Most conversations about AI in financial services often focus on efficiency, faster fraud detection, or lower operational costs and reduced manual errors. While these benefits are real and significant, they represent optimisation of existing models, not transformation into new ones.
Our research across 1,000 financial services leaders revealed a different story. 83% anticipate that agentic AI will unlock entirely new business models and revenue streams. They see autonomous systems not as better tools for current processes but as enablers of products, services and relationships that were not viable before.
As organisations become AI-native, embedding this technology into their work, the question turns to what is possible, rather than what is faster. Today’s innovation leaders must consider which opportunities to pursue and how to balance protecting existing revenue against new business models that could disrupt it.
Beyond efficiency: agentic AI as a catalyst for reinvention
The optimism around agentic AI's transformative potential is striking:
- 84% of leaders believe organisations that embrace agentic AI will gain a distinct competitive advantage in identifying and capturing new market opportunities
- 85% say it is already enabling financial institutions to personalise services for underserved or previously unreachable customer segments
- 81% expect it will enable faster entry into new markets
These improvements signal a structural change in what financial services can offer and who can access them. Human-delivered advisory services, personalised wealth management and continuous financial guidance have long been possible but have been too expensive to offer every customer. Agentic AI changes the equation. Autonomous systems can deliver sophisticated, personalised guidance and services to every customer regardless of account balance or transaction volume.
Scaling an outstanding service
Agentic AI opens profitable access to new markets by enabling trusted, relationship-driven services at scale. Younger customers already rely on AI for guidance, making financial advice a natural extension of existing behaviour and allowing banks to build lifelong relationships early. It also reduces geographic and infrastructure barriers by delivering services digitally without the need for physical branches or large advisory teams. This supports the ability to bring services to rural, underserved and emerging communities. The wider social impact could be substantial, with AI helping to expand financial literacy and access to wealth-building tools far beyond what traditional, human-led models can reach.
Beyond allowing the scaling of these services, it also shortens the time it takes to bring them to market, creating a competitive landscape where speed becomes decisive. As AI takes on coding, infrastructure configuration and testing, teams can iterate far more quickly, compounding their advantage over slower competitors. Always-on, self-optimising systems then extend this edge, delivering personalised service around the clock in ways traditional, human-led models cannot match.
Here, we explore examples of where agentic AI could expand beyond traditional offerings, opening new opportunities within the coming years.
Democratised wealth management
Agentic AI moves wealth management from aspiration to reality. For example, an AI financial advisor can be scaled to an 18-year-old with £100 a month in disposable income, allowing them to receive sophisticated financial planning previously reserved for clients with £100,000 investment portfolios. With deep customer data, it can adjust recommendations as circumstances change, explain offset mortgage strategies, pension optimisation and tax efficiency in plain language, all tailored to someone just starting their financial journey.
This creates wealth-building opportunities for people who would never have qualified for traditional wealth management services before. It also creates new revenue models for financial institutions willing to serve this market at scale.
Dynamic credit and risk assessment
With agentic AI, risk assessments could move beyond historical snapshots to real-time behavioural understanding. Traditionally, someone who historically missed payments may carry a poor credit score despite demonstrating consistent financial discipline since then. Behavioural analytics that track spending patterns, savings habits and financial decision-making can build a more accurate picture of creditworthiness than these traditional models.
This opens access to credit for customers who would have been declined automatically while also reducing risk for lenders who can assess someone's current – rather than past – behaviour.
Advisory banking relationships
In this AI-first landscape, banks could know and advise their customers better than ever, taking their relationship from purely transactional to proactive. For example, a bank that understands its customers' behavioural patterns can suggest booking next year's holiday deposit before prices rise, based on account capacity and historical interest. It can recommend financial products aligned with life changes, helping actively manage a customer's financial well-being.
However, such a close relationship requires trust and transparency. Customers need to believe their bank is optimising for their benefit, not just cross-selling products. Done well, it transforms banking from a necessary infrastructure to a valued partnership.
Agentic orchestration
The next step could see the creation of entirely new service categories that take and analyse data from multiple sources to provide an outstanding level of service. Imagine an AI agent that understands your family's preferences, checks calendars, evaluates budget constraints and then presents holiday options optimised across multiple dimensions. It could negotiate across flights, accommodation and activities to find combinations that maximise value for a customer’s specific priorities, explaining trade-offs and helping decision-making.
This would go beyond a comparison site’s capabilities, providing intelligent orchestration on the customer's behalf. The agent acts with autonomy within parameters, continuously learning what matters to the customer and adapting recommendations accordingly. With this level of service, financial services become invisible infrastructure supporting experiences, rather than transactions requiring attention.
Risk versus reward
The innovation potential of agentic AI creates a strategic dilemma for incumbent financial institutions. New business models may cannibalise existing revenue, market expansion into underserved segments may conflict with premium positioning, and always-on AI advisory services may reduce the perceived value of existing offerings.
So, how can leaders address this tension? Some are creating incubated AI-native units that operate separately from core business, allowing innovation to proceed without legacy constraints, while protecting existing operations from disruption.
However, this can be challenging internally without wider support and the right culture of experimentation. Resources are withheld, cooperation becomes reluctant and stakeholders spend energy relentlessly defending the initiative rather than building it.
The alternative approach is integration: evolving the entire organisation rather than building separate units. This is slower and more complex but avoids creating internal competition between old and new business models. It also allows leveraging existing customer relationships, trust and operational scale rather than building from zero.
The right choice depends on organisational culture, competitive positioning and risk tolerance. What matters is making a conscious choice rather than allowing the tension to paralyse action. The organisations that will capture that potential are those willing to experiment, accept that not every initiative will succeed and balance the tension between protecting what exists whilst building what comes next.
Explore how early movers are navigating this transformation and what separates experimentation from execution.