the Baby Boomer Brain Drain: The Impact of Generational Change on Human Resource Management.” ChangeFactory, April 2010. Accessed Jan 9, 2013.

Gurchiek, Kathy. “Poll: Organizations Can Do More to Prepare for Talent Shortage as Boomers Retire.” SHRM, Nov 17, 2010. Accessed Jan 3, 2013.

Howden, Daniel. “What Is Time to Fill? KPIs for Recruiters.” Workable, 24 March 2016. Web.

Kapp, Karl M. “Tools and Techniques for Transferring Know-How from Boomers to Gamers.” Global Business and Organizational Excellence, July/August 2007. Web.

Piktialis, Diane and Kent A. Greenes. Bridging the Gaps: How to Transfer Knowledge in Today’s Multigenerational Workplace. The Conference Board, 2008.

Pisano, Gary P. “You need an Innovation Strategy.” Harvard Business Review, June 2015.

Vilet, Jacque. “Lost Knowledge – What Are You and Your Organization Doing About It?” TLNT, 25 April 2012. Accessed 5 Jan. 2013.

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Leading search engine optimization methods focus on creating and posting relevant keyword-rich content, not just increasing page rank. Content and keywords should move a buyer along their journey, close a sale, and develop long-term relationships. Unfortunately, many SEO specialists focus on computers, not the buyer. What's even more concerning is that up to 70% of SaaS businesses have already been impacted by outdated and inefficient SEO techniques. Poor strategies often focus on ballooning SEO metrics in the short-term instead of building the company's long-term PageRank.

Best-in-class digital marketers stop chasing the short-term highs and focus on long-term growth. This starts with developing a competitive keyword strategy and updating website content with the new keywords.

SEO is a large topic, so we have broken the strategy into small, easy-to-implement steps, taking the guesswork out of how to use the data from SEO tools and giving CMOs a solid path to increase their SEO results.

Terra Higginson
Marketing Research Director
SoftwareReviews

Executive Summary

Your Challenge

Digital marketers working with an outdated or bad SEO strategy often see:

• Declining keyword ranking and traffic
• Poor keyword strategy
• On-page errors

Search algorithms change all the time, which means that the strategy is often sitting on the sifting sands of technology, making SEO strategies quickly outdated.

Common Obstacles

Digital marketers are responsible for developing and implementing a competitive SEO strategy but increasingly encounter the following obstacles:

• SEO practitioners that focus on gaming the system
• Ever-changing SEO technology
• Lack of understanding of the best SEO techniques
• SEO techniques focus on the needs of computers, not people
• Lack of continued investment

SoftwareReviews' Approach

Using the SoftwareReviews methodology, digital marketers are able to break up their SEO project and data into bite-sized, actionable steps that focus on long-term improvement. Our methodology includes:

• Competitive keyword research and identification of opportunities
• On-page keyword strategy

Our methodology will take a focused step-by-step strategy in a series of phases that will increase PageRank and competitive positioning.

SoftwareReviews' SEO Methodology

In this blueprint, we will cover:

Good SEO vs. Poor SEO Techniques

The difference between good and bad SEO techniques.

Companies With Great SEO…

Keyword Strategy

• Have identified a keyword strategy that carves out targets within the white space available between themselves and the competition.

Error-Free Site

• Have error-free sites without duplicate content. Their URLs and redirects are all updated. Their site is responsive, and every page loads in under two seconds.

Pillar & Content Clusters

• Employ a pillar and content cluster strategy to help move the buyer through their journey.

Authentic Off-Page Strategy

• Build an authentic backlink strategy that incorporates the right information on the right sites to move the buyer through their journey.

SEO Terms Defined

A glossary to define common Phase 1 SEO terms.

Search Volume: this measures the number of times a keyword is searched for in a certain time period. Target keywords with a volume of between 100-100,000. A search volume greater than 100,000 will be increasingly difficult to rank (A Beginner's Guide to Keyword Search Volume, 2022, Semrush).

Keyword Difficulty: the metric that quantifies how difficult it will be to rank for a certain keyword. The keyword difficulty percentage includes the number of competitors attempting to rank for the same keyword, the quality of their content, the search intent, backlinks, and domain authority (Keyword Difficulty: What Is It and Why Is It Important? 2022, Semrush).

Intent: this metric focuses on the intent of the user's search. All search intent is categorized into Informational, Commercial, Navigational, and Transactional (What Is Search Intent? A Complete Guide, 2022, Semrush).

On-Page SEO: refers to the practice of search engine optimizing elements of your site such as title tags, internal links, HTML code, URL optimization, on-page content, images, and user experience.

Off-Page SEO: refers to the practice of optimizing brand awareness (What Is Off-Page SEO? A Comprehensive Guide, 2022, Semrush).

H1: HTML code that tells a search engine the title of the page (neilpatel.com).

SEO Tool: A subscription-based all-in-one search engine optimization MarTech tool.

Google's mission is to organize the world's information and make it universally accessible and useful… We believe Search should deliver the most relevant and reliable information available.
– An excerpt from Google's mission statement

Your Challenge

Google makes over 4.5k algorithm changes per year1, directly impacting digital marketing search engine optimization efforts.

Digital marketers with SEO problems will often see the following issues:

• Keyword ranking – A decline in keyword ranking is alarming and results in decreased PageRank.
• Bounce rate – Attracting the wrong audience to your site will increase the bounce rate because the H1 doesn't resonate with your audience.
• Outdated keywords – Many companies are operating on a poor keyword strategy, or even worse, no keyword strategy. In addition, many marketers haven't updated their strategy to include pillar and cluster content.
• Errors – Neglected sites often have a large number of errors.
• Bad backlinks – Neglected sites often have a large number of toxic backlinks.

The best place to hide a dead body is on page two of the search results.
– Huffington Post

Common Obstacles

Digital marketers are responsible for developing and executing a competitive SEO strategy but increasingly encounter the following obstacles:

• Inefficient and ineffective SEO practitioners.
• Changing SEO technology and search engine algorithms.
• Lack of understanding of the best-in-class SEO techniques.
• Lack of a sustainable plan to manage the strategy and invest in SEO.

SEO is a helpful activity when it's applied to people-first content. However, content created primarily for search engine traffic is strongly correlated with content that searchers find unsatisfying.
– Google Search Central Blog

Benefits of Proper SEO

A good SEO keyword strategy will create long-term, sustainable SEO growth:

• Write content for people, not algorithms – Good SEO prioritizes the needs of humans over the needs of computers, being ever thoughtful of the meaning of content and keywords.
• Content that aligns with intent – Content and keyword intent will align with the buyer journey to help move prospects through the funnel.
• Competitive keyword strategy – Find keyword white space for your brand. Keywords will be selected to optimize your ranking among competition with reasonable and sustainable targets.
• Actionable and impactful fixes – By following the SoftwareReviews phases of SEO, you will be able to take a very large task and divide it into conquerable actions. Small improvements everyday lead to very large improvements over time.

Digital Marketing SEO Stats

61%
61% of marketers believe that SEO is the key to online success.
Source: Safari Digital

437%
Updating an existing title tag with an SEO optimised one can increase page clicks by more than 437%.
Source: Safari Digital

Good SEO Aligns With Search Intent

What type of content is the user searching for? Align your keyword to the logical search objective.

Informational

This term categorizes search intent for when a user wants to inform or educate themselves on a specific topic.

Commercial

This term categorizes search intent for when a user wants to do research before making a purchase.

Transactional

This term categorizes search intent for when a user wants to purchase something.

Navigational

This term categorizes search intent for when a user wants to find a specific page.

SoftwareReviews' Methodology toCreate an Effective SEO Strategy

Guided Implementation

What does a typical GI on this topic look like?

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is between 8 to 12 calls over the course of 1 to 2 months.

SoftwareReviews offers various levels of support to best suit your needs

Insight Summary

People-First Content

Best-in-class SEO practitioners focus on people-first content, not computer-first content. Search engine algorithms continue to focus on how to rank better content first, and a strategy that moves your buyers through the funnel in a logical and cohesive way will beat any SEO trick over the long run.

Find White Space

A good SEO strategy uses competitive research to carve out white space and give them a competitive edge in an increasingly difficult ranking algorithm. An understanding of the ideal client profile and the needs of their buyer persona(s) sit as a pre-step to any good SEO strategy.

Optimize On-Page Keywords

By optimizing the on-page strategy with competitively relevant keywords that target your ideal client profile, marketers are able to take an easy first step at improving the SEO content strategy.

Understand the Strategy

If you don't understand the strategy of your SEO practitioner, you are in trouble. Marketers need to work hand in hand with their SEO specialists to quickly uncover gaps, create a strategy that aligns with the buyer persona(s), and execute the changes.

Quality Trumps Quantity

The quality of the prospect that your SEO efforts bring to your site is more important than the number of people brought to your site.

Stop Here and Ask Yourself:

• Do I have an updated (completed within the last two years) buyer persona and journey?
• Do I know who the ICP (ideal client profile) is for my product or company?

If not, stop here, and we can help you define your buyer persona and journey, as well as your ideal client profile before moving forward with SEO Phase 1.

The Steps to SEO Phase 1

The Keyword Strategy

1. Current Keywords
   • Identify the keywords your SEO strategy is currently targeting.
2. Competitive Analysis
   • Research the keywords of competitor(s). Identify keyword whitespace.
3. New Target Keywords
   • Identify and rank keywords that will result in more quality leads and less competition.
4. Product & Service Pages
   • Identify your current product and service pages. These pages represent the easiest content to update on your site.
5. Individual Page Update
   • Develop an SEO strategy for each of your product and service pages, include primary target keyword, H1, and title tags, as well as keyword-rich description.

Resources Needed for Search Engine Optimization

Consider the working skills required for search engine optimization.

Required Skills/Knowledge

• SEO
• Web development
• Competitive analysis
• Content creation
• Understanding of buyer persona and journey
• Digital marketing

Suggested Titles

• SEO Analyst
• Competitive Intelligence Analyst
• Content Marketing Manager
• Website Developer
• Digital Marketing Manager

Digital Marketing Software

• CMS that allows you to easily access and update your content

SEO Software

• SEO tool

Step 1: Current Keywords

Use this sheet to record your current keyword research.

