Manual-heavy prospecting
Business development relied on manual research and outreach, capping how many opportunities the team could evaluate.
Healthcare services
More commercial reach, without more headcount
An agentic research layer embedded in a business development workflow
A healthcare services provider whose partnership ambitions were outgrowing the capacity of its business development team. The work was manual, the market signals were scattered, and the explicit constraint was that AI had to support the team's judgment rather than replace it.
- Sector
- Healthcare services
- Footprint
- Accessible, affordable care delivery
- Engagement
- AI-driven business development
- Pillars
- Productive · Resilient
Client identity withheld. Sector and focus are generalised to preserve anonymity. A reference call with the engagement sponsor can be arranged.
The constraint was capacity, not ambition
Business development ran on manual research and outreach. That is not a criticism of the team — it is how most of this work is done — but it caps the number of opportunities anyone can evaluate in a week, and the cap has nothing to do with how good the people are.
The signals that would tell them where to focus existed. They were simply scattered across disconnected sources, none of which talked to each other, so knowing which opportunity deserved attention this week required somebody to go and look, every week.
Meanwhile the ambition was growing faster than the bandwidth. More partnerships were wanted than the existing team could pursue, and the available answers were to hire, to lower the standard of qualification, or to change how the groundwork gets done.
The client set one constraint explicitly: AI had to support the judgment of the business development people, not replace it. That shaped everything. A system that ranks and drafts, with a person deciding, is a different design from a system that contacts people on your behalf — and in a sector where relationships are the product, the second would have cost more than it saved.
The challenges
Scattered market insight
Relevant market and competitive signals sat across disconnected sources, making it hard to know where to focus next.
Growth outpacing capacity
Ambitions for new partnerships were growing faster than the existing team's bandwidth to pursue them.
What we did
- 01
Define
Target segments, the ideal account profile, and — the part that takes the longest and matters most — which market signals are actually worth acting on. Most of what can be collected is noise, and deciding that in advance is what keeps the system useful.
- 02
Build
An agentic research layer that scans the sources, qualifies opportunities against the profile and drafts account briefs. The output is a brief for a person to act on, not an action taken on their behalf.
- 03
Execute
Embedded into the team's daily workflow, with named owners, a review cadence and a qualification standard. A system that sits beside the workflow gets used for a fortnight; a system inside it gets used.
- 04
Focus shift
Team time moved off manual groundwork and onto relationships and closing — which is the actual point of the engagement, and the only outcome worth measuring.
What changed
Structured prioritisation
The team works from a structured, insight-led method for identifying and ranking opportunities, rather than from whoever had time to research this week.
A shorter path to contact
A faster route now runs from market signal to qualified conversation, with the groundwork handled before a person picks it up.
A framework that scales
Define, Build, Execute gives the organisation an approach it can keep applying to the next workflow as it grows.
If you are in the same position
This is the most transferable AI use case we see, and it has nothing to do with healthcare. Any team whose output is capped by how much manual groundwork precedes a human conversation has the same shape of problem — business development, recruitment, claims, procurement, credit.
Decide what the system is allowed to do before you build anything. 'Research and draft, a person decides' and 'act on our behalf' are different systems with different risks, and the second is almost never what a relationship-driven business actually wants.
Spend the time on the definition stage. What counts as a signal worth acting on is a business judgment, and a system built on a loose definition produces volume that people learn to ignore — which is a worse outcome than not building it.
And put it inside the daily workflow, with a named owner and a review cadence. The difference between an AI pilot and an AI capability is almost entirely whether it was embedded in how the work already runs.
Leave with your top three risks documented
Thirty minutes with a senior practitioner. No slideware, no sales engineer.