A convincing demo takes days. A system that real people rely on takes much longer, and that gap is where most AI initiatives lose momentum.
Analysts see the pattern. Gartner predicted in 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, pointing to poor data quality, inadequate risk controls, escalating costs and unclear business value. In 2025 it went further for agentic AI, predicting that over 40% of projects would be cancelled by the end of 2027, for similar reasons. Notice what is missing from those lists: model quality.
Three places projects stall
No strategy. The project began with a technology, not a problem. Nobody can say which decision or process it improves, or how success would be measured, so the business case never firms up.
No implementation. The prototype ran on a sample of clean data, outside the systems people use. Connecting it to real data, identity, permissions and workflows is engineering work that was never planned or budgeted.
No operations. Nobody owns the system after launch. Quality drifts, costs creep up and the first serious error ends the project’s credibility.
What to do differently
Choose the first use case on business value and feasibility, not enthusiasm. Check that the data and systems it needs are within reach before building anything. Prototype with real data and the people who will use the result. Decide at the outset who will run, monitor and improve it, and what “good” looks like in numbers.
None of this is glamorous, and all of it matters more than the choice of model.
How we approach it
We start with the opportunity, work alongside your teams to prove it quickly on real data, integrate and deploy it into production, and stay to operate and improve it. That is the idea behind our AI Discovery work and Forward Deployed Engineering.