Editor's note: This is AI Impact, Newsweek's weekly newsletter where each week, we will explore how business leaders are unlocking real value through artificial intelligence. Tap or click here to get this newsletter delivered to your inbox. Readiness is low.” Genpact President and CEO Balkrishan “BK” Kalra used that line near the end of last week’s “AI Impact Forum” webinar, after a conversation about what companies have to fix as they move AI beyond experiments and into everyday operations.
His argument was that the difficult part increasingly sits inside the enterprise. Data may still be fragmented, business processes can vary across teams and markets and employees need enough fluency with the technology to help redesign how work gets done. More capable models do not clear away those problems on their own.
Kalra was especially direct about where companies are now: “Time of proof of concepts is gone. Time of experiments is gone.” He said the next phase is about scale and whether those deployments improve business performance. I get into that argument in my recap of the webinar (where you can also watch the full discussion), including why Kalra believes data, process and talent debt can constrain what companies are able to do with AI.
This week’s Signal Capture picks up one very specific version of the readiness problem. Companies may already have the customer data they want for an AI initiative and still discover that the permission attached to it does not cover the new use. Read on for why existing customer permissions can become a problem when AI gives old data a new purpose.
Signals from the frontlines of AI adoption Syrenis CEO: Existing Customer Consent May Not Cover New AI Uses Adam Binks, CEO of Syrenis, which makes enterprise consent and preference management software, has watched companies revisit existing customer permissions as they try to use the same old data in new AI systems. Binks described a retailer that had years of customer contact data and had built an AI-driven outreach model around it. Before launch, the company reviewed permissions collected for earlier campaigns in a handful of markets.
The retailer went back to customers with a plain-language explanation of the new purpose and relaunched with a smaller audience, which Binks said performed better than the original list would have. The same mismatch can arise when companies repurpose information for an AI use that falls outside what the customer originally understood and agreed to. The starting point, Binks said, is establishing why the data was collected, what the customer was told at the time and whether a new application is consistent with that permission.
Extract — continue reading at the source.
