/* ---- Google Analytics Code Below */

Saturday, July 20, 2019

Enterprise AI Strategy

Useful overview of what it takes.  Integrate it with real process, real goals, real results,  not just small tests.

Anatomy of an enterprise-scale AI strategy
Looking to move beyond point solutions and proofs of concept? Here’s what it takes to develop to a holistic AI strategy honed for business results.
 By Maria Korolov in CIO

When it comes to AI, companies typically test the waters proof of concepts or small-scale use cases, taking advantage of vendor offerings, such as new features in their existing SaaS platforms.

If things go well, they pursue another project, then another — and soon they’re relying on a sprawl of incompatible systems, competing data lakes, problems with cost overruns, duplication of efforts, and an inability to scale, not to mention privacy, compliance or ethics problems.

[ Cut through the hype with our practical guide to machine learning in business and find out the10 signs you’re ready for AI — but might not succeed. | | Get the latest insights with our CIO Daily newsletter. ]

At some point, the benefits of AI become obvious enough, and the pain of continuing on their present path so acute, that companies step back to develop a cohesive strategy for an enterprise-wide AI-powered transformation.

"The tendency to get overwhelmed in individual technologies is not only drowning organizations in technical debt but discouraging them because they don't see a path forward to sustainable and scalable AI," said Traci Gusher, partner in data, analytics and artificial intelligence practice at KPMG.

Here’s a look at how organizations can ensure the shift from pilot projects to full-scale AI fluency goes right.  .... " 

No comments: