MIT Sloan The Trust Architecture of Industrial AI 2

The Trust Architecture
of Industrial AI

Part 2 | The Execution Gap

Why Industrial AI Prescriptions Don’t Become Action

Industrial AI is getting better at telling you what to do. The harder problem – and the one most organizations aren’t solving – is getting it done. This study, based on 68 respondents across industrial operations, examines the conditions under which AI-generated prescriptions either reach the plant floor or quietly disappear.
of respondents execute fewer than 1 in 4
AI-generated recommendations
0 %
execute fewer than half - regardless of
recommendation quality
0 %
report execution rates above 75% the
narrow group where AI delivers full value
0 %

Five Barriers · Why Action Stalls

The system isn't resisting AI.
It's resisting change it wasn't ready for.

These five barriers account for the gap between what AI recommends
and what actually gets done.

01.  Workforce adoption & change management 60%
02.  Execution conflicts with production priorities 46%
03.  Low trust in recommendation credibility 40%
04.  Recommendations not operationally actionable 38%
05.  Lack of clear ownership for execution 26%

2.1 barriers reported per respondent on average – execution failure is systemic, not a single-point problem.

The Cycle · Why It Compounds

Where this paper sits.

The execution gap isn’t just a lost recommendation. It’s a break in the feedback loop that industrial AI depends on to prove its value

Al generates
prescription

Technically sound recommendation reaches the operator

Prescription goes
unexecuted

Deferred, questioned, or ignored on the plant floor

No observable outcome

Without action, there is nothing to validate

Trust & scale stall

No proof. No confidence. No case to expand.

The Series · Three Parts

Where this paper sits.

PART I - PUBLISHED

The Trust Architecture of
Industrial Al

How contextualization shapes
prediction accuracy – and why most deployments fail before they begin.

Read Part I

PART II - NOW AVAILABLE

The
Execution Gap

Why technically sound Al prescriptions fail to become action – and the five barriers that keep the gap open.

PART III - COMING SOON

From Execution
to Impact

Measuring and sustaining financial
and operational value from industrial Al at scale.

Unlock Part 2:
Closing the execution gap

Discover how to convert Prescriptive AI into
measurable Production Outcomes (Reliability
, Efficiency, Throughput) – full Part 2 available now.
Download Part 2 - Full Study
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