AI as Re-Engineering: Why AI Works in Demos But Breaks in Production

AI failures rarely show up in demos. They surface in production, inside the workflows that determine margin, throughput, and risk.

AI works in controlled environments because those environments remove the very conditions that define real operations: variability, exceptions, handoffs, and accountability. In production, those conditions are unavoidable. And most systems were never designed to absorb intelligence under that kind of pressure.

When organizations bolt AI tools onto existing systems, they introduce intelligence without redesigning how decisions are made, owned, and executed. The result is friction across workflows where decisions should move cleanly into execution.

"Demos don't take into account all the elements of the organization's ecosystem," says Rachel Stuve, Director of AI Enablement at Sparq. "They may not take into account a bad process or a broken process. They may not take into account cultural adoption."

This pattern is widespread. While 88% of organizations surveyed by McKinsey report using AI in at least one business function, nearly two-thirds remain stuck in pilots. Only 39% see enterprise-level returns. The issue is intelligence being introduced into systems that were never designed to carry it.

Why AI Breaks Down in Production Environments

Demos succeed by narrowing scope. Production fails when scope expands to reality.

In real operational systems, AI must function across interconnected workflows, conflicting incentives, incomplete data, and decisions with real financial consequences. When those conditions aren’t designed for explicitly, AI introduces variability the system can’t absorb, slowing execution and increasing manual intervention.

Operational Complexity

Production systems operate under far more variables than demos acknowledge, with dozens of real-world factors influencing outcomes minute by minute.

Derek Perry, Sparq's Chief Technology Officer, describes a common failure mode: "If you think of building a shipping forecast as an example, you may consider historical information, day of week information, and some load information," he says. "But you're not contemplating the 30 other factors that you would see in production." For example, "Is there fog in Albany, Georgia? That's a scenario that came up in a real-life production deployment."

In these environments, missed variables force manual overrides, erode trust in the system, and push decisions back into spreadsheets and inboxes.

Fragmented Workflows

Work in production environments rarely follows a clean, linear path. It crosses departments, involves multiple handoffs, and relies on informal knowledge that exists in people's heads rather than documented processes.

When workflows aren’t explicitly mapped and owned, AI has no stable surface to operate on. Insights arrive, but execution stalls because no system is responsible for carrying the decision forward.

Misaligned Incentives

AI often reveals conflicts that already exist inside organizations. Different teams may have competing priorities that AI exposes but can't resolve.

Without redesigning incentives and decision rights, AI deployments stall regardless of technical capability. The system surfaces what should happen, but the organization isn’t structured to act on it.

Unclear Ownership

In demos, decision-making is hypothetical. In production, decisions affect customers, revenue, compliance, and risk.

Many organizations deploy AI without defining who owns AI-driven decisions once they move into live operations. When outcomes deteriorate, responsibility diffuses, and confidence in the system collapses.

AI concentrates accountability by pushing decisions into workflows where outcomes are visible and owned.

Why This Matters Now

As AI capabilities stabilize, the constraint shifts to operations.

Organizations that embed intelligence directly into execution are compressing decision cycles, reducing rework, and compounding operational gains. Those that leave AI isolated in tools continue to absorb the cost of pilots, workarounds, and manual intervention. When systems remain unchanged, every attempted deployment adds friction. Rework accumulates. Confidence erodes. The cost of moving AI into production rises with each iteration.

Most AI failures today occur at the operational layer, where systems were never designed to carry intelligence into live execution. Addressing that late is expensive. Addressing it deliberately is how AI starts to pay off.

AI as Re-Engineering

AI’s real value is the ability to reshape how decisions are made and executed across complex systems.

"The power of AI is in the fact that it can look at a vast amount of data and connect it in ways that simple task automation can't," Stuve says. "You truly can re-engineer a process." For example, instead of just helping users write emails, AI can identify the best person to contact in a given situation based on the user's role and other contacts.

Designing Workflows That Can Carry Intelligence

Re-engineering with AI means redesigning workflows so intelligence is embedded directly into the moments where decisions carry consequence. That requires systems that can absorb variability, surface exceptions, and route outcomes predictably, without pushing work back to humans as a default.

Foundations Before Precision

This approach starts with stable operational foundations. Inputs are structured. Outputs are validated. Exceptions are handled deliberately. Decisions move through defined paths instead of ad hoc escalation.

Rather than applying AI everywhere, re-engineering focuses intelligence where it compounds: inside earnings-critical workflows where small improvements create outsized financial impact. The result is a system designed to improve as conditions change.

Human Judgement Where It Compounds

Re-engineering also clarifies the role of humans. AI handles pattern recognition and consistency at scale. Humans retain judgment where tradeoffs, risk, and nuance matter most.

Context Before Iteration

From the outset, this requires modeling the full operational context, not just the narrow use case. As Perry explains, "You can go straight into building a demo and construct something that is of narrow complexity, narrow value, but when you factor in all of the unique complexities at that stage, you set yourself up for a more successful production deployment." Such deployments also save time in the long run because you avoid downstream rework.

This approach contradicts the prevailing agile methodology of building quickly and iterating. Perry acknowledges the tension. "I'm not saying that view is completely wrong," he says. "I'm saying in an AI age, that view doesn't lead to the fastest of outcomes, the most optimal of outcomes, because you're allowing change absent the context that you need to understand a problem."

Agile iteration still matters, but in AI-driven systems, iteration without context increases rework and risk. Re-engineering shifts effort earlier, so change compounds instead of destabilizing execution.

The Path Forward

The shift to AI re-engineering starts with identifying where failure is expensive. Organizations should prioritize workflows that directly affect revenue, margin, or customer trust–areas where delays, errors, or manual workarounds already create financial drag.

Stuve recommends focusing on processes where exceptions pile up and decisions slow execution: "What are the big pain points within your company? What are the things that either cause a lot of manual work, or they're constantly breaking, or they have a lot of downstream impact if they're not corrected?"

Starting with a narrow scope doesn't mean thinking small. Early wins establish the foundations that make broader transformation faster and safer.

The organizations that succeed with AI won’t be the ones with the most pilots, but the ones that redesign how decisions move through their operational backbone, so intelligence strengthens execution instead of fighting it.