Publish an Annual AI Performance Report

Organizations are investing heavily in AI, but most lack a structured and repeatable way to define, measure, and communicate AI performance. You need to:

Develop an approach to measure business value and ROI across multiple business functions.
Assess the performance of your AI program to drive accountability, outcomes, and year-over-year improvement.

Our Advice

Critical Insight

Making progress against this mandate is challenging, because:

There’s no clear view into the initiatives in progress across the organization.
There’s no agreement on KPIs, baselines, dashboards, and reporting approach, leading to fragmented data and inconsistent metrics.

As a result, leaders struggle to justify continued AI investment or make informed decisions about scaling, funding, or improving their AI portfolio.

Impact and Result

Use this methodology to prepare for and develop an annual AI performance report to measure, benchmark, and communicate AI value consistently. You will:

Define the scope of your measurement program.
Identify standardized, business-aligned KPIs so every AI initiative is assessed in a comparable, repeatable way across the organization.
Create a polished, executive-facing report that helps inform and drive decision-making on your AI portfolio.

Publish an Annual AI Performance Report Research & Tools

1. Publish an Annual AI Performance Report Deck – Measure your AI performance to maximize return and mitigate risks with your AI investments.

This storyboard provides a structured approach to measuring and communicating the business value of AI initiatives, helping organizations understand how to evaluate AI performance and move beyond ad hoc reporting toward a standardized, enterprise-wide view of AI impact.

This storyboard introduces the concept of an annual AI performance report and outlines how organizations can assess AI initiatives across key value dimensions, consolidate performance insights, and use these insights to support strategic decision-making, portfolio governance, and future AI investments.

2. AI Annual Performance Report Executive Presentation Template – Use this template to support the proposal discussion for your AI Performance Report.

Use this tool to consolidate AI performance data and narratives into a single, executive-ready view of business impact, helping organizations overcome siloed initiatives and inconsistent metrics and enabling leaders to make informed decisions on where to invest in, improve, scale, or stop AI initiatives and steer the AI roadmap for the next year.

3. AI Value and Performance Evaluation Tool – Use this tool to define and assess the value of your AI initiatives and the performance of your organization against its AI targets.

This workbook helps you measure performance and value against defined KPIs, compare baseline and achieved outcomes, and determine whether AI investments are delivering expected business value.

Publish an Annual AI Performance Report

Measure your AI performance to maximize return and mitigate risks with your AI investments.

Analyst perspective

Connect AI initiatives with measurable business value and organizational priorities.

As organizations increasingly integrate artificial intelligence into core operations, the ability to measure, communicate, and govern AI performance and value has become essential. An annual AI performance report provides a structured mechanism for organizations to evaluate the outcomes of their AI initiatives and communicate their impact to key stakeholders. By consolidating insights on AI-driven value, operational effectiveness, and responsible AI practices, organizations can ensure that AI investments remain aligned with enterprise strategy and business priorities.

Use the methodology and tools in this research to connect AI strategy to execution by translating technical deployments into measurable business outcomes. It enables leaders to assess how AI initiatives contribute to customer and stakeholder satisfaction, financial performance, process improvement, operational effectiveness, and organizational innovation.

Publishing an annual AI performance report also strengthens transparency and accountability across the enterprise. A structured performance report provides visibility into how AI initiatives perform, the value realized, and the governance mechanisms used to support responsible and trustworthy AI practices.

Sumegha Sama.