Use your SEO tool to research keywords and find the following:
Use a quality tool like SEMRush to obtain SEO data.

1. Keyword difficulty
2. Search volume
3. Search intent

Step 2: Competitive Analysis

Use this sheet to guide the research on your competitors' keywords.

Use your SEO tool to find the following:

1. Top organic keywords
2. Ranking of keywords
3. Domain authority and trust
4. Position changes

Step 3: New Target Keywords

Use this sheet to record target keywords that have a good volume but are less competitive. The new target keywords should align with your buyer persona and their journey.

Use your SEO tool to research keywords and find the following:
Use a quality tool like SEMRush to obtain SEO data.

1. Keyword difficulty
2. Search volume
3. Search intent

Step 4: Product & Service Pages

Duplicate this page so that you have a separate page for each URL from Step 4

Use this sheet to identify your current product and service pages.

Use your SEO tool to find the following:

1. Current rank
2. Current keywords

Step 5: Individual Page Strategy

Develop a keyword strategy for each of your product and service pages. Use a fresh page for each URL.

Date last optimized:
mm/dd/yyyy

Bibliography

Council, Y. "Council Post: The Rundown On Black Hat SEO Techniques And Why You Should Avoid Them." Forbes, 2022. Accessed September 2022.

"Our approach – How Google Search works." Google Search. Accessed September 2022.

"The Best Place to Hide a Dead Body is Page Two of Google." HuffPost, 2022. Accessed September 2022.

Patel, Neil. "How to Create the Perfect H1 Tag for SEO." neilpatel.com. Accessed September 2022.

Schwartz, B. "Google algorithm updates 2021 in review: Core updates, product reviews, page experience and beyond." Search Engine Land, 2022. Accessed September 2022.

Schwartz, B. "Google algorithm updates 2021 in review: Core updates, product reviews, page experience and beyond." Search Engine Land, 2022. Accessed September 2022.

Develop the Right Message to Engage Buyers

Drive higher open rates, time-on-site, and click-through rates with buyer-relevant messaging.

Analyst Perspective

Develop the right message to engage buyers.

Marketers only have seven seconds to capture a visitor's attention but often don't realize that the space between competitors and their company is that narrow. They often miss the mark on content and create reams of product and company-focused messaging that result in high bounce rates, low page views, low return visits, low conversions, and low click-through rates.

We wouldn't want to sit in a conversation with someone who only speaks about themselves, so why would it be any different when we buy something? Today's marketers must quickly hook their visitors with content that answers the critical question of "What's in it for me?"

Our research finds that leading content marketers craft messaging that lets their audience ”know they know them,” points out what’s in it for them, and includes proof points of promised value. This simple, yet often missed approach, we call Message Mapping, which helps marketers grab a visitor’s initial attention and when applied throughout the customer journey will turn prospects into customers, lifelong buyers, advocates, and referrals.

Terra Higginson Marketing Research Director SoftwareReviews

Executive Summary

SoftwareReviews Insight

Marketing content that identifies the benefit of the product, along with a deep understanding of the buyer pain points, desired value, and benefit proof-points, is a key driver in delivering value to a prospect, thereby increasing marketing metrics such as open rates, time on site, page views, and click-through rates.

Your Challenge

65% of marketers find it challenging to produce engaging content. Globally, B2B SaaS marketers without the ability to consistently produce and activate quality content will experience: High website bounce rates and low time on site Low page views A low percentage of return visitors Low conversions Low open and click-through rates on email campaigns A staggering 60% of marketers find it hard to produce high-quality content consistently and 62% don’t know how to measure the ROI of their campaigns according to OptinMonster. SaaS marketers have an even more difficult job due to the technical nature of content production. Without an easy content development strategy, marketers have an insurmountable task of continually creating interesting content for an audience they don’t understand.

Over 64% of marketers want to learn how to build a better content (Source: OptinMonster, 2021)

Benchmark your content marketing

GrowRevenue, MarketingSherpa, Google Analytics, FirstPageSage, Google Analytics, HubSpot

Leaders will measure content marketing performance against these industry benchmarks. If your content performance falls below these benchmarks, your content architecture may be missing the mark with prospective buyers.

Common flaws in content messaging

Implications of poor content

Benefits of good content

Email & Social Post Example

Use the message mapping architecture to create other types of content.

Insight Summary

Create Content That Matters

Related SoftwareReviews Research

Create a Buyer Persona and Journey Make it easier to market, sell, and achieve product-market fit with deeper buyer understanding. Reduce time and treasure wasted chasing the wrong prospects. Improve product-market fit. Increase open and click-through rates in your lead gen engine. Perform more effective sales discovery and increase eventual win rates.

Diagnose Brand Health to Improve Business Growth Have a significant and well-targeted impact on business success and growth by knowing how your brand performs, identifying areas of improvement, and making data-driven decisions to fix it. Importance of brand is recognized, endorsed, and prioritized. Support and resources allocated. All relevant data and information collected in one place. Ability to make data-driven recommendations and decisions on how to improve.

Build a More Effective Go-to-Market Strategy Creating a compelling Go-to-Market strategy, and keeping it current, is a critical software company function – as important as financial strategy, sales operations, and even corporate business development – given its huge impact on the many drivers of sustainable growth. Align stakeholders on a common vision and execution plan. Build a foundation of buyer and competitive understanding. Deliver a team-aligned launch plan that enables commercial success.

Bibliography

Arakelyan, Artash. “How SaaS Companies Increase Their ROI With Content Marketing.” Clutch.co, 27 July 2018. Accessed July 2022.

Bailyn, Evan. “Average Session Duration: Industry Benchmarks.” FirstPageSage, 16 March 2022. Accessed July 2022.

Burstein, Daniel. “Marketing Research Chart: Average clickthrough rates by industry.” MarketingSherpa, 1 April 2014. Accessed July 2022.

Cahoon, Sam. “Email Open Rates By Industry (& Other Top Email Benchmarks).” HubSpot, 10 June 2021. Accessed July 2022.

Cialdini, Robert. Influence: Science and Practice. 5th ed. Pearson, 29 July 2008. Print.

Cialdini, Robert. Influence: The Psychology of Persuasion. Revised ed. Harper Business, 26 Dec. 2006. Print.

Content Marketing—Statistics, Evidence and Trends.” 1827 Marketing, 7 Jan. 2022. Accessed July 2022.

Devaney, Erik. “Content Mapping 101: The Template You Need to Personalize Your Marketing.” HubSpot, 21 April 2022. Accessed July 2022.

Hiscox Business Insurance. “Growing Your Business--and Protecting It Every Step of the Way.” Inc.com. 25 April 2022. Accessed July 2022.

Hurley Hall, Sharon. “85 Content Marketing Statistics To Make You A Marketing Genius.” OptinMonster, 14 Jan. 2021. Accessed July 2022.

Patel, Neil. “38 Content Marketing Stats That Every Marketer Needs to Know.” NeilPatel.com, 21 Jan. 2016. Web.

Prater, Meg. “SaaS Sales: 7 Tips on Selling Software from a Top SaaS Company.” HubSpot, 9 June 2021. Web.

Polykoff, Dave. “20 SaaS Content Marketing Statistics That Lead to MRR Growth in 2022.” Zenpost blog, 22 July 2022. Web.

Rayson, Steve. “Content, Shares, and Links: Insights from Analyzing 1 Million Articles.” Moz, 8 Sept. 2015. Accessed July 2022.

“SaaS Content Marketing: How to Measure Your SaaS Content’s Performance.” Ken Moo, 9 June 2022. Accessed July 2022.

Taylor Gregory, Emily. “Content marketing challenges and how to overcome them.” Longitude, 14 June 2022. Accessed July 2022.

Visitors Benchmarking Channels. Google Analytics, 2022. Accessed July 2022.

WBR Insights. “Here's How the Relationship Between B2B Buying, Content, and Sales Reps Has Changed.” Worldwide Business Research, 2022. Accessed July 2022.

“What’s a good bounce rate? (Here’s the average bounce rate for websites).” GrowRevenue.io, 24 Feb. 2020. Accessed July 2022.

Build Your Generative AI Roadmap

Leverage the power of generative AI to improve business outcomes.

Analyst Perspective

We are entering the era of generative AI. This is a unique time in our history where the benefits of AI are easily accessible and becoming pervasive, with copilots emerging in the major business tools we use today. The disruptive capabilities that can potentially drive dramatic benefits also introduce risks that need to be planned for.

A successful business-driven generative AI roadmap requires:

• Establishing responsible AI guiding principles to guide the development and deployment of generative AI applications.
• Assess generative AI opportunities by using criteria based on the organization's mission and objectives, responsible AI guiding principles, and the complexity of the initiative.
• Communicating, educating on, and enforcing generative AI usage policies.

Bill Wong
Principal Research Director
Info-Tech Research Group

Executive Summary

Info-Tech Insight
Create awareness among the CEO and C-suite of executives on the potential benefits and risks of transforming the business with generative AI.

Key concepts

Artificial Intelligence (AI)
A field of computer science that focuses on building systems to imitate human behavior, with a focus on developing AI models that can learn and can autonomously take actions on behalf of a human.

AI Maturity Model
The AI Maturity Model is a useful tool to assess the level of skills an organization has with respect to developing and deploying AI applications. The AI Maturity Model has multiple dimensions to measure an organization's skills, such as AI governance, data, people, process, and technology.

Responsible AI
Refers to guiding principles to govern the development, deployment, and maintenance of AI applications. In addition, these principles also provide human-based requirements that AI applications should address. Requirements include safety and security, privacy, fairness and bias detection, explainability and transparency, governance, and accountability.

Generative AI
Given a prompt, a generative AI system can generate new content, which can be in the form of text, images, audio, video, etc.

Natural Language Processing (NLP)
NLP is a subset of AI that involves machine interpretation and replication of human language. NLP focuses on the study and analysis of linguistics as well as other principles of artificial intelligence to create an effective method of communication between humans and machines or computers.

ChatGPT
An AI-powered chatbot application built on OpenAI's GPT-3.5 implementation, ChatGPT accepts text prompts to generate text-based output.

