RoundTable Buys a Skincare Brand, and Healthcare Private Equity Meets AI

A Lake Forest private equity firm just bought a skincare company, and the deal is a useful window into healthcare private equity AI. On February 2, 2026, RoundTable Healthcare Partners acquired Colorescience, a mineral-based skincare and sun-protection brand sold through dermatologists, plastic surgeons, and med spas, from a shareholder group led by 1315 Capital. Terms were not disclosed. RoundTable called it the firm's ninth consumer-healthcare platform.

I read deals like this the way an operator would, not the way a headline does. RoundTable is a serious buyer with a long healthcare track record. The interesting question is not the brand. It is what the next round of value creation looks like across a portfolio like this, and where AI actually fits once the lawyers are done.

What RoundTable Actually Bought in Carlsbad

RoundTable bought a profitable, channel-driven consumer-healthcare brand. Colorescience, founded in 2000 and based in Carlsbad, California, sells mineral-based sunscreen and skincare through a professional channel of dermatologists, plastic surgeons, aestheticians, and med spas, plus direct-to-consumer and e-commerce. Its current management team has built the brand since 2013. RoundTable acquired it from a shareholder group led by 1315 Capital, and according to the firm's deal announcement, it is the third platform from RoundTable's $800 million Equity Fund VI.

The firm itself is the part worth sitting with. RoundTable was founded in 2001, is based in Lake Forest, and invests only in healthcare. It has raised $4.25 billion across nine funds and made 98 investments. That is not a tourist in the space. It is a buyer with repeat-CEO relationships and a real operating playbook, which is exactly the kind of firm where an AI conversation should start with the workflow, not the tool.

Why Does a Disciplined Healthcare Buyer Care About AI Now?

Because the value-creation playbook has changed, and a brand like Colorescience is data-heavy in every place that matters. A consumer-healthcare brand runs on professional-channel relationships, direct-to-consumer marketing, inventory planning, and clinical claims. Each of those is a data problem before it is a marketing problem. That is where AI shows up first, not in some moonshot.

The broader market has already moved. In its Private Equity Midyear Report 2026, Bain calls AI one of the most important value-creation opportunities across the portfolio, and calls inaction a strategic choice rather than a neutral one. Execution is the hard part. In its Asia-Pacific Private Equity Report 2026, Bain reports that about half of general partners in that region say AI initiatives in their portfolio companies are meeting expectations, while the rest say results have fallen short. That gap is not about model access. It is about execution discipline, which is the thing operating-oriented firms are supposed to be good at. For the longer argument on why operating improvement now drives returns, see our piece on how AI has become private equity's new return engine.

Where AI Lands First in a Consumer-Healthcare Portfolio

AI lands first on the repeatable, data-heavy work that sits underneath the brand. For a company like Colorescience, that means demand forecasting and inventory across both the professional channel and direct-to-consumer, surfacing reorder and churn signals in the dermatologist and med-spa account base, compressing diligence and 100-day planning at the deal level, and standardizing reporting across portfolio companies so the operating partner sees the same numbers everywhere. None of that replaces judgment. It clears the desk so judgment has room.

The firms getting real results are not the ones that bought the most tools. Bain's midyear read is that the companies seeing the greatest impact are not layering AI onto existing processes; they redesign workflows and strengthen the data foundation first, then bring AI to it. Bolting a tool onto a broken process just gives you a faster broken process. This is the same point we make about where AI agents actually help an advisory practice: the workflow is the product, and the software is the container.

Three Ways a Healthcare PE Firm Could Put AI to Work

Hypothetical workflows a firm could run, each with a prompt you can paste into Claude today.

Use Case 1: An AI-Opportunity Map Across the Portfolio

Rank the candidates by return before you fund a single pilot.

A healthcare PE firm could commission an audit that ranks AI opportunities across each portfolio company by return and effort, so the operating team funds the few that move EBITDA instead of scattering pilots that go nowhere. The point of the map is to argue about priorities on one page, before money moves.

Use Case 2: A 100-Day AI Plan for a Newly Acquired Brand

Sequence AI work behind the integration, not on top of it.

Right after a deal closes, an operating partner might want a phased 100-day plan that places AI work behind the existing integration priorities rather than competing with them. The plan has to be realistic for a team that is also handling the integration, which means every item gets an owner, a target, and a dependency.

Use Case 3: A Build-Versus-Buy Brief Before Any Tool Purchase

Weigh buying against building against doing nothing, on two pages.

Before a portfolio company signs with an AI vendor, a firm could ask for a short brief that compares buying, building, and waiting. Most firms should buy or partner for commodity capability and build only where their data is a real moat. A two-page brief forces that decision into the open instead of letting a vendor demo make it. If a team does decide to build, the tooling has matured fast, as we cover in our look at the Claude Agent SDK for a Lake Forest private equity shop.

How Should a Firm Decide What to Build Versus Buy?

Start with an audit and an opportunity map, not a contract. Adoption is compounding: Bain's 2025 Generative AI in M&A report found that 36 percent of the most active acquirers already use generative AI for M&A, and more than half of practitioners expect to integrate it into their dealmaking by 2027. None of that settles who should build, and this is where firms burn money. Building in-house is expensive, ties up engineering talent a portfolio company rarely has, and races vendor roadmaps that improve every month. The sensible default is to buy or partner for commodity capability and build only where the firm's own data is a genuine moat.

This is the heart of an AI Consulting and Strategy engagement: an operational audit, an AI-opportunity map ranked by return, a clear build-versus-buy call, and a phased roadmap before anyone signs a vendor. That is the work we do at Bace Agency, and it is the cheapest insurance against funding a pilot that never ships. A disciplined buyer like RoundTable already runs deals this way. The AI version is the same instinct, pointed at a newer set of tools.

What This Does Not Replace

AI does not replace the operating judgment that made RoundTable a 98-investment firm. It does not pick the platform, set the thesis, or manage a CEO relationship. It does not sign off on a clinical claim or a label. What it does is take the repeatable work around those decisions, the forecasting, the reconciliation, the first-pass diligence read, and do it faster so the operating team spends its hours on the calls that actually move a number. The firm that treats AI as a way to free up judgment, not replace it, is the one that gets the return.

Watch the next platform RoundTable adds, and watch whether the firms competing with it on the next deal show up with an operating plan that already has AI sequenced into it. If you run a North Shore firm weighing the same question, the first step is small: map where the hours go and rank the opportunities by return. We can do that in a free 30-minute AI audit, in person on the North Shore or by video, whether or not you work with us afterward.

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