2026 State of AI in Financial Services: How Value and Risk Vary by Subsector

At the industry level, financial services is leaning further into AI than other sectors. 45% use AI multiple times a day, compared with 37% across all industries surveyed.

That momentum takes a different form in each financial services subsector. Our 2026 State of AI for Business and Finance report finds that investment banking reports the clearest gains in decision quality, alongside the highest exposure to negative consequences from bad AI output; private equity (PE) and venture capital (VC) express the strongest budget growth expectations and the highest trust in enterprise-grade AI; asset management and hedge funds has the tightest governance but also the lowest AI urgency; and wealth management has the lowest trust in enterprise-grade AI and verifies output the least frequently.

Four subsectors, four different balances of AI value and risk.

(Findings below draw on 275 respondents across four financial services subsectors. PE and VC's smaller sample of 32 respondents means its findings should be read as directional rather than conclusive. )

Financial Services Runs Ahead of the Overall Market

Financial services professionals report higher trust in enterprise-grade AI, use it more frequently, expect stronger budget growth, and are more likely than respondents overall to view enterprise AI for research as competitively necessary. They also report having experienced more negative consequences from inaccurate or misleading output.

• Trust: 82% of financial services respondents trust enterprise-grade AI, compared with 78% overall.
• Usage: 67% use AI at least daily in financial services, compared with 65% overall. 45% use it multiple times a day, compared with 37% overall.
• Budget growth expectations: 84% expect their AI budget to grow, compared with 80% overall.
• Competitive belief: 65% believe firms that don’t adopt enterprise-grade AI for research will underperform, compared with 60% overall.
• Negative consequences: 67% have been burned by bad AI output, compared with 62% overall.

But these industry-wide averages flatten meaningful differences underneath them. Financial services subsectors diverge on usage, governance, trust, and risk.

Trust, Usage, Verification, and Harm

Both trust in enterprise-grade AI tools and AI usage are a little higher in financial services than the overall survey rate. Within the sector, PE and VC stand out by combining the highest enterprise-grade trust and verification rate with the lowest exposure to negative consequences from inaccurate or misleading AI output.

Trust:

• PE and VC lead in enterprise-grade AI tool trust at 85%, followed by investment banking (81%), both higher than the overall rate of 78%. PE and VC also have the largest gap in trust between consumer-grade AI tools and enterprise-grade AI tools (63% and 85%, respectively).
• Wealth management shows the lowest trust of enterprise-grade AI tools at 74%.

Verification:

• PE and VC lead here too, with 86% verifying output always or often.
• Wealth management also trails here. Only 69% verify AI output always or often, below every other subsector and the 79% overall rate.

Usage:

• Asset management and hedge funds lead in daily AI usage, with 70% using AI at least once a day, the highest rate among the four subsectors.
• Asset management and hedge funds and wealth management tie for the highest share using AI multiple times a day, at 43% each.

Burned by bad output:

• Investment banking reports the highest rate of having experienced negative consequences due to inaccurate or misleading AI output (70%).
• PE and VC sit at the opposite end, with the lowest rate of negative consequences at 53%.

Decision-Making Benefits: Investment Banking Leads in Most Areas

Investment banking reports the strongest AI benefits of any subsector in decision quality and insight discovery.

• Investment banking leads in reporting that AI helps them make better decisions at 73%, above the 60% overall rate. It also leads at using AI to find insights: 90% of IB responders say AI surfaces an insight they wouldn't have found otherwise at least weekly, the highest of any subsector. Despite its strongest-in-class benefits, investment banking also rejects AI for risk management more than any other subsector. Among respondents doing investment research, 36% say they will never use AI for risk assessment and management.
• Asset management and hedge funds are the opposite. They, collectively, have the highest rate of surfacing a new insight less than once a month (10% vs. 5% overall). And only 76% find a novel insight at least weekly, compared with 84% overall. This is the lowest rate among all surveyed subsectors.
• Wealth management has the highest rate of AI helping increase confidence in decisions made (71% vs 55% overall), followed by PE and VC at 66%. Yet wealth management also draws the sharpest line of any subsector around a specific use case: 45% say they will never use AI to generate investment research reports.
• PE and VC get the highest benefit of AI helping arrive at decisions more quickly (69% vs 64% overall).

