Across every industry, boards are approving AI budgets. Inside many enterprises, however, the reality is the same. Pilots never scale, tools sit unused, and transformation programmes struggle to justify their investment. In this episode of the Tech Transformed podcast, host Trisha Pillay sits down with Darin Patterson, VP of Product Advocacy and Market Strategy at Make, to find out what separates the organisations genuinely operationalising AI from those still running expensive experiments.

AI Adoption Gap

Enterprise AI investment is accelerating. What is not accelerating at the same pace is business value. Patterson is direct about why he believes that most organisations are measuring the wrong things, assigning ownership to the wrong people, and deploying tools before they have defined the problem.

"The AI adoption gap is real," Patterson tells Pillay, "and it starts at the top. Leaders are approving investments without a clear framework for what success looks like."

For C-suite executives, this is a critical signal. AI adoption is not primarily a technology challenge; it is an organisational one. Strategy, culture, and accountability structures determine if AI initiatives produce compounding returns or accumulate as technical debt.

Ownership Models

One of the most instructive conversations in this episode concerns who should own AI inside an enterprise. Patterson's position is that ownership must live with the people closest to the business function being transformed.

"Ownership models are often unclear," he says. "And unclear ownership is where AI initiatives go to die."

When AI is owned exclusively by a central IT or data science function, it becomes disconnected from the operational realities of the teams it is meant to serve. When it is owned entirely by individual business units without central governance, you get fragmented tooling, inconsistent data practices, and security exposure. The hybrid model Patterson advocates centralises governance standards, security, and infrastructure while pushing execution authority down to functional leaders. This structure creates accountability at the point of value creation rather than at a remove from it.

For C-level executives building or restructuring their AI operating model, the actionable question is: do the leaders of each business unit have both the mandate and the capability to own AI outcomes in their domain?

Stop Starting With the Tool

A pattern Patterson sees consistently across enterprises is what he calls tool-first thinking. An organisation identifies a capable AI platform, deploys it, and then attempts to work backwards to the business problem it should solve.

"Focus on your business process first," he advises. "The tool is never the strategy."

This is especially relevant for executives evaluating vendor proposals. The quality of an AI platform matters far less than the clarity of the problem definition sitting upstream of it. Organisations that achieve sustainable AI ROI typically begin by mapping their highest-friction processes, quantifying the cost of those inefficiencies, and only then evaluating which AI capability best addresses the root cause. The discipline of process-first thinking also prevents a common failure mode by automating a broken process rather than fixing it. AI applied to a flawed workflow does not eliminate the flaw but rather accelerates it.

Culture Is the Multiplier

Patterson also points to a softer but critical success indicator, which is cultural adoption. If the teams closest to an AI deployment are not using it willingly and consistently, the business case will not hold, regardless of what the pilot showed.

The final, and perhaps most important, dimension Patterson raises is culture. Technical capability and strategic clarity are necessary but not sufficient conditions for AI success at scale. The organisations that are genuinely ahead are those that have invested in building an AI-literate workforce, not just an AI-enabled one.

"Invest in people as much as you invest in AI," Patterson says. "The technology will keep improving. Your competitive advantage comes from people who know how to use it well."

For C-level leaders, this means reframing AI investment as a human capability programme as much as a technology programme. Training, change management, and psychological safety around experimentation are not soft additions to an AI strategy, but they are core to its delivery.

Listen to the full conversation with Darin Patterson on the Tech Transformed podcast. Connect with Darin on LinkedIn and explore Make's automation platform at make.com.

Takeaways

AI adoption challenges
Organisational culture and AI
Ownership models for AI
Measuring AI success
Operational AI examples

Chapters

00:00 The AI Adoption Landscape

03:01 Bridging the ROI Gap in AI

05:48 Ownership and Responsibility in AI Implementation

08:57 Strategic Approaches to AI

11:57 Measuring Success in AI Initiatives

15:00 Cultural Transformation for AI Success

18:53 Real-World AI Implementation Examples

24:00 Advice for C-Level Leaders on AI Investment