AWS Co-Founder Matt Domo: Why AI Investments are Stalling

Recent analysis from Forrester discovered only 10-15% of AI pilots successfully scale into long-term production – meaning most enterprise AI investments stall before delivering real impact.

Well placed to explain why is Matt Domo, a Co-Founder of Amazon Web Services (AWS) and a creator of foundational Microsoft enterprise technologies. Today, he advises Fortune 500 companies, government agencies and universities on turning AI from stalled pilots into measurable competitive advantage.

Here, Matt tells AI Magazine how organisations can move from experimentation to enterprise-scale execution.

Why do most AI initiatives fail at the executive level?

Most AI initiatives don’t fail because the technology is weak. They fail because the organisation around it isn’t aligned.

At the executive level, the breakdown shows up in three places: unclear ownership of outcomes, misaligned incentives across teams and operating models that weren’t built for how AI changes decision-making. Leaders fund pilots, but they don’t redesign how work happens. They treat them as technology projects instead of using them to change how the business operates.

Without that alignment, AI gets layered on top of existing processes instead of reshaping them. It looks like progress, but it doesn’t translate into measurable results.

How can companies scale AI pilots to enterprise-wide deployment?

AI doesn’t scale through more pilots. It scales through standardisation.

Companies that succeed define a repeatable path from pilot to production, assign clear ownership of outcomes, and integrate AI into core workflows instead of layering it on top. If every team is starting from scratch, you don’t have scale, you have scattered experiments.

Scaling happens when the organisation changes how work gets done, not just where AI is used.

Which ROI metrics truly convince boards of AI investments?

To guide leadership, focus on metrics that directly link AI impact to financial results and strategic goals. Boards aren’t convinced by activity. They’re convinced by measurable impact tied to the P&L.

The metrics that matter are straightforward: cost reduction, revenue lift, and faster decision cycles. If AI is reducing expense, increasing revenue, or accelerating how the business operates, it holds up. If it isn’t, it doesn’t.

What doesn’t work is vague reporting like "usage" or "engagement". Boards want clear attribution: what changed, by how much and how it ties directly to financial outcomes.  

How can organisations avoid 'AI-washing' and misaligned projects?

Organisations avoid AI-washing by starting with the outcome, not the tool.

The discipline is simple: define the business result first, assign clear ownership of that outcome and only then determine where AI actually improves the workflow.  If AI doesn’t change how work gets done or how decisions are made, it’s not adding value.

Misalignment occurs when teams are incentivised to launch initiatives rather than deliver results.  The fix is to tie every AI effort to a measurable objective and hold a single owner accountable for it.

What’s the best way to speed up AI-driven decision-making in large organisations?

Speed in AI-driven decision-making doesn’t come from better models. It comes from clearer ownership and fewer handoffs.

In large organisations, decisions slow down because accountability is fragmented and every step requires alignment across multiple teams.  The fix is to define who owns the outcome, standardise the inputs those decisions rely on and reduce the number of approvals required to act.

AI can surface better insights, but unless the organisation is structured to act on them quickly, those insights sit in dashboards. Speed comes from aligning decision rights with the people closest to the outcome.