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Insightful Experience
An Honest Look at AI Adoption and Returns
A diagnostic framework for growth-stage leadership teams
Leadership teams at growth-stage companies need to understand what AI can actually deliver for their organization right now, and what's standing in the way of more. That starts with an honest diagnostic: what's actually in place, what's blocking returns, and what needs to happen next. The diagnostic framework behind this work was built from MIT and Harvard Data Science Initiative executive education curricula, filtered through 15 years of running the operations AI is supposed to transform.
Why I Built This
I spent 15 years running post-sales operations at growth-stage technology companies: customer success, support, professional services. I sat on executive teams navigating board pressure, revenue targets, and organizational scaling. I led teams through multiple enterprise technology rollouts and witnessed countless adoption failures, not just within my own organizations but across the client companies we served. I also saw adoptions succeed. And I know what separated them. The technology was rarely the problem. The blocker was almost always organizational: unclear ownership, misaligned incentives, unmapped workflows, teams not brought along, governance absent until something broke.
When AI started accelerating, I recognized the pattern: the same organizational gaps, but with higher stakes and less runway to recover.
I pursued executive education at MIT and Harvard to understand what makes AI adoption succeed or fail at the organizational level, and I've been working hands-on with AI tools myself to understand what the technology can and can't do. I'm no expert on the technical side, but I understand enough to know when organizational readiness is the real constraint.
Through all of this, I've seen the pattern continue. A $15M SaaS VP told me his team isn't considering AI because "we can't trust what it will do." A peer described a CEO managing his business through a chatbot in his office, asking it questions then calling executives on the carpet based solely on what it tells him. He is losing his leadership team's loyalty. At a recent HDSI webinar for alumni that discussed digital twins, 80 attendees unanimously rejected the idea that companies could own their digital twin data without deletion rights or ongoing compensation after they leave the business. There's a polarization happening, and the organizational work to bridge these gaps is being skipped.
This diagnostic framework emerged from both my work and continuing education: a structured way to surface the organizational gaps that determine whether AI adoption succeeds. My operational background is strongest in go-to-market and post-sales—the areas where I spent my career building teams, implementing technology, and navigating the human side of organizational change. The diagnostic itself is company-level, but that operational lens shapes how I read what's working, what's breaking down, and where the real blockers sit.
The Problem
The pressure on leadership teams to adopt AI is real and legitimate. Boards are asking about it. Investors are measuring for it. Competitors are moving on it. But most organizations are finding the gap between AI investment and AI returns wider than expected.
Seventy-three percent of companies report that at least some AI investments did not meet expectations over the past year. (Source: G-P 2026 Report.) AI is producing gains in efficiency and incremental optimization. What it has not yet delivered is the transformational revenue impact or differentiated innovation the original business case promised. Only 39% of organizations attribute measurable earnings impact to AI at the enterprise level, despite tactical productivity gains. (Source: Deloitte State of AI in the Enterprise.) Leadership teams are reporting ROI in process minutes saved, not market position gained.
Companies are directing 93% of their AI investment toward technology and architecture, and 7% toward organizational and human readiness. (Source: Deloitte CTO Bill Briggs, Fortune, December 2025.) That 7% is precisely where returns are either captured or lost.
For growth-stage companies, the stakes are particularly high. Sales cycles are compressing and contract lengths are shortening. (Source: ICONIQ State of Go-to-Market 2026.) There is less runway to recover from a failed implementation or a team that was never brought along. The organizations finding their footing tend to share a starting point: before making technology decisions, they made organizational ones.
Where This Work Comes From
Two executive education programs form the intellectual foundation: MIT's AI Leadership and Strategy curriculum through Sloan School of Management, and Harvard Data Science Initiative's Agentic AI Intensive. Both center on a principle that is consistently underestimated: sustainable AI outcomes depend as much on organizational and human foundations as on the technology itself.
MIT's curriculum focuses on AI as a sociotechnical challenge, teaching how change travels through organizations and why real drivers of outcomes remain invisible until they surface as problems. Harvard's program brought precision through the AGENT framework, a structured methodology for auditing, gauging, engineering, navigating, and tracking AI workflows that accounts for organizational and human factors alongside technical ones. Running through both: governance questions that apply at every stage: who has authority over AI decisions, who must consent to them, and who carries responsibility when outcomes fall short.
The operational filter matters as much as the academic one. Academic frameworks provide structure. Operational experience provides judgment.
Structure Meets Judgment
This framework emerged from combining those two perspectives. The structure comes from the curricula: dimensional thinking, governance frameworks, readiness models. The judgment comes from having run the operations AI is supposed to transform and knowing what separates successful adoption from expensive failure.
Academic rigor without operational context produces recommendations that don't survive contact with reality. Operational experience without structured thinking produces pattern-matching that misses the systemic issues.
This work sits at the intersection.
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?
How It Works
Every engagement begins with the diagnostic. The assessment explores the six framework dimensions through open-ended questions designed to surface an honest picture of where the organization stands. The output is a written assessment of what was heard, what it reveals, and where to focus next. Individual contributions to the assessment are treated with discretion.
Perspectives inform the findings without attribution unless a contributor has explicitly agreed to be identified.
What the diagnostic surfaces determines what happens next. Some organizations will focus on foundational work. Others are positioned to continue investing in AI-specific initiatives. In many cases, both happen concurrently: pilots running alongside foundational work, each informing the other.
The work is done collaboratively and from the inside out. The organization's own knowledge, leadership context, and customer reality shape the output. Every recommendation emerges from the organization's reality. The goal is to leave the organization more capable of navigating AI decisions independently and sustaining that capability long after the engagement ends.
Who This Is For
This work is designed for leadership teams at growth-stage companies navigating the gap between AI ambition and organizational reality. These are companies past early-stage uncertainty and not yet at enterprise scale, moving through an inflection point generating real pressure from boards, investors, and the organization itself.
The engagement may begin before the first significant AI investment, when leadership wants clarity before committing. It may come after an implementation that didn't deliver as expected. Or it may come when growth has created structural complexity that would be compounded, not resolved, by adding AI without a foundation.
What these moments share is a leadership team willing to be honest about where the organization actually is, and committed to doing the organizational work the technology requires.
