Bringing proprietary AI in-house is not a workable plan that has occasional side effects. It is a bad move with a straight line to failure – and that’s not hyperbole. Gartner reports that 85% of enterprise AI initiatives fail directly, meaning they never advance beyond the prototype (proof-of-concept) stage. Producing a fieldable product is an outright impossibility for most enterprise teams. Those that beat the odds by getting past the prototype stage still only stand a desperate hope for success. Success with proprietary AI requires surviving a gauntlet of execution challenges covering training at scale, generalization, performance, field qualification testing, software engineering, systems integration, human-centered design, and other workstreams. Mix in a highly technical domain that is packed with esoteric engineering challenges and the typical enterprise stands no chance. I’m sorry, but that’s the reality.
Abandon all efforts to get better at Scrum, SAFe, or whatever Agile snake oil you’ve been pitched. None of those make a difference. Whether your organization is good at Scrum, or terrible at it, the outcome is the same. As for SAFe, the closer you get – the worse things are. For ordinary software development
Multiple factors could brake check the rapid advance of AI in the West. From increased data hoarding to zealous regulation, the West could begin to under-leverage AI in ways that are advantageous to adversaries.