Field noteOct 1, 20262 min read1 source

Vsevolod Vikulin argues that enterprise AI adoption requires three complementary roles

In one minute

Thesis: Enterprise AI implementation is best treated as a division of labor across platform building, contextual deployment, and prioritization—not as a mandate for one central team.

Watch condition: Watch for organizations publishing evidence that locally deployed AI workflows can be governed, reused, and maintained at scale without a proportional expansion of specialist teams. That evidence would help test whether FDEs are a durable bridge or a transitional role.

Thesis

Enterprise AI implementation is best treated as a division of labor across platform building, contextual deployment, and prioritization—not as a mandate for one central team.

Observed facts

  • The article says McKinsey’s analysis of 63 generative-AI use cases across 16 functions attributed about 75% of identified value to four areas, while its broader estimate including other knowledge work was substantially larger.

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  • It also cites Zylo’s estimate that companies with more than 10,000 employees use an average of 660 SaaS applications.

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Mechanism

Central teams can provide infrastructure, standards, and reusable tools, while people close to operational work are more likely to notice small, company-specific tasks. FDEs are proposed as a bridge: they translate local processes into deployments and can feed reusable patterns back to the platform team.

Why now

The article argues that current AI use is concentrated in prominent functions while the long tail of work remains comparatively untouched, making organizational ownership a live implementation question.

Implications

  1. Organizations considering the model should distinguish platform governance from workflow discovery and deployment, and test whether local implementations can be safely reused.

  2. The framework also suggests that employee-led automation may grow as tools become easier to operate, but this is a forecast, not an established outcome.

Evidence boundary

This is an authored analysis and historical analogy, not a controlled comparison demonstrating that the proposed roles improve returns or apply universally.

Watch conditions

  1. Watch for organizations publishing evidence that locally deployed AI workflows can be governed, reused, and maintained at scale without a proportional expansion of specialist teams. That evidence would help test whether FDEs are a durable bridge or a transitional role.

Evidence / sources

  1. 1vikulin.ai/posts/who-implements-ai
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