Sumegha Sama
Research Analyst – AI
Info-Tech Research Group

Executive summary

Your Challenge	Common Obstacles	Info-Tech’s Approach
Organizations are investing heavily in AI, but most lack a structured and repeatable way to define, measure, and communicate AI performance. You’re taking the lead on AI adoption in your organization, and you need to: Develop an approach to measure business value and return on investment (ROI) across multiple business functions. Assess the performance of your AI program to drive accountability, outcomes, and year-over-year improvement.	Making progress against this mandate is challenging because: There’s no clear view into the initiatives in progress across the organization. There’s no agreement on KPIs, baselines, dashboards, and reporting approach, leading to fragmented data and inconsistent metrics. As a result, leaders struggle to justify continued AI investment or make informed decisions about scaling, funding, or improving their AI portfolio.	Use this methodology to prepare for and develop an Annual AI performance report to measure, benchmark, and communicate AI value consistently. Define the scope of your measurement program. Identify standardized, business-aligned KPIs so every AI initiative is assessed in a comparable, repeatable way across the organization. Create a polished, executive-facing report that helps inform and drive decision-making on your AI portfolio.

Your Challenge

Common Obstacles

Info-Tech’s Approach

Organizations are investing heavily in AI, but most lack a structured and repeatable way to define, measure, and communicate AI performance.

You’re taking the lead on AI adoption in your organization, and you need to:

Develop an approach to measure business value and return on investment (ROI) across multiple business functions.
Assess the performance of your AI program to drive accountability, outcomes, and year-over-year improvement.

Making progress against this mandate is challenging because:

There’s no clear view into the initiatives in progress across the organization.
There’s no agreement on KPIs, baselines, dashboards, and reporting approach, leading to fragmented data and inconsistent metrics.
As a result, leaders struggle to justify continued AI investment or make informed decisions about scaling, funding, or improving their AI portfolio.

Use this methodology to prepare for and develop an Annual AI performance report to measure, benchmark, and communicate AI value consistently.

Define the scope of your measurement program.
Identify standardized, business-aligned KPIs so every AI initiative is assessed in a comparable, repeatable way across the organization.
Create a polished, executive-facing report that helps inform and drive decision-making on your AI portfolio.

Info-Tech Insight
Organizations struggle to measure and govern AI investments effectively. A standardized annual AI performance report transforms fragmented initiatives into a measured portfolio, enabling leaders to prove value, prioritize investments, and scale AI with evidence-backed decisions.

Your challenge

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

Establish a structured and repeatable approach to track, measure, and communicate AI performance across the enterprise.
Create a single consolidated view of AI initiatives across business functions, reducing siloed reporting and inconsistent metrics.
Define clear, comparable performance metrics to assess AI value, ROI, and business impact year over year.
Enable executives to identify which AI initiatives should be scaled, improved, or stopped based on evidence, not anecdotes.
Produce an executive-ready annual AI performance report that supports informed investment decisions and future AI roadmap planning.

74% of companies struggle to achieve and scale measurable AI value.
Source: Integrate.io, 2026

The image contains a screenshot of the thought model on Publish an Annual AI Performance Report.

Common obstacles

These barriers are challenging for many organizations:

Lack of clear AI performance metrics and baselines, resulting in fragmented data, inconsistent KPIs, and limited visibility across AI use cases and business functions.
AI performance measured at a model or technical level rather than against business outcomes, value realization, and enterprise impact.
Inconsistent reporting and unclear ownership, with performance reviews varying by team and no central governance or standard oversight model.
No standardized dashboards or review cadence, preventing leaders from tracking progress, comparing initiatives, or identifying underperforming AI investments.
Limited ability to prove AI value, making it difficult to prioritize initiatives, justify continued funding, or make informed scale, improve, or stop decisions.

Info-Tech’s methodology to publish an annual AI performance report

	Phase 1: Define AI Value & Success	Phase 2: Measure Your Performance	Phase 3: Build Your AI Performance Report
Phase Steps	1.1 Identify the AI initiatives in your funnel 1.2 Define value dimensions for your organization 1.3 Apply AI guiding principles to value and performance measurement 1.4 Define project value	2.1 Establish your measurement approach 2.2 Measure your progress	3.1 Communicate impact and drive decisions 3.2 Build a communication plan for your roadmap
Phase Outcomes	Top initiatives AI value framework Measurement approach	Validated performance metrics, targets, and measurement formulas Calculated performance results Indexed performance scores	Communications plan for all impacted stakeholders Annual AI performance report presentation