Governance and Shadow AI

Tool governance varies by subsector.

• PE and VC: A genuinely split picture: 37% run two or more unapproved AI tools, tied with investment banking for the highest share of any subsector, yet 57% run none at all, above the 49% overall rate.
• Investment banking: 37% use two or more unapproved tools, but only 39% use none, the lowest share of any subsector.
• Asset management and hedge funds: Report the lowest rate of people running two or more unapproved tools at 17%. 59% don’t run any unapproved tools, the highest of all finance subsectors.

Barriers to Adoption Converge on Security

Security leads as the top barrier to broader AI adoption in three of the four subsectors. Only asset management and hedge funds cite something else (regulatory compliance) as their leading concern.

• PE and VC: Security is the leading barrier (52%), with cost a close second (45%, the highest of the four). Lack of skilled personnel is the lowest of any subsector (10%).
• Asset management and hedge funds: Regulatory compliance is a top barrier (45%), followed closely by security concerns (41%). Manual search is the single biggest time sink for 45% when searching for internal information.

Budget and Competitive Conviction: PE and VC Leads Both

PE and VC report the strongest budget-growth expectations and the greatest conviction that enterprise AI will become competitively necessary for research.

• Expect AI budget to grow:
  • PE and VC lead at 91% (vs. 80% overall).
  • Asset management and hedge funds lag at 71%.
• Believe non-adopters of enterprise AI for research will underperform:
  • PE and VC lead at 74% (vs 60% overall).
  • Asset management and hedge funds trail at 58%.

Feature Priorities: Accuracy Leads Everywhere Except PE and VC

Accuracy is the top-ranked AI tool feature across financial services, with one clear exception.

• PE and VC: Only 13% rank accuracy first, the lowest of any finance subsector, and there's no single leading feature. Data privacy and security, ease of use, and source transparency all tie at 19% each.
• Asset management and hedge funds rank accuracy highest of any subsector. 29% name it their single most important feature, more than the overall average of 24%.

What This Means

Taken together, the survey results suggest that the financial services sector is more committed to AI than the overall market. Higher trust, usage, investment expectations, and competitive conviction coexist with greater exposure to inaccurate or misleading AI output. However, beneath those industry-wide patterns, each subsector faces a different combination of opportunities, risks, and adoption barriers.

• Investment banking: clearest reward, highest harm. Combines the strongest gains in decision quality and insight discovery of any subsector with the most widespread use of unapproved AI tools. It also has the highest rate of negative consequences from inaccurate or misleading AI output.
• PE and VC: most trusting, strongest budget outlook, widest trust gap. Lead on enterprise-grade AI trust and on verification, checking output always or often more than any other subsector, while also reporting the highest expectations for budget growth. and the lowest rate of negative consequences from inaccurate or misleading AI output.
• Asset management and hedge funds: tightest governance, lowest AI urgency. Have the most people who don’t run unapproved tools. They also have the lowest budget growth expectations of any subsectors, are the least convinced that skipping enterprise-grade AI for research carries real competitive risk, and find a novel insight at least weekly less often than any other subsector.
• Wealth management: lowest enterprise-grade AI trust, highest decision confidence. Reports the lowest trust in enterprise-grade AI and verifies output less frequently than any other subsector, yet reports the strongest gains in decision confidence through AI usage.

When it comes to AI, financial services firms are broadly confident, invested, and security-conscious. But the truth about the real risk and value of AI adoption sits in the details of each subsector.

Methodology note: Figures are based on respondents identifying their organization's primary area of specialization within finance, insurance & real estate: Investment banking (108), asset management and hedge funds (70), wealth management (65), and private equity and venture capital (32). Private equity and venture capital's smaller sample means its findings should be read as directional rather than conclusive.

For full findings, see The 2026 State of AI for Business and Finance.

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