Introduction

The saying “there are decades where nothing happens; and there are weeks where decades happen” is particularly applicable to the timeline of Artificial Intelligence development and adoption.  After decades of behind-the-scenes incremental improvements in the technologies underlying AI, we have witnessed in the last few years a period of rapid growth of AI usage in our personal and business lives.  Take ChatGPT as an example. User growth more than doubled year on year from 2024 to 2025, with website traffic and query volume also more than doubling over this period. All manner of businesses – from financial services, healthcare to media and the legal services – are scrambling to take advantage of the potential that AI presents, while at the same time soberly assessing the long-term impact AI may have on their business models and their people.

Like the rest of the business community, private capital is having its own AI moment. Different parts of the industry are moving, at varying paces and often in their own ways, to adopt and deploy AI in various parts of their businesses, including marketing, investor onboarding, due diligence, deal execution, deal monitoring and exit activity. Private capital managers are also using their influence as investors to drive greater AI adoption across their portfolios. As we start 2026 – which could prove to be a watershed year for AI (or another watershed year following its rapid growth in 2025) – we have identified a few themes that private capital managers should consider as they look to future-proof their businesses in the increasingly AI-dominant world in which they are operating.

What is next for private capital in 2026? Moving from AI chatbots to AI agents

If the last two years were defined by AI chatbots, 2026 will be defined by the adoption by enterprises of the next wave in AI: AI agents. Unlike chatbots, which are largely passive tools that can be prompted to generate text, images, voice or video, AI agents can go a step further and take action in the real world. These agents can not only reason and research, but they can also plan and execute multi-step workflows – often without constant human oversight.

The commercialization of AI agents in 2026 represents a significant shift in how companies will adopt and use AI. Instead of tools that help write content, AI agents will be leveraged to automate entire workflows end-to-end – from resolving customer support tickets to conducting complex deal research. These agents do not sleep; they can map the market continuously. And teams of AI agents can coordinate with each other, turning simple workflows into complex team sports, with individual agents becoming specialists in particular domains or processes.

As agentic technologies become more widespread in 2026, firms will move beyond viewing AI merely as a tool for efficiency and start to focus on agents as a new (and highly competitive) unit of their workforce – one that has the potential to realize significant gains, if governed correctly. Culturally, the rise of agentic AI will therefore lead to a new type of investment professional: the AI-fluent supervisor, who has the skills to manage and orchestrate teams of AI agents that can run workflows and processes across the enterprise. Firms’ AI governance strategies will also need to mature, as companies develop new technical and compliance strategies to manage their increasingly autonomous technology portfolio. We also expect to see significant progress in the tools being adopted across industries to help make AI agents work in practice – from new protocols governing how agents interact and communicate, to more sophisticated dashboards providing oversight and clarity into how the agents are operating.

Managing AI risk across a private capital portfolio

Private capital managers seeking to responsibly scale AI across their portfolios in 2026 must adopt a structured, risk‑aware approach that embeds governance, compliance and value creation into every stage of AI use. Since the beginning of the legal AI revolution in the last few years, private capital has shifted from simply leveraging AI for operational optimization within individual portfolio companies to actively managing its AI risk exposure across portfolio companies at the fund level.

AI adoption is complex. It raises privacy concerns, security threats, increasingly complex and overlapping regulatory regimes quality control challenges. How, and to what extent, should a private capital fund approach AI risk within its portfolio – and what should fund managers look to implement in 2026? Fund managers must work to train their personnel and employees within portfolio companies to develop uniform, but industry-specific, AI best practices, accountability systems and playbooks. The objective is to fully monetize AI’s efficiencies without compromising overall portfolio risk profile of an investment portfolio.

In 2026, we expect to see more private capital managers opting for, and rolling out, better AI systems across their portfolios – systems that operate transparently, are explainable, scalable and incorporate continuous monitoring. This will be especially critical where AI influences pricing, underwriting or customer‑facing decisions. Robust AI structures not only reduce regulatory and reputational exposure, but also enhance exit valuations, as AI adoption and maturity is increasingly viewed as a differentiating asset.

The barbell effect of AI

The so-called middle market of private capital – generally, funds between $100m and $5bn that target deals in the $25m to $1bn+ range – has been an important and enduring source of dealmaking, talent development and innovation for the US and global economy since the inception of the industry. It has proven to be remarkably resilient over multiple market cycles, changing economic circumstances and opportunity sets. Many of the iconic names in the big cap sector of private capital today established their track records, teams and brands based on their successful operation in the middle market.  

In the current environment, many middle-market managers are facing a range of stresses, including: sluggish fundraising dynamics; a hyper-competitive landscape for deals; the challenge of staying relevant in a crowded marketplace of fund offerings; sub-optimal exit opportunities at current valuations; the high cost of acquiring and retaining top talent; and succession planning.  Added to this list of obstacles that private capital managers are facing is the risk posed by the advancement and adoption of AI across the industry. 

You may ask: doesn’t AI present significant opportunities for private capital as a whole? It does, subject to what may be an emerging “barbell effect.”  By barbell effect, we mean that AI has the potential to disproportionately benefit the two ends of the private capital market -- new and emerging managers who are able to leverage readily available and inexpensive AI tools to compensate for their lack of resources, on the one hand, and large, scaled managers with the resources to build proprietary AI and data flywheels, on the other – while presenting middle market managers with a new set of competitive challenges.

Startups and emerging managers are natural beneficiaries of AI’s declining cost curve. Founders can now build their firms with far smaller teams and lower startup and ongoing operating costs. AI can reduce barriers to entry by making it cheaper and easier for smaller managers to operate their businesses including in the areas of marketing, deal sourcing, diligence, portfolio monitoring, and investor reporting and support. The days of two young stars raising their first fund out of a garage may be back – at least for those technically fluent enough to deploy AI effectively in building their business. 

