AI strategy advisory for a private equity firm's portfolio companies, working across industries to identify where AI would pay off and what it would take to implement.
Private Equity Portfolio Companies
8/4/2024
Help portfolio companies across engineering, healthcare technology, food processing, and fitness identify where AI could address real operational problems, and separate those opportunities from the ones not worth pursuing.
A workshop-based advisory process moving each company from problem identification through solution design to an implementation roadmap with architecture, data requirements, and cost estimates.
Engagement
Industries
Output
Decisions
In early 2025 we advised a private equity firm’s portfolio companies on AI strategy and adoption. Through a series of workshops with businesses in engineering, healthcare technology, food processing, and fitness, we worked to identify where AI would produce a return, define what to build, and set out how to build it.
Client: Portfolio companies of a private equity firm.
Engagement: Workshop-based advisory across four industries, covering problem identification, solution design, and implementation planning.
We worked with stakeholders at each company to find operational problems worth solving, rather than starting from available technology and looking for somewhere to apply it. The problems that surfaced clustered around operational efficiency, customer engagement, workforce productivity, complex decision-making, and field service support.
The discipline here is mostly subtractive. Companies arrive with a list of things AI might do; the useful work is establishing which of them would change a business outcome and which would produce an impressive demonstration and nothing else.
For each viable use case we designed an approach fitted to the company’s actual infrastructure and constraints. The recurring questions were build versus buy, what a proof of concept would need to establish, and whether a deployment partner made more sense than an internal effort.
Matching the technology to the problem is the step most often skipped. A company that has decided it needs a large language model before it has characterized the problem usually ends up with an expensive solution to something a simpler method would have handled.
Each engagement produced a roadmap the company could act on: an architectural overview, the key assumptions and data requirements, preliminary cost and resource estimates, integration and testing phases, and the monitoring and maintenance the system would need once it was running.
Each company came out with a defined set of use cases, a technology approach fitted to its own operations, and a roadmap specific enough to resource and schedule. Several of the more speculative ideas were deliberately left on the table, which was itself a useful result.
Client identities are withheld under confidentiality.
