Whether you remember the “before times,” when the internet didn’t exist, or you’re a digital native who can’t imagine a world that wasn’t always online, the impact of technological innovation is inescapable. In a matter of years, digital advances have transformed society and its day-to-day operations. Now, the latest iteration, agentic AI, promises to be even more disruptive. For the financial services sector, agentic AI represents a foundational shift in how it can harness innovation and accelerate growth.
How is this surge in AI distinctive from previous waves of technical advancement? In the words of OpenAI CEO Sam Altman, tasks that once took a month could soon be completed in an hour by AI agents. Instead of using large language models in isolation, imagine a multi-AI system that can reason, learn and act autonomously. AI agents can accomplish all of that, executing tasks across the value chain from customer service to risk management, with minimal help from humans. Agentic AI isn’t just speeding up service but automating entire workflows.
When implemented effectively, this technology can dramatically affect the bottom line. Recent data from the Capgemini Research Institute shows that, by 2028, AI agents could deliver up to $450 billion in annual economic value through revenue growth and cost savings, across 14 countries. Yet our research shows only 10% of financial services firms have deployed AI agents at scale, even as 70% of such firms expect these processes to be agent-led within the next 18 to 36 months. They see the future in agentic AI, but they have yet to act.
For early adopters, this new frontier is an opportunity to evolve cloud platforms from infrastructure providers to innovation enablers. Agentic AI is not just an efficiency game changer; it’s a cultural shift for the industry towards a cloud-enabled, collaborative AI ecosystem.
Cloud moves past old barriers with AI enablement
Where legacy technologies once limited growth, the emergence of cloud made it possible and practical for companies to enact scalable, intelligent automation at speed. Now that agentic AI is a reality, financial institutions can reimagine their core operations even more ambitiously.
But here’s where most firms get stuck. That gap — between pilot promise and production reality — represents the industry’s real AI challenge. This isn’t an adoption problem; it’s an architecture problem. Treating cloud strategy and AI strategy as siloed initiatives keeps firms trapped in the pilot phase, unable to move from controlled experiments to enterprise deployment.
The firms breaking out understand this: Cloud strategy and AI strategy aren’t separate anymore. Deploying AI agents means scaling dynamically across multiple systems with real-time data. When utilized strategically, the cloud becomes an AI orchestration platform, not just infrastructure.
Successful financial services firms are starting their agentic AI journey by focusing on functions that have traditionally been inefficient, such as labor- and time-intensive customer onboarding and “Know Your Client” processes. AI agents are delivering faster, more accurate outcomes and significantly mitigating risk, especially in wealth management and insurance segments.
Other AI agent superpowers include its decision-making capability and its goal-oriented behavior, which enable it to accomplish specific objectives based on context, such as automating complex, multi-stage credit underwriting processes. Loan underwriting AI agents, for example, are already reducing application processing time by 90%, making it a win-win for both employees and customers. Another benefit is AI agents’ ability to develop personalized customer profiles, something that could take a human employee weeks or months to accomplish. By introducing AI technology into these workflows, you can optimize for speed and divert employee energy to other advanced tasks.
One U.K.-based global health insurer was struggling with fragmented information across multiple platforms, overwhelming employees who were searching for data. Google Agentspace and Vertex AI were deployed to support employees with AI-powered agents for tasks and workflows. These tools unified search across all enterprise platforms and automated content generation to significantly reduce manual effort, among other benefits.
Designing a governance roadmap
Any investment in AI must start with a strategic roadmap that integrates cloud and AI capabilities, fosters innovation and also ensures compliance. Some companies are rethinking foundational workflows across operating, revenue and business models. Others are finding levers to optimize productivity within existing workflows, then using the savings realized to fund more comprehensive transformation.
Whichever path you pursue, there must still be a focus on responsible, secure implementation, and safeguards must be maintained. Having humans work closely with intelligent agents ensures important oversight, while still freeing people up for high-value tasks, such as strategic innovation and improved customer service. Financial institutions also acknowledge the need to prioritize AI ethics, compliance with data privacy laws and good governance; this can be achieved through native capabilities, such as retrieval-augmented generation, audit trails and explainability. Taking the time to meet these standards is crucial in an ever-evolving regulatory landscape, especially considering that 92% of organizations we surveyed said that AI agents will help them reach new geographies in the future.
Agentic AI is more than just technological disruption. It represents a smart shift in mindset, redefining human-machine interaction. Just as with the adoption of the internet, we believe that agentic AI will significantly change our lifestyles and behaviors as we build trust to leave complex tasks, and even financial decisions, to autonomous systems. Financial services firms that strike the right balance in this journey will have a competitive edge in a rapidly evolving digital economy.
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