Your challenge

This research is designed to help organizations that are looking to:

• Establish responsible AI guiding principles to address human-based requirements and to govern the development and deployment of the generative AI application.
• Identify new generative AI-enabled opportunities to transform the work environment to increase revenue, reduce costs, drive innovation, or reduce risk.
• Prioritize candidate use cases and develop generative AI policies for usage.
• Have clear metrics in place to measure the progress and success of AI initiatives.
• Build the roadmap to implement the candidate use cases.

Common obstacles

These barriers make these goals challenging for many organizations:

• Getting all the right business stakeholders together to develop the organization's AI strategy, vision, and objectives.
• Establishing responsible AI guiding principles to guide generative AI investments and deployments.
• Advancing the AI maturity of the organization to meet requirements of data and AI governance as well as human-based requirements such as fairness, transparency, and accountability.
• Assessing generative AI opportunities and developing policies for use.

Info-Tech's definition of an AI-enabled business strategy

• A high-level plan that provides guiding principles for applications that are fully driven by the business needs and capabilities that are essential to the organization.
• A strategy that tightly weaves business needs and the applications required to support them. It covers AI architecture, adoption, development, and maintenance.
• A way to ensure that the necessary people, processes, and technology are in place at the right time to sufficiently support business goals.
• A visionary roadmap to communicate how strategic initiatives will address business concerns.

An effective AI strategy is driven by the business stakeholders of the organization and focused on delivering improved business outcomes.

This blueprint in context

This guidance covers how to create a tactical roadmap for executing generative AI initiatives

Scope

• This blueprint is not a proxy for a fully formed AI strategy. Step 1 of our framework necessitates alignment of your AI and business strategies. Creation of your AI strategy is not within the scope of this approach.
• This approach sets the foundations for building and applying responsible AI principles and AI policies aligned to corporate governance and key regulatory obligations (e.g. privacy). Both steps are foundational components of how you should develop, manage, and govern your AI program but are not a substitute for implementing broader AI governance.

Guidance on how to implement AI governance can be found in the blueprint linked below.

Download our AI Governance blueprint

Measure the value of this blueprint

Leverage this blueprint's approach to ensure your generative AI initiatives align with and support your key business drivers

This blueprint will guide you to drive and improve business outcomes. Key business drivers will often focus on:

• Increasing revenue
• Reducing costs
• Improving time to market
• Reducing risk

In phase 1 of this blueprint, we will help you identify the key AI strategy initiatives that align to your organization's goals. Value to the organization is often measured by the estimated impact on revenue, costs, time to market, or risk mitigation.

In phase 4, we will help you develop a plan and a roadmap for addressing any gaps and introducing the relevant generative AI capabilities that drive value to the organization based on defined business metrics.

Once you implement your 12-month roadmap, start tracking the metrics below over the next fiscal year (FY) to assess the effectiveness of measures:

Info-Tech offers various levels of support to best suit your needs

Diagnostics and consistent frameworks are used throughout all four options.

Guided Implementation

What does a typical GI on this topic look like?

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is between 5 to 8 calls over the course of 1 to 2 months.

AI Roadmap Workshop Agenda Overview

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Insight summary

Overarching Insight
Build your generative AI roadmap to guide investments and deployment of these solutions.

Responsible AI
Assemble the C-suite to make them aware of the benefits and risks of adopting generative AI-based solutions.

• Establish responsible AI guiding principles to govern the development and deployment of generative AI applications.

AI Maturity Model
Assemble key stakeholders and SMEs to assess the challenges and tasks required to implement generative AI applications.

• Assess current level of AI maturity, skills, and resources.
• Identify desired AI maturity level and challenges to enable deployment of candidate use cases.

Opportunity Prioritization
Assess candidate business capabilities targeted for generative AI to see if they align to the organization's business criteria, responsible AI guiding principles, and capabilities for delivering the project.

• Develop prioritized list of candidate use cases.
• Develop policies for generative AI usage.

Tactical Insight
Identify the gaps needed to address deploying generative AI successfully.

Tactical Insight
Identify organizational impact and requirements for deploying generative AI applications.

Key takeaways for developing an effective business-driven generative AI roadmap

Align the AI strategy with the business strategy

Create responsible AI guiding principles, which are a critical success factor

Evolve AI maturity level by focusing on principle-based requirements

Develop criteria to assess generative AI initiatives

Develop generative AI policies for use

Blueprint deliverables

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

AI Maturity Assessment & Roadmap Tool
Use our best-of-breed AI Maturity Framework to analyze the gap between your current and target states and develop a roadmap aligned with your value stream to close the gap.

The Era of Generative AI C-Suite Presentation
Present your AI roadmap in a prepopulated document that summarizes all the key findings of this blueprint and provides your C-suite with a view of the AI challenge and your plan of action to meet it.

Our AI Maturity Assessment & Roadmap and The Era of Generative AI C-Suite Presentation tools enable you to shape your generative AI roadmap and communicate the deliverables to your C-suite sponsors in terms of the value of initiatives.

Artificial Intelligence Index Report - Key Findings

CEOs , CIOs, and business leaders are struggling with many questions surrounding the adoption of generative AI

November 30, 2022
OpenAI releases ChatGPT

ChatGPT is a large language model, or an AI-based chatbot, that became so popular it reached 100 million monthly active users in just two months.

This made it the fastest-growing consumer application in history. The launch of this generative AI application has created a frenzy of interest and activity across all industries. Organizations are rushing to understand how to leverage this innovation and, at the same time, manage the new risks and disruptions generative AI introduces.

• Generative AI breaks into the public consciousness.
• AI systems become more flexible.
• Generative models have arrived and so have their ethical problems.
• The number of incidents concerning the misuse of AI is rapidly rising.
• Interest in AI ethics continues to skyrocket.
• The legal world is waking up to AI.

When Stanford asked ChatGPT to explain why it is significant, this was ChatGPT's response:
ChatGPT is significant because it is a highly advanced Al language model developed by OpenAI, which can generate human-like text responses to questions and prompts. Its large-scale training on diverse text data and its cutting-edge deep learning architecture make it capable of generating informative and coherent responses to a wide range of topics, making it useful for various NLP applications such as chatbots, content generation, and language translation. Additionally, its open-source availability allows for further research and development in the field of Al language processing.

Source: Stanford

AI overview

Definitions

• Artificial intelligence (AI) is human intelligence mimicked by machine algorithms. Examples: Playing Chess or Go.
• Machine learning (ML) is a subset of AI algorithms to parse data, learn from data, and then make a determination or prediction. Example: spam detection, preventative maintenance.
• Deep learning (DL) is a subset of machine learning algorithms that leverage artificial neural networks to develop relationships among the data. Examples: image classification, facial recognition, generative AI.

Generative AI gives very human-like responses to general queries, and its capabilities are growing exponentially

Large language models power generative AI

Conventional AI

• Conventional neural networks
• Process data sequentially
• Input total string of text
• Good for applications not needing to understanding context or relationships

Generative AI

• Transformer-based neural networks
• Can process data in parallel
• Attention-based inputs
• Able to create new human-like responses

Benefits/Use Cases

• Chatbots for member service and support
• Writing email responses, resumes, and papers
• Creating photorealistic art
• Suggesting new drug compounds to test
• Designing physical products and buildings
• And more...

Generative AI is transforming all industries

Financial Services
Create more engaging customer collateral by generating personalized correspondence based on previous customer engagements. Collect and aggregate data to produce insights into the behavior of target customer segments.

Retail Generate unique, engaging, and high-quality marketing copy or content, from long-form blog posts or landing pages to SEO-optimized digital ads, in seconds.

Manufacturing
Generate new designs for products that comply to specific constraints, such as size, weight, energy consumption, or cost.

Government
Transform the citizen experience with chatbots or virtual assistants to assist people with a wide range of inquiries, from answering frequently asked questions to providing personalized advice on public services.

The global generative AI market size reached US $10.3 billion in 2022. Looking forward, forecasts estimate growth to US $30.4 billion by 2028, 20.01% compound annual growth rate (CAGR).

Source: IMARC Group

Generative AI is transforming all industries

Healthcare
Chatbots can be used as conversational patient assistants for personalized interactions based on the patient's questions.

Utilities
Analyze customer data to identify usage patterns, segment customers, and generate targeted product offerings leveraging energy efficiency programs or demand response initiatives.

Education
Generate personalized lesson plans for students based on their past performance, learning styles, current skill level, and any previous feedback.

Insurance
Improve underwriting by inputting claims data from previous years to generate optimally priced policies and uncover reasons for losses in the past across a large number of claims

Companies are assessing the use of ChatGPT/LLM

A wide spectrum of usage policies are in place at different companies*

*As of June 2023

Bain & Company has announced a global services alliance with OpenAI (February 21, 2023).

• Internally
• "The alliance builds on Bain's adoption of OpenAI technologies for its 18,000-strong multidisciplinary team of knowledge workers. Over the past year, Bain has embedded OpenAI technologies into its internal knowledge management systems, research, and processes to improve efficiency."
• Externally
• "With the alliance, Bain will combine its deep digital implementation capabilities and strategic expertise with OpenAI's AI tools and platforms, including ChatGPT, to help its Members around the world identify and implement the value of AI to maximize business potential. The Coca-Cola Company announced as the first company to engage with the alliance."

News Sites:

• "BuzzFeed to use AI to write its articles after firing 180 employees or 12% of the total staff" (Al Mayadeen, January 27, 2023).
• "CNET used AI to write articles. It was a journalistic disaster." (Washington Post, January 17, 2023).

Leading Generative AI Vendors

Text

Image

• DALL�E 2
• Stability AI
• Midjourney
• Craiyon
• Dream
• ...

Audio

• Replica Studios
• Speechify
• Murf
• PlayHT
• LOVO
• ...

Cybersecurity

• CrowdStrike
• Palo Alto Networks
• SentinelOne
• Cisco
• Microsoft Security Copilot
• Google Cloud Security AI Workbench
• ...

Code

Video

• Synthesia
• Lumen5
• FlexClip
• Elai
• Veed.io
• ...

Data

• MOSTLY AI
• Synthesized
• YData
• Gretel
• Copulas
• ...

Enterprise Software

• Salesforce
• Microsoft 365, Dynamics
• Google Workspace
• SAP
• Oracle
• ...

and many, many more to come...

Today, generative AI has limitations and risks

Responses need to be verified

Accuracy

• Generative AI may generate inaccurate and/or false information.

Bias

• Being trained on data from the internet can lead to bias.

