Teach AI to Work Like a Member of Your Team -

Teach AI to Work Like a Member of Your Team

By reverse engineering your team’s workflow, you can customize generic AI tools to fit your processes.

Authored by CXOs for CXOs

Authors

Synopsis

Across boardrooms, one theme is becoming clear: enterprise leaders are not questioning if AI can create value — they’re asking why it hasn’t yet. Sparked by a discussion about how the work graph and interaction data can help AI deployments in customers, between Rohan Narayana Murty and Ravi Kumar S (CEO, Cognizant) and brought to life in an actual customer environment, this article is a manifesto on fixing AI’s productivity paradox in the enterprise.

At Soroco, we see this every day – large enterprises roll out powerful AI models with high expectations. But the productivity gains are marginal. The business impact is unclear. And the tools, while sophisticated, are disconnected from how work teams really get work done. But this is exactly what Soroco, and its work graph have been excavating. The work graph contains the interaction data necessary to bridge the gap between the promise of enterprise AI and its current limitation – missing context.

Read our latest article in Harvard Business Review (HBR) on how interaction data helps orgs solve the key bottleneck for widespread AI adoption, including a live customer case study. We introduce reverse mechanistic localization (RML), a new operating model for enterprise AI.

Written in conjunction with Workfabric AI, a new spinoff from Soroco, as well as our partner Cognizant. Co-authored with Hemanth Yamijala and George Nychis.

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