Enterprise hits and misses - SAP to acquire SmartRecruiters, but why? Dirty data borks AI, and context engineering is here

Lead story - Dirty data sinks AI, but can digital twins "ground" AI agents?

I carry on about accuracy in gen AI output - but accuracy comes down to use case, regulation, company culture, and data relevance.

Some use cases are more forgiving than others. But one thing is certain: enterprise AI needs the right (quality) data. Start with the quality side, via Sarah's The data isn't fit for purpose, but let's crash on with AI anyway! Workiva's global study exposes pragmatism and recklessness combined. As per Workviva's survey of 2,300 professionals involved in financial and ESG reporting, Sarah notes:

[Workviva] found that while 88% of respondents have seen ROI improvements in AI investments - 96% time saving, 94% productivity, and 92% cost efficiencies - two in three thought their companies currently lack the high-quality data, AI governance and training ready to use AI effectively.

And yet: "Despite ...