Phase 1:

Define AI Value & Success

Phase 2:

Measure Your Performance

Phase 3:

Build Your AI Performance Report

Phase Steps

1.1 Identify the AI initiatives in your funnel

1.2 Define value dimensions for your organization

1.3 Apply AI guiding principles to value and performance measurement

1.4 Define project value

2.1 Establish your measurement approach

2.2 Measure your progress

3.1 Communicate impact and drive decisions

3.2 Build a communication plan for your roadmap

Phase Outcomes

Top initiatives
AI value framework
Measurement approach

Validated performance metrics, targets, and measurement formulas
Calculated performance results
Indexed performance scores

Communications plan for all impacted stakeholders
Annual AI performance report presentation

Blueprint Deliverables

Key deliverable

Annual AI Performance Report Executive Presentation Template

This template enables you to shape your generative AI roadmap and communicate the value of the initiatives to your C-suite sponsors.

Supporting deliverable

AI Value and Performance Evaluation Tool

Use this structured tool to assess whether an AI initiative delivers measurable business value across defined performance dimensions before scaling or further investment.

Blueprint benefits

IT Benefits	Business Benefits
Develop consistent KPIs and methodologies for measuring AI performance. Eliminate fragmented reporting and enable repeatable, enterprise-wide measurement. Gain visibility into AI initiatives across planning, pilot, and production stages. Improve resource prioritization and reduce duplication of tools and efforts.	Develop a structured approach to quantify AI impact across cost, productivity, revenue, and risk. Consolidate measures of AI performance across the organization into a single enterprise view to align AI initiatives with your business strategy and organizational goals. Identify AI initiatives delivering measurable value. Enable leaders to make evidence-based decisions on where to invest in scaling or stop AI efforts.

IT Benefits

Business Benefits

Develop consistent KPIs and methodologies for measuring AI performance.
Eliminate fragmented reporting and enable repeatable, enterprise-wide measurement.
Gain visibility into AI initiatives across planning, pilot, and production stages.
Improve resource prioritization and reduce duplication of tools and efforts.

Develop a structured approach to quantify AI impact across cost, productivity, revenue, and risk.
Consolidate measures of AI performance across the organization into a single enterprise view to align AI initiatives with your business strategy and organizational goals.
Identify AI initiatives delivering measurable value.
Enable leaders to make evidence-based decisions on where to invest in scaling or stop AI efforts.

Measure the value of this blueprint

Leverage this blueprint to institutionalize AI performance measurement, enabling transparent value reporting and informed investment decision-making.

Project Outcome	Measured Value Delivered
Pick the projects that truly return value, rather than experiments.	For example: Code Generation Software Testing Document and Workflow Automation: Save 20 hours week, reduce data extraction and labelling time by 50%. Knowledge Management
Highlight your successes for an executive audience.	Clear executive-ready summaries of AI performance and business outcome. For example, portfolio-level metrics such as cost savings, productivity gains, and adoption rates to support leadership decisions and future AI investments.
Build your performance reporting framework using Info-Tech’s AI performance and value framework.	Save time and avoid rework. For example: 20 stakeholders @ 4 hours each * $100/hour = $8,000
Demonstrate your AI program’s value and impact across the organization.	Quantify and communicate AI-driven value across financial impact, customer outcomes, operational efficiency, and innovation.
Improve visibility into your AI portfolio.	Reduce time spent collecting and consolidating AI performance data. For example: Save 40-60 reporting hours per cycle.

12-month performance framework.

Following the implementation of the 12-month performance framework, monitor these metrics to prove enterprise AI value and ROI delivery.

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

DIY Toolkit	Guided Implementation	Workshop	Executive & Technical Counseling	Consulting
"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."	"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."	"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."	"Our team and processes are maturing; however, to expedite the journey we'll need a seasoned practitioner to coach and validate approaches, deliverables, and opportunities."	"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 are used throughout all five options.

