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21 Apr 2026

Agentic AI

What it means for funds and why 2026 is the year to pay attention

7 minute read time

Alternative investment funds are feeling the pressure on multiple fronts. Tighter diligence windows, rising LP expectations and cost-to-serve challenges are all driving managers to squeeze greater efficiency and accuracy out of their processes. To meet these demands, many are turning to emerging technologies, with RBSI’s recent Pulling Together thought leadership report showing that 66% of funds expect artificial intelligence to deliver significant automation gains, while 62% predict productivity improvements.

“Nearly all alternative investment funds are using artificial intelligence in some form, with AIMA research finding that GenAI is employed in 95% of hedge funds,” says Lewis Lane, Innovation Manager at RBSI. “AI is already widely embedded across research and ingestion, document review, trade analytics, risk assessment and reporting, and investment decisions.”

There are still adoption hurdles to be surmounted, however, with 71% of funds citing digital transformation as their biggest operational challenge. Our report shows that the top barriers to AI integration are security/privacy (69%), regulatory uncertainty (66%), and lack of understanding of use cases (45%). To overcome these issues, collaboration, trust and strong partnerships are imperative, with funds working in tandem with service providers and regulators.

Introducing Agentic AI

To plug into the benefits of AI for deal speed, investor servicing and operational resilience, fund managers don’t have to be tech experts, building and integrating applications – but they do have to understand its potential. This includes comprehending the scope of the evolution of Agentic AI.

Where AI is prompted to respond, Agentic AI is prompted to act. Agentic AI can be instructed to plan, make decisions and take action towards a predetermined goal with limited human intervention. While AI can be thought of as a smart assistant able to follow commands, Agentic AI is a junior analyst which can devise, perform and report on a task.

“Agentic AI breaks tasks down into moves or steps, where it has to choose the most suitable tool, execute for impact and then review outcomes and adapt ‘game-play’,” explains Lane. “This has sizeable impact on how funds operate as users of AI, shifting from ‘AI as a tool’ to ‘AI as a delegated operator’.”

Agentic AI is set to start graduating from pilot projects to practical workflows in 2026. “This is the year funds stop experimenting and start operating with Agentic agents,” predicts Lane. “As with many emerging technologies, front office functions are often constrained by sensitivity and are slower to build trust, whereas the back office is typically quicker to implement and benefit from Agentic AI automation – in reconciliations, investor reporting and exception management.”

Differentiating between AI and Agentic AI

Artificial Intelligence (AI): Technology that enables computers to perform tasks that have previously required human intelligence, such as analysing data, recognising complex patterns or making predictions. Standard AI systems respond to inputs and follow rules or learned processes, and usually require humans to tell them which tasks to undertake.

Agentic AI: The next evolution of artificial intelligence, instead of answering questions or running single tasks, Agentic systems act as autonomous agents. They can break down complex tasks into steps, select and use tools, request and access specific data, execute workflows across multiple systems, and continuously learn and improve over time within set guardrails.

Alternative investment fund use cases: AI versus Agentic agents

AI will increasingly free up fund managers to focus on strategic work. Instead of chasing data or drafting routine reports, they will be able to spend more of their time on building relationships with LPs, making informed investment decisions, and driving portfolio value creation. Our report shows that 80% of funds agree that AI will enable more routine tasks to be performed in-house, speeding up processes and reducing reliance on outsourcing, while 45% expect it to improve market analysis and forecasting.

The introduction of Agentic AI provides even greater efficiencies, interoperability and explainability – and it’s full capabilities are yet to come online. “Right now, Agentic AI makes sense in high value/low risk use cases such as risk monitoring, compliance checks, research summaries, document review and portfolio surveillance,” explains Lane. “Where is it not yet being leveraged fully is in trade execution, portfolio rebalancing, capital allocation and liquidity management.”

Existing AI capabilities can already make impressive inroads into the daily workload of fund managers – but comparing current use cases with the capacity of Agentic AI agents highlights just how much potential remains untapped.

Deal origination: AI research tools drive faster screening of opportunities, confidential information memorandums and pitch decks, collate key metrics and flag potential risks.
Agentic AI: Automatically pulls data from filings, news and CRM databases, then compiles a shortlist of deals that fit managers’ investment thesis ready for them to review.

