Financial Services Lead Enterprise AI Adoption as 85% Boost Budgets

Financial services firms have put AI to work on more tasks than healthcare and media combined, and they're not done yet. The PYMNTS Intelligence report titled "Same Direction, Different Roads: How Financial Services, Media & Advertising, and Healthcare Are Navigating Enterprise AI, Together and Apart" reveals a sector that's already ahead of the curve.

The research surveyed 60 senior technology executives at U.S. enterprises with at least $1 billion in annual revenue during March 2026. Financial services and insurance reached high adoption on 27 of 75 AI-supported tasks, compared with 16 in media and advertising and 10 in healthcare. That's 26 combined for the other two sectors, one fewer than financial services alone.

According to the PYMNTS report, the financial services story isn't that banks, insurers and related firms are using artificial intelligence everywhere. It's that they're using it most heavily where the business case is clearest: revenue, risk, compliance and forecasting.

Those are areas where financial institutions already have structured processes, documented workflows and established governance, conditions that make AI easier to deploy and easier to trust. Think about the physical reality of this: finance teams clicking through standardized forms, following audit trails, and reconciling numbers against regulatory requirements. AI slots into that existing machinery without requiring a complete rebuild of how work gets done.

Key findings from the report include 65% of financial services and insurance firms using AI for revenue recognition and accounting close. That's the highest adoption rate listed for the sector and reflects AI's role in helping firms manage finance functions that require accuracy, consistency and speed. Sixty percent use AI for credit risk assessment and scoring, and 60% use it for sales forecasting and pipeline optimization.

Those use cases show how financial firms are applying AI to both sides of the operating model: protecting against bad outcomes while sharpening revenue planning. The numbers aren't abstract—they represent actual workflows where someone clicks a button, reviews flagged transactions, or approves a credit decision with AI-generated recommendations displayed on screen.

Eighty-five percent of financial services and insurance firms are increasing AI budgets over the next 12 months. The top justifications are productivity and efficiency gains, cited by 65%, and strategic or competitive positioning, also cited by 65%. That's a lot of money flowing into infrastructure, tooling, and talent (which is probably why CTOs are both excited and exhausted).

The report also points to where growth is still coming. Adoption lags in customer-facing areas: churn prediction and retention targeting sits at 30%, know your customer (KYC) and know your business (KYB) and identity verification at 20%, and A/B testing at just 10%. Those numbers show AI has gained the most traction where rules and controls are already well established, and that expanding into customer experience and product work will require a different approach.

That's where the sector's next phase of deployment is likely to take shape. Moving from back-office automation to customer-facing applications means dealing with messier data, more ambiguous outcomes, and higher stakes for brand reputation. It's the difference between automating a spreadsheet calculation and deciding what message a customer sees on their mobile banking app.

The main barrier isn't budget or executive support. Thirty percent of financial services executives cite data quality and fragmentation as their biggest organizational challenge, the highest single barrier in the sector. In practical terms, fragmented data limits how far AI can move beyond structured back-office tasks into broader decisioning and customer work.

Financial firms appear to have the resources and the will to close that gap, and the ones that do it first are likely to extend their lead. Independent reporting from LetsDataScience corroborates the PYMNTS findings on adoption rates and budget increases.

Industry observers note the PYMNTS findings align with broader reporting that financial services has concentrated AI effort on revenue, risk, compliance, and forecasting, where measurement and regulatory requirements make outcomes easier to validate. For practitioners, this means operational controls, feature traceability, and explainability tooling are frequently near the top of implementation checklists in finance deployments.

What matters for practitioners and vendors: monitor how firms translate increased AI budgets into tooling choices (model governance, explainability, MLOps) and whether spending concentrates on vendor solutions or internal platforms. Also watch adoption breadth: the report measures high adoption across specific tasks, but observers will want to see whether those deployments move from pilot-to-production at scale and how firms instrument monitoring and controls.

The sector-level differences in AI adoption and near-term budget increases matter to practitioners planning deployments or vendor strategies. It is important but not frontier-shifting. The real question is whether the 85% budget increase translates into actual production deployments or just more proof-of-concept projects gathering dust on a server rack.

Whether users actually pay for it remains the real question. Financial services has the data, the budget, and the regulatory pressure to deploy AI at scale. But turning that investment into measurable competitive advantage—rather than just keeping pace with competitors—will require more than throwing money at the problem. Time will tell if the 27 high-adoption tasks become the foundation for something transformative or just a more efficient version of the same old banking.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn