While overt AI "flops" are less common than they were three years ago, the pattern of failure for AI projects is still much the same.
Leadership selects an AI project without a strong understanding of (a) it's long-term value, (b) it's technical viability, or (c) the phases the project will take to go from pilot to deployment.
Project teams operate with sub-optimal data, often without the data science talent they need, and under unrealistic expectations of short-term ROI (as if AI were IT).
The project never comes close to deployment.
Assessing project opportunities and having the right expectations about AI's adoption challenges is paramount to converting pilots into actual deployments.
Especially for firms new to artificial intelligence, it's crucial to demonstrate progress and success with AI early on - in order to win approval, confidence, and additional budget to expand AI efforts productively.
Nothing builds confidence like actually putting an AI application i...
You've landed on exclusive content for Emerj Plus Members
Emerj Plus Membership
Consistent coverage of emerging AI capabilities across sectors.
Exclusive AI Capabilities Matrix
An explorable, visual map of AI applications across sectors.
Exclusive AI White Paper Library
Every Emerj online AI resource downloadable in one-click
Best Practices and executive guides
Generate AI ROI with frameworks and guides to AI application