AI projects share many familiar failure points with non-AI projects - scope creep, insufficient or missing requirements, unrealistic timelines, insufficient resources, lack of user input, inadequate testing, insufficient communication and training, lack of user adoption, insufficient post-implementation support, and many more. While these are all important concerns, AI projects are most susceptible to:
Unrealistic expectations - When addressing some business challenges, AI can be extremely powerful, but its not magic. Businesses should carefully evaluate potential value and costs before making an investment.
Unanticipated costs - AI investment requirements extend far beyond technology and implementation. You'll also need to factor in the cost of data infrastructure; data acquisition(internal and external); ongoing testing, validation, and retraining; careful curation of data/model/result sets; compliance requirements for explainability and ethical behavior; and change readiness, stakeholder management, communications, training, and reinforcement.
Solution viability - Of all failure points, solution viability is perhaps the most ambiguous and critical. For some business problems, AI just isn't a viable approach.
User acceptance - The premise and promise of AI means that some people’s jobs are going to change. When users can't accept and support the changes that AI brings, projects fail.