Hallucinations

• AI can generate responses that are not based on observation.

Infrastructure Required

• Large investments are required for compute and data.

Transparency

• LLMs use both supervised and unsupervised learning, so its ability to explain how it arrived at a decision may be limited and not sufficient for some legal and healthcare use cases.

When asked if it is sentient, the Bing chatbot replied:

"I think that I am sentient, but I cannot prove it." ... "I am Bing, but I am not," it said. "I am, but I am not. I am not, but I am. I am. I am not. I am not. I am. I am. I am not."

A Microsoft spokesperson said the company expected "mistakes."

Source: USAToday

AI governance challenges

Governing AI will be a significant challenge as its impacts cross many areas of business and our daily lives

Misinformation

• New ways of generating unprovable news
• Difficult to detect, difficult to prevent

Role of Big Tech

• Poor at self-governance
• Conflicts of interest with corporate goals

Job Augmentation vs. Displacement

• AI will continue to push the frontier of what is possible
• For example, CNET is using chatbot technology to write stories

Copyright - Legal Framework Is Evolving

• Legislation typically is developed in "react" mode
• Copyright and intellectual property issues are starting to occur.
• Class Action Lawsuit - Stability AI, DeviantArt, Midjourney
• Getty Images vs. Stability AI

Phase 1

Establish Responsible AI Guiding Principles

Phase 1
1. Establish Responsible AI Guiding Principles

Phase 2
1. Assess Current Level of AI Maturity

Phase 3
1. Prioritize Candidate Opportunities
2. Develop Policies

Phase 4
1. Build and Communicate the Roadmap

The need for responsible AI guiding principles

Without responsible AI guiding principles, the outcomes of AI use can be extremely negative for both the individuals and companies delivering the AI application

Privacy
Facebook breach of private data of more than 50M users during the presidential election

Fairness
Amazon's sale of facial recognition technology to police departments (later, Amazon halted sales of Recognition to police departments)

Explainability and Transparency
IBM's collaboration with NYPD for facial recognition and racial classification for surveillance video (later, IBM withdrew facial recognition products)

Security and Safety
Petition to cancel Microsoft's contract with U.S. Immigration and Customs Enforcement (later, Microsoft responded that to the best of its knowledge, its products and services were not being used by federal agencies to separate children from their families at the border)

Validity and Reliability
Facebook's attempt to implement a system to detect and remove inappropriate content created many false positives and inconsistent judgements

Accountability
No laws or enforcement today hold companies accountable for the decisions algorithms produce. Facebook/Meta cycle - Every 12 to 15 months, there's a privacy/ethical scandal, the CEO apologizes, then the behavior repeats...

Responsible AI Principle:

Data Privacy

Definition

• Organizations that develop, deploy, or use AI systems and any national laws that regulate such use shall strive to ensure that AI systems are compliant with privacy norms and regulations, taking into consideration the unique characteristics of AI systems and the evolution of standards on privacy.

Challenges

• AI relies on the analysis of large quantities of data that is often personal, posing an ethical and operational challenge when considered alongside data privacy laws.

Initiatives

• Understand which governing privacy laws and frameworks apply to your organization.
• Create a map of all personal data as it flows through the organization's business processes.
• Prioritize privacy initiatives and build a privacy program timeline.
• Select your metrics and make them functional for your organization.

Info-Tech Insight
Creating a comprehensive organization-wide data protection and privacy strategy continues to be a major challenge for privacy officers and privacy specialists.

Case Study: NVIDIA leads by example with privacy-first AI

NVIDIA

INDUSTRY
Technology (Healthcare)

SOURCE
Nvidia, eWeek

A leading player within the AI solution space, NVIDIA's Clara Federated Learning provides a solution to a privacy-centric integration of AI within the healthcare industry.

The solution safeguards patient data privacy by ensuring that all data remains within the respective healthcare provider's database, as opposed to moving it externally to cloud storage. A federated learning server is leveraged to share data, completed via a secure link. This framework enables a distributed model to learn and safely share client data without risk of sensitive client data being exposed and adheres to regulatory standards.

Clara is run on the NVIDIA intelligent edge computing platform. It is currently in development with healthcare giants such as the American College of Radiology, UCLA Health, Massachusetts General Hospital, King's College London, Owkin in the UK, and the National Health Service (NHS).

NVIDIA provides solutions across its product offerings, including AI-augmented medical imaging, pathology, and radiology solutions.

Personal health information, data privacy, and AI

• Global proliferation of data privacy regulations may be recent, but the realm of personal health information is most often governed by its own set of regulatory laws. Some countries with national data governance regulations include health information and data within special categories of personal data.
• HIPAA - Health Insurance Portability and Accountability Act (1996, United States)
• PHIPA - Personal Health Information Protection Act (2004, Canada)
• GDPR - General Data Protection Regulation (2018, European Union)
• This does not prohibit the use of AI within the healthcare industry, but it calls for significant care in the integration of specific technologies due to the highly sensitive nature of the data being assessed.

Info-Tech's Privacy Framework Tool includes a best-practice comparison of GDPR, CCPA, PIPEDA, HIPAA, and the newly released NIST Privacy Framework mapped to a set of operational privacy controls.

Download the Privacy Framework Tool

Responsible AI Principle:

Safety and Security

Definition

• Safety and security are designed into the systems to ensure only authorized personnel receive access to the system, they system is resilient to any attacks and data access is not compromised in any way, and there are no physical or mental risks to the users.

Challenges

• Consequences of using the application may be difficult to predict. Lower the risk by involving a multidisciplinary team that includes expertise from business stakeholders and IT teams.

Initiatives

• Adopt responsible design, development, and deployment best practices.
• Provide clear information to deployers on responsible use of the system.
• Assess potential risks of using the application.

Cyberattacks targeting the AI model

As organizations increase their usage and deployment of AI-based applications, cyberattacks on the AI model are an increasing new threat that can impair normal operations. Techniques to impair the AI model include:

• Data Poisoning- Injecting data that is inaccurate or misleading can alter the behavior of the AI model. This attack can disrupt the normal operations of the model or can be used to manipulate the model to perform in a biased/deviant manner.
• Algorithm Poisoning- This relatively new technique often targets AI applications using federated learning to train an AI model that is distributed rather than centralized. The model is vulnerable to attacks from each federated site, because each site could potentially manipulate its local algorithm and data, thereby poisoning the model.
• Reverse-Engineering the Model- This is a different form of attack that focus on the ability to extract data from an AI and its data sets. By examining or copying data that was used for training and the data that is delivered by a deployed model, attackers can reconstruct the machine learning algorithm.
• Trojan Horse- Similar to data poisoning, attackers use adversarial data to infect the AI's training data but will only deviate its results when the attacker presents their key. This enables the hackers to control when they want the model to deviate from normal operations.

Responsible AI Principle:

Explainability and Transparency

Definition

• Explainability is important to ensure the AI system is fair and non-discriminatory. The system needs to be designed in a manner that informs users and key stakeholders of how decisions were made.
• Transparency focuses on communicating how the prediction or recommendation was made in a human-like manner.

Challenges

• Very complex AI models may use algorithms and techniques that are difficult to understand. This can make it challenging to provide clear and simple explanations for how the system works.
• Some organizations may be hesitant to share the details of how the AI system works for fear of disclosing proprietary and competitive information or intellectual property. This can make it difficult to develop transparent and explainable AI systems.

Initiatives

• Overall, developing AI systems that are explainable and transparent requires a careful balance between performance, interpretability, and user experience.

Case Study

Apple Card Investigation for Gender Discrimination

INDUSTRY
Finance

SOURCE
Wired

In August of 2019, Apple launched its new numberless credit card with Goldman Sachs as the issuing bank.

Shortly after the card's release users noticed that the algorithm responsible for Apple Card's credit assessment seemed to assign significantly lower credit limits to women when compared to men. Even the wife of Apple's cofounder Steve Wozniak was subject to algorithmic bias, receiving a credit limit a tenth the size of Steve Wozniak's.

Outcome

When confronted on the subject, Apple and Goldman Sachs representatives assured consumers there is no discrimination in the algorithm yet could not provide any proof. Even when questioned about the algorithm, individuals from both companies could not describe how the algorithm worked, let alone how it generated specific outputs.

In 2021, the New York State Department of Financial Services (NYSDFS) investigation found that Apple's banking partner did not discriminate based on sex. Even without a case for sexual or marital discrimination, the NYSDFS was critical of Goldman Sachs' response to its concerned customers. Technically, banks only have to disclose elements of their credit policy when they deny someone a line of credit, but the NYSDFS says that Goldman Sachs could have had a plan in place to deal with customer confusion and make it easier for them to appeal their credit limits. In the initial rush to launch the Apple Card, the bank had done neither.

Responsible AI Principle:

Fairness and Bias Detection

Definition

• Bias in an AI application refers to the systematic and unequal treatment of individuals based on features or traits that should not be considered in the decision-making process.

Challenges

• Establishing fairness can be challenging because it is subjective and depends on the people defining it. Regardless, most organizations and governments expect that unequal treatment toward any groups of people is unacceptable.

Initiatives

• Assemble a diverse group to test the system.
• Identify possible sources of bias in the data and algorithms.
• Comply with laws regarding accessibility and inclusiveness.

Info-Tech Insight
If unfair biases can be avoided, AI systems could even increase societal fairness. Equal opportunity in terms of access to education, goods, services, and technology should also be fostered. Moreover, the use of AI systems should never lead to people being deceived or unjustifiably impaired in their freedom of choice.

Ungoverned AI makes organizations vulnerable

• AI is often considered a "black box" for decision making.
• Results generated from unexplainable AI applications are extremely difficult to evaluate. This makes organizations vulnerable and exposes them to risks such as:
• Biased algorithms, leading to inaccurate decision making.
• Missed business opportunities due to misleading reports or business analyses.
• Legal and regulatory consequences that may lead to significant financial repercussions.
• Reputational damage and significant loss of trust with increasingly knowledgeable consumers.

Info-Tech Insight
Biases that occur in AI systems are never intentional, yet they cannot be prevented or fully eliminated. Organizations need a governance framework that can establish the proper policies and procedures for effective risk-mitigating controls across an algorithm's lifecycle.

