Why Execution Remains Industrial AI’s Hardest Problem

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Industrial AI has grown increasingly capable at predicting what might go wrong. Ensuring that those insights translate into consistent action on the shop floor has proved far harder.

Across heavy industries, from steel and cement to chemicals and mining, plants have spent the past decade digitizing equipment, wiring sensors, and deploying increasingly sophisticated monitoring systems. 

Visibility has improved, but decision-making has not. Alerts are generated, anomalies flagged, and dashboards refreshed, but action still depends on fragmented workflows, human judgment, and production pressures that routinely override caution.

The result is an execution gap that many manufacturers now describe as one of the central constraints in industrial AI.

A new research initiative by MIT Sloan Management Review India, in collaboration with Infinite Uptime, will examine whether prescriptive AI can close that gap and under what operating conditions it succeeds or fails. Infinite Uptime works with heavy industries as a production outcomes partner, offering a prescriptive AI orchestration system to support semi-autonomous manufacturing.

The focus is not on prediction accuracy alone, but on whether AI systems can consistently drive action and deliver outcomes that operators are willing to validate.

The three-part whitepaper series will center on what some users of Infinite Uptime describe as the “99% Trust Loop,” a benchmark the research will test.

MIT Sloan Management Review India will interrogate why industrial AI initiatives stall between insight and execution. By focusing on user-validated outcomes rather than model performance alone, the research aims to establish clearer benchmarks for what operationally credible AI looks like inside real plants.

The premise is deliberately demanding: AI recommendations only matter if they are acted upon, confirmed by users, and validated against real operating outcomes. Anything less, the argument goes, is insight without consequence.

From Prediction to Prescription

Most industrial AI deployments today stop at prediction. Systems estimate remaining useful life, detect anomalies, or rank failure risks. What happens next is typically outside the system. 

Maintenance teams interpret alerts. Production managers weigh trade-offs. Decisions are delayed, diluted, or ignored altogether, especially when downtime threatens near-term output.

Prescriptive AI attempts to narrow that gap. Instead of flagging what might fail, it recommends specific actions, explains why they matter, and tracks whether those actions were taken. Crucially, it also verifies outcomes, closing the loop between recommendation and result.

The research will examine how this shift changes behavior on the shop floor. It contrasts the prevailing model of fragmented sensors and dashboards that stop at alerts with an approach exemplified by platforms such as Infinite Uptime’s Plant Orchestration System (PlantOS), which seek to integrate context, prescription, execution, and validation into a single operating loop. The aim is not full autonomy but semi-autonomous execution, where machines, systems, and operators work from a single, validated operational view.

Trust Remains the Hard Problem

At the core of the study is a practical reality often glossed over in AI strategy decks: operators do not act on systems they do not trust.

In many plants, AI recommendations compete with decades of experience, informal heuristics, and incentive structures that prioritize throughput over preventive intervention. When AI outputs lack context or fail to demonstrate impact, skepticism is rational, not cultural resistance.

The “99% Trust Loop” is designed to confront this directly. Recommendations are contextualized. Actions are confirmed by users. Outcomes are validated at the machine level. Over time, this creates an auditable link between data, decisions, and results, reducing the distance between what the system suggests and what operators believe.

The research will explore how such a design affects decision latency, execution discipline, and organizational confidence, particularly in environments where downtime carries both financial and safety implications.

Three Outcomes, One Operating Layer

Another focus of the study is whether a single prescriptive AI platform can credibly deliver multiple operational outcomes at once.

Infinite Uptime argues that its PlantOS can drive three outcomes in parallel: asset reliability measured through mean time between failures (MTBF), process and energy efficiency, and throughput. In most plants, these objectives are managed by different teams using different tools, often with conflicting priorities.

By integrating mechanical, electrical, and process-induced fault detection into one operating layer, the platform aims to replace fragmented optimization with coordinated execution. The research will assess whether this consolidation reduces trade-offs or simply relocates complexity.

Evidence From the Field

Case evidence exists, though it is more operational than promotional.

At Star Cement, Infinite Uptime’s platform was deployed across four plants, integrating data from 19 systems including programmable logic controllers (PLCs), distributed control systems (DCS), energy meters, SAP enterprise resource planning systems, maintenance logs, and quality reports. Rather than adding sensors, the system unified existing infrastructure to create plant-wide context.

The deployment helped prevent 46 hours of unplanned downtime, increased throughput by 10 tons per hour, reduced specific heat consumption by about 920,000 kilocalories, and lifted mean time between failures by roughly 5%, according to company data shared as part of the research. The company reports a ten-times return on investment in under six months, with 99% of prescriptions acted upon and outcomes validated by plant teams.

“The biggest change was the immediate establishment of a single source of truth,” said Dhawan Soni, Electrical and Instrumentation Head at Star Cement. “We moved from reactive chaos to proactive control.”

What distinguishes this case, Infinite Uptime said, is not the headline ROI but the insistence on user validation. Industrial plants are full of systems that claim impact but never prove it. Here, recommendations only counted if operators confirmed results at the machine level.

Similar deployments span steel, cement, chemicals, mining, and tire manufacturing across countries. The unresolved question, and the one this research will probe, is whether such outcomes are reproducible at scale or dependent on unusually disciplined operating cultures.

From Pilots to Proof

The timing of the research reflects a broader shift in manufacturing. After years of experimentation, patience with pilots that never graduate into production-critical systems is wearing thin. The question has moved from what AI can predict to what it can reliably deliver.

The first paper in the series will examine context and prediction accuracy. The second will focus on execution and why prescriptions are or are not acted upon. The final paper will assess user-validated outcomes and what it takes to sustain trust over time.

If industrial AI is entering a more serious phase, it will be defined by whether systems earn the right to be believed, acted on, and held accountable for results.