How Oak Hill Advisors Achieved 6X Return on Investment with Hebbia

Oak Hill Advisors, the $108 billion global credit-focused alternative investment firm, has emerged as a case study in how private credit teams can deploy artificial intelligence to generate measurable returns. The firm, which operates as the private markets platform of T. Rowe Price, reported achieving a sixfold return on its AI investment through the strategic deployment of the Hebbia platform across its public and private credit businesses.

The results reflect a broader pattern unfolding across private credit, where firms managing trillions in assets are moving beyond experimental AI pilots toward production-scale deployments. With the private credit market now exceeding $3 trillion in assets under management and projected to reach $5 trillion by 2029, leading firms are embracing automation to capture new opportunities at scale. Oak Hill Advisors' experience offers a roadmap for how investment firms can approach AI adoption with the same rigor they apply to portfolio construction.

Oak Hill Advisors did not treat its AI initiative as a technology experiment. Instead, the firm approached implementation with the discipline it applies to any investment decision, identifying high-value use cases, measuring impact, and reinvesting in what produced results. According to Hebbia’s published case study, OHA partnered with Hebbia to deploy the platform across credit operations with specific performance targets in mind.

The implementation centered on three pillars. First, the firm built credit agreement workflows that captured institutional expertise in a no-code format, transforming dense legal documents into structured data within seconds. Hours of manual review collapsed into minutes, allowing analysts to redirect attention toward higher-value deal analysis. Second, OHA launched an internal competition across all its AI platforms, challenging analysts to build use cases that improved efficiency and decision-making. Hebbia emerged as the clear winner, outperforming alternatives and ultimately becoming the firmwide standard. Third, the firm tied license allocation to consistent usage, ensuring that adoption remained accountable and that licenses went to analysts who actively embedded the technology into their workflows.

The financial results at Oak Hill Advisors followed a compounding trajectory that illustrates how AI value can multiply as organizations mature their deployments. Initial implementation delivered a twofold return on time savings alone. As the firm expanded use cases across its credit operations, that figure doubled to fourfold. Refining the quality and depth of analysis through advanced research capabilities pushed ROI to its current sixfold level.

Sonja Renander, Managing Director at Oak Hill Advisors, attributed the results to the firm's engagement-driven strategy. "We've seen a 6X+ ROI on our investment with Hebbia, using engagement and internal competitions to spark high-impact use cases that solve real analyst pain points and make teams eager to adopt the platform," Renander stated in the case study. The approach suggests that technology adoption in financial services succeeds when it addresses genuine operational challenges rather than chasing innovation for its own sake.

Beyond the headline ROI figure, OHA reported efficiency gains that reached a 75 percent reduction in review times for various workflows. The platform also accelerated onboarding for newer analysts, allowing them to benefit from the accumulated knowledge of seasoned associates. This capability addresses an important opportunity in private credit: making institutional expertise accessible across the organization rather than concentrated with individual professionals.

The operational realities of private credit help explain why firms like Oak Hill Advisors are investing in AI infrastructure. Each potential investment involves analysis of hundreds of pages spanning credit agreements, financial statements, and diligence materials. Teams that can extract precise terms, identify relevant benchmarks, and compare opportunities against precedent transactions gain a significant advantage in evaluating and executing deals efficiently.

Research from the 2025 Private Credit Technology Summit highlighted that AI is automating operational, process-driven tasks such as deal screening, investment committee memo creation, financial analysis, and portfolio monitoring. The technology is being used to generate first drafts of memos, spread financials, and conduct desktop research. Summit participants noted that ingestion and normalization of unstructured data, including credit agreements and portfolio company financials, represent key areas where AI delivers substantial value.

The private credit sector has characteristics that make automation particularly valuable. Data arrives in varied formats, and portfolio companies report differently, creating opportunities for technology to standardize and streamline processes. Bridging front, middle, and back-office functions through technology and shared data platforms helps firms scale operations as the market continues its growth trajectory. Industry surveys indicate that nearly half of private credit professionals plan to increase spending on data automation and machine learning by at least 25 percent compared to prior years.

Oak Hill Advisors brings more than three decades of credit investing experience to its operations. The firm has invested over $52 billion in more than 440 private credit transactions as of mid-2025, employing an opportunistic, credit-driven approach across North American and European markets. T. Rowe Price acquired the firm in December 2021 to accelerate its expansion into alternative investment markets.

The firm's scale and complexity position it as a meaningful test case for AI deployment in credit operations. OHA manages capital across private credit, high yield bonds, leveraged loans, stressed and distressed debt, and collateralized loan obligations. Its investor base includes pension funds, sovereign wealth funds, insurance companies, foundations, and family offices. The diversity of strategies and stakeholders creates opportunities for AI to add value across multiple workflows.

Credit teams at firms of this caliber handle substantial deal flow across private credit, syndicated loans, and bonds, all requiring speed, accuracy, and analytical depth. The firm described its objective not as whether AI could help, but whether it could cut review times, scale expertise across the organization, and prove returns that exceeded implementation costs.

Oak Hill Advisors' approach to measuring AI value differs from how many organizations track technology investments. Rather than focusing on softer metrics like user satisfaction or adoption rates, OHA calculated returns using the same framework it would apply to any investment. Time savings translated into quantifiable capacity gains. Expanded use cases multiplied initial returns. Improved analytical quality pushed ROI higher still.

This measurement discipline appears central to sustainable AI adoption in financial services. Hebbia has reported similar results across its client base, which includes over one-third of the largest asset managers by assets under management. Hebbia recently crossed one billion pages processed, growing from 47 million pages just twelve months earlier. That volume suggests financial institutions have moved beyond pilots toward production-scale deployment.

The platform's architecture addresses specific requirements of credit analysis. It maintains full document integrity with precise source linking, allowing teams to surface individual clauses or specific disclosures that prove decisive for investment decisions. Analysts can verify findings against original documents rather than accepting outputs that provide conclusions without traceable reasoning chains. This transparency supports the high standards of accuracy that private credit professionals maintain when evaluating covenant calculations, basket limitations, and other critical deal terms.

Oak Hill Advisors' results arrive at an opportune moment for private credit firms seeking to enhance their operational capabilities. Limited partners increasingly recognize the value of managers who deploy capital efficiently and manage portfolios with sophisticated infrastructure. Technology capabilities have become a competitive differentiator that distinguishes market leaders.

The Hebbia deployment at OHA demonstrates that AI adoption in private credit succeeds when it solves specific analyst needs rather than pursuing automation as an abstract goal. Hebbia’s competition-based approach to identifying use cases ensured that adoption emerged from genuine operational opportunities rather than top-down mandates. Tying license allocation to usage created accountability that sustained engagement beyond initial implementation.

For private credit teams exploring similar investments, the OHA experience highlights several best practices. Start with high-value workflows where time savings translate directly into capacity for additional deal analysis. Measure returns with the same rigor applied to portfolio investments. Create adoption incentives that reward genuine engagement. Position AI infrastructure as a strategic asset that enhances the firm's competitive positioning.

The private credit market's continued expansion creates favorable conditions for firms that invest in operational excellence. As the asset class approaches $5 trillion, organizations that can evaluate more opportunities with greater analytical depth will be well positioned to identify and capture the most compelling returns. Oak Hill Advisors' sixfold ROI demonstrates what becomes possible when firms treat AI adoption with the same discipline they bring to investment decisions.