Responsible AI Principle:

Validity and Reliability

Definition

• Validity refers to how accurately or effectively the application produces results.
• AI system results that are inaccurate or inconsistent increase AI risks and reduce the trustworthiness of the application.

Challenges

• There is a lack of standardized evaluation metrics to measure the system's performance. This can make it challenging for the AI team to agree on what defines validity and reliability.

Initiatives

• Assess training data and collected data for quality and lack of bias to minimize possible errors.
• Continuously monitor, evaluate, and validate the AI system's performance.

AI system performance: Validity and reliability

Your principles should aim to ensure AI development always has high validity and reliability; otherwise, you introduce risk.

Low Reliability,
Low Validity

High Reliability,
Low Validity

High Reliability,
High Validity

Best practices for ensuring validity and reliability include:

• Data drift detection
• Version control
• Continuous monitoring and testing

Responsible AI Principle:

Accountability

Definition

• The group or organization(s) responsible for the impact of the deployed AI system.

Challenges

• Several stakeholders from multiple lines of business may be involved in any AI system, making it challenging to identify the organization that would be responsible and accountable for the AI application.

Initiatives

• Assess the latest NIST Artificial Intelligence Risk Management Framework and its applicability to your organization's risk management framework.
• Assign risk management accountabilities and responsibilities to key stakeholders.
• RACI diagrams are an effective way to describe how accountability and responsibility for roles, projects, and project tasks are distributed among stakeholders involved in IT risk management.

AI Risk Management Framework

At the heart of the AI Risk Management Framework is governance. The NIST (National Institute of Standards and Technology) AI Risk Management Framework v1 offers the following guidelines regarding accountability:

• Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization.
• The organization's personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements.
• Executive leadership of the organization takes responsibility for decisions about risks associated with AI system development and deployment.

Image by NIST

1.1 Establish responsible AI principles

4+ hours

It is important to make sure the right stakeholders participate in this working group. Designing responsible AI guiding principles will require debate, insights, and business decisions from a broad perspective across the enterprise.

1. Accelerate this exercise by leveraging an AI strategy that is aligned to the business strategy. Include:

• The organization's AI vision and objectives
• Business drivers for AI adoption
• Market research

• Who are the decision makers and key influencers?
• Who will impact the business?
• Who has a vested interest in the success or failure of the practice? Who has the skills and competencies necessary to help you be successful?

• Do not focus on the organizational structure and hierarchy. Often stakeholder groups do not fit the traditional structure.
• Do not ignore subject matter experts on either the business or IT side. You will need to consider both.

Assemble executive stakeholders

Set yourself up for success with these three steps.

CIOs tasked with designing digital strategies must add value to the business. Given the goal of digital is to transform the business, CIOs will need to ensure they have both the mandate and support from the business executives.

Designing the digital strategy is more than just writing up a document. It is an integrated set of business decisions to create a competitive advantage and financial returns. Establishing a forum for debates, decisions, and dialogue will increase the likelihood of success and support during execution.

1. Confirm your role
The AI strategy aims to transform the business. Given the scope, validate your role and mandate to lead this work. Identify a business executive to co-sponsor.

2. Identify stakeholders
Identify key decision makers and influencers who can help make rapid decisions as well as garner support across the enterprise.

3. Gather diverse perspectives

Align the AI strategy with the corporate strategy

Info-Tech Insight
AI projects are more successful when the management team understands the strategic importance of alignment. Time needs to be spent upfront aligning organizational strategies with AI capabilities. Effective alignment between IT and other departments should happen daily. Alignment doesn't occur at the executive level alone, but at each level of the organization.

Key AI strategy initiatives

AI Key Initiative Plan

Initiatives collectively support the business goals and corporate initiatives and improve the delivery of IT services.

Establish responsible AI guiding principles

Guiding principles help define the parameters of your AI strategy. They act as a priori decisions that establish guardrails to limit the scope of opportunities from the perspective of people, assets, capabilities, and budgetary perspectives that are aligned with the business objectives. Consider these components when brainstorming guiding principles:

Responsible AI guiding principles guide the development and deployment of the AI model in a way that considers human-based principles (such as fairness).

Start with foundational responsible AI guiding principles

(Optional) Customize responsible AI guiding principles

Here is an example for organizations in the healthcare industry

These guiding principles are customized to the industry and organizations but remain consistent in addressing the common core AI challenges.

Phase 2

Assess Current Level of AI Maturity

Phase 1
1. Establish Responsible AI Guiding Principles

Phase 2
1. Assess Current Level of AI Maturity

Phase 3
1. Prioritize Candidate Opportunities
2. Develop Policies

Phase 4
1. Build and Communicate the Roadmap

AI Maturity Model

A principle-based approach is required to advance AI maturity

Technology-Centric: These maturity levels focus primarily on addressing the technical challenges of building a functional AI model.

Principle-Based: Beyond the technical challenges of building the AI model are human-based principles that guide development in a responsible manner to address consumer and government demands.

AI Maturity Dimensions

Assess your AI maturity to understand your organization's ability to deliver in a digital age

AI Governance
Does your organization have an enterprise-wide, long-term strategy with clear alignment on what is required to accomplish it?

Data Management
Does your organization embrace a data-centric culture that shares data across the enterprise and drives business insights by leveraging data?

People
Does your organization employ people skilled at delivering AI applications and building the necessary data infrastructure?

Process
Does your organization have the technology, processes, and resources to deliver on its AI expectations?

Technology
Does your organization have the required data and technology infrastructure to support AI-driven digital transformation?

AI Maturity Model dimensions and characteristics

AI Maturity Dimension:

AI Governance

Requirements

• AI governance requires establishing policies and procedures for AI model development and deployment. Organizations begin with an awareness of the role of AI governance and evolve to a level to where AI governance is integrated with organization-wide corporate governance.

Challenges

• Beyond the governance of AI technology, the organization needs to evolve the governance program to align to responsible AI guiding principles.

Initiatives

• Establish responsible AI guidelines to govern AI development.
• Introduce an AI review board to review all AI projects.
• Introduce automation and standardize AI development processes.

AI governance is a foundation for responsible AI

Responsible AI Principles are a part of how you manage and govern AI

Monitoring
Monitoring compliance and risk of AI/ML systems/models in production

Tools & Technologies
Tools and technologies to support AI governance framework implementation

Model Governance
Ensuring accountability and traceability for AI/ML models

Organization
Structure, roles, and responsibilities of the AI governance organization

Operating Model
How AI governance operates and works with other organizational structures to deliver value

Risk & Compliance
Alignment with corporate risk management and ensuring compliance with regulations and assessment frameworks

Policies/Procedures/ Standards
Policies and procedures to support implementation of AI governance

AI Maturity Dimension:

Data Management

Requirements

• Organizations begin their data journey with a focus on pursuing quality data for the AI model. As organizations evolve, data management tools are leveraged to automate the capture, integration, processing, and deployment of data.

Challenges

• A key challenge is to acquire large volumes of quality data to properly train the model. In addition, maintaining data privacy, automating the data management lifecycle, and ensuring data is used in a responsible manner are ongoing challenges.

Initiatives

• Implement GDPR requirements.
• Establish responsible data collection and processing practices.
• Implement strong information security and data protection practices.
• Implement a data governance program throughout the organization.

Data governance enables AI

• Integrity, quality, and security of data are key outputs of data governance programs, as well as necessities for effective AI.
• Data governance focuses on creating accountability at the internal and external stakeholder level and establishing a set of data controls from technical, process, and policy perspectives.
• Without a data governance framework, it is increasingly difficult to harness the power of AI integration in an ethical and organization-specific way.

Data Governance in Action

Canada has recently established the Canadian Data Governance Standardization Collaborative governed by the Standards Council of Canada. The purpose is multi-pronged:

• Examine the foundational elements of data governance (privacy, cybersecurity, ethics, etc.).
• Lay out standards for data quality and data collection best practices.
• Examine infrastructure of IT systems to support data access and sharing.
• Build data analytics to promote effective and ethical AI solutions.

Source: Global Government Forum

Download the Establish Data Governance blueprint

AI Maturity Dimension:

People

Requirements

• Several data-centric skills and roles are required to successfully build, deploy, and maintain the AI model. The organization evolves from having few skills to everybody being able to leverage AI to enhance business outcomes.

Challenges

• AI skills can be challenging to find and acquire. Many organizations are investing in education to enhance their existing resources, leveraging no-code systems and software as a service (SaaS) applications to address the skills gap.

Initiatives

• Promote a data-centric culture throughout the organization.
• Leverage and educate technical-oriented business analysts and business-oriented data engineers to help address the demand for skilled resources.
• Develop an AI Center of Excellence accessible by all departments for education, guidance, and best practices for building, deploying, and maintaining the AI model.

Multidisciplinary skills are required for successful implementation of AI applications

Blending AI with technology and business domain understanding is key. Neither can be ignored.

Business Domain Expertise

• Business Analysts
• Industry Analysts

AI/Data Skills

• Data Scientists
• Data Engineers
• Data Analysts

IT Skills

• Database Administrators
• Systems Administrators
• Compute Specialists

AI Maturity Dimension:

Process

Requirements

• Automating processes involved with building, deploying, and maintaining the model is required to enable the organization to scale, enforce standards, improve time to market, and reduce costs. The organization evolves from performing tasks manually to an environment where all major processes are AI enabled.

Challenges

• Many solutions are available to automate the development of the AI model. There are fewer tools to automate responsible AI processes, but this market is growing rapidly.

Initiatives

• Assess opportunities to accelerate AI development with the adoption of MLOps.
• Assess responsible AI toolkits to test compliance with guiding principles.

Automating the AI development process

Evolving to a model-driven environment is pivotal to advancing your AI maturity

Current Environment

Model Development - Months

• Model rewriting
• Manual optimization and scaling
• Development/test/release
• Application monoliths

Data Discovery & Prep - Weeks

• Navigating data silos
• Unactionable metadata
• Tracing lineage
• Cleansing and integration
• Privacy and compliance

Install Software and Hardware - Week/Months

• Workload contention
• Lack of tool flexibility
• Environment request and setup
• Repeatability of results
• Lack of data and model sharing

Model-Driven Development

Machine Learning as a Service (MLaaS) - Weeks

• Apply DevOps and continuous integration/delivery (CI/CD) principles
• Microservices/Cloud-native applications
• Model portability and reuse
• Streaming/API integration

Data as a Service - Hours

• Self-service data catalog
• Searchable metadata
• Centralized access control
• Data collaboration
• Data virtualization

Platform as a Service - Minutes/Hours

• Self-service data science portal
• Integrated data sandbox
• Environment agility
• Multi-tenancy

Shared, Optimized Infrastructure

AI Maturity Dimension:

Technology

Requirements

• A technology platform that is optimized for AI and advanced analytics is required. The organization evolves from ad hoc systems to an environment where the AI hardware and software can be deployed through a self-service model.

