AI projects often fail before model quality becomes decisive. Teams commit budget without first testing the idea against the people, systems and market it depends on.
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For pharma Commercial and Omnichannel teams, the pattern is familiar: an impressive demo, then a quiet stop before anything reaches field teams, campaigns or healthcare professionals. The budget is spent and the business problem is still there.
The good news is that the reasons why AI projects fail are predictable. Most of them can be checked before a single line of production code is written.
How often do AI projects fail?
According to RAND, which interviewed 65 experienced data scientists and engineers, more than 80% of AI projects fail by some estimates. That is twice the failure rate of IT projects that do not involve AI.
Generative AI has not changed the picture. In 2024, Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. Its 2026 analysis found the real share was at least 50%, due to poor data quality, inadequate risk controls, escalating costs or unclear business value. Gartner also expects over 40% of agentic AI projects to be canceled by the end of 2027.
These causes point mainly to business decisions and operating conditions, not model performance alone. Most of those decisions are made, or skipped, before the build begins.
Why AI projects fail: five root causes in Commercial and Omnichannel
RAND groups the causes of AI project failure into five patterns. Each has a recognizable version in pharma commercial work. The examples below are illustrative, not client cases.
1. The problem is not clearly defined. “Use AI to plan better campaigns” is an ambition, not a decision. A campaign-planning assistant built without agreeing which planning step it should improve, or how success is measured, produces drafts that brand teams admire and then ignore.
2. The data is not ready. An HCP segmentation model is only as good as the records behind it. When the same physician appears differently across CRM, master data and consent systems, segments look precise but field teams do not trust them.
3. Technology comes before the workflow. A team adopts generative AI to localize global content for US markets, then discovers there is no defined place for MLR review. Output arrives faster than reviewers can approve it, so nothing is published sooner.
4. The path to production is missing. A journey personalization pilot recommends the next best action in a sandbox, but it is not connected to CRM, channels or consent. It works in the demo and has nowhere to go.
5. The problem is too hard for now. An emerging KOL identification model promises to spot tomorrow’s experts automatically, without verifiable scientific sources or a role for Medical Affairs. That limit should be discovered in weeks, not after a year of development.
Why AI pilots stall when success criteria are unclear
A common thread runs through all five causes: most AI pilots are judged after the fact. Success criteria appear once the demo exists, so the question becomes “Does this look promising?” instead of “Did this meet the threshold we agreed?” The next phase is approved on momentum, and the unresolved risks return later as cost overruns, compliance concerns or low adoption.
Better validation before significant investment prevents many of these problems.
How to prevent AI project failure before you build
Before committing budget to an AI solution, three questions need a verified answer rather than an assumption.
THREE QUESTIONS TO ANSWER BEFORE YOU BUILD
Does it fit how your people work? Will brand, regional and field teams use the output in their real workflow, and who reviews it?
Can it be built on your systems? Are the data, integrations, permissions and governance in place, or what must change first?
Are the market and timing right? Is the business case clear now, and is the value large enough to justify the build?
Each question is a risk. If you are already sure of an answer, there is nothing to validate. If you are not, it should be tested against a threshold agreed before the work starts.
This is how Ingemark’s Discover & Advise works: it turns an uncertain AI idea into a verified decision before significant budget is committed.
What you get from Discover & Advise
A Vision Statement. The decision to improve, what success means and the thresholds each risk must meet, agreed before any assessment starts.
One to three assessments. Functional, technical or market, chosen by which risks are still open. You pay to validate what is uncertain, not what you already know.
An analysis document. Findings measured against each threshold, with a UI prototype where testing the look and feel with users helps.
A Business Case with one recommendation. The outcome may be go with a defined scope, go with a narrower scope, improve what exists, or no-go. Each recommendation includes the reasoning behind it.
Discover & Advise is an analysis and advisory engagement with no production development. Scope and price are fixed before the work begins, and a small, senior team of product, design, architecture and business analysis specialists runs the engagement, so it stays predictable.
A no-go is a valid outcome. Paying to avoid the wrong investment costs far less than discovering the problem after a full build. When the answer is go, you move into delivery with validated assumptions, a defined scope and an owner for each decision.
SCENARIO: THE $400,000 DECISION – WHY A NO-GO CAN BE A WIN
This scenario is hypothetical, not a client case.
A US brand team plans a $400,000 build for AI-driven journey personalization across email, rep-triggered email and an HCP portal. Before the build, a technical assessment checks whether consent and channel data can support timely recommendations.
It finds that consent status is updated in weekly batches and, in one channel, cannot be linked to CRM records at all. The threshold agreed at the start, recommendations only for HCPs with verified and current consent, cannot be met. The Business Case recommends no-go for the full build and a narrower first step: fix consent synchronization, then start with one channel. The team avoids committing $400,000 to a solution it could not have used as designed.
The recommendations rest on delivery experience in regulated MedTech. For a NASDAQ 100 medical device manufacturer, Ingemark built a master data management platform and an event-driven global integration platform. They show the data and integration groundwork that AI use cases in Commercial and Omnichannel depend on, rather than a finished pharma Commercial AI solution. Ingemark is also ISO 27001 and ISO 9001 certified.
Questions teams ask before they start
Why not validate the idea ourselves with AI tools? AI tools can produce an analysis or a prototype quickly. They cannot take responsibility for the judgment. Discover & Advise gives you an independent, senior assessment against thresholds you agreed in advance, and we stand behind the recommendation, including when it is no-go.
What if the answer is no-go? Then the engagement has done its job. You have spent a fraction of the build budget to avoid a larger mistake, and you usually leave with a narrower opportunity that is worth pursuing.
What if we are already sure about some of the risks? Then we do not test them. Each assessment covers one open risk. If nothing is open, we will tell you that you are ready to build.
Start with one decision, not one model
The most reliable way to avoid becoming another AI failure statistic is to narrow the starting point. Pick one recurring Commercial or Omnichannel decision that is slow, fragmented or hard to scale across brands and regions, and test it properly before you build.
Download the Discover & Advise decision guide to see how the engagement works, what you receive and how the assessments are chosen.
Prefer to start with a conversation? You can also book a 30-minute conversation with Ingemark. We will review one workflow with you, identify which of the three risks (workflow fit, technical feasibility and market timing) are still open and tell you honestly whether validation is worth it.
Sources
RAND Corporation, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI
Gartner, Why 50% of GenAI Projects Fail — And How to Beat the Odds
Gartner, Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027
