Benchmarking AI Adoption in Financial Services

Financial institutions have spent billions integrating artificial intelligence into their operations, but measuring the actual impact of those investments has remained elusive. Banks understand that AI will define their future competitiveness, but without clear benchmarks, they struggle to assess whether they are ahead or behind their peers.

Evident Insights, a data intelligence firm focused on AI adoption, has stepped into this gap. CEO Alexandra Mousavizadeh built the company after leading the Global AI Index, a framework that ranked nations by the strength of their AI ecosystems. Recognizing the private sector’s demand for similar measurement tools, she founded Evident to quantify AI maturity at major financial institutions.

“We got pulled into thinking hard about how to actually measure AI adoption and maturity for businesses,” Mousavizadeh said. “We started with banks because that’s where the demand was.”

Two years after launching, Evident has become a go-to resource for financial institutions looking to understand where they stand in AI adoption—and what it takes to stay competitive.

Why AI Benchmarking Matters

For many banks, AI investment decisions have been driven by intuition and internal priorities rather than an objective assessment of market positioning. AI adoption varies widely among institutions, and without clear comparisons, companies risk either overspending on AI tools with limited returns or underinvesting and falling behind.

Evident's methodology aims to solve this problem by analyzing both public and private data to track how financial institutions integrate AI into their operations.

• Public Benchmark: The company compiles information on AI talent acquisition, R&D efforts, external partnerships, and stated investments. This data forms the basis of a comparative ranking across institutions.

• Private Benchmark: Evident also works directly with banks that provide proprietary data in exchange for detailed diagnostics on their internal AI performance. This allows institutions to compare real-world AI deployment metrics against an anonymized peer set.

By combining these sources, Evident provides financial institutions with quantifiable insights into their AI adoption efforts, moving beyond vague corporate statements about digital transformation.

The AI Arms Race in Banking

Mousavizadeh describes the AI landscape in banking as a sprint with increasing existential risk.

“You listen to any of the banks, and they’re saying, ‘This cannot happen fast enough,’” she said. “The ones already using AI at scale are reinvesting, doing more with less, growing market share, and increasing efficiencies. The ones that aren’t? They’re at risk of going out of business faster than I initially expected.”

Banks leading in AI adoption are using machine learning to automate loan underwriting, detect fraud, personalize customer interactions, and streamline compliance reporting. But more critically, they are improving operational efficiency at a scale that creates a growing gap between early adopters and laggards.

According to Evident’s research, top-tier AI adopters are not just maintaining market position—they are actively expanding it.

“The banks that are furthest ahead are also the ones hiring the most,” Mousavizadeh said. “They’re growing because they can scale operations efficiently. The ones that aren’t adopting AI? They’re the ones cutting jobs.”

This competitive imbalance is likely to deepen as regulatory expectations evolve. AI-powered compliance automation is reducing costs for proactive banks, while those slower to adapt will continue spending more on manual processes.

Expanding Beyond Banks

Evident’s success in financial services has set the stage for broader expansion. The company recently closed a Series A funding round to accelerate its reach into new verticals.

“In the near term, we’re adding four new areas within financial services: insurance, payments, asset management, and private equity,” Mousavizadeh said. “After that, we’ll most likely be expanding into energy, manufacturing, retail, and pharma.”

The long-term goal is to build a global standard for AI adoption measurement across industries.

By applying the same benchmarking approach to different sectors, Evident aims to create an AI adoption index that helps companies assess their standing not just within their own industry, but across the global economy.

AI Models Are Commoditizing—Data is the Differentiator

While AI technology continues to improve, its growing accessibility is changing how companies think about differentiation.

“We are moving toward zero-cost AI models,” Mousavizadeh noted. “That’s great for businesses adopting AI but not great for companies trying to sell proprietary models.”

Open-source models like DeepSeek and Mistral are rapidly closing the gap with closed-source systems from OpenAI and Anthropic. As model architecture becomes increasingly commoditized, proprietary data is emerging as the true competitive advantage.

Financial institutions sit on vast reserves of customer transactions, risk profiles, and operational insights—datasets that external AI models can’t replicate. The ability to structure and leverage that data effectively is what will ultimately determine success in AI adoption.

“The big banks with enormous amounts of data are best positioned,” Mousavizadeh said. “If you’re data-rich, you can build products and models that are significantly harder to replicate.”

The Hardest Part of AI Adoption? Making the Data Useful

Despite the rush to implement AI, the biggest challenge isn’t data collection—it’s making sense of it.

“You get all this data, and you have the entire mosaic on your clients, but what does it actually tell you?” Mousavizadeh said. “Our business isn’t just providing data. It’s helping institutions interpret it. That’s the hard part.”

AI alone doesn’t solve decision-making. Organizations still need to contextualize insights, align them with business objectives, and translate them into action.

This is where financial institutions face their biggest AI bottleneck—not in the algorithms, but in turning raw AI outputs into usable intelligence.

Key Takeaway: AI Adoption is Now a Business Imperative

The findings from Evident point to an unavoidable conclusion: AI adoption in financial services is no longer optional.

• Leading banks aren’t just using AI—they are widening the gap. Institutions that effectively integrate AI are scaling faster, reducing costs, and expanding their market position.

• AI models are becoming less of a differentiator—data is the new competitive edge. The value of AI increasingly depends on proprietary data and how well it is structured.

• The hardest challenge isn’t AI implementation—it’s extracting value from it. Having AI capabilities doesn’t automatically create an advantage. Success depends on turning AI-driven insights into clear, strategic action.

Mousavizadeh sees this shift accelerating, with AI becoming a fundamental determinant of financial institutions’ long-term viability.

“The AI revolution isn’t coming—it’s already here,” she said. “The question is, who will keep up?”