AI Can Be Cyborg or Centaur. But Can Enterprises Monetise It?
Satish Viswanathan, former global data and AI lead at Accenture and author of The Weight of Intelligence, discusses the gap between AI capability and enterprise value creation. In 2024, many companies treated token consumption as a proxy for AI success, even including it in hiring commitments. Viswanathan identifies this as misguided thinking, noting that consuming tokens without strategic alignment costs money without generating revenue. He frames the challenge as 'enterprise metabolisation'—the ability to absorb and deploy AI effectively. Success requires alignment across model capability, data architecture, system integration, human processes, organisational structure, value economics and
Key points
- Token consumption alone doesn't generate revenue or prove AI capability
- Enterprises need data architecture, infrastructure, people and processes to use AI effectively
- 'Enterprise metabolisation' is the gap between AI access and productive deployment
- Six factors determine enterprise AI success: capability, data, integration, people, economics and governance
Summary and key points written by AI from the headline and a short excerpt, so they can be incomplete or wrong. Analytics India Magazine did the reporting; read the original for the full story.
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