The clamour of anticipation around new applications for artificial intelligence is as fevered as ever. The problem for me is that expectations are not informed by a robust appreciation of the practical requirements for innovating with AI. As an adviser to businesses on bringing such innovation to market, my advice is simple: to scale rapidly, large-scale AI-enabled projects must be built on firm foundations to allow multidisciplinary development teams to thrive.
Chief among the reasons is that, in engineering terms, developing AI is a complex, non-linear process. Frankly, you can expend a great deal of time and effort with very little progress to show for it. It is the antithesis of agile approaches that deliver incremental advances. Even if you do make solid progress, rapid acceleration at scale will never be guaranteed if the foundations are not fit for purpose. Pushing ahead without a good level of confidence in robust groundwork is risky indeed.
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