What is the single most common cause of failed AI projects?

Starting from a tool instead of a workflow. When the tool is chosen first, teams look for somewhere to apply it, and the resulting pilot has no owner, no baseline, and no definition of done.

How do you know a use case is a bad fit for AI?

Look for three signals: the task requires an answer that must be exactly right every time with no review possible, the underlying data is incomplete or inconsistent, or the process changes so often that any measurement would be meaningless.

How small should a first AI project be?

Small enough that one team owns it end to end and you can measure the result inside 90 days. Breadth comes after you have one workflow that people prefer to the old way.

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Framework

Best PracticesLearned From Failed Pilots

These are the practices that separate AI work that survives contact with production from the pilots that quietly end after the first quarter.

Eight practices that hold up

Start from the workflow, not the tool

Name the specific task, the person who owns it, and the decision the output feeds. If you cannot name all three, you have a technology interest rather than a project.

Baseline before you build

Record current cycle time, error rate, and volume first. Without a baseline, every result becomes a matter of opinion at review time.

Decide where AI will not be used

Write the exclusions down: decisions that stay human, data that never leaves your systems, output that always needs sign-off. Clear limits make the approved uses easier to defend.

Keep a human review step where consequences are real

Financial, legal, safety, and customer-facing output needs an approver. Design the review to take seconds, or people will route around it.

Build an evaluation set early

Collect thirty to fifty representative cases with known correct answers. It turns every later change into a measured decision instead of a vibe check.

Keep vendors swappable

Put an abstraction layer between your workflow and the model provider so a change in price, quality, or terms is a configuration change rather than a rebuild.

Budget for the whole cost

Licenses are the visible part. Integration, review labor, monitoring, retraining, and change management usually cost more than the subscription.

Measure adoption, not activity

Query counts prove nothing. Track how many people use the new workflow as their default, and how many still keep the old process running in parallel.

Patterns That Predict Failure

Choosing a platform before any workflow has been scoped

Running a pilot with no owner, no baseline, and no exit criteria

Feeding a model documents nobody has confirmed are current

Treating fluent output as verified output

Announcing headcount implications before the work is understood

Skipping training and expecting adoption from a launch email

Adding agent steps to hide an unreliable first step

Judging success on demo quality instead of production behavior

A Workable First 90 Days

Weeks 1 to 2: pick one workflow, name its owner, and record the baseline numbers

Weeks 3 to 4: write the exclusions, the review step, and the evaluation set

Weeks 5 to 8: build the narrowest version that touches real data, with monitoring on day one

Weeks 9 to 12: run it with one team, measure against the baseline, then decide to widen, fix, or stop

Related Resources

Readiness Checklist

Assess data, organization, technology, and process readiness.

Use Case Finder

Find which workflows are worth scoping first.

Templates

Scoping, baseline, and governance documents to reuse.

Apply This to Your Own Roadmap

We will review your planned AI work against these practices and tell you which parts we would stop, keep, or resequence.

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