AI adoption in alternative asset management is entering a more practical phase. Early efforts focused largely on identifying where AI could add value and testing individual use cases. As those experiments produce results, firms are turning to the harder work of deploying AI at scale, integrating it into established workflows and putting the governance and controls in place to support production use. That shift is bringing greater focus to which applications are delivering measurable value, where they can be scaled and what infrastructure is needed to support them.
AI Adoption Is Spreading Across the Investment Lifecycle
AI deployment is expanding across functions, although the level of adoption and maturity varies considerably.
On the investment and deal side, AI is accelerating due diligence by automating the review of financials, contracts and data rooms, while also generating first drafts of investment memos and investment committee materials. Deal teams face significant pressure to move quickly, and AI can compress timelines for work that previously required days of analyst effort.
In the middle and back office, use cases tend to be more operational. These include NAV workpaper review and validation, cash flow monitoring, variance detection, reconciliations and know-your-customer (KYC) and anti-money laundering (AML) processes. These are high-volume, repetitive activities where accuracy matters and errors can carry real consequences. The case for automation is strong, but so is the need for deterministic checks and appropriate human oversight.
In investor relations and portfolio monitoring, firms are using AI to automate due diligence questionnaire (DDQ) responses, support data room and confidential information memorandum (CIM) review, and streamline investor reporting. Interest is also growing in AI agents, which can perform tasks autonomously rather than simply generate content. Early applications include workflows such as email triage and meeting summaries.
Certain Use Cases Are Consistently Outperforming Others
Not all AI deployments are delivering the same level of value. Relatively straightforward use cases with clear business impact, including covenant monitoring, data room review, document extraction and DDQ automation, are consistently demonstrating value. Managers also identify fee and waterfall validation, finance close and deal origination as high-value opportunities, particularly where processes combine significant manual effort with complex underlying data.
Custom large language models (LLMs), by contrast, are notably absent from that list. Firms that have explored building proprietary models frequently find that the effort and ongoing investment do not justify the expected return. The clearer business case often lies in applying existing models to well-defined workflows, with the surrounding processes, controls and data infrastructure doing much of the work required to make those applications useful.
Governance Remains the Central Challenge
Across conversations with chief technology officers (CTOs), chief operating officers (COOs) and transformation leaders at alternative managers, governance consistently emerges as a central concern. The issues are specific: data confidentiality, model accuracy and hallucination risk, access controls, regulatory auditability and the ability to maintain human oversight as AI use expands.
Alongside these technical considerations is the broader business challenge of demonstrating return on investment, and many organizations are still building the business case for AI. Change management adds another layer of complexity. AI can alter workflows that teams have relied on for years, and achieving consistent adoption across a business requires more than deploying the technology itself. It requires sustained attention to how people use it and where responsibility remains with human teams.
There is also an emerging structural risk in the proliferation of siloed AI tools and agents. When individual teams deploy separate solutions without centralized oversight, governance requirements can multiply. Over time, fragmented adoption can make it harder to understand which tools are being used, what data they can access and where automated decisions or actions are taking place.
Orchestration Requires More Than Automation
These are challenges we have been working through at SS&C GlobeOp as we apply AI within our own operations. The scale of our workflows makes the need for a structured approach particularly clear. Each year, we process more than four million loan agent notices and more than 300,000 portfolio trade confirmations, along with 260,000 funding memos and around 100 credit agreements each month. At those volumes, applying AI responsibly requires more than automating individual tasks.
SS&C WorkHQ, SS&C's agentic automation platform for enterprise, provides an orchestration layer that connects agents, automation and human workflows. It provides centralized governance, auditability and security guardrails, controls model access and data permissions, and incorporates deterministic checks, reconciliation logic and human approval steps into automated processes. This is particularly important in fund administration, where efficiency cannot come at the expense of accuracy or accountability.
Clients are increasingly asking to see how SS&C uses AI in its own operations, with a focus on real-world applications rather than concept demonstrations. Deploying SS&C WorkHQ internally first gives SS&C direct experience with the governance framework, edge cases and failure modes that emerge in production environments before these capabilities are applied to client workflows. That experience informs both how we develop the platform and how we advise clients on responsible AI deployment.
Contact us to learn more about SS&C WorkHQ and how SS&C is helping alternative asset managers scale AI with the controls and governance required for production use.