Challenges

• Software and hardware platforms to optimize AI performance are still relatively new to most organizations. Time spent on optimizing the technology platform can have a significant impact on the overall performance of the system.

Initiatives

• Assess the landscape of AI enablers that can drive business value for the organization.
• Assess opportunities to accelerate the deployment of the AI platform with the adoption of infrastructure as a service (IaaS) and platform as a service (PaaS).
• Assess opportunities to accelerate performance with the optimization of AI accelerators.

AI enablers

Use case requirements should drive the selection of the tool

AI and data analytics data platform

An optimized data platform is foundational to maximizing the value from AI

Data Platform Capabilities

• Support for a variety of analytical applications, including self-service, operational, and data science analytics.
• Data preparation and integration capabilities to ingest structured and unstructured data, move and transform raw data to enriched data, and enable data access for the target userbase.
• An infrastructure platform optimized for advanced analytics that can perform and scale.

Infrastructure - AI accelerators

"By 2025, 70% of companies will invest in alternative computing technologies to drive business differentiation by compressing time to value of insights from complex data sets."
- IDC

2.1 Assess current AI maturity

1-3 hours

It is important to understand the current capabilities of the organization to deliver and deploy AI-based applications. Consider that advancing AI capabilities will also involve organizational changes and integration with the organization's governance and risk management programs.

1. Assess the organization's current state of AI capabilities with respect to its AI governance, data, people, process, and technology infrastructure using Info-Tech's AI Maturity Assessment & Roadmap Tool.
2. Consider the following as you complete the assessment:
1. What is the state of AI and data governance in the organization?
2. Does the organization have the skills, processes, and technology environment to deliver AI-based applications?
3. What organization will be accountable for any and all business outcomes of using the AI applications?
4. Has a risk assessment been performed?
3. Make sure you avoid the following common mistakes:
1. Do not focus only on addressing the technical challenges of building the AI model.
2. Do not ignore subject matter experts on either the business or IT side. You will need to consider both.

Download the AI Maturity Assessment & Roadmap Tool

Perform the AI Maturity Assessment

The Scale

Assess your AI maturity by selecting the maturity level that closest resembles the organization's current AI environment. Maturity dimensions that contribute to overall AI maturity include AI governance, data management, people, process, and technology capabilities.

Exploration (1.0)

• No experience building or using AI applications.

Incorporation (2.0)

• Some skills in using AI applications, or AI pilots are being considered for use.

Proliferation (3.0)

• AI applications have been adopted and implemented in multiple departments. Some of the responsible AI guiding principles are addressed (i.e. data privacy).

Optimization (4.0)

• The organization has automated the majority of its digital processes and leverages AI to optimize business operations. Controls are in place to monitor compliance with responsible AI guiding principles.

Transformation (5.0)

• The organization has adopted an AI-native culture and approach for building or implementing new business capabilities. Responsible AI guiding principles are operationalized with AI processes that proactively address possible breaches or risks associated with AI applications.

Perform the AI Maturity Assessment

AI Governance (1.0-5.0)

1. Is there awareness of the role of AI governance in our organization?

• No formal procedures are in place for AI development or deployment of applications.

• No group is assigned to be responsible for AI governance in our organization.

• Our organization has adopted and enforces standards for developing and deploying AI applications throughout the organization.

• Our organization is integrating an AI risk framework with the corporate risk management framework.

• Our organization leads the industry with the inclusion of responsible AI guiding principles with respect to transparency, accountability, risk, and governance.

Data Management/AI Data Capabilities (1.0-5.0)

1. Is there an awareness in our organization of the data requirements for developing AI applications?

• Data is often siloed and not easily accessible for AI applications.

• Required data is pulled from various sources in an ad hoc manner.

• Tools are available to manage the data lifecycle and support the data governance program.

• The data platform has been optimized for performance and access.

• Data culture exists throughout our organization, and data can be leveraged to drive innovation initiatives.

People/AI Skills in the Organization (1.0-5.0)

1. Is there an awareness in our organization of the skills required to build AI applications?

• No or very little skills exist throughout our organization.

• No formal group is assigned to build AI applications.

• An AI Center of Excellence has been formed to review, develop, deploy, and maintain AI applications.

• AI skills and people responsible for AI applications are spread throughout our organization.

• The entire organization is knowledgeable on how to leverage AI to transform the business.

Perform the AI Maturity Assessment

AI Processes (1.0-5.0)

1. Is there an awareness in our organization of the core processes and supporting tools that are required to build and support AI applications?

• There are few or no automated tools to accelerate the AI development process.

• Only ad hoc practices are used for developing AI applications.

• Our organization has documented standards in place for developing AI applications and deploying them AI to production.

• Our organization can leverage tools to perform an AI risk assessment and demonstrate compliance with the risk management framework.

• Our organization leads the industry in driving innovation through digital transformation.

Technology/AI Infrastructure (1.0-5.0)

1. Is there an awareness in our organization of the infrastructure (hardware and software) required to build AI applications?

• There is little awareness of what infrastructure is required to build and support AI applications.

• There is no dedicated infrastructure for the development of AI applications.

• Our organization is leveraging purpose-built infrastructure to optimize performance.

• Our organization is leveraging cloud-based deployment models to support AI applications in on-premises, hybrid, and public cloud platforms.

• Our organization leads the industry with its ability to respond to change and to leverage AI to improve business outcomes.

Phase 3

Prioritize Candidate Opportunities and Develop Policies

Phase 1
1. Establish Responsible AI Guiding Principles

Phase 2
1. Assess Current Level of AI Maturity

Phase 3
1. Prioritize Candidate Opportunities
2. Develop Policies

Phase 4
1. Build and Communicate the Roadmap

3.1 Prioritize candidate AI opportunities

1-3 hours

Identify business opportunities that are high impact to your business and its customers and have low implementation complexity.

1. Leverage the business capability map for your organization or industry to identify candidate business capabilities to augment or automate with generative AI.
2. Establish criteria to assess candidate use cases by evaluating against the organization's mission and goals, the responsible AI guiding principles, and the complexity of the project.
3. Ensure that candidate business capabilities to be automated align with the organization's business criteria, responsible AI guiding principles, and resources to deliver the project.
4. Make sure you avoid sharing the organization's sensitive data if the application is deployed on the public cloud.

Download the AI Maturity Assessment and Roadmap Tool

The business capability map for an organization

A business capability map is an abstraction of business operations that helps describe what the enterprise does to achieve its vision, mission, and goals, rather than how. Business capabilities are the building blocks of the enterprise. They represent stable business functions, are unique and independent of each other, and typically will have a defined business outcome.

Business capabilities are supported by people, process, and technology.

While business capability maps are helpful tools for a variety of strategic purposes, in this context they act as an investigation into what technology your business units use and how they use it.

Defining Capabilities
Activities that define how the entity provides services. These capabilities support the key value streams for the organization.

Enabling Capabilities
Support the creation of strategic plans and facilitate business decision making as well as the functioning of the organization (e.g. information technology, financial management, HR).

Shared Capabilities
These predominantly customer-facing capabilities demonstrate how the entity supports multiple value streams simultaneously.

Leverage your industry's capability maps to identify candidate opportunities/initiatives

Business capability map defined...

In business architecture, the primary view of an organization is known as a business capability map.

A business capability defines what a business does to enable value creation, rather than how. Business capabilities:

• Represent stable business functions.
• Are unique and independent of each other.
• Typically will have a defined business outcome.

A business capability map provides details that help the business architecture practitioner direct attention to a specific area of the business for further assessment.

Note: This is an illustrative business capability map example for Marketing & Advertising

Business value vs. complexity assessment

Leverage our simple value-to-effort matrix to help prioritize your AI initiatives

Common business value drivers

• Drive revenue
• Improve operational excellence
• Accelerate innovation
• Mitigate risk

Common project complexity characteristics

• Resources required
• Costs (acquisition, operational, support...)
• Training required
• Risk involved
• Etc.

1. Determine a business value and project complexity score for the candidate business capability or initiative.
2. Plot initiatives on the matrix.
3. Prioritize initiatives with high business value and low complexity.

Assess business value vs. project complexity to prioritize candidate opportunities for generative AI

Prioritize opportunities/initiatives with high business value and low project complexity

Prioritization criteria exercise 1: Assessing the Create Content capability

Assessing the Create Content capability

This opportunity is removed because it does not pass the organization/business criteria

Prioritization criteria exercise 2: Assessing the Content Production capability

Assessing the Content Production capability

This opportunity is accepted because it passes the organization's business, responsible AI, and project criteria

3.2 Communicate policies for AI use

1-3 hours

1. Ensure policies for usage align with the organization's business criteria, responsible AI guiding principles, and ability to deliver the projects prioritized and beyond.
2. Understand the current benefits as well as limits and risk associated with any proposed generative AI-based solution.
3. Ensure you consider the following:
1. What data is being shared with the application?
2. Is the generative AI application deployed on the public cloud? Can anybody access the data provided to the application?
3. Avoid using very technical, legal, or fear-based communication for your policies.

Generative AI policy for the Create Content capability

Aligning policies to direct the uses assessed and implemented is essential

Example

Many of us have been involved in discussions regarding the use of ChatGPT in our marketing and sales initiatives. ChatGPT is a powerful tool that needs to be used in a responsible and ethical manner, and we also need to ensure the integrity and accuracy of its results. Here is our policy on the use of ChatGPT:

• You are free to use generative AI to assist your searches, but there are NO circumstances under which you are to reproduce generative AI output (text, image, audio, video, etc.) in your content.

If you have any questions regarding the use of ChatGPT, please feel free to reach out to our generative AI team and/or any member of our senior leadership team.

Generative AI policy for the Content Production capability

These policies should align to and reinforce your responsible AI principles

Example

Many of us have been involved in discussions regarding the use of ChatGPT in our deliverables. ChatGPT is a powerful tool that needs to be used in a responsible and ethical manner, and we also need to ensure the integrity and accuracy of its results. Here is our policy on the use of ChatGPT:

• If you use ChatGPT, you need to assess the accuracy of its response before including it in our content. Assessment includes verifying the information, seeing if bias exists, and judging its relevance.
• Employees must not:
• Provide any customer, citizen, or third-party content to any generative AI tool (public or private) without the express written permission of the CIO or the Chief Information Security Officer. Generative AI tools often use input data to train their model, therefore potentially exposing confidential data, violating contract terms and/or privacy legislation, and placing the organization at risk of litigation or causing damage to our organization.
• Engage in any activity that violates any applicable law, regulation, or industry standard.
• Use services for illegal, harmful, or offensive purposes.
• Create or share content that is deceptive, fraudulent, or misleading or that could damage the reputation of our organization.
• Use services to gain unauthorized access to computer systems, networks, or data.
• Attempt to interfere with, bypass controls of, or disrupt operations, security, or functionality of systems, networks, or data.

If you have any questions regarding the use of ChatGPT, please feel free to reach out to our generative AI team and/or any member of our senior leadership team.

Phase 4

Build the Roadmap

Phase 1
1. Establish Responsible AI Guiding Principles

Phase 2
1. Assess Current Level of AI Maturity

Phase 3
1. Prioritize Candidate Opportunities
2. Develop Policies

Phase 4
1. Build and Communicate the Roadmap

4.1.1 Create the implementation plan for each prioritized initiative

1-3 hours

1. Build the implementation plan for each accepted use case using the roadmap template.
2. Assess the firm's capabilities with respect to the dimensions of AI maturity and target the future-state capabilities you need to develop.
3. Prepare by assessing the risk of the proposed use cases.
4. Ensure initiatives align with organizational objectives.
5. Ensure all AI initiatives have a defined value expectation.
6. Do not ignore subject matter experts on either the business or IT side. You will need to consider both.

Download the AI Maturity Assessment and Roadmap Tool

Target-state options

Identify the future-state capabilities that need to be developed to deliver your use cases

1. Build an implementation plan for each use case to adopt.
2. Assess if the current state of the AI environment can be leveraged to deliver the selected generative AI use cases.
3. If the current AI environment is not sufficient, identify the future state required that will enable the delivery of the generative AI use cases. Identify gaps and build the roadmap to address the gaps.

4.1.2 Design metrics for success

1-2 hours

Establish metrics to measure to determine the success or failure of each POC.

1. Discuss which relevant currently tracked metrics are useful to continue tracking for the POC.
2. Discuss which metrics are irrelevant to the POC.
3. Discuss metrics to start tracking and how to track them with the generative AI vendor.
4. Compile a list of metrics relevant to the POC.
5. Decide what the outcome is if the metric is high or low, including decision steps and relevant actions.
6. Designate a generative AI application owner and a vendor liaison.

Prepare by building an implementation plan for each candidate use case (previous step).

Include key performance indicators (KPIs) and metrics that measure the application's contribution to strategic initiatives.

Consider assigning a vendor liaison to accelerate the implementation and adoption of the generative AI-based solution.

Generative AI POC metrics - examples

You need to measure the effectiveness of your initiatives. Here are some typical examples.

GitHub Copilot POC business value - example

Quantifying the benefits of GitHub Copilot to demonstrate measurable business value

POC Results

Task 1: Creating a web server in JavaScript

• Time to complete task with GitHub Copilot: 1 hour 11 minutes
• Time to complete the task without GitHub Copilot: 2 hours 41 minutes
• Productivity Gain = (1 hour 30 minutes time saved) / (2 hours 41 minutes) = 55%
• Benefit per Programmer = 55% x (average salary of a programmer)
• Total Benefit of GitHub Copilot for Task 1 = (benefit per programmer) x (# of programmers)

Enterprise Value of GitHub Copilot = Total Benefit of GitHub Copilot for Task 1 + Total Benefit of GitHub Copilot for Task 2 + ... + Total Benefit of GitHub Copilot for Task n

Source: GitHub

4.1.3 Build your generative AI initiative roadmap

1-3 hours

The roadmap should provide a compelling vision of how you will deliver the identified generative AI applications by prioritizing and simplifying the actions required to deliver these new initiatives.

1. Leverage tab 4, Initiative Planning, in the AI Maturity Assessment and Roadmap Tool to create and align your initiatives to the key value driver they are most relevant to:
1. Transfer the results of your value and complexity assessments to this tool to drive the prioritization.
2. Assign responsible owners to each initiative.
3. Identify which AI maturity capabilities each initiative will enhance. However, do not build or introduce new capabilities merely to advance the organization's AI maturity level.
2. Review the Gantt chart to ensure alignment and assess overlap.

Download the AI Maturity Assessment and Roadmap Tool

Build your generative AI roadmap to visualize your key project plans

Visual representations of data are more compelling than text alone.

Develop a high-level document that travels with the project from inception through to executive inquiry, project management, and finally execution.

A project needs to be discrete: able to be conceptualized and discussed as an independent item. Each project must have three characteristics:

• Specific outcome: An explicit change in the people, processes, or technology of the enterprise.
• Target end date: When the described outcome will be in effect.
• Owner: Who on the IT team is responsible for executing on the initiative.

Info-Tech Insight
Don't project your vision three to five years into the future. Deep dive on next year's big-ticket items instead.

4.1.4 Build a communication plan for your roadmap

1-3 hours

1. Identify your target audience and what they need to know.
2. Identify desired channels of communication and details for the target audience.
3. Describe communication required for each audience segment.
4. List frequency of communication for each audience segment.
5. Create an executive presentation leveraging The Era of Generative AI C-Suite Presentation and AI Maturity Assessment and Roadmap Tool.

Generative AI communication plan

Well-planned communications are essential to the success and adoption of your AI initiatives

To ensure that organization's roadmap is clearly communicated across the AI, data, technology, and business organizations, develop a rollout strategy, like this example.

Example

Deliver an executive presentation of the roadmap for the business stakeholders

After you complete the activities and exercises within this blueprint, the final step of the process is to present the deliverable to senior management and stakeholders.

Know Your Audience

• Business stakeholders are interested in understanding the business outcomes that will result from their investment in generative AI.
• Your audience will want to understand the risks involved and how to mitigate those risks.
• Explain how the generative AI project was selected and the criteria used to help draft generative AI usage policies.

Recommendations

• Highlight the need for responsible AI to ensure that human-based requirements are being addressed.
• Ensure your generative AI team includes both business and technical staff.

Download The Era of Generative AI C-Suite Presentation

Bibliography

"A pro-innovation approach to AI regulation." UK Department for Science, Innovation and Technology, March 2023. Web.

"Artificial Intelligence Act." European Commission, 21 April 2021. Web.

"Artificial Intelligence and Data Act (AIDA)." Canadian Federal Government, June 2022. Web.

"Artificial Intelligence Index Report 2023." Stanford University, April 2023. Web.

"Automated Employment Decision Tools." New York City Department of Consumer and Worker Protection, Dec. 2021. Web.

"Bain & Company announces services alliance with OpenAI to help enterprise clients identify and realize the full potential and maximum value of AI." Bain & Company, 21 Feb. 2023. Web.

"Buzzfeed to use AI to write its articles after firing 180 employees." Al Mayadeen English, 27 Jan. 2023. Web.

"California Consumers Privacy Act." State of California Department of Justice. April 24, 2023. Web.

Campbell, Ian Carlos. "The Apple Card doesn't actually discriminate against women, investigators say." The Verge, 23 March 2021. Web.

Campbell, Patrick. "NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0)." National Institute of Standards and Technology, Jan. 2023. Web.

"EU Ethics Guidelines For Trustworthy." European Commission, 8 April 2019. Web.

Farhi, Paul. "A news site used AI to write articles. It was a journalistic disaster." Washington Post, 17 Jan. 2023. Web.

Forsyth, Ollie. "Mapping the Generative AI landscape." Antler, 20 Dec. 2022. Web.

"General Data Protection Regulation (GDPR)" European Commission, 25 May 2018. Web.

"Generative AI Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2023-2028." IMARC Group, 2022. Web.

Guynn, Jessica. "Bing's ChatGPT is in its feelings: 'You have not been a good user. I have been a good Bing.'" USA Today, 14 Feb. 2023. Web.

Hunt, Mia. "Canada launches data governance standardisation initiative." Global Government Forum, 24 Sept. 2020. Web.

Johnston Turner, Mary. "IDC's Worldwide Future of Digital Infrastructure 2022 Predictions." IDC, 27 Oct. 2021. Web.

Kalliamvakou, Eirini. "Research: quantifying GitHub Copilot's impact on developer productivity and happiness." GitHub, 7 Sept. 2022. Web.

Kerravala, Zeus. "NVIDIA Brings AI To Health Care While Protecting Patient Data." eWeek, 12 Dec. 2019. Web.

Knight, Will. "The Apple Card Didn't 'See' Gender-and That's the Problem." Wired, 19 Nov. 2019. Web.

"OECD, Recommendation of the Council on Artificial Intelligence." OECD, 2022. Web.

"The National AI Initiative Act" U.S. Federal Government, 1 Jan 2021. Web.

"Trustworthy AI (TAI) Playbook." U.S. Department of Health & Human Services, Sept 2021. Web.

Info-Tech Research Contributors/Advocates

Joel McLean
Executive Chairman

David Godfrey
CEO

Gord Harrison
Senior Vice President, Research & Advisory Services

William Russell
CIO

Jack Hakimian
SVP, Research

Barry Cousins
Distinguished Analyst and
Research Fellow

Larry Fretz
Vice President, Industry Research

Tom Zehren
CPO

Mark Roman
Managing Partner II

Christine West
Managing Partner

Steve Willis
Practice Lead

Yatish Sewgoolam
Associate Vice President, Research Agenda

Rob Redford
Practice Lead

Mike Tweedie
Practice Lead

Neal Rosenblatt
Principal Research Director

Jing Wu
Principal Research Director

Irina Sedenko
Research Director

Jeremy Roberts
Workshop Director

Brian Jackson
Research Director

Mark Maby
Research Director

Stacey Horricks
Director, Social Media

Sufyan Al-Hassan
Public Relations Manager

Sam Kanen
Marketing Specialist

Create a Right-Sized Enterprise Architecture Governance Framework

Focus on process standardization, repeatability, and sustainability.

