Prescriptive AI That Respects Reality: Recommendations You Can Actually Execute

Prescriptive AI That Respects Reality: Recommendations You Can Actually Execute

Midnight on a production line isn’t the time for a generic alert. When a critical asset starts drifting, teams don’t want a chatbot—they need a specific diagnosis and a plan they can run safely, with the parts and people they actually have on hand. That’s the gap between “detect” and “do,” and it’s where the next wave of agentic AI will be judged.

During the AI Frontiers 2025 session “Agentic AI in Manufacturing: From Copilots to Autonomous Systems,” one theme cut through: agents must graduate from warning lights to action-literate systems. In practical terms, that means moving beyond “something might be wrong” to the specific fault, why it’s happening, how severe it is, what to do, and when—all conditioned by the plant’s real constraints. This perspective, articulated by Infinite Uptime’s founder Raunak Bhinge, reframes success for manufacturers: speed still matters, but doability matters more.

Why action literacy beats more alerts

Classic anomaly detection stops at “unusual vibration” or “temperature spike.” Operators then scramble to translate that into work: source parts, find a slot, coordinate crews, and negotiate with production. Most of the value leaks there.

Action-literate agents aim to close that gap. They tie a signal to a failure mode, price the risk of acting now versus later, check feasibility across systems, then deliver a ready-to-run plan. In heavy industries—steel, cement, chemicals—this isn’t a convenience; it’s the difference between timely maintenance and costly, sometimes dangerous, outcomes.

What a credible prescription looks like

A useful recommendation isn’t a paragraph—it’s a job you can do. The minimum viable prescription should include:

• Root cause and severity: down to the component (e.g., gearbox stage/bearing ID), with a clear severity band and confidence.

• Timing that makes economic sense: weighs false positives (unnecessary stoppage) against false negatives (missed failure) using your own downtime cost, quality risk, and changeover impact.

• Feasibility check: confirms the right spare is in stores (or the vendor lead time), a crew is available, and a maintenance window won’t derail the schedule.

• Ready-to-execute work pack: prefilled work order with parts/tools, lockout/tagout steps, permits, and an execution window that fits the plan of record.

• Safety and context: tested behavior for alarm storms, sensor drift, and product mix changes—especially before any auto-action is enabled.

If any piece is missing, the “recommendation” is still homework for the operator.

A build plan that manufacturers can run this quarter

1. Pick one failure mode on one asset family
Choose a high-loss target (critical rotating equipment is a common win). Document signatures, failure thresholds, and acceptable intervention windows.

2. Codify severity and response
Create a small matrix that maps condition bands to actions: monitor, plan, prepare, execute. Bake in safety rules and escalation paths.

3. Wire the constraints into the agent
Connect the systems that determine whether a plan is doable:

• MES (Manufacturing Execution System) for schedules and changeovers
• CMMS (Computerized Maintenance Management System) for work orders and crews
• ERP/Inventory for spares and supplier lead times
• HMI/SCADA (Human–Machine Interface/Supervisory Control and Data Acquisition) for live process state

4. Generate the job pack, not just the nudge
Have the agent assemble parts, tools, SOPs (Standard Operating Procedures), permits, and a proposed slot. Reduce orchestration down to an approval click.

5. Validate in the messy middle
Run operational acceptance tests across shifts: handle data dropouts, sensor drift, seasonal product mix, and stacked alarms. Track results against a defined control.

6. Stage autonomy with intent
Begin with “recommend + prepare.” Move to “recommend + execute with approval” for low-risk tasks once accuracy and economics hold up. Keep high-risk steps human-gated.

Metrics that prove this isn’t another science project

If it’s working, you’ll see impact where operations feel it—not just in a model score.

• Time-to-action: detection → approved work order → first wrench turn

• Avoided downtime minutes and scrap reduction by asset family

• Intervention precision: share of agent-led jobs resolved without rework or rollback

• Operator acceptance: percent of plans accepted as-is; reasons for overrides (which feed the next release)

These metrics also help finance attribute gains to the agent, not just broader continuous improvement.

Governance and safety never go out of style

No agent should push setpoints at the control layer without passing rigorous tests. Treat PLC (Programmable Logic Controller) interfaces as high-risk, segment networks, and maintain an instant rollback path. In regulated environments, preserve a full audit trail and clear reasoning for each action; explainability is not optional.

Why this matters now

Budgets are shifting from pilots to scale, and leadership is asking a sharper question: which systems change outcomes, not just dashboards? Action-literate agents—those that respect inventory, lead times, schedules, and safety—shorten the path from signal to a good decision. That’s the kind of result plant managers will demand and fund.

Raunak Bhinge’s contribution to the discussion is a practical filter manufacturers can apply today: if a recommendation can’t be executed with the people and parts you actually have, it’s not yet useful. Build agents that know the work as well as the data, and you’ll see fewer “cry-wolf” moments and more well-timed fixes.

Source: “Agentic AI in Manufacturing: From Copilots to Autonomous Systems” (AI Frontiers 2025), sponsored by Cybus and Infinite Uptime, Inc.

Related articles:

• The Hidden Advantage of Generative AI in Manufacturing: Speed, Not Just Smarts
• From Data-Driven Ambitions to Sensing Factories: A Strategic Path for AI in Manufacturing