Graphwise Talk #4: 5 Patterns of Enterprise AI Failure: How to Spot Yours?
384 enterprise AI and data leaders told us where their projects are actually stuck. The pattern was the same, over and over: 42% still keep their data’s meaning consistent in spreadsheets. 64% call data silos their biggest barrier to scaling AI. 73% have no shared layer of meaning across their apps at all.
If your pilot never turned into a program, or one bad output was enough to make leadership pull the plug, you’re probably living through one of five recognizable failure patterns. If you’re a decision maker or budget owner accountable for your AI initiatives, that pattern is costing you more than time.
On September 3, we invite you to join our next Graphwise Talk #4 we host together with Panos Alexopoulos, Data and AI practitioner, Author, Educator and Jim Buonocore, Business Solutions Consultant, #EPAM_Systems.
We walk you through 5 failure patterns we investigated: what each looks like from the inside, why it happens, and what the organizations getting real ROI are doing differently.
- What’s causing enterprise AI projects to stall;
- Where you stand compared to the rest of the industry;
- How to mitigate the risks and lay down a foundation for trustworthy, scalable AI.
Submit your questions when you register, and we’ll help you figure out which pattern you’re in and what to do about it.