Guided Implementation

What does a typical GI on this topic look like?

Phase 1	Phase 2	Phase 3
Call #1: Identify the AI initiatives to include in the annual AI performance report. Call #2: Define the value dimensions and metrics used to measure AI outcomes across the organization.	Call #3: Collect baseline and current performance data and evaluate AI initiatives using the AI value and performance evaluation framework. Call #4: Validate results with initiative owners and analyze the business value realized across the defined value dimensions.	Call #5: Develop the annual AI performance report structure and summarize key insights and performance highlights. Call #6: Build the communication plan and prepare the report for executive review and stakeholder communication.

Phase 1

Phase 2

Phase 3

Call #1: Identify the AI initiatives to include in the annual AI performance report.

Call #2: Define the value dimensions and metrics used to measure AI outcomes across the organization.

Call #3: Collect baseline and current performance data and evaluate AI initiatives using the AI value and performance evaluation framework.

Call #4: Validate results with initiative owners and analyze the business value realized across the defined value dimensions.

Call #5: Develop the annual AI performance report structure and summarize key insights and performance highlights.

Call #6: Build the communication plan and prepare the report for executive review and stakeholder communication.

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 6 calls delivered across 3 phases over the course of 4 to 6 months.

Workshop overview

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

	Day 1	Day 2	Day 3	Day 4	Day 5
	Define AI Reporting Scope	Establish the Measurement Framework	Measure AI Performance	Analyze Results and validate insights	Build the Annual AI Performance Report
Activities	1.1 Identify the AI initiatives to include in the report. 1.2 Identify initiative owners and stakeholders. 1.3 Define the objectives of the annual AI performance report. 1.4 Prioritize the top initiatives to evaluate.	2.1 Define value dimensions (customer satisfaction, financial value, process improvement, operational effectiveness, innovation). 2.2 Identify relevant KPIs for each initiative. 2.3 Define measurement formulas and targets. 2.4 Assign metric ownership and reporting frequency.	3.1 Collect baseline and current performance data. 3.2. Use the AI Value and Performance Evaluation Tool to evaluate initiatives. 3.3 Measure performance across defined value dimensions. 3.4. Document results and supporting evidence.	4.1 Review evaluation results with stakeholders. 4.2 Identify value realized and improvement opportunities. 4.3. Cross-reference value metrics with responsible AI principles. 4.4 Identify key insights and performance highlights.	5.1 Create the annual AI performance report structure. 5.2. Develop executive summary and key performance highlights. 5.3 Build a communication plan for stakeholders. 5.4 Review next steps and improvement actions.
Deliverables	Defined AI initiative scope and owners	AI measurement framework and KPI definitions	AI performance evaluation results	Validated insights and value analysis	Draft annual AI performance report and communication plan

Day 1

Day 2

Day 3

Day 4

Day 5

Define AI Reporting Scope

Establish the Measurement Framework

Measure AI Performance

Analyze Results and validate insights

Build the Annual AI Performance Report

Activities

1.1 Identify the AI initiatives to include in the report.

1.2 Identify initiative owners and stakeholders.

1.3 Define the objectives of the annual AI performance report.

1.4 Prioritize the top initiatives to evaluate.

2.1 Define value dimensions (customer satisfaction, financial value, process improvement, operational effectiveness, innovation).