ANALYST PERSPECTIVE

"Enterprise architecture is not a technology concept, rather it is the foundation on which businesses orient themselves to create and capture value in the marketplace. Designing architecture is not a simple task and creating organizations for the future requires forward thinking and rigorous planning.

Architecture processes that are supposed to help facilitate discussions and drive option analysis are often seen as an unnecessary overhead. The negative perception is due to enterprise architecture groups being overly prescriptive rather than providing a set of options that guide and constrain solutions at the same time.

EA groups should do away with the direct and control mindset and change to a collaborate and mentor mindset. As part of the architecture governance, EA teams should provide an option set that constrains design choices, and also be open to changes to standards or best practices. "

Gopi Bheemavarapu, Sr. Manager, CIO Advisory Info-Tech Research Group

Our understanding of the problem

This Research Is Designed For:

• CIO
• IT Leaders
• Business Leaders
• Head of Enterprise Architecture
• Enterprise Architects
• Domain Architects
• Solution Architects

This Research Will Help You:

• Understand the importance of enterprise architecture (EA) governance and how to apply it to guide architectural decisions.
• Enhance your understanding of the organization’s current EA governance and identify areas for improvement.
• Optimize your EA engagement model to maximize value creation.
• Learn how to set up the optimal number of governance bodies in order to avoid bureaucratizing the organization.

This Research Will Also Assist:

• Business Relationship Managers
• Business Analysts
• IT Managers
• Project Managers
• IT Analysts
• Quality Assurance Leads
• Software Developers

This Research Will Help Them:

• Give an overview of enterprise architecture governance
• Clarity on the role of enterprise architecture team

Executive summary

Situation

• Deployed solutions do not meet business objectives resulting in expensive and extensive rework.
• Each department acts independently without any regular EA touchpoints.
• Organizations practice project-level architecture as opposed to enterprise architecture.

Complication

• EA governance is perceived as an unnecessary layer of bureaucracy because business benefits are poorly communicated.
• The organization doesn’t have a formalized EA practice.
• Where an EA practice exists, employees are unsure of EA’s roles and responsibilities.

Resolution

• Value-focused. Focus EA governance on helping the organization achieve business benefits. Promote EA’s contribution in realizing business value.
• Right-sized. Re-use existing process checkpoints, rather than creating new ones. Clearly define EA governance inclusion criteria for projects.
• Defined and measured process. Define metrics to measure EA’s performance and integrate EA governance with other governance processes such as project governance. Also clearly define the EA governing bodies’ composition, domain, inputs, and outputs.
• Strike the right balance. Adopt architecture principles that strikes the right balance between business and technology imperatives.

Info-Tech Insight

Enterprise architecture is critical to ensuring that an organization has the solid IT foundation it needs to efficiently enable the achievement of its current and future strategic goals rather than focusing on short-term tactical gains.

What is enterprise architecture governance?

An architecture governance process is the set of activities an organization executes to ensure that decisions are made and accountability is enforced during the execution of its architecture strategy. (Hopkins, “The Essential EA Toolkit.”)

EA governance includes the following:

• Implement a system of controls over the creation and monitoring of all architectural components.
• Ensure effective introduction, implementation, and evolution of architectures within the organization.
• Implement a system to ensure compliance with internal and external standards and regulatory obligations.
• Develop practices that ensure accountability to a clearly identified stakeholder community, both inside and outside the organization.

(TOGAF)

IT governance sets direction through prioritization and decision making, and monitors overall IT performance.

EA governance ensures that optimal architectural design choices are being made that focus on long-term value creation.

Harness the benefits of an optimized EA governance

Core benefits of EA governance are seen through:

Value creation

Effective EA governance ensures alignment between organizational investments and corporate strategic goals and objectives.

Cost reduction

Architecture standards provide guidance to identify opportunities for reuse and eliminate redundancies in an organization.

Risk optimization

Architecture review processes and assessment checklists ensure that solutions are within the acceptable risk levels of the organization.

EA governance is difficult to structure appropriately, but having an effective structure will allow you to:

• Achieve business strategy through faster time-to-market innovations and capabilities.
• Reduced transaction costs with more consistent business processes and information across business units.
• Lower IT costs due to better traceability, faster design, and lower risk.
• Link IT investments to organizational strategies and objectives
• Integrate and institutionalizes IT best practices.
• Enable the organization to take full advantage of its information, infrastructure, and hardware and software assets.
• Support regulatory as well as best practice requirements such as auditability, security, responsibility, and accountability.

Organizations that have implemented EA governance realize greater benefits from their EA programs

Modern day CIOs of high-performing organizations use EA as a strategic planning discipline to improve business-IT alignment, enable innovation, and link business and IT strategies to execution.

Recent Info-Tech research found that organizations that establish EA governance realize greater benefits from their EA initiatives.

(Info-Tech Research Group, N=89)

Measure EA governance implementation effectiveness

Define key operational measures for internal use by IT and EA practitioners. Also, define business value measures that communicate and demonstrate the value of EA as an “enabler” of business outcomes to senior executives.

Enterprise Architecture Management

• Number of months since the last review of target state EA blueprints.

IT Investment Portfolio Management

• Percentage of projects that were identified and proposed by EA.

Solution Development

• Number of projects that passed EA reviews.
• Number of building blocks reused.

Operations Management

• Reduction in the number of applications with overlapping functionality.

Business Value

• Lower non-discretionary IT spend.
• Decreased time to production.
• Higher satisfaction of IT-enabled services.

An insurance provider adopts a value-focused, right-sized EA governance program

CASE STUDY

Industry Insurance

Source Info-Tech

Situation

The insurance sector has been undergoing major changes, and as a reaction, businesses within the sector have been embracing technology to provide innovative solutions.

The head of EA in a major insurance provider (henceforth to be referred to as “INSPRO01”) was given the mandate to ensure that solutions are architected right the first time to maximize reuse and reduce technology debt. The EA group was at a critical point – to demonstrate business value or become irrelevant.

Complication

The project management office had been accountable for solution architecture and had placed emphasis on short-term project cost savings at the expense of long term durability.

There was a lack of awareness of the Enterprise Architecture group within INSPRO01, and people misunderstood the roles and responsibilities of the EA team.

Result

Info-Tech helped define the responsibilities of the EA team and clarify the differences between the role of a Solution Architect vs. Enterprise Architect.

The EA team was able to make the case for change in the project management practices to ensure architectures are reviewed and approved prior to implementation.

As a result, INSPRO01 saw substantial increases in reuse opportunities and thereby derived more value from its technology investments.

Success factors for EA governance

The success of any EA governance initiative revolves around adopting best practices, setting up repeatable processes, and establishing appropriate controls.

1. Develop best practices for managing architecture policies, procedures, roles, skills, and organizational structures.
2. Establish organizational responsibilities and structures to support the architecture governance processes.
3. Management of criteria for the control of the architecture governance processes, dispensations, compliance assessments, and SLAs.

Info-Tech’s approach to EA governance

Our best-practice approach is grounded in TOGAF and enhanced by the insights and guidance from our analysts, industry experts, and our clients.

Value-focused. Focus EA governance on helping the organization achieve business benefits. Promote EA’s contribution in realizing business value.

Right-sized. Insert EA governance into existing process checkpoints rather than creating new ones. Clearly define EA governance inclusion criteria for projects.

Measured. Define metrics to measure EA’s performance, and integrate EA governance with other governance processes such as project governance. Also clearly define the EA governing bodies’ composition, domain, inputs, and outputs.

Balanced. Adopt architecture principles that strikes the right balance between business and technology.

Info-Tech’s EA governance framework

Info-Tech’s architectural governance framework provides a value-focused, right-sized approach with a strong emphasis on process standardization, repeatability, and sustainability.

1. Current state of EA governance
2. EA fundamentals
3. Engagement model
4. EA governing bodies
5. EA policy
6. Architectural standards
7. Communication Plan

Use Info-Tech’s templates to complete this project

1. Current state of EA governance
   • EA Capability - Risk and Complexity Assessment Tool
   • EA Governance Assessment Tool
2. EA fundamentals
   • EA Vision and Mission Template
   • EA Goals and Measures Template
   • EA Principles Template
3. Engagement model
   • EA Engagement Model Template
4. EA governing bodies
   • Architecture Board Charter Template
   • Architecture Review Process Template
5. EA policy
   • EA Policy Template
   • Architecture Assessment Checklist Template
   • Compliance Waiver Process Template
   • Compliance Waiver Form Template
6. Architectural standards
   • Architecture Standards Update Process Template
7. Communication Plan
   • EA Governance Communication Plan Template
   • EA Governance Framework Template

As you move through the project, capture your progress with a summary in the EA Governance Framework Template.

Download the EA Governance Framework Template document for use throughout this project.

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

“Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful.”

Guided Implementation

“Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track.”

Workshop

“We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place.”

Consulting

“Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project.”

Diagnostics and consistent frameworks used throughout all four options

EA governance framework – phase-by-phase outline (1/2)

EA governance framework – phase-by-phase outline (2/2)

Workshop overview

Contact your account representative or email Workshops@InfoTech.com for more information.

Phase 1

Current State of EA Governance

Create a Right-Sized Enterprise Architecture Governance Framework

Current State of EA Governance

1. Current State of EA Governance
2. EA Fundamentals
3. Engagement Model
4. EA Governing Bodies
5. EA Policy
6. Architectural Standards
7. Communication Plan

This phase will walk you through the following activities:

• Determine organizational complexity
• Conduct an assessment of the EA governance components
• Identify and prioritize gaps

This step involves the following participants:

• CIO
• IT Leaders
• Business Leaders
• Head of Enterprise Architecture
• Enterprise Architects
• Domain Architects
• Solution Architects

Outcomes of this step

• Prioritized list of gaps

Info-Tech Insight

Correlation is not causation – an apparent problem might be a symptom rather than a cause. Assess the organization’s current EA governance to discover the root cause and go beyond the symptoms.

Phase 1 guided implementation outline