Financial Services Increases AI Investment Faster Than Wider Market, While Maturity Remains Level

HTEC research finds 42% of financial services and insurance executives report significantly increased AI investment, compared with 28% across industries, while AI maturity remains in line with the wider market

HTEC, the global AI technology and engineering leader, today released its research on AI Value realization in financial services and Insurance. Organizations in this sector are increasing AI investment more aggressively than the wider market, but the sector has yet to establish a corresponding lead in AI maturity, according to new research from HTEC.

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HTEC, the global AI technology and engineering leader, today released its research on AI Value realization in financial services and Insurance. Organizations in this sector are increasing AI investment more aggressively than the wider market, but the sector has yet to establish a corresponding lead in AI maturity, according to new research from HTEC.

The HTEC AI Value Realization Report 2026: Financial Services & Insurance, found that among FSI respondents currently pursuing AI adoption, 88% report increasing investment over the past 12 months. Forty-two per cent describe the increase as significant, compared with 28% across all industries. Yet 44% of FSI organizations are at the scaling stage or already AI-native, exactly the same proportion as the overall sample.

The findings suggest that high levels of investment alone are not translating into a sector-wide maturity advantage. Seventy-eight per cent of FSI respondents agree that people and processes limit the value organizations derive from AI more than technology does.

“Financial institutions are investing heavily, but technology alone does not determine whether AI creates value,” said Philip Otley, Global Managing Partner at HTEC. “Results depend heavily on what happens around the technology: how AI is integrated into workflows, who owns the outcome, and whether employees are equipped to use it consistently.”

Better decisions, not simply lower costs

The research also points to what FSI leaders want AI investment to deliver.

Better decision quality is the most desired outcome of AI investment, cited by 31% of executives, compared with 17% who prioritize cost reduction.

The finding reinforces the importance of being able to demonstrate value beyond traditional efficiency measures. Improvements in decision quality require credible baselines, trustworthy data, and evidence connecting AI-supported decisions to business outcomes.

However, the research shows significant differences in when organizations establish and assess financial returns. Thirty-four per cent require a formal ROI model before an AI initiative is approved, while 29% assess returns only after implementation.

Together, these findings indicate that defining and measuring value throughout the AI investment lifecycle remains an important part of the maturity challenge.

AI risk measurement remains inconsistent

The research identifies a parallel challenge around governance.

Forty-two per cent of FSI respondents consider AI risk informally alongside value, while only 16% measure risk consistently across their portfolios.

As AI becomes more deeply embedded into financial services workflows, HTEC believes appropriate oversight will become increasingly important, particularly where AI supports consequential decisions within regulated operations.

“Not every AI-assisted decision requires the same level of oversight,” said Lawrence Whittle, Chief Strategy Officer at HTEC. “The challenge is designing review processes that are proportionate to the risk involved, while ensuring that people have enough context and authority to exercise meaningful judgment when it matters.”

Organizations are also relying more heavily on external expertise

The research suggests that the complexity of moving AI beyond individual initiatives is also affecting how FSI organizations work with external partners.

21.5% of FSI respondents report greater dependence on external partners or consultants than they had anticipated, indicating that moving AI from isolated use cases towards enterprise-wide deployment increasingly requires strategy, data, engineering, and governance to work together. Multidisciplinary teams can work alongside internal stakeholders to connect these capabilities as use cases move from roadmap to production.

Turning investment into value

Taken together, the findings suggest that the next phase of AI adoption in financial services and insurance will depend not simply on deploying more technology, but on strengthening the conditions around it.

The research points towards five areas: clear business outcomes, trustworthy data, defined ownership, consistent measurement, and proportionate governance.

For HTEC, this also changes the role of technology partners. Financial institutions increasingly need strategy and engineering to connect with implementation and organizational change, together with the transparency, safeguards, and traceability required to understand how AI-supported decisions were reached and whether they delivered the intended outcome.

About the Research

The findings are based on responses from 235 financial services and insurance C-suite executives within a global survey of 1,500. The research was commissioned by HTEC and conducted by Censuswide in July 2026.

The full HTEC AI Value Realization Report 2026: Financial Services & Insurance is available at https://htec.com/insights/reports/the-htec-ai-value-realization-report-2026-financial-services-insurance/.

About HTEC

HTEC Group Inc. HTEC is a global technology and engineering company that helps organizations build AI capabilities from silicon to outcomes. HTEC helps its customers build an adaptive, vendor-agnostic AI stack designed to evolve as technologies, models, and platforms change.

Through its forward-deployed model, HTEC works alongside customer’s teams to connect AI to business strategy and create a repeatable capability consistently and faster each time. OneLoopAi is at the heart of this approach and brings greater transparency in measuring progress and continuously improves how AI is developed and deployed. The result is a stronger path from AI investment to measurable business value and AI at scale.

With more than 15 years of experience working with AI and machine learning, HTEC combines deep engineering expertise with strategy, product, design, and data capabilities to turn AI ambition into production.

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