KBRA Examines AI’s Gradual but Uneven Impact on Software Borrowers and Private Credit Markets
KBRA has released an in-depth research report analyzing how artificial intelligence is reshaping risk dynamics across software companies and private credit portfolios. The study provides a comprehensive view of how lenders, sponsors, and borrowers are responding to the emergence of AI, while also assessing whether the technology poses a systemic threat to credit quality in the near term.
The firm’s central conclusion is measured and nuanced: while artificial intelligence introduces new forms of disruption, its impact on credit risk is likely to be gradual, contained, and manageable within existing portfolio structures. Rather than triggering widespread defaults, AI is expected to drive greater differentiation in performance across borrowers and investment vehicles.
AI Risk: Diffuse, Gradual, and Largely Contained
According to KBRA’s analysis, the risks associated with AI adoption in the software sector are not uniform. Instead, they are dispersed across companies with varying degrees of exposure depending on their business models, financial health, and strategic positioning.
Most software companies within private credit portfolios are considered resilient enough to navigate this transition. These firms generally possess sufficient liquidity, operational flexibility, and time to adapt to technological changes. As a result, KBRA does not anticipate a broad-based deterioration in credit quality solely attributable to AI.
However, the report identifies a subset of companies—particularly those backed by financial sponsors and facing near-term debt maturities—that may experience heightened pressure. These borrowers are often more vulnerable to disruption due to structural weaknesses, including slower growth, declining revenues, or limited capacity to invest in new technologies.
While these risks are real, KBRA emphasizes that they are unlikely to escalate into systemic issues. Instead, they are expected to manifest as isolated cases of stress, contributing to only a modest increase in default rates across the broader market.
Greater Impact from Macroeconomic Conditions
Interestingly, KBRA places greater emphasis on macroeconomic factors as the primary drivers of credit risk in the near term. The report highlights several external pressures that could have a more immediate and significant impact on borrowers than AI itself.
These include ongoing geopolitical tensions—such as a prolonged conflict in the Middle East—alongside elevated energy prices, persistent inflation, and continued disruptions in global supply chains. Additionally, the current “higher-for-longer” interest rate environment is increasing borrowing costs and tightening financial conditions for many companies.
In this context, AI is viewed as a secondary risk factor, one that interacts with broader economic challenges rather than acting as the dominant force shaping credit outcomes.
Deep Dive into the Software and Technology Cohort
To support its conclusions, KBRA conducted a detailed analysis of 495 companies within what it defines as the Software and Technology cohort. This group includes 415 software firms, 40 companies in information and telecommunications, and 40 in internet and data services.
Collectively, these businesses represent approximately 20% of KBRA’s broader portfolio of more than 2,400 middle-market, sponsor-backed borrowers worldwide. This substantial sample provides a robust foundation for evaluating how AI-related risks are distributed across different segments of the technology sector.
The firm developed a framework to categorize each company based on its relative exposure to AI disruption. This framework considers factors such as competitive positioning, reliance on legacy technologies, investment capacity, and sensitivity to shifts in customer demand.
A particular focus was placed on borrowers with near-term debt maturities, as these companies are more exposed to refinancing risks and may have less flexibility to adapt to changing market conditions.
Evidence of Emerging Risk Differentiation
One of the report’s key findings is the growing divergence in financial performance among companies with higher exposure to AI-related disruption. KBRA identified 165 companies as having relatively elevated AI risk, and this group already exhibits weaker financial characteristics compared to the broader cohort.
These higher-risk companies tend to show slower revenue growth and a greater incidence of declining sales. Their operating performance aligns more closely with traditionally lower-performing sectors such as chemicals, materials, and consumer retail, rather than the typically high-growth software industry.
This underperformance suggests that AI is not the sole driver of risk, but rather an amplifying factor for existing weaknesses. Companies that are already struggling operationally may find it more difficult to adapt to technological shifts, increasing their vulnerability.
At the same time, KBRA notes that lenders and sponsors appear to be aware of these dynamics and are actively managing their exposure. This proactive approach reduces the likelihood of unexpected losses.
