Executive Summary
Agentic AI is creating a foundational shift in enterprise performance. Unlike task-specific automation, agentic AI systems are designed to perceive, decide, act and adapt independently. These intelligent agents introduce new value across business functions: from accelerating planning cycles to enhancing precision of decisions and reducing risk. Yet, for all its promise, agentic AI often stalls at the pilot stage. Why? Because enterprises lack a robust framework to measure its real impact.
ISG's Agentic AI Measurement Framework is a methodology built around two complementary frameworks: 1) a framework that uses a function-specific Objectives and Key Results (OKR) model, and 2) a framework that uses KPIs based on the Observe, Orient, Decide, Act (OODA) model. The Agentic AI Measurement Framework provides enterprises with the structure they need to align AI investments to strategic intent, measure performance of autonomous agents in business terms and enable continuous benchmarking and improvement.
Drawing on ISG's applied research and field work, the framework is adaptable across industries and maturity levels. It is designed to help business and technology leaders move agentic AI beyond experimentation and begin measuring what truly matters.
Introduction – The AI Shift Enterprises Can’t Ignore
Agentic AI Makes Businesses Smarter, Not Just Faster
Agentic AI marks an essential evolution in enterprise operations. Agentic AI is designed to execute business processes through autonomous actions. Unlike traditional automation technologies, agentic AI systems independently observe their environments, interpret contextual cues, make strategic decisions and autonomously execute actions aligned with overarching organizational objectives. This transformative shift demands immediate attention and adoption, as organizations face escalating market complexities and fierce competition, where agility and autonomous intelligence increasingly determine enterprise success and sustainability.
Why Your AI Projects Are Stalling
Despite considerable investment and enthusiasm surrounding agentic AI, enterprises frequently encounter difficulties advancing beyond the initial pilot stages. This stall primarily results from inadequate measurement frameworks unable to quantify the nuanced and multifaceted impacts of agentic AI. Traditional performance metrics, designed for simple automation scenarios, neglect the strategic complexities and adaptive capabilities inherent to agentic AI, hindering organizations' ability to accurately evaluate success and make informed scaling decisions.
The Problem with Traditional Automation Metrics
Traditional automation metrics, like throughput, transaction volume or direct cost reductions, were developed for linear, predictable processes. However, these metrics significantly fall short when assessing agentic AI, which provides core value in strategic adaptability, decision accuracy and contextually aware performance. Consequently, enterprises urgently require the establishment of advanced, comprehensive measurement frameworks that accurately capture strategic alignment, adaptive decision-making quality, resilience and the ability to innovate autonomously in response to dynamic market conditions.
Agentic AI Changes Everything About Decision-Making
Agentic AI fundamentally redefines the essence of organizational decision-making by autonomously managing and navigating highly complex and unpredictable scenarios in real-time. Unlike traditional systems that follow fixed, predetermined rules, agentic AI dynamically assesses trade-offs, promptly responds to environmental shifts and proactively proposes innovative strategies, thus significantly elevating decision-making precision, strategic agility and the potential for competitive differentiation.
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Introducing a Framework Built for Enterprise Realities
ISG’s Agentic AI Measurement Framework gives enterprises a pragmatic and actionable approach specifically designed to quantify the impact of agentic AI. Based on substantial applied research and extensive industry practice, the framework combines precise business-function-aligned OKRs with granular, clearly defined KPIs structured around the OODA decision-making cycle. This ensures comprehensive and meaningful assessment of agentic AI performance, directly tied to real-world strategic and operational priorities.
Figure 1: Measurement Framework for Agentic AI
Crafting OKRs that Reflect Real Enterprise Goals
The development of impactful OKRs beings with a rigorous three-layer diagnostic methodology:
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Process decomposition: This first step meticulously maps existing functional workflows, clearly identifying optimal points of agentic AI deployment.
- AI capability matching: This assesses each potential opportunity based on three dimensions:
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Augmentation: Where AI assists human decision-making by increasing speed, quality or capacity.
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Automation: Where AI replicates human actions with minimal intervention.
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Innovation: Where AI introduces capabilities not previously possible within existing workflows.
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Readiness and calibration: This step ensures realistic goal setting, aligning AI ambitions with the enterprise’s current technological maturity, data capabilities and cultural adaptability to optimize results and strategic coherence. This also establishes the time horizon in which the goals can be realistically achieved.
Aligning Goal-Oriented OKRs to Function-Specific Priorities
Enterprises need to align identified opportunities with enterprise and function-specific priorities, building OKRs for agentic AI across different functions. Below, we can see how the framework manifests across the levels of a few functions to address their priorities.
Finance: Agentic AI can improve capital allocation, forecasting and financial reporting.
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Executive objective: Improve enterprise financial agility; key results include increased forecast accuracy by 25%, reduced capital planning time by 30% and enhanced return on working capital by 15%.
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Operational objective: Streamline financial reconciliation with key results of 50% reduction in manual data entry, 40% faster reconciliation and 35% fewer errors.
Legal: Agentic AI can help legal teams benefit from both speed and risk management.
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Executive objective: Mitigate enterprise legal risk through AI strategy with key results including 30% faster risk identification, 25% reduced review cycle time and 20% improved audit success.
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Senior IC objective: Enhance research and contract drafting with key results that include reduced manual drafting time by 40%, reduced external counsel costs by 25%, accelerated resolution by 35%.
Sales: Agentic AI in sales can help convert activity into quality outcomes.
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Executive objective: Increase revenue pipeline effectiveness with key results including a 40% boost in qualified leads, a 35% increase in forecast accuracy and a 25% reduction in cycle time.
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Manager objective: Enhance real-time deal coaching; key results of improved win rates by 30%, 25% greater cross-sell opportunities and reduced admin time by 20%.
HR: Agentic AI can help HR leaders in both hiring and retention.
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Executive objective: Transform talent acquisition and mobility; key results include halved time-to-hire, 30% greater quality-of-hire and 20% greater internal mobility.
Maximizing agentic AI’s value requires better process decomposition and better identification of level-specific OKRs within each of your organization’s functions.
KPIs Designed for Intelligent Action
Where OKRs define strategic intent, KPIs measure how well plans are executed. To measure agentic AI performance accurately, the OODA loop is the foundation for developing strategic KPIs. Originally designed for high-stakes combat scenarios, the OODA loop effectively captures essential performance dimensions, including real-time data accuracy, context comprehension, decision-making clarity and confidence and precise action execution:
About the authors
Shriram Natarajan
