Artificial Intelligence has moved from hype to inevitability. But for most mid-market organisations, the real question isn’t “Should we use AI?” it’s:
“Where do we start, and how do we make sure it actually delivers value?”
That’s exactly why we launched the First Focus $100k AI Investment Fund initiative.
The AI Investment Fund was designed to help 5 forward-thinking organisations move beyond experimentation and into real, measurable AI implementation, with clear commercial outcomes. Instead of simply talking about AI strategy, we’re co-investing time, expertise, and capability into practical use cases that improve productivity, reduce cost, enhance customer experience, and unlock new revenue opportunities.
This case study is relevant if your business:
Executive Summary
For ultra‑high‑net‑worth advisory firms, investment due diligence is not just a process — it is the product. The depth of analysis, the discipline of governance, and the quality of judgement behind every allocation decision are what separate trusted advisers from the rest of the market.
For Lipman Burgon, a private wealth boutique advisory firm serving ultra‑high‑net‑worth clients, family offices, charities and endowments, this reality creates both a competitive advantage and a structural constraint. Their investment team’s rigour is a defining strength but it also comes with heavy operational cost.
Each new investment opportunity can require hundreds of pages of documentation to be screened, interrogated, summarised and validated. Even an initial high‑level assessment can consume one to two full days of analyst time. A full due‑diligence cycle can take anywhere from two to four weeks.
As market opportunity increased and client expectations rose, Lipman Burgon faced a familiar challenge: how do you scale deep, high‑quality decision‑making without either diluting standards or expanding headcount?
Rather than experimenting with surface‑level productivity tools, the firm made a deliberate decision to apply AI where it mattered most at the front end of investment due diligence. In partnership with First Focus, Lipman Burgon is building an AI‑assisted due diligence capability designed to compress timelines, unlock scale, and preserve the human judgement that defines their value proposition.
This case study explores how they approached the problem, why they chose this use case over others, how ROI was modelled, and what responsible AI adoption looks like in a high‑stakes financial services environment.
Lipman Burgon operates in a segment of the market where the cost of being wrong is exceptionally high. Before any recommendation or allocation of capital is made, the firm’s investment team undertakes an exhaustive due‑diligence process on fund managers, strategies and structures
The firm prides itself on the pedigree of its investment staff and the depth of analysis behind every decision. This process includes multiple gated stages, starting with an initial screening and escalating into a deep dive across performance data, risk frameworks, governance, personnel, market exposure and more.
Even the earliest gate is not trivial.
As Nandita states: “Even that very high‑level screening can take an analyst one or two days to just screen an investment at that stage. Is there a binary gate? Do we go ahead or not?”
Once an opportunity passes that gate, the workload increases dramatically.
“That deep dive process can then take the analyst anywhere between two weeks to an average of four weeks just to comb through data… the corpus of information can often span several hundred pages.”
This level of rigour is non‑negotiable, but it is also resource‑intensive. As deal flow increases, the only traditional way to scale this work is to add more highly skilled analysts, which is both costly and slow.
Each review required analysts to:
Like many organisations, Lipman Burgon had already been experimenting with AI across the business. Content creation, marketing support and communications were all considered as potential early use cases.
However, rather than forcing AI into areas where tone, nuance and originality are critical, Lipman Burgon deliberately pivoted toward a use case where AI’s strengths are most effective: factual ingestion, structured analysis and summarisation at scale.
Lipman Burgon proposed an internal, secure AI assistant designed to remove the heavy manual lift from investment due diligence. Human judgement remains central, and analysts retain control over final decisions, validation, and interpretation while the AI handles the repetitive, time-intensive first pass.
By automating the front end of the due diligence process, the firm expects to:
On conservative estimates, this translated to annual efficiency gains in excess of $100,000, while improving depth of coverage rather than diluting it. This was not about saving time for its own sake. It was about leverage and scale without compromising trust.
Rather than replacing analysts, Lipman Burgon proposed an AI‑assisted due diligence workflow designed to handle the heavy lifting.
The pattern is broadly applicable across many knowledge‑intensive businesses:
In Lipman Burgon’s case, this includes:
All processing occurs within a secure environment and integrates with Microsoft 365 workflows, including SharePoint and Teams.
From the outset, Lipman Burgon and First Focus agreed that adoption and governance needed to be designed in, not tested later.
Key design principles include:
The project has been intentionally structured with gated stages, allowing accuracy, usefulness and analyst confidence to be evaluated before expanding scope or automation depth.
This approach reduces adoption risk and ensures the system supports, rather than disrupts, existing investment processes.
While this use case sits in investment research, the same pattern applies wherever businesses face structured decision making at scale.
Examples include:
In each case, the value comes from automating information extraction and structuring, while preserving human judgement where it matters most.
If successful, the solution will allow Lipman Burgon to operate with institutional‑grade efficiency while retaining boutique‑level rigour.
Expected outcomes include:
Most importantly, investment professionals can spend less time searching for information and more time applying experience and insight.
This initiative was selected as a Tier One AI Investment Fund project because it met the core criteria we look for:
It demonstrates how AI and automation deliver their strongest returns when applied to high‑stakes, repeatable knowledge work, not generic productivity experiments.
AI projects do not finish when the initial build is delivered. For forward‑thinking organisations, real return comes from ongoing support, iteration and operational ownership as models mature, data grows and business needs evolve.
That is exactly why we created CORE.
CORE is our managed AI and automation service, designed to help organisations move beyond pilots and point solutions and turn initiatives like this into long‑term productivity engines.
With CORE, we support clients to:
If you are investing in AI to drive real, measurable outcomes, CORE provides the structure and continuity required to make that investment compound.
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