GenAI adoption is increasingly measurable.
Many of the world’s most influential financial services firms — like Allianz, J.P. Morgan, and Mastercard — have taken concrete action to adopt genAI technology.
The genAI adoption efforts have shaped 2 years’ worth of corporate strategy, unveiling key priorities — from the rise of agentic commerce to customer service copilots — across the competitive landscape.
Using CB Insights data, we identified and analyzed 100 real-world applications of genAI from 69 companies across banking, insurance, and payments.
Download the book to explore all 100 applications, and read on for 5 key takeaways and a breakdown of our methodology.
Key takeaways
1. Cross-functional platforms are now table stakes.
24% of applications center on deploying general-use genAI platforms to employees.
Prominent firms like BBVA have established enterprise-wide genAI capabilities across their organizations (typically via enterprise-wide deployments of platforms like Microsoft Copilot or ChatGPT). Early adopters — like Klarna, which shared in May 2024 that 87% of its employees are using OpenAI technology — now have over a year of genAI operational experience at scale, which can guide the development of more complex applications in the future.
Looking forward, financial services firms without a plan to provide genAI access to employees risk competitive disadvantage. Over the past 2 years, simply providing genAI capabilities to employees has shifted from cutting-edge innovation to standard operations.
2. Microsoft and OpenAI permeate the adoption landscape.
33% of applications analyzed disclose involvement from either Microsoft or OpenAI.
Microsoft and OpenAI (in which Microsoft has significantly invested) overwhelmingly permeate the landscape of genAI applications analyzed. Many of these applications anchor on foundational capabilities, from which organizations can build more complex applications and agents. Anthropic, Amazon Web Services, and Google Cloud follow a similar deployment pattern across multiple companies in the sector.
Looking forward, financial services firms should prepare for increasingly blurred “build, buy, or partner” decisions. The prevalence of genAI model developers (like OpenAI and Anthropic) and big tech partners (like Microsoft and Google) provide financial services executives with more flexibility to customize their tech solutions than what has traditionally been the case with many point-solution providers.
3. Emerging genAI vendors face a fierce competitive landscape.
Median Mosaic Scores among genAI startups analyzed are in the top 3% globally.
The 100 analyzed genAI applications include engagement from 25 startups as tech vendors, ranging from pre-seed companies like Twin — which offers an agent for invoice collection — to late-stage giants like Anthropic. These startups have a median CB Insights Mosaic Score — which measures the overall health and growth potential of private companies — of 732 out of 1,000, as of July 30, 2025.
Looking forward, financial services firms should prepare for increasingly capable tech vendors seeking to sell their genAI products. These vendors must exhibit a clear advantage over the alternative of building in-house solutions.
4. Customer-facing genAI will become increasingly prevalent.
16% of applications center on customer engagement & self-service capabilities.
Firms like ING, Wells Fargo, and Truist show that customer-facing genAI assistants are capable of powering millions of customer interactions. Customer-facing genAI deployment will accelerate as companies like Mastercard, Visa, and PayPal deploy applications centered on “agentic commerce,” where customers can autonomously shop and complete transactions with AI payments agents.
Looking forward, financial services firms need to develop a gameplan for how they will engage customers with agentic AI. The market opportunities for enterprise agents and copilots are growing, so customer-facing applications will quickly emerge.
5. Impact is now tangible, but success definitions remain elusive.
Only 30% of applications disclose quantitative tangible impact from deployment.
Most of the application sources analyzed lack disclosure of tangible impact (i.e., numbers, percentages, or figures to quantify effectiveness). Among the impact metrics that are available, the top-cited focus on operational considerations like call-handle times.
Looking forward, any financial services firm has the opportunity to define “what good genAI adoption looks like” across the sector. The lack of clear success definitions creates an opportunity for financial services firms to stand out among peers.
Methodology
We used CB Insights’ Business Graph — including data points like Dealmaking, Business Relationships, Earnings Transcripts, and Media Mentions — and third-party company releases to identify 100 real-world genAI applications across banking, insurance, and payments. These applications were disclosed between July 2023 and April 2025.
Then, using CB Insights’ Team of Agents, we analyzed these applications across 10 categories. Applications are detailed based on disclosure date, and are not exhaustive of a given company’s genAI initiatives. Applications and categorizations are not mutually exclusive or exhaustive of activity within their respective industries.
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