2.2 Identify relevant KPIs for each initiative.

2.3 Define measurement formulas and targets.

2.4 Assign metric ownership and reporting frequency.

3.1 Collect baseline and current performance data.

3.2. Use the AI Value and Performance Evaluation Tool to evaluate initiatives.

3.3 Measure performance across defined value dimensions.

3.4. Document results and supporting evidence.

4.1 Review evaluation results with stakeholders.

4.2 Identify value realized and improvement opportunities.

4.3. Cross-reference value metrics with responsible AI principles.

4.4 Identify key insights and performance highlights.

5.1 Create the annual AI performance report structure.

5.2. Develop executive summary and key performance highlights.

5.3 Build a communication plan for stakeholders.

5.4 Review next steps and improvement actions.

Deliverables

Defined AI initiative scope and owners

AI measurement framework and KPI definitions

AI performance evaluation results

Validated insights and value analysis

Draft annual AI performance report and communication plan

Phase 1

Define AI Value and Success

Phase 1	Phase 2	Phase 3
1.1 Identify AI initiatives in your funnel 1.2 Define value dimensions 1.3 Apply AI guiding principles to value and performance measurement 1.4 Define project value	2.1 Establish your measurement approach 2.2 Measure your progress	3.1 Communicate impact & drive decisions 3.2 Build a communication plan for your roadmap

Phase 1

Phase 2

Phase 3

1.1 Identify AI initiatives in
your funnel

1.2 Define value dimensions

1.3 Apply AI guiding principles to value and performance measurement

1.4 Define project value

2.1 Establish your measurement approach

2.2 Measure your progress

3.1 Communicate impact & drive decisions

3.2 Build a communication plan for your roadmap

Phase Activities

Identify AI initiatives in the funnel, define organizational value dimensions, apply AI guiding principles for value and performance measurement, and determine the overall project value and impact.

Phase Outcomes

Development of an AI value framework, a structured measurement framework, and list of top AI initiatives aligned with business strategy and value creation.

Measure the value AI brings to your organization

AI value must be defined in business outcomes, not technical performance, to enable accountability, comparability, and sustained ROI.

Context	The Core Issue	Our Definition
Organizations continue to invest heavily in AI yet struggle to articulate and measure the value delivered clearly. AI success is often assessed using technical or activity-based metrics (e.g. model accuracy, number of pilots) that do not reflect business impact.	Lack of a consistent business-led definition of AI value leads to: Unclear ROI. Fragmented measurement approaches across functions, projects, and products. Limited executive confidence in AI decision-making.	AI performance is the extent to which AI systems and initiatives effectively deliver against defined business and operational objectives across the AI value framework. AI value is the measurable contribution of AI initiatives to enterprise outcomes across financial performance, operational effectiveness, stakeholder impact, and strategic advancement.

Context

The Core Issue

Our Definition

Organizations continue to invest heavily in AI yet struggle to articulate and measure the value delivered clearly.
AI success is often assessed using technical or activity-based metrics (e.g. model accuracy, number of pilots) that do not reflect business impact.

Lack of a consistent business-led definition of AI value leads to:

Unclear ROI.
Fragmented measurement approaches across functions, projects, and products.
Limited executive confidence in AI decision-making.

AI performance is the extent to which AI systems and initiatives effectively deliver against defined business and operational objectives across the AI value framework.

AI value is the measurable contribution of AI initiatives to enterprise outcomes across financial performance, operational effectiveness, stakeholder impact, and strategic advancement.

Info-Tech Insight

At some point, if it hasn’t happened already, the business stakeholders investing in AI in your organization will ask what they’ve received in return for their investment. This is difficult to answer if you haven’t prepared yourself to answer it. Establishing a structured performance measurement framework enables organizations to quantify realized value, demonstrate , and build confidence in continued AI investment.

Misaligned performance measures create a watermelon effect

The watermelon effect

Organizations can hit all their defined delivery targets, such as on-time and on-budget delivery or model accuracy, but fail to deliver real business value. This misalignment results in the watermelon effect, where performance indicators appear green on the surface, but under the surface, they’re red because key stakeholders aren’t satisfied with the (unmeasured) value they’re getting from your solution.

If you fail to capture the right performance metrics, you’ll miss opportunities to demonstrate impact. You’ll also miss value gaps, limiting your ability to intervene early and correct course.

Identify your AI initiatives and where they are in the PoC to proof pipeline

Exploration & Proof of Concept	Prototype	Pilot	Production, Deploy & Scale
Clarify the business problem. Define expected outcome KPI/value hypothesis. Run small technical experiments. Check feasibility (data, model, integration, cost).	Build a working version (basic UI/workflow). Test with limited users. Improve performance and usability. Validate that the solution fits the process.	Deploy to limited users/region. Track defined KPIs. Compare against baseline (before/after or control group). Monitor operational stability.	Full deployment across the organization. Implement monitoring & governance. Optimize cost and performance. Continuous measurement of KPIs.
Move to Prototype when: • Value hypothesis is defined. • Feasibility and sponsorship align.	Move to Pilot when: • Pilot users confirm usefulness. • Minimum performance is met. • Pilot scope is agreed upon.	Move to Production when: • KPI uplift is proven. • ROI is validated. • Stability and compliance are achieved.	Success when: • Impact is sustained. • Operations are reliable. • Project scales safely.
DEFINE VALUE		PROVE VALUE	MEASURE VALUE

1.1 Identify AI initiatives in your funnel

Build an inventory of in-flight AI initiatives to help establish the scope and focus of your AI performance measurement program.

Create a list of the AI initiatives currently underway across the organization.
Identify the current lifecycle stage of each initiative (i.e. PoC, Prototype, Pilot, or Production). Categorize each initiative into the appropriate lifecycle stage within the funnel in the template on the next slide.
Identify the business sponsor/owner for each initiative in brackets next to the initiative name.
Review the list and eliminate any for which you are not responsible for measuring performance or value.
Identify the top initiatives in the list. These likely have an outsized impact on the organization’s goals, may be more complex, require additional spend, and are tightly aligned to the organizational strategy.

Input	Output
Existing project/initiative documentation	List of AI initiatives in scope for the performance measurement program, with identified sponsors/owners Top initiatives identified
Materials	Participants
Template on the next slide	Organizational AI lead (delegate work as appropriate)

Example: AI initiative funnel portfolio

Exploration & Proof of Concept	Prototype	Pilot	Deploy & Scale
AI-powered meeting summarization and action extraction (User Enablement Lead) Policy/procedure Q&A assistant (HR Lead) Automated document classification and tagging (Records Management Lead) Customer email intent detection and routing (Customer Support Operations Manager) Natural language search across enterprise content (Knowledge Management Lead) Fraud detection and transaction monitoring (Fraud Risk Manager) Demand forecasting and inventory optimization (Supply Chain Planning Manager) Marketing content generation with brand guardrails (Marketing Operations Lead) Personalized customer recommendations (Customer Experience Manager) Regulatory reporting automation (Compliance Reporting Manager)	Finance forecasting variation analyzer (FP&A Manager) HR resume screening and skills matching (Talent Acquisition Manager) Contact clause identification and risk flagging (Legal Operations Manager) Sales call summarization with opportunity insights (Sales Operations Manager) IT incident categorization and prioritization (IT Service Management Lead) Invoice processing and exception handling (Accounts payable Manager)	* Customer churn prediction for priority segments ( Customer Analytics Manager) * GitHub Copilot (DevOps Manager) Service desk ticket resolution recommender (IT Service Desk Manager)	* Service desk triage and routing (IT Support Operations Manager) Fraud detection, triage, and routing (Fraud Operations Manager)

* Top Initiatives

Define and measure value with Info-Tech’s AI Performance and Value Framework

Use our comprehensive performance and value framework as your starting point to assess AI value.

Value Dimension	Who tends to care about these dimensions?
Customer & Stakeholder Satisfaction Score	Service-driven organizations, customer-facing functions
Financial Value	Most organizations, but especially those launching AI-enabled products and cost-sensitive enterprises
Process Improvement	Operations-heavy functions, shared services, IT, manufacturing, financial services
Innovation, Learning & Growth	AI transformation-led enterprises, capability-building functions
AI Operational Effectiveness	Technically mature AI organizations, digital-native firms, regulated industries, AI CoEs