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Making it work, Part 2 of 2

Part 2 picks up after the solution decision, where buying the tool turns out to have been the easy part. It covers redesigning the work itself: building hybrid workflows out of deterministic, generative and human layers, scoping a pilot that can return a negative result, training to the role, governing the output, and measuring whether any of it changed the work. For proposal, capture and BD leadership in regulated, high-compliance environments, with a checklist closing every chapter. Part 1, Are you ready for AI?, covers the readiness and selection work that comes first.

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In this guide we cover

1

No single layer is sufficient on its own.

Deterministic tooling is exact and auditable but cannot write a narrative. Generative AI is fast but will invent things if left ungrounded. Human judgment carries context and accountability but does not scale to a 200 page solicitation under deadline. Error risk is a function of task structure, and task structure is something you design.

2

Grounding is four properties, not a setting.

Scope, attribution, freshness, and refusal. A system that never returns “no source found” is not grounded, it is just confident. The vendor supplies the mechanism, your content library decides whether it has anything to hold onto.

3

A pilot buys you the right to go big.

One intact team, one deliverable type, a fixed 60 to 90 day window, a baseline captured before anyone logs in, and written stop criteria. Governance, measurement and a retraining path belong on day one, not phase two.

4

Adoption drifts when the reinforcement stops.

In an anonymized VisibleThread pilot with a large defense contractor, usage fell close to 40% the moment the cadence ended, and recovered to 90% in four weeks only because measurement and ownership were already in place. Track outcomes rather than logins, and pair every participation metric with an outcome metric.

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