Jeremy Goro leads financial services strategy and partnerships at Google. A thought leader in the field, he recently co-authored the book. The next frontier of AI for financial services leaders Join financial experts from Oliver Wyman, Innovate Finance, and Google as we dive into the industry’s transition to agent AI.
United Arab Emirates (UAE) We are experiencing a wave of agent AI adoption.this technology is poised to become a core engine of growth across both the public and private sectors. Unlike traditional automation, these intelligent systems plan, decide on, and autonomously execute complex multi-step workflows on behalf of customers and employees. No industry is unaffected, but banks and fintechs will be at the absolute epicenter of this transformation.
“AI is poised to fundamentally reshape the future of financial services in the Middle East and the rest of the world, as it evolves from providing basic assistance to executing autonomous actions,” said Adrian Oest, partner at consulting firm Oliver Wyman. “In this new landscape, marketing teams are pivoting from manual execution to strategic architects managing AI agents that deliver hyper-personalized experiences on a scale previously reserved for the world of science fiction.”
In our latest whitepaper report, “The Next Frontier of AI for Financial Services Leaders””Written in collaboration with Oliver Wyman and Innovate Finance, this book analyzes the key growth drivers behind this rapid change and explores the new economic realities that are emerging as agent AI takes over.
AI moves from assistance to execution
Transitioning from the first wave Generation AI Towards the second wave of agent AI. And I obviously have an appetite. According to research from Oliver Wyman, more than 80% of financial services professionals have already expressed their intention to treat AI agents as “colleagues.”1
Today, marketing teams might ask an AI assistant to summarize detailed market research. Tomorrow, a single prompt will enable AI agents to design, build, and launch entire campaigns from scratch.
Tools like personal AI agents gemini spark They are already driving this transition by working in the background without requiring people’s attention. This allows teams to automate complex multi-step workflows, such as automating continuous data monitoring, freeing up time for high-level strategy.
Andy Thornley, head of regulation at fintech industry body Innovate Finance, said the changes would change the way financial institutions operate. “By embedding AI agents directly into the real world, customer journeyBanks and fintechs can profitably tap underserved market segments, provide consumers with automated guidance on complex and historically gated financial decisions, and further democratize financial advice. ”
This agent-like reality is enhanced by major technological advances. today’s Large-scale language model (LLM) Humans have much sharper reasoning, even though they still need to oversee complex economic choices. Expanding the context window digests large amounts of data, making it more valuable, and advanced memory remembers your settings over time.
Ultimately, these breakthroughs will enable autonomous actions, allowing AI agents to securely connect to external systems and regulated financial models to independently perform real-world tasks.
Today, marketing teams might ask an AI assistant to summarize detailed market research. Tomorrow, a single prompt will enable AI agents to design, build, and launch entire campaigns from scratch.
Dual strategy of existing banks and fintech
Our research shows that established banks are leveraging their deep pockets to successfully pilot AI and achieve significant gains in back-office efficiency. Management is now deploying intelligent agents Streamline identity verification, automate compliance reviews, and optimize marketing analytics.
Meanwhile, digitally native fintechs have the agility to embed agent AI across the value chain to enable customer-facing advances. The public expects fintech to be experimental, giving them the freedom to test, iterate, and disrupt without the constraints faced by traditional banks.
For example, Stripe, a financial infrastructure technology company, is running AI. Payment platform model (PFM) Efforts are being made at scale to optimize flows and detect fraud across the company’s total annual processing volume, which reached $1.9 trillion last year. To demonstrate how fintechs can quickly scale AI innovation, Stripe recently Major partnership with Googlelaunches merchant checkout feature that allows businesses to sell directly to internal consumers AI mode and gemini.
Ultimately, to unlock agent AI across the financial ecosystem; Scalable cloud infrastructuresupportive regulation, secure identity verification, and trust for AI to operate autonomously.
Three different AI economies now available
Our research shows that widespread adoption of agent AI will create three distinct economies, providing new opportunities for financial brands to unlock new sources of value.
1. Agent Twin Economy
AI agent responsiveness is at an all-time high. However, major bottlenecks still remain. Customer data is fragmented across a sea of apps, leaving banks with outdated snapshots of their customers rather than the holistic view needed for a seamless “support economy.”
