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Continuing Brightwave's Investment Research Spotlight Series

In the first edition of this newsletter, we focused on the ways in which AI is accelerating investment research workflows for middle-market private equity professionals. The takeaway: while AI platforms remain in early stages of development across financial research, they are evolving quickly with new use cases across deal sourcing, due diligence, and portfolio management.

In our second Spotlight Series, we’re focusing on the private credit industry, synthesizing what we’ve heard from leading professionals at industry conferences, conversations with customers, and more. Private credit firms are confronting the realities of compounding changes to their business models, exacerbated by a shortage of deals and a more volatile macroeconomic environment. In this new world, AI has become essential for private equity success. Finding effective sources of leverage is becoming not an option, but a requirement, to stay competitive.

Private Credit Markets Will Grow From $1.5T To $2.8T By 2028

Private financing solutions have always been attractive to both borrowers and lenders for their flexible fixed-income instruments and ability to absorb volatility in markets where equity valuations can swing wildly. Led by the popularity of direct lending, this is reflected in the impressive growth of the private credit market over the last decade. With consistent double-digit CAGR, Morgan Stanley estimates that private credit could balloon from a $1.5 trillion market at the beginning of 2024 to as much as $2.8 trillion by 2028.

As the financial services ecosystem grows more complex, so has the range of business models within the private credit space. Private credit firms are seeking increasingly diverse sources of capital, partnering with traditional investment banks for deal access, and merging internal functions to best serve client needs as a one-stop shop solutions provider.

How Traditional Banks Are Disrupting The Private Credit Landscape

At the same time, intensifying competition from traditional asset managers and banks threatens to take market share from private credit firms, both established and boutique. Direct lending has historically driven the growth in private credit markets, but competition from larger funds and partnership models with banks will likely force diversification into other credit pools, such as real estate or infrastructure. Not all private credit firms will be able to do so successfully; scale and reputation may favor only established players who can fundraise from larger capital bases. For smaller or mid-sized private credit firms, making successful investment decisions will require a more creative form of leverage.

Expand Your Deal Coverage Without Adding Headcount Through AI Tools

The tone on the ground for private credit is cautiously optimistic: while returns have so far held steady in 2025, a rising inflationary environment could trigger higher defaults, especially for companies that are short on cash and have a smaller cushion to deal with inflationary pressures. This makes credit asset selection critical: deal teams must have a solid grasp of the different upside and downside cases across multiple economic scenarios.

Deal sourcing can cover a larger territory in private credit than within other private markets, such as private or growth equity where teams are more narrowly focused on specific industries or verticals. AI platforms can help scale up coverage of niche or lower-middle-market opportunities that were historically time-consuming or difficult to find. When sourcing opportunities, an associate can drop all available materials into the platform and quickly get a distilled briefing on the company’s business model, financial health, and key risks. If the target operates in a niche sector, an AI tool’s ability to find semantic matches might incorporate definitions or context from similar cases (or even integrate third-party market data if available) to help the team understand the landscape.

This means private credit funds can consider a wider funnel of deals — including those outside their immediate comfort zone — because the initial time investment to evaluate each one is drastically lower. The speed of initial screening is a competitive advantage because it allows teams to broaden their universe of potential deals and quickly select ones that match their goals.

Using AI Research Tools To Scale Lower-Middle-Market Opportunities

This scalability especially benefits direct lenders chasing lower-middle-market (LMM) deals, where information is often sparse and time to respond is short. Using pre-built, user-defined reports and templates, AI tools can automatically map a new opportunity against lending criteria (including the borrower’s EBITDA, leverage ratio, industry, and proposed terms) and compare them to the fund’s typical “credit box.” Standardizing initial deal reviews with clear criteria allows junior analysts to cover more deals in parallel without sacrificing quality, creating higher throughput. This is especially valuable for smaller or mid-sized managers who have limited resources and headcount, allowing them to compete with larger players that have bigger analyst teams.

Gain Competitive Advantage With AI-Enhanced Due Diligence

The due diligence process is critical for private credit teams seeking to understand the fundamentals of a company, including both the capital structure and business risk that it carries. Each potential deal comes with hundreds or even thousands of pages of documents, from CIMs and financial statements to detailed loan agreements and compliance reports. Investment professionals must often sift through this trove under intense time pressure, creating a growing diligence dilemma: how can credit teams be thorough yet fast, especially when managing multiple deals in parallel?

Practical AI Applications For Risk Detection, Reporting, And Compliance

AI platforms that are designed for the diligence process can accelerate this process while maintaining high-quality standards. Below, we outline a few key use cases in private credit:

• Surface credit risks: Many successful private credit strategies, especially in direct lending, center around downside protection through different contractual provisions. These provisions are often noted in dense legal documents or buried in footnotes, traditionally requiring a time-intensive manual approach. Now, AI tools can be used to extract critical contractual clauses across loan and security agreements — such as change-of-control provisions, covenant thresholds, or subordination terms — that might affect the loan’s risk profile.
• Create initial workflow outputs: Whether the desired output is an investment committee memo or a credit screening checklist, generative AI tools are quickly improving in their ability to pre-populate first drafts of sophisticated reports. AI platforms can quickly and accurately process source documents, identify relevant sections, and produce synthesized responses with relevant citations to the source material. Analysts remain critical in this process to review and validate the outputs, but no longer have to do the time-consuming work of developing initial drafts.
• Develop portfolio monitoring benchmarks: Post-loan, AI platforms can help private credit in ongoing monitoring of borrower communications and compliance by tracking credit health and early warning signals. Any deterioration that could threaten repayment is a lender concern. Continuous monitoring might look like reports on monthly financials, covenant compliance certificates, or management commentary for issues. Surfacing patterns in a borrower’s updates is particularly pertinent to credit risk management and scanning for covenant breaches, which can be difficult to do at scale without an AI tool.

Building An Effective AI-Enhanced Credit Workflow For Your Team

AI-driven research platforms are rapidly becoming an essential part of the private credit toolkit, not a futuristic add-on. Slowly, firms are adjusting to a new workflow paradigm where human expertise is augmented by new developments in LLM technology. Teams can spend more time on strategic insights and robust diligence by leveraging AI tools and reports to create a comprehensive initial pass.

Prepare Your Private Credit Strategy For The Future With Research Innovations

For private credit firms operating in a hyper-competitive environment, adopting AI-powered deep research is quickly becoming a strategic imperative. Those who harness these tools can execute thorough diligence in compressed timelines, cover more opportunities without expanding headcount, and standardize best practices across deals. In a zero-sum market, that translates to a real competitive edge in winning attractive loans and avoiding traps. It also reduces the risk of post-close surprises by ensuring critical issues are caught upfront. For smaller firms without extensive headcount or research resources, AI can play a role in leveling the playing field with larger institutions through enhanced productivity and coverage. As the private credit landscape continues to evolve, those firms that embrace AI-driven research platforms are likely to find themselves not only working faster, but also making better-informed lending decisions with confidence. The future of private credit investing will belong to the fast and informed, and AI is the catalyst making that possible.