Near-Term Maturity Risks
Another area of focus in the report is the concentration of near-term debt maturities among higher-risk borrowers. Approximately 25% of the companies identified as having elevated AI risk are scheduled to mature before the end of the second quarter of 2027.
This proportion is notably higher than the 19% observed across the broader population of borrowers analyzed in 2025. The combination of elevated AI exposure and impending refinancing needs creates a potential pressure point for these companies.
Nevertheless, KBRA’s modeling suggests that even in a worst-case scenario—where all 41 of these higher-risk companies default—the overall impact on credit metrics would remain manageable. The firm’s Middle Market Default Monitor (KMDM), a forward-looking indicator of default risk, would increase modestly but remain within acceptable thresholds.
Specifically, the default rate would rise from 3.4% to 4.8% by borrower count and from 2.0% to 2.9% by value. These figures indicate that the system has sufficient capacity to absorb potential losses without significant disruption.
Diversification as a Key Risk Mitigator
A critical factor supporting this resilience is the high level of diversification within private credit portfolios. The 41 higher-risk loans are distributed across more than 90 investment vehicles managed by 28 different direct lenders.
In most cases, individual exposure to any single borrower is relatively small, with a median allocation of less than 2.5% per vehicle. Additionally, these companies are backed by a diverse group of 34 sponsors, further reducing concentration risk.
This dispersion of exposure limits the potential impact of any individual default and helps stabilize overall portfolio performance. As a result, KBRA does not expect significant ratings changes for its rated direct lending vehicles, even in the face of AI-related stress.
Changing Behavior Among Lenders and Sponsors
Although the direct impact of AI on defaults may be limited, the report highlights a noticeable shift in behavior among lenders and sponsors. Uncertainty surrounding AI’s long-term implications is already influencing how capital is allocated and managed.
Lenders are increasingly incorporating additional safeguards into loan agreements, such as tighter covenants and higher pricing. In some cases, loan spreads have increased by 100 basis points or more, reflecting a higher perceived risk.
There is also evidence that lenders are becoming more selective in extending maturities, particularly for companies that are underperforming or perceived as vulnerable to technological disruption. This more cautious approach could contribute to higher refinancing risk for weaker borrowers.
Sponsors, meanwhile, are reassessing their willingness to provide additional capital to portfolio companies. Factors such as declining valuation multiples, increased technology investment requirements, and broader market uncertainty are influencing these decisions.
Early Signs of AI-Driven Market Shifts
While the full impact of AI is expected to unfold over time, KBRA notes that early indicators are already visible. Lenders report changes in customer behavior, including longer sales cycles and shifts in spending priorities toward AI-related initiatives.
These trends are beginning to affect revenue growth and profit margins for some companies, particularly those that are slow to adapt. As customers reallocate budgets to invest in AI capabilities, traditional software offerings may face increased competition and pricing pressure.
Although these changes are still in the early stages, they suggest that AI is gradually reshaping the competitive landscape, with implications for both borrowers and investors.
Outlook: Measured Risk, Increasing Differentiation
Looking ahead, KBRA expects AI to play an increasingly important role in shaping credit outcomes within the software and private credit sectors. However, the firm maintains that the associated risks are manageable and unlikely to trigger widespread disruption.
Instead, the primary effect of AI will be to amplify differences between companies—rewarding those that successfully adapt while exposing the weaknesses of those that do not. This dynamic will likely lead to more varied performance across private credit investment vehicles.
For investors, this means that careful selection and active management will become even more critical. Understanding each borrower’s exposure to AI, as well as its ability to respond to technological change, will be key to navigating the evolving landscape.
KBRA’s research provides a balanced perspective on the intersection of artificial intelligence and credit risk. While acknowledging the transformative potential of AI, the report concludes that its impact on private credit portfolios will be gradual, uneven, and largely contained.
Macroeconomic conditions remain the dominant risk factor in the near term, but AI is beginning to influence both market behavior and borrower performance. As these trends continue to develop, the ability of lenders, sponsors, and companies to adapt will determine how risks and opportunities are ultimately realized.
In the meantime, the resilience of diversified portfolios and the proactive strategies of market participants suggest that the private credit ecosystem is well positioned to absorb the challenges posed by this next wave of technological change.
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