At the other end of the barbell, large AUM managers are uniquely positioned to exploit AI because of their scale. With deep balance sheets, long track records, and broad and diverse portfolios, they can invest heavily in proprietary data infrastructure, internal AI tools and cross-portfolio data aggregation. These managers can create powerful feedback loops – using portfolio data to improve sourcing, diligence, value creation, risk management and exits, which in turn generate more data and better models.

This barbell effect has the potential to accelerate consolidation among middle-market managers. Proprietary data, and how firms use it in their investment analysis and decision-making, will become increasingly central to success. Managers with large, robust data loops and bigger balance sheets to invest in data and AI infrastructure, may begin to pull away from the rest of the pack. The result could be a wave of acquisitions and combinations driven less by traditional cost synergies and more by technology and data synergies, as firms seek either to scale sufficiently to compete or to plug into platforms where AI advantages can compound across strategies and asset classes.

AI as an accelerant of retail market growth

The trend towards democratizing retail investor access to private capital is gaining momentum both in the US and globally. Experts expect that retail AUM in the US alone will grow from its current estimated $80bn level to over $2tn by the end of the decade. Regulators are also attempting to facilitate this shift, including in the US through the promised liberalization of rules allowing individuals to invest in private capital through their 401(k) retirement plans. Retail investment is likely to grow at a fast pace with or without the assistance of AI, but AI is likely to play an important role in accelerating that pace. In this sense, AI may prove to be what smartphones were to e-commerce. 

First, AI lowers the operational and economic costs associated with serving retail and high-net-worth investors. Onboarding costs – including KYC/AML checks, accreditation verification, suitability assessments, subscription processing and disclosures – have traditionally made retail private markets expensive, slow and hard to scale. AI-driven automation can streamline these processes, reducing marginal cost per investor and making smaller ticket sizes economically viable.

Second, AI materially improves personalization and investor matching. Machine learning models can analyze investor profiles – risk tolerance, liquidity needs, time horizon, tax sensitivity and behavioral patterns – and dynamically align them with appropriate private market products. Rather than one-size-fits-all offerings, managers can deliver tailored exposure across strategies, vintages and liquidity profiles. This level of personalization increases investor confidence and engagement, addressing one of the key psychological barriers to retail participation in illiquid assets.

Third, AI enhances education, transparency and communication among the investing public, all of which are essential for retail adoption. Natural language tools can translate complex private capital concepts – capital calls, capital account statements and other periodic reporting, distribution notices, and tax reporting – into clear, digestible explanations tailored to each investor’s level of sophistication. As retail investor comfort and understanding increase, so does allocation willingness.

Finally, AI enables private capital managers to scale their retail investor base without losing control – a prerequisite for sustained retail growth. Managers can monitor investor behavior, liquidity stress points and concentration risks in real time, enabling more resilient fund design and proactive risk management. Over time, this data feedback loop improves product structuring and regulatory engagement, reinforcing trust with both investors and gatekeepers.

Taken together, it is easy to see how AI has the potential to both accelerate and industrialize the retailization of private capital. AI is uniquely capable of flattening the information and sophistication gap between institutional and retail investors, while making it easier and more cost-effective to support a large base of smaller-ticket investors.

What does it mean to be a fiduciary in an AI-enabled world?

US fund managers are typically fiduciaries under both federal and state law and are also subject to contractual standards of care under their constituent documents. A core fiduciary duty is the duty of care, which requires a manager to exercise the care, skill and diligence that a reasonably prudent fund manager pursuing a similar investment strategy would use.

Important elements of such care, skill and diligence include making decisions on an informed basis and consistent with clients’ objectives and best interests; conducting appropriate investigation and analysis; using sound judgment consistent with market practice; seeking best execution of client transactions as appropriate; and overseeing service providers.

What does this mean in an AI-enabled world where more of the information and analysis that managers rely on in making investment decisions is produced by AI rather than humans? Here are some rules of thumb managers should consider in exercising their fiduciary responsibilities in the context of an increasingly AI-centric environment:

• Have honest and ongoing conversations with your service providers – law firms, accountants and other advisers as well as fund administrators, custodians and broker-dealers – about how they are using AI and what controls they have in place to ensure quality control, human oversight and data protection.
• Implement “human in the loop” or “human over the loop” controls to ensure that professionals with subject-matter expertise review key AI-driven outputs, conclusions and actions at the appropriate point in each workflow.
• Treat AI as a decision-support tool, not the decision maker. The buck stops with humans.
• The more sophisticated and complex the workflow – particularly when AI agents are involved – the more important it will be to involve IT, legal and other key stakeholders in determining appropriate level of human oversight and verification.
• Develop internal AI policies and refine them over time based on experience.
• Set the tone from the top: employees should remain vigilant and maintain a healthy skepticism about what machines produce.
• Accurately disclose how your firm uses AI, including both its potential benefits and risks.

Beyond fiduciary considerations, managers should also bear in mind that people can often distinguish AI-generated communication from human communication – at least for now.  This matters when communicating with investors, counterparties and other stakeholders, particularly where authenticity and trust are central to the firm’s brand.

Conclusion

When it comes to technological advancement, it is often difficult to distinguish hype from genuine inflection points. Real change tends to unfold over time and unevenly.  For example, the early iterations of smartphones were cool in their own way, but the revolution of the smartphone occurred over a number of years, alongside advances in networks, software and shifts in how people communicated and consumed information.

AI and private capital may follow a similar path. But make no mistake: change is happening.  The challenge for the private capital managers will be to understand what that change looks like – both its upside and its risks – and to position themselves to maximize favorable outcomes for their franchises, employees and stakeholders.

In the end, fortune will favor the prepared.