PoV: The Real Risk in Enterprise AI Isn’t Model Bias—It’s Decision Distance

pov

The Real Risk in Enterprise AI Isn’t Model Bias—It’s Decision Distance

21 May, 2025 | 15 min read

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pov

The Real Risk in Enterprise AI Isn’t Model Bias—It’s Decision Distance

21 May, 2025 | 15 min read

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Executive Summary

AI adoption in enterprises is booming. But value realization? That’s a different story. Most businesses are still struggling to turn machine intelligence into business outcomes. The problem isn’t just technical. It’s structural. And it often gets ignored.

This Point of View explores the concept of “decision distance”: the gap between AI-generated insights and the actual business decisions they’re meant to support. Unlike model bias, which gets all the headlines, decision distance operates quietly in the background—causing delays, eroding trust, and bottlenecking impact.

We unpack why this distance exists, what it costs businesses, and how agentic AI systems like Turinton’s Insights AI platform can help bridge the gap. Spoiler: accuracy isn’t enough. If AI insights don’t drive action, they don’t matter.

1. Why It’s Not About the Model

Enterprise AI projects are failing at an alarming rate. Up to 75% stall or get cancelled before delivering ROI. And it’s not because the models don’t work. In fact, only about 10% of AI failures stem from the algorithms themselves. Most of the time, the problem is everything around the model.

There’s a widening disconnect between the data science teams building AI systems and the business teams meant to use them. You’ve got highly accurate predictions sitting in dashboards, ignored. Not because they’re wrong. But because the people who are supposed to act on them don’t trust them, can’t interpret them, or don’t even see them in time.

This is what we mean by decision distance. And it’s emerging as the true bottleneck to AI value.

2. What Is Decision Distance, Exactly?

Think of decision distance as a delay—not just in time, but in understanding, trust, and execution. It comes in three forms:

Operational distance shows up when AI insights are trapped in siloed systems and never make their way into day-to-day workflows.

Cognitive distance happens when users don’t understand the AI output or can’t see how it connects to their decision-making.

Here’s what continues to hold companies back:

Organizational distance appears when there’s a disconnect between the teams building the models and the ones making the decisions.

Even when a model is accurate, if there’s friction at any of these levels, the insights go nowhere. Or worse, they’re misused.

3. The Problem with Our Obsession Over Bias

Bias in AI matters. But focusing solely on bias gives enterprises a false sense of control. Yes, you should check for fairness, audit for discrimination, and stress-test for edge cases. But what’s often overlooked is the fact that many AI models don’t fail because they’re unfair.

They fail because they’re irrelevant. Or too late. Or too confusing. Or too disconnected from the way decisions are actually made.

Organizations often spend 30–50% of their AI development cycle on bias mitigation, especially in regulated industries. But usability and integration? That gets just 10–20%. And yet that’s where adoption breaks down.

4. When Great Models Die in the Lab

Take IBM Watson for Oncology. It promised AI-driven cancer treatment recommendations. But doctors didn’t trust the outputs. The data wasn’t representative, and the suggestions felt disconnected from local clinical realities. Despite billions invested, the system was shut down.

Or look at McDonald’s AI drive-thrus. Great in theory. Terrible in practice. The voice system misunderstood orders. Employees overrode it. Customers got frustrated. The project was abandoned.

The models weren’t necessarily broken. But they weren’t usable. They weren’t embedded. They were too far away from where real decisions got made.

5. The Hidden Cost Of Distance

Decision distance isn’t just frustrating. It’s expensive.

It slows down critical decisions by forcing teams to rely on gut instincts instead of timely AI insights.

It wastes investments, because every unused model still racks up costs in storage, maintenance, and opportunity.

It delays value, especially in industries where timing—like in operations, finance, and healthcare—has a direct cost.

In healthcare alone, delays in acting on AI recommendations can cost hundreds of millions annually.

6. Why the Last Mile Is Always the Hardest

Enterprises love AI pilots. But most projects get stuck there. The last mile—getting the insight from the lab into a decision workflow—is where things fall apart.

It’s not just a tech challenge. It’s a design challenge. A trust challenge. A process challenge.

Employees often don’t know how to use the system effectively, leading to low adoption.

Even when the AI suggests something useful, users are unsure about the next step to take.

That’s the reality in most companies. The AI works. But the people and the processes around it don’t.

7. What Enterprises Should Be Solving For

Here’s the shift that needs to happen: Stop solving for model accuracy alone. Start solving for decision latency.There’s a difference between an experimental agent and one you can trust to run operations.

That means measuring how long it takes from the moment an AI insight is produced to when an action is taken based on it.

It also means understanding whether people actually trust and comprehend the output.

It’s about tracking how often AI recommendations are followed through, not just how accurate they were.

Finally, it requires identifying where exactly in the workflow things are breaking down or being ignored.

And it also means embedding AI directly in the tools and processes people already use—so action is just one click away.

8. Enter Agentic AI: Closing the Gap

This is where agentic AI makes a difference.

Turinton’s Insights AI platform is built around autonomous agents that don’t just deliver insights. They nudge, explain, follow up, and adapt.

Discover agents surface new trends or outliers in your data, helping uncover opportunities or risks you may not have seen.

Observe agents monitor your systems in real time, alerting you to anomalies as they happen.

Correlate agents connect dots across data points, identifying root causes or emerging patterns.

Explore agents allow you to run simulations, ask natural language questions, and drill into answers.

These agents don’t live in separate dashboards—they show up in the systems your teams already use. They shrink the distance between insight and action.

9. What Happens When the Gap Closes

When AI is embedded and usable, everything moves faster.

Decision latency can drop by up to 70%, especially in fast-paced environments like IT or customer support.

Trust in AI increases naturally, because users see the value firsthand instead of having to be convinced.

Business outcomes improve—faster resolutions, higher conversion rates, fewer missed opportunities.

One Turinton customer cut mean time to resolution by 60% using Observe and Correlate agents. Another saw a 25% sales lift after Explore agents uncovered high-value leads.

10. The Real Benchmark: Decisions, Not Models

Too often, AI success is measured in precision scores. But the real metric is: Did it change a business outcome?

That’s the north star.

Track how quickly AI insights turn into decisions.

Ask users if the AI made their work easier, not just if it was accurate.

11. Final Thoughts: The Future of AI Is Decision-First

Model bias still matters. But the bigger risk is building smart systems that no one uses. That no one trusts. That no one acts on.

Decision distance is fixable. But only if we stop thinking of AI as a model problem, and start treating it as a decision problem.

Turinton’s platform is designed to solve exactly that. By embedding agentic AI into the heart of the business, it brings insights closer to the people who need them. And closer to the moment they matter most.

Because in the end, it’s not the model that moves the business forward. It’s the decision it helps you make.

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About Author

Vikrant Ladbe is a technology leader with 20+ years of experience, specializing in cloud-native applications, IoT, and AI-driven systems. He scaled a successful enterprise acquired by LTIMindtree and has led large-scale digital transformation initiatives for global clients.

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Vikrant Labde

Vikrant Labde

Executive Summary

3. The Problem with Our Obsession Over Bias

Vikrant Labde

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