Introducing Agent Twin. These consumer-owned personal AI assistants securely store aggregated data and digital identities based on predefined permissions. It will evolve from managing passwords and calendars to handling complex product selections and provider interfaces in the background. This shifts the paradigm from inefficient data tracking to secure, permissioned collaboration, enabling richer content-driven insights and faster interactions.
Simplifying the experience for both customers and providers opens the door to powerful new opportunities. Oest says this newfound transparency allows Agenttic Twin to provide highly personalized and streamlined financial assistance at scale, helping customers set themselves up for greater success.
Additionally, Thornley adds that future revenue growth will largely depend on becoming the top choice for these digitally enabled companies. He said, “For financial companies to stand out, they need to work with their twin agent AI assistants through clear, logic-driven communication that aligns with the values programmed into their AI twins. This approach complements traditional marketing creativity with the data-backed precision these systems require.”
Naturally, this change puts security at the forefront. As AI twins act as a protective shield for customers, banks and fintechs must be fully transparent about fees and demonstrate strict privacy compliance to gain trust. Institutions that pioneer robust authentication and regain ownership of their data will gain a significant competitive advantage.
By putting control back in the hands of customers and gaining consent through real, demonstrable value, data becomes a shared asset, elevating consumers from passive users to active partners in their financial journey.
2. Aid economy
We are already seeing glimpses of the aid economy model being developed in the banking sector. City Wealth Announces City Sky. was developed in google cloud using Gemini Enterprise Agent Platform and Google DeepMind Frontier ModelCiti Sky acts as an always-on, AI-powered wealth team member. Leveraging Google DeepMind Real-time digital avatar technology in line Gemini live audio and video models, the platform aims to move beyond traditional banking interfaces to fluid, conversational intelligence. It highlights how large financial institutions are quickly recognizing and expanding advanced agent technology by providing financial guidance and support and enabling customers to act on advice instantly.
We are seeing a similar rapid expansion of agent technology on the institutional side. Deutsche Bank deploys DB Lumina. Developed with Google Cloud Vertex AI DB Lumina acts as an intelligent research assistant for the bank’s global analysts. Through conversational chat, analysts can query complex documents, translate insights, and summarize large volumes of reports. protected by Search extension generation (RAG) With this architecture, the platform cross-references queries with internal databases, central bank data, and global tax returns to ensure verified, fact-based responses. This allows you to track detailed trends, such as central bank sentiment scoring, with verifiable inline quotes. By moving teams from manual data collection to high-level strategy, we highlight how large banking operations are deeply embedding generative AI into their core workflows.
As this landscape matures, we expect to see different types of agent models defining user behavior.
“In an agent-first model, consumers can delegate all the heavy lifting to a single personal AI assistant,” Oest says. “This agent acts as a gatekeeper, sourcing information, comparing options, and completing transactions end-to-end on their behalf. Brands must compete to be chosen by agents, not consumers.”
after that, AI-powered searchwhich combines traditional web browsing with generative AI synthesis. using technologies such as AI overview and AI mode Shorten the path to purchase and open the door to dynamic, context-driven advertising.
Finally, businesses will be able to embed brand agents into their digital storefronts. These on-site AI concierges can use a visitor’s unique context to guide them to the perfect product. This drives conversions while the company retains full ownership of the customer relationship.
3. Adaptive customer experience
Just for today 21% of bank customers are satisfied Along with personalization initiatives. The next wave of AI will address this issue by moving customer journeys from static paths to real-time agent experiences driven by contextual data such as product searches and actual user behavior.
By utilizing Gemini Enterprise Agent Platformfinancial companies can deploy professional AI agents at scale. They act based on a best practice framework called. Generation UIAI not only interacts with humans, but also dynamically renders custom interface components on the fly, such as personalized mortgage calculators and onboarding modules. This facilitates a continuous feedback loop that learns from people’s clickstream behaviors and preferences and adjusts interface redesigns without manual coding.
Andy Thornley, Head of Regulation at Innovate Finance, says this evolution opens up exciting opportunities to create a truly proactive customer experience.
“Instead of waiting for people to say what they want, organizations can adapt in real time to create personalized spaces that stay one step ahead of customers’ needs,” he explains. “This makes welcoming new customers easier, smoothing the onboarding process with customized content, increasing trust, and increasing marketing ROI.”
Additionally, understanding the true intentions of customers allows institutions to provide the right service at the right time. Also, prioritizing customers’ immediate needs over general promotions shows them that they are truly valued and is the fastest way to build lasting trust and loyalty.
