AI in the lab creates excitement.
AI in the flow of work creates tension.

And tension is where value lives.

Most AI initiatives look impressive in controlled environments—clean datasets, defined prompts, cooperative users, and predictable outcomes. Demos work. Pilots impress. Dashboards look green.

But the real test begins when AI leaves the lab and enters everyday workflows.

That’s where tradeoffs surface.

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The Hidden Tradeoffs of Production AI

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Once AI is embedded into real processes, teams are forced to confront decisions that were easy to ignore during experimentation:

• Speed vs Accuracy
  Faster responses may increase productivity—but at what cost to correctness or trust?
• Automation vs Judgment
  Which decisions should AI make autonomously, and where must humans remain in the loop?
• Consistency vs Exceptions
  AI thrives on patterns, but businesses run on edge cases. Who owns exception handling?

These are not technical limitations.
They are organizational and operational choices.

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Why Most Pilots Stall

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Many teams treat these tensions as problems to eliminate.
The teams that succeed do the opposite.

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They:

• Make the tensions explicit
• Assign clear ownership
• Define escalation paths and decision rights
• Design governance into the flow of work—not as an afterthought

In other words, they manage the tension instead of pretending it doesn’t exist.

That is usually the difference between:

• AI that stays trapped in pilots
• And AI that becomes a durable part of how work actually gets done

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The Real Shift

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Scaling AI is less about better models and more about better decisions:

• Who decides when AI is right?
• Who is accountable when it’s wrong?
• How are tradeoffs resolved under real-world pressure?

Until those questions are answered, AI will remain impressive—but fragile.

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What have you observed when AI moves from the lab into production workflows?

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Frequently Asked Questions (FAQs)

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Q. What does “AI in the lab” typically mean?
AI in the lab refers to controlled environments where models are tested on clean data, predefined workflows, and cooperative users. Success is measured by benchmarks, demos, or pilot KPIs rather than sustained business outcomes. These settings minimize ambiguity, edge cases, and accountability.

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Q. Why does AI behave differently in real workflows compared to pilots?
Production environments introduce variability: incomplete data, shifting priorities, exceptions, regulatory constraints, and human judgment calls. AI must operate alongside existing systems, incentives, and decision rights — factors that are often abstracted away in lab settings.

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Q. Why are speed vs accuracy and automation vs judgment not technical problems?
Because the technology can often support multiple configurations. The challenge is deciding:
• How much error is acceptable for a given process
• Where humans must intervene
• Who owns outcomes when AI is wrong
These are governance, risk, and operating-model decisions, not model limitations.

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Q. What causes most AI pilots to fail to scale?
Common reasons include:
• No clear ownership for AI decisions
• Undefined escalation paths for exceptions
• Misaligned incentives between teams
• Treating governance as a compliance step instead of a design input
Without resolving these, pilots remain fragile and untrusted.

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Q. How do successful teams handle “tension” instead of avoiding it?
They explicitly design for it by:
• Defining decision boundaries for AI vs humans
• Assigning accountability for outcomes
• Embedding approvals and checkpoints into workflows
• Treating exceptions as first-class citizens, not errors
This turns tension into a managed system property rather than a failure mode.

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Q. What does it mean to design AI “in the flow of work”?
It means AI operates inside real processes — finance closes, customer support, compliance reviews, software delivery — with clear roles, metrics, and controls. AI becomes part of how work happens, not a side tool or standalone experiment.

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Q. How should organizations measure success beyond pilots?
Success metrics shift from:
• Model accuracy → business impact
• Demo performance → cycle-time reduction
• Task automation → decision quality and trust
If these aren’t measured, AI adoption stalls regardless of technical quality.

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Q. What’s the key takeaway for leaders investing in AI?
Scaling AI is less about better models and more about better decisions. If tensions are ignored, AI stays in pilots. If tensions are designed for, AI becomes durable infrastructure.

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