Skip to content

AI Adoption in Financial Services: Balancing Risks and Regulations

AI adoption is accelerating. Governance is struggling to keep pace.

AI is quickly becoming part of everyday operations across the alternative investment industry. But speed of adoption is a poor measure of success. What matters is how responsibly the technology is governed once it’s in.

Managers aren’t simply deploying another software platform. They’re introducing technology that can influence investment and operational workflows, touch sensitive investor and portfolio data, and create oversight responsibilities that don’t fit neatly into existing technology, security or compliance programmes.

A question we hear increasingly often from COOs and CTOs is a simple one: how do we know if we’re ready to adopt AI safely? The honest answer is that many firms are not. Not for lack of capable teams, but because they haven’t yet defined the standards and processes needed to define what “ready” means for their firm. That definition is a governance job, not a technology one.

Shadow AI is already here

One of the biggest misconceptions is that AI adoption begins when leadership approves a new platform. In reality, it usually starts long before that.

Employees are already using generative AI to summarise meetings, analyse spreadsheets and prepare first drafts of everything from presentations to DDQ responses. At the same time, vendors are embedding AI into the systems managers already rely on: order and portfolio management platforms, fund administration, CRM, research tools, compliance monitoring, and cybersecurity products.

The result is that in many firms, AI is influencing day-to-day operations without centralised visibility. Leadership may not know where it’s in use, what data is being shared, whether confidential information is leaving approved systems, or whether employees understand the firm’s expectations for responsible use. AI will be used either way. The open question is whether it’s used deliberately.

Governance comes before tool selection

Before evaluating vendors or deploying firm-wide solutions, managers should be able to answer a short set of fundamental questions:

  • Where is AI already being used across the firm, and what data can it access?
  • Who owns AI governance and oversight?
  • How are AI-generated outputs reviewed and validated, and what happens when they’re inaccurate, biased or incomplete?
  • What policies guide employee use of AI tools?
  • How are third-party AI vendors assessed for security, privacy and compliance risk?
  • How will AI systems be monitored as they evolve?

Without clear answers, firms increase their risk while limiting the long-term value AI can deliver. Governance isn’t about slowing innovation. It’s a simple test: could you defend every AI decision in an audit, a board meeting, or an investor’s due diligence questionnaire?

Alternative investment managers face pressures

Managers must balance innovation with investor confidentiality, regulatory obligations, cybersecurity considerations, and operational resilience, often with lean teams. And AI doesn’t sit in one department. It touches research, trading support, operations, investor relations, compliance, vendor management and executive decision-making.

Without a centralised approach, adoption becomes fragmented. Different teams evaluate tools independently and against different standards, vendors introduce new capabilities without formal review, and employees adopt tools outside approved policy. The result is inconsistent oversight, regulatory risk, duplicated spend, and missed opportunities.

The cost of getting it wrong goes beyond compliance

Regulatory exposure attracts the most attention, but it’s only part of the picture. Poor governance can mean confidential investor or portfolio information exposed through unauthorised tools, vendor risks that were never assessed during onboarding, and a fragmented strategy that erodes the trust of investors and counterparties.

Scrutiny is rising too. Regulators, cyber insurers, and allocators are all asking sharper questions, and operational due diligence questionnaires increasingly cover how AI is governed. Firms that treat these expectations as a ceiling rather than a floor will spend the next few years catching up.

The greatest risk isn’t adopting AI. It’s adopting AI without understanding how to manage it.

Where to start

Firms don’t need a large programme to make progress. A practical starting point is a short readiness exercise: map where AI is in use, classify the data it touches, assign ownership, set an acceptable use policy for staff, and add AI to vendor risk assessments. None of this is sophisticated. That’s the point. The firms that fare best with AI won’t be the ones with the most advanced tools, but the ones that got the fundamentals right before scaling.

Build governance first, and everything that follows gets easier. Adoption, oversight, and conversations with investors and regulators are coming either way – the firms that come out ahead are preparing now.