AI strategy, then an internal build that speeds up RFP responses

The situation

For a consulting business like Knowit Cloud & Code, the quality of an RFP answer depends on finding the right people and the most relevant past work quickly. That meant searching, under deadline, across roughly a thousand consultant CVs and a history of delivered projects. The wider question was where AI belonged across the business, for internal productivity and for what they offer clients.

What we did

We started with the strategy, and then chose what to build first.

• AI strategy with leadership: an AI playbook covering both internal productivity and customer offerings.
• Use case discovery and prioritization, to choose the first build from a ranked list of candidates.
• Inspiration and enablement sessions to share the strategy with a wider audience.
• Leading an internal development team through building the RFP matchmaker: a tool that matches an incoming RFP against the consultant CV and project database and suggests candidate consultants and past projects.

Why the RFP matchmaker was the right first build

It fit the first of our three paths to AI value, boosting productivity. It improved a workflow the business already understood and measured. The data already existed, it was clear who owned the workflow, and the people doing the work could see the result within one RFP cycle.

What changed

A small team of salespeople now matches consultants to an incoming RFP in a few hours. The same work used to take about a week. Answering an RFP also no longer means sending a question to fifty team managers to find out who is available and who fits. The search now runs against the database, which saves the time those managers spent on each RFP. That time was a cost to the business, although it did not appear in the budget.