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Insightful Experience
An Honest Look at AI Adoption and Returns
A diagnostic framework for growth-stage leadership teams
The Work Looks Different for Every Organization
This full overview walks through the academic foundation, the judgment it takes to apply it, and what makes this diagnostic different from a standardized assessment.
Leadership teams need to understand what AI can actually deliver right now, and what's standing in the way of more. This diagnostic surfaces what's in place, what's blocking returns, and where to focus next.
THE PROBLEM
73% of companies report AI investments didn't meet expectations. Only 39% see measurable earnings impact despite tactical gains. (G-P 2026, Deloitte 2026). The pattern: teams casually "using AI" without real adoption. CEOs managing through chatbots and losing leadership team confidence. Organizations refusing AI because "we can't trust what it will do." Companies direct 93% of AI investment toward technology, only 7% toward organizational readiness. (Deloitte 2025). That 7% is where returns are captured or lost.
THE APPROACH
Built from MIT AI Leadership & Strategy and Harvard's Agentic AI Intensive, filtered through 15 years running the operations AI is supposed to transform. Academic frameworks provide structure. Operational experience provides judgment. This work sits at the intersection.
THE DELIVERABLE
A written assessment of where the organization stands across all six dimensions: what was heard, what it reveals about current readiness, and where to focus next. The assessment is designed to be shared at the leadership level and serves as a starting point for the right conversation, informed by the organization's actual context and constraints.
WHO THIS IS FOR
Leadership teams at growth-stage companies navigating the gap between AI ambition and organizational reality. Companies past early-stage uncertainty and not yet at enterprise scale, moving through an inflection point that is generating real pressure from boards, investors, and the organization itself. Teams willing to be honest about where the organization actually is, and committed to doing the organizational work the technology requires.
The Framework
Vision and Leadership Alignment
Has leadership aligned on the strategic purpose of AI adoption, and is that direction clearly communicated to the people being asked to act on it?
Data and Knowledge Architecture
Does the organization have a coherent knowledge architecture that AI can leverage, or is critical information locked in disconnected systems and individuals?
People, Culture and Change Readiness
Is the organization positioned to adopt AI constructively, with the leadership clarity, communication, and change infrastructure that sustainable adoption requires?
Opportunity Clarity and Workflow Fit
Have the highest-value AI opportunities been mapped to specific workflows, with measurable outcomes and implementation risk defined?
Operating Model and Accountability
Are roles, ownership, and decision-making authority clear enough to support consistent, accountable AI adoption across the organization?
Governance, Ethics, and Trust Design
Has the organization defined what AI will do, how it will do it, and what it will not do? Has it built the oversight and transparency structures that employees and customers need to trust AI-assisted interactions?