67% of AI projects fail — the same 12 mistakes come up systematically
Several studies suggest that nearly two out of three AI projects in SMBs never reach the production phase. After supporting more than 60 companies, we have identified 12 recurring mistakes that explain the majority of these failures. The good news: 10 of these 12 mistakes occur before the first development and are therefore avoidable through better scoping. Here is the complete list, classified by project phase, with concrete solutions for each.
The problem
AI projects rarely fail for technological reasons. In 80% of cases, the failure is due to scoping, organizational or change management mistakes. Yet these mistakes are predictable and documented — they repeat from one project to the next, from one company to another.
The 12 mistakes fall into three phases:
- Scoping phase (mistakes 1 to 4): starting from the technology rather than the problem, underestimating data quality, not defining measurable KPIs, no business sponsor.
- Development phase (mistakes 5 to 8): aiming for perfection rather than usefulness, neglecting integration with the IT system, ignoring end users during development, under-budgeting data preparation.
- Deployment phase (mistakes 9 to 12): not planning for production maintenance, ignoring change management, not measuring ROI, the eternal POC syndrome that never makes it to production.
Each mistake taken in isolation seems avoidable. But their combination creates a domino effect that dooms the project. The key is to put guardrails in place at each phase to detect and correct these drifts as early as possible.
The AI solution
To counter these 12 mistakes, we propose a prevention framework structured around three complementary mechanisms, applicable to any AI project in SMBs and mid-market companies.
Business-problem-oriented scoping
Every AI project should start with a one-page scoping sheet that answers 5 questions: which business problem are we solving? what is the current baseline (time, cost, error)? what is the expected gain and how do we measure it? are the required data available and of sufficient quality? who is the business sponsor and who are the end users? If a single answer is missing, the project is not ready.
Short validation loops
Break each project into iterations of 2 to 3 weeks with a testable deliverable at each step. Involve end users from the first iteration. This approach detects misdirection in 2 weeks instead of 2 months. Go/No-Go criteria are assessed at each iteration, not only at the end of the POC. Find our LLMOps practices to structure your iterations.
Anti-failure dashboard
A tracking dashboard that monitors the 5 early warning signs: sponsor engagement (number of meetings/month), data quality (completeness score), user satisfaction (internal NPS), schedule adherence, and scope evolution. Any red signal triggers a project review within 48 hours. Our maturity audit includes this dashboard.
Implementation
Here are the three concrete actions to put in place before and during each AI project to avoid the 12 fatal mistakes.
Pre-project scoping checklist (before spending 1 euro)
Before launching an AI project, validate these 8 criteria: clearly defined and quantified business problem, available data of sufficient quality (test on a sample), identified and committed business sponsor, success KPIs defined with Go/No-Go thresholds, budget covering data + development + deployment + year-1 maintenance, identified and involved end users, realistic schedule with intermediate milestones, available skills (internal or external). Minimum score: 6/8 to launch.
Structured bi-weekly project reviews
Every 2 weeks, bring together the business sponsor, project manager and lead user for 30 minutes. Review: progress vs. schedule, intermediate results vs. target KPIs, warning signs (data, adoption, scope), decisions to make. Document each review and the decisions taken. This simple discipline eliminates 70% of drifts.
Formal Go/No-Go at the end of the POC
After 6 to 8 weeks of POC, organize a formal review with the management committee. Present: results vs. success criteria, projected ROI over 12 months, production rollout plan (budget, schedule, resources), identified risks and mitigations. A Go requires that 80% of the criteria be met. In case of a No-Go, document the learnings and reallocate the budget. A well-managed No-Go is better than a zombie project.
Results
Frequently asked questions
What is the most frequent mistake in AI projects in SMBs?
Mistake number 1 is to start from the technology rather than the business problem. "We want to do AI" is not an objective. The right approach is to identify a concrete business problem (processing time too long, high error rate, lack of responsiveness) then assess whether AI is the best solution. 40% of AI projects fail simply because the problem was not well defined.
How do you know if an AI project is going to fail before it's too late?
Five early warning signs: no clearly identified business sponsor, insufficient or inaccessible data after 2 weeks of investigation, no measurable baseline to calculate ROI, active resistance from end users, and scope that keeps expanding (scope creep). If you check 3 or more signs, stop and reframe before continuing.
Should you stop an AI project that isn't delivering results?
Yes, if the Go/No-Go criteria are not met after the POC phase. A POC that doesn't demonstrate at least 60% of the expected value has little chance of succeeding in production. It's better to reallocate the budget to a more promising use case. The failure of a POC is not a waste, it's valuable information that prevents a much more costly investment.
How do you avoid the eternal POC syndrome?
Set a deadline of 6 to 8 weeks for the POC from the outset, with quantified success criteria. Plan the budget and resources for the production phase as soon as the POC is validated. Appoint an industrialization lead distinct from the POC lead. Check out our guide on moving from POC to production.
For technical profiles
Comparison of AI project management frameworks suited to SMBs:
| Criterion | CRISP-DM adapted for SMBs | Agile ML (Scrum + ML) | Classic Waterfall |
|---|---|---|---|
| Adaptability to SMBs | Excellent (6 clear phases) | Good (requires agile maturity) | Low (too rigid) |
| Handling data uncertainty | Dedicated exploration phase | Exploratory sprints | Not planned |
| User involvement | At each phase | Continuous (sprint review) | Beginning and end only |
| Intermediate Go/No-Go | Yes (between each phase) | Yes (end of sprint) | Just 1 (end of project) |
| Documentation | Standardized templates | Variable (user stories) | Exhaustive |
| Learning curve | Low | Medium | Low |
| Typical project duration | 2-4 months | 2-6 months | 4-8 months |
Our recommendation: use adapted CRISP-DM for the first 2-3 AI projects (clear structure, ready-to-use templates), then evolve toward Agile ML once the team has gained maturity. Waterfall should be avoided for AI projects because it doesn't handle the uncertainty inherent to data and models.