Guide

Common Reasons AI Projects Stall and How to Address Them

By Todd Creek Updated September 8, 2026 3 min read

An AI project can encounter problems in the model, the software, or the surrounding business process. The checks below focus on operational gaps that a project team can investigate before committing to implementation.

Define an operational objective

“Use AI” does not specify what should change. Describe the task and the improvement needed, such as reducing manual preparation of a report. Agree with the people doing the work on what a useful result would look like.

Check access and quality early

A proposed workflow may rely on missing fields, inconsistent records, or data that cannot be accessed under current permissions. Inspect representative examples and confirm the integration requirements. Include cleanup or access work in the estimate.

Assign responsibility after launch

The workflow will need support as inputs and requirements change. Name both the operational owner and the technical support contact. Clarify who reviews failures, approves changes, and decides whether the system should be paused.

Test whether the selected tool fits

A product may be capable without being appropriate for the workflow. Compare it with the actual requirements and alternatives. Be willing to change the proposed approach when a trial exposes significant integration or review costs.

Plan the surrounding process changes

Identify which existing steps the tool replaces and what users will do differently. Provide training, but also check whether the new workflow is easier to follow and whether its output can be used. Running both the old and new processes indefinitely can erase the expected benefit.

Agree on evidence of improvement

Establish a baseline and a small set of measures before rollout. Include quality and correction effort as well as speed. Without consistent measurement, the team may have difficulty determining whether the change helped.

Give unresolved issues an owner

These questions often cross departmental boundaries. Keep a list of open decisions, the person responsible, and the evidence needed. A missing owner or unclear permission should remain visible until it is resolved.

Use the review to shape the project

A readiness review can expose these gaps and change the proposed scope. It does not guarantee success or remove the need to test the system itself. Use its findings to define a realistic pilot and operating plan.

The pilot planning guide covers acceptance criteria, ownership, integration, and costs at production volume.

Questions and answers

What percentage of AI projects fail?

There is no single percentage that applies to every type of AI project. Studies use different definitions, samples, and timeframes. Evaluate the specific risks in your proposed workflow rather than relying on an unattributed headline figure.

Can an assessment prevent every failure?

No. It can identify uncertainties and dependencies before implementation. Design quality, testing, adoption, and ongoing support still affect the outcome.

TC

Todd Creek

Todd Creek is the founder of Rock Creek Performance Partners. He works with clients on AI strategy and workflow automation. Connect on LinkedIn ↗

Keep reading

Related insights

For help evaluating a workflow, see what an AI opportunity assessment includes.