Due diligence: Document agents extract search terms and compile investment committee-ready packs at speed. With auditable actions, fund managers can accelerate and control the due diligence process, with AI reading through hundreds of pages of contracts and financials to highlight covenants, lease terms and anomalies.
Agentic AI: Autonomously completes the entire workflow, extracts data from the data room, organises it into a standard diligence template, and drafts an IC-ready summary for the management team.

Investor relations: As LP expectations of faster reporting and greater transparency grow, their queries can be answered quickly and accurately, with AI generating draft commentary for quarterly reports based on portfolio KPIs.
Agentic AI: Automates LP Q&A responses using approved data, escalates complex queries to the team, and ensures compliance with side letters before sending.

Compliance: AI checks documents for regulatory language, highlighting compliance related terms to reduce risk and manual burden.
Agentic AI: Applies ‘policy as code’ to every action, validates compliance before sending reports, logs all steps for audit, and sends alerts if any item needs human approval.

Cost-to-serve pressure: AI cuts manual effort in operations, IR, fund administration and compliance by handling repetitive tasks such as data entry and reconciliation.
Agentic AI: Orchestrates workflows across systems including CRM, order management systems and portfolio dashboards so managers don’t have to jump between screens to transfer data. Where data is fragmented, such as across CRM notes, Excel trackers, PDFs and emails, Agentic agents stitch and normalise it, reducing manual intervention.

Enhanced market intelligence: By processing vast volumes of market data and news content, AI can identify trends relevant to the sector and deliver that information at pace.
Agentic AI: Maintains a live watchlist of targets, sends alerts regarding regulatory changes, ESG events, and drafts meeting briefings.

How fund managers can extract material benefit from Agentic AI

To ensure Agentic AI works for them, fund managers should help refine their organisation’s approach to the integration of Agentic agents. Raising questions rooted in the challenges they face and how Agentic AI can be applied to their day-to-day will help to shape its adoption.

Understanding the problem

Where are the pain points? What specific tasks could Agentic agents automate to speed up workflows?
What’s the impact of eliminating these pain points? What could be achieved with time savings or better quality output?
Is AI already being used for tasks such as risk analysis and reporting, or piloting workflows? How effective is it proving?

Defining authority and protecting trust

What can Agentic agents do within the fund structure versus what needs to remain a human task?
How does AI impact legal, ethical, governance and risk obligations? What’s being done to protect sensitive investor and portfolio data?
How can these processes be easily explained to clients and regulators? What guardrails are in place to ensure compliance?
Is there a single source of truth for key fund data, or is it spread across systems?

Fuelling innovation

Are the processes supported by sufficient data quality and audit trails? If not, how can data and reporting be improved?
Can use cases for Agentic agents be tested in an agile, low-cost, safe space to prove impact and value?
How will Agentic AI reduce reliance on outsourcing or change how the fund works with service providers?

Approaching with caution

Testing, use cases and user confidence will all need to grow and develop before funds embrace Agentic AI across the processes outlined above. “It’s logical to build confidence, accuracy and senior buy-in for Agentic AI in a sandbox environment at first,” points out Lane. “And even in that environment, AI autonomy should be constrained in any scenarios that include a risk of irreversible damage, be it reputational, financial or regulatory.”

An understanding of the full value of AI is yet to be unlocked in fund management, believes Lane. “Monitoring progress and adjusting tech and wider strategy accordingly is fundamental in achieving differentiation or even staying at pace. We shouldn’t see its potential to be fully autonomous as a barrier to experimenting with Agentic AI, but should instead start with human-in-the-loop models to build understanding and confidence. In this way we will see Agentic AI moving beyond a minority use case by the end of 2026 and into 2027.”

Whatever approach funds take, now is the time to seize the initiative. “The urgency to embrace digital innovation cannot be overstated; these new technologies will underpin sustained growth and competitive advantage but only for those who take action,” concludes Lane.

If you would like to discuss any of the themes covered in this article further, please reach out to your Relationship Manager.

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Agentic AI

7 minute read time

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