Why This Matters

Most AI conversations in private equity stay at the headline level: model releases, predictions about what comes next.

‍Independent Sponsors need something more useful, what can actually be implemented by the following week. On July 15, ISN hosted a webinar moderated by Sam Tidswell-Norrish with BluWave Founder and CEO Sean Mooney and Chief Product and Technology Officer James Aylward, built around exactly that premise: the foundational steps to take first, the tools that can be deployed without a drawn out technology overhaul, the mistakes that slow implementation down, and how AI applies specifically to sourcing, diligence, portfolio operations, fundraising, and firm management.

BluWave is the private equity market network and enablement platform that connects private equity firms, independent sponsors, portfolio companies, and independent companies with vetted third-party service providers, independent consultants, interim executives, AI advisors and tools, board-level leaders, and senior advisors.

BluWave is itself a PE-backed, lower-middle-market company of 75 employees. It has implemented these actions internally, and connects the PE firms and portfolio companies it serves with the resources to do the same.

The urgency is showing up in BluWave's own data. Demand for AI enablement services across its network has grown 240% year over year, measured over the trailing 90 days, the largest spike the firm has tracked. For Independent Sponsors managing a concentrated portfolio without a large internal team, that is a signal that peers and competitors are already moving, and that the cost of waiting compounds every quarter.

Start with the LLM, not the Tech Project

The first action is the simplest to start and the easiest to underestimate. Mooney and Aylward were direct on this point: roll out a large language model, Claude, ChatGPT, or another enterprise-grade tool, to everyone in the company on day one, using a team or enterprise plan so company data is not used to train the underlying model. The goal is giving every employee access to what Aylward called the sum total of human knowledge represented in these systems, not restricting access to a pilot group.

Training matters as much as access. Mooney was clear that generic, video-based training does not work, comparing it to sitting through a defensive driving course. The better approach is project-based: give employees a real work scenario to run through the tool, the same way a new hire would be tested on a work sample. BluWave trained its entire team this way, and Aylward emphasized treating the LLM as a conversational thinking partner that should be probed and iterated with, not a single command-and-response search engine. The payoff compounds once a company connects its own data to the LLM, moving from general knowledge to insight grounded in the business's own numbers and relationships.

The Data Foundation is Easy to Skip and Expensive to Fix Later

The second action is where AI moves from a personal productivity tool to an enterprise asset, and it is the one most likely to get skipped. Aylward described the starting point as standing up a modern data warehouse, such as Snowflake or Databricks, and ingesting the systems that hold the company's core data: ERP, CRM, and the spreadsheets and emails that otherwise sit disconnected.

On top of the warehouse, companies need a semantic layer so the LLM understands what the data actually means, not just where it sits. Building that layer is an ongoing process rather than a one-time project: start with the most used and useful data, then keep extending and refining. Getting this right typically requires a data architect, a role Mooney noted is expensive enough that most lower middle market companies should rent the expertise first or share it across a portfolio rather than hiring it in-house immediately.

Tool Selection Follows the Data, Not the Other Way Around

The third action is sequencing which tools to deploy and when. Companies do not need to wait for a data layer to get started: off-the-shelf tools already deliver value without one, including document AI for summaries and contract or RFP review, meeting intelligence and transcription tools, LLM plugins inside Excel, PowerPoint, and Word, and AP, expense, and customer service automation.

Once the data foundation is in place, the same AI capability extends across functional areas in a more integrated way, from strategic planning and FP&A analysis to interactive customer success and cross-system enterprise agents. The webinar's framing was practical rather than sequential: value from bounded tools starts within roughly three months, while enterprise-wide value builds toward month twelve and beyond as the data foundation matures underneath it.

Governance is the Foundation

The fourth action is not optional and needs to start on day one alongside the others, not after. Aylward framed governance as what allows a company to move quickly and safely at the same time, starting with a written AI usage policy that every employee with system access has actually gone through, with proof of completion. The single biggest rule: company data goes through company-approved AI tools only. Employees running company data through a personal, free version of a public LLM effectively hands a firm's most valuable asset, its own data, to that tool's training set.

Beyond the policy itself, governance means assigning data access rights by role rather than giving every employee access to everything, monitoring and logging how data is used to guard against large-scale exfiltration, and applying the same limited-permission, monitored approach to any AI agents deployed inside the business. Change management is ongoing, not a one-time rollout task, and Mooney noted that cybersecurity discipline matters more now than it did before AI tools became this capable.

The Cost of Waiting is the Real Risk

Mooney's closing point to the group was blunt: most companies are sleeping through this shift, underestimating both what is already available and what is coming. Tool costs are likely to keep falling and capability keep rising, which means the compounding cost sits on the other side of the ledger, in the businesses that do nothing. Competitors and portfolio company peers are already acting, and every quarter of delay widens the gap. The advice from both speakers was to start now, in whichever of the four areas is most accessible, since early steps build the comfort and momentum needed for the next ones.

What Actually Matters Now

1. Roll out an enterprise-grade LLM to the full company on day one, pair it with project-based training instead of generic courses, and connect it to company data once the basics are in place.
2. Stand up a data warehouse and begin building a semantic layer now. This is an ongoing process, not a single project, and the data architect role can be rented or shared across a portfolio before it needs to be hired.
3. Start with bounded, off-the-shelf AI tools that do not require a data layer, and plan for cross-system and enterprise-wide tools once the data foundation matures, typically within twelve months.
4. Put a written AI usage policy and role-based data access rights in place from day one. Governance is not a later phase, it runs underneath every other action area.

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