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.
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.
