MIT’s State of AI in Business 2025 reporting revealed a stark truth: 95% of enterprise AI pilots fail. It’s becoming the most widely cited statistic in enterprise AI adoption — and for good reason. In an era of massive AI investment, only 5% of projects deliver measurable ROI. If any other technology failed this consistently, the CFO would shut it down overnight.

Consider a bank that deployed an “AI-powered churn dashboard.” It predicted which clients were at risk but offered only confidence scores — no reasoning, no transparency. Relationship managers ignored it.

The problem isn’t weak models. It’s the way they’re delivered to humans. Without trust, even accurate AI becomes another failed pilot.

What Workers Actually Want

Stanford’s Future of Work with AI Agents work found that workers don’t fear AI. Their findings reinforce a critical theme in enterprise AI adoption: workers don’t fear AI; they fear losing control. 

Workers want AI to handle busy work so they can focus on meaningful, high-value tasks.

Think about a healthcare operations manager. They do not want an algorithm dictating how to staff a unit. What they do want is an assistant that automatically transcribes care notes into the right fields of an electronic health record. That saves nurses from clerical work, giving them more time to spend with patients.

The difference is simple: augmentation, not replacement.

Why Most AI Strategies Fail

Many enterprises design AI as a replacement strategy, rather than an augmentation strategy.

A global consumer goods company trained a custom model to summarize sales trends. It looked impressive, but the model couldn’t access live systems. It summarized PDFs instead. Employees still had to export, align, and clean data manually before they could use it.

The model wasn’t inaccurate. It was irrelevant. That is why 95% of AI dies in pilot purgatory. This disconnect is one of the top reasons enterprise AI strategies fail to scale beyond experimentation. Coverage of the MIT results suggests that a “learning gap” and poor workflow integration are the real culprits.

Trust Before Automation

At my previous company, Downstream, when we built machine learning automations for Amazon ad bidding, we learned this lesson firsthand. Marketers didn’t want to hand over budgets to a black box. They wanted a spectrum of control.

We provided them with options: start with recommendations, add rules-based automation, and progress toward full autonomy at their own pace. Adoption only scaled once users trusted the system.

Just as drivers won’t give up the steering wheel on day one, workers won’t embrace AI without visibility and oversight. 

Trust isn’t a feature. It’s the adoption curve.

DataLab: AI That Earns Trust

We built DataLab to give people autonomy and build trust in the relationship they have with their data insights. It’s designed specifically for enterprises struggling with AI adoption, data silos, and pilot-to-production friction. 

Imagine a retail brand manager asking: “Why did sales of Product X drop 12% last month?”

BEFORE:

  • An analyst would pull sales data from Amazon Vendor Central, ad data from the Amazon Ads console, and inventory reports from a warehouse system, then manually reconcile them all. Hours were lost before any insight appeared.

AFTER:

  • With DataLab, the manager asks once. DataLab integrates with Snowflake, Databricks, and more. It reconciles mismatched IDs, runs the analysis, and responds: "Sales dropped because 22% of ad spend went to out-of-stock products. Your competitor’s share of voice increased by 8%. Inventory replenishment lagged by five days compared to normal." 
  • ‍The manager can see the reasoning, inspect the SQL queries, and correct any issues that appear to be incorrect. Once confident, they can delegate: “Alert me when more than 10% of ad spend is wasted on out-of-stocks.”

Trust builds step by step, until automation feels like empowerment instead of risk.

The Second Mile: Safe Autonomy via DataLab MCP

Earning trust is the first mile.

The next frontier is action. This is where agents come in. 

In the simplest terms, an AI agent is software that can take action on your behalf — a workflow-aware system that doesn’t just analyze data but acts on it. For example, an AI agent can monitor a workflow, check conditions, trigger an update, or execute a task.

But handing action to an agent requires a level of trust far higher than asking for a summary. If you can’t see what it’s doing or why, you will never let it operate unsupervised.

How does this work with Alkemi’s DataLab? Using DataLab’s MCP endpoints, an agent can safely ask DataLab questions and take only the actions you’ve allowed it to take. Think of it like giving the agent a controlled toolbox: it can use the tools you approve, in the way you approve, and nothing more.

The agent never connects directly to your databases or systems. Instead, everything flows through DataLab, which checks permissions, runs the analysis, and delivers only the information or actions that are safe. This means the agent can help with real tasks—like monitoring conditions or triggering alerts—while you keep full visibility, control, and an audit trail of every step it takes.

When you’re ready to move beyond insights, DataLab lets you move gradually — from “recommend,” to “approve-to-run,” to “auto-run within limits” — so autonomy always grows at the pace your team trusts.

No More Failed AI Projects

AI projects don’t fail because the models are weak. They fail because adoption breaks. Workers reject systems that don’t match how they operate or that hide how decisions are made.

The organizations that escape the 95% failure rate do one thing differently: they build trust first.

The companies that win with AI won’t be the ones trying to replace workers. They’ll be the ones designing human-centered systems that eliminate busywork and then translate insight into safe, governed autonomy through DataLab MCP endpoints. Every agent action flows through a single controlled layer, so AI never operates outside your guardrails.

If AI is still a “side project” in your org, you’re already in the 95%. It’s time to stop building disposable pilots and start building AI that earns trust and delivers results.

If you’re evaluating AI platforms or looking to improve AI ROI in 2025, trust-centered systems are the differentiator.

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👉 Ready to see how trust-centered AI can deliver ROI? Connect with us and learn more.

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