For leaders in the financial sector, the experimental phase of generative AI has concluded and the focus for 2026 is operational integration.

While early adoption centred on content generation and efficiency in isolated workflows, the current requirement is to industrialise these capabilities. The objective is to create systems where AI agents do not merely assist human operators, but actively run processes within strict governance frameworks.

This transition presents specific architectural and cultural challenges. It requires a move from disparate tools to joined-up systems that manage data signals, decision logic, and execution layers simultaneously.

Financial institutions integrate agentic AI workflows

The primary bottleneck in scaling AI within financial services is no longer the availability of models or creative application, it is coordination. Marketing and customer experience teams often struggle to convert decisions into action due to friction between legacy systems, compliance approvals, and data silos.

Saachin Bhatt, Co-Founder and COO at Brdge, notes the distinction between current tools and future requirements: “An assistant helps you write faster. A copilot helps teams move faster. Agents run processes.”

For enterprise architects, this means building what Bhatt terms a ‘Moments Engine’. This operating model functions through five distinct stages:

• Signals: Detecting real-time events in the customer journey.

• Decisions: Determining the appropriate algorithmic response.

• Message: Generating communication aligned with brand parameters.

• Routing: Automated triage to determine if human approval is required.

• Action and learning: Deployment and feedback loop integration.

Most organisations possess components of this architecture but lack the integration to make it function as a unified system. The technical goal is to reduce the friction that slows down customer interactions. This involves creating pipelines where data flows seamlessly from signal detection to execution, minimising latency while maintaining security.

Governance as infrastructure

In high-stakes environments like banking and insurance, speed cannot come at the cost of control. Trust remains the primary commercial asset. Consequently, governance must be treated as a technical feature rather than a bureaucratic hurdle.

The integration of AI into financial decision-making requires “guardrails” that are hard-coded into the system. This ensures that while AI agents can execute tasks autonomously, they operate within pre-defined risk parameters.

Farhad Divecha, Group CEO at Accuracast, suggests that creative optimisation must become a continuous loop where data-led insights feed innovation. However, this loop requires rigorous quality assurance workflows to ensure output never compromises brand integrity.

For technical teams, this implies a shift in how compliance is handled. Rather than a final check, regulatory requirements must be embedded into the prompt engineering and model fine-tuning stages.

“Legitimate interest is interesting, but it’s also where a lot of companies could trip up,” observes Jonathan Bowyer, former Marketing Director at Lloyds Banking Group. He argues that regulations like Consumer Duty help by forcing an outcome-based approach.

Technical leaders must work with risk teams to ensure AI-driven activity attests to brand values. This includes transparency protocols. Customers should know when they are interacting with an AI, and systems must provide a clear escalation path to human operators.

Data architecture for restraint

A common failure mode in personalisation engines is over-engagement. The technical capability to message a customer exists, but the logic to determine restraint is often missing. Effective personalisation relies on anticipation (i.e. knowing when to remain silent is as important as knowing when to speak.)

Jonathan Bowyer points out that personalisation has moved to anticipation. “Customers now expect brands to know when not to speak to them as opposed to when to speak to them.”

This requires a data architecture capable of cross-referencing customer context across multiple channels – including branches, apps, and contact centres – in real-time. If a customer is in financial distress, a marketing algorithm pushing a loan product creates a disconnect that erodes trust. The system must be capable of detecting negative signals and suppressing standard promotional workflows.

“The thing that kills trust is when you go to one channel and then move to another and have to answer the same questions all over again,” says Bowyer. Solving this requires unifying data stores so that the “memory” of the institution is accessible to every agent (whether digital or human) at the point of interaction.

The rise of generative search and SEO

In the age of AI, the discovery layer for financial products is changing. Traditional search engine optimisation (SEO) focused on driving traffic to owned properties. The emergence of AI-generated answers means that brand visibility now occurs off-site, within the interface of an LLM or AI search tool.

“Digital PR and off-site SEO is returning to focus because generative AI answers are not confined to content pulled directly from a company’s website,” notes Divecha.

For CIOs and CDOs, this changes how information is structured and published. Technical SEO must evolve to ensure that the data fed into large language models is accurate and compliant. 

Organisations that can confidently distribute high-quality information across the wider ecosystem gain reach without sacrificing control. This area, often termed ‘Generative Engine Optimisation’ (GEO), requires a technical strategy to ensure the brand is recommended and cited correctly by third-party AI agents.

Structured agility

There is a misconception that agility equates to a lack of structure. In regulated industries, the opposite is true.

Agile methodologies require strict frameworks to function safely. Ingrid Sierra, Brand and Marketing Director at Zego, explains: “There’s often confusion between agility and chaos. Calling something ‘agile’ doesn’t make it okay for everything to be improvised and unstructured.”

For technical leadership, this means systemising predictable work to create capacity for experimentation. It involves creating safe sandboxes where teams can test new AI agents or data models without risking production stability.

Agility starts with mindset, requiring staff who are willing to experiment. However, this experimentation must be deliberate. It requires collaboration between technical, marketing, and legal teams from the outset.

This “compliance-by-design” approach allows for faster iteration because the parameters of safety are established before the code is written.

What’s next for AI in the financial sector?

Looking further ahead, the financial ecosystem will likely see direct interaction between AI agents acting on behalf of consumers and agents acting for institutions.

Melanie Lazarus, Ecosystem Engagement Director at Open Banking, warns: “We are entering a world where AI agents interact with each other, and that changes the foundations of consent, authentication, and authorisation.”

Tech leaders must begin architecting frameworks that protect customers in this agent-to-agent reality. This involves new protocols for identity verification and API security to ensure that an automated financial advisor acting for a client can securely interact with a bank’s infrastructure.

The mandate for 2026 is to turn the potential of AI into a reliable P&L driver. This requires a focus on infrastructure over hype and leaders must prioritise:

• Unifying data streams: Ensure signals from all channels feed into a central decision engine to enable context-aware actions.

• Hard-coding governance: Embed compliance rules into the AI workflow to allow for safe automation.

• Agentic orchestration: Move beyond chatbots to agents that can execute end-to-end processes.

• Generative optimisation: Structure public data to be readable and prioritised by external AI search engines.

Success will depend on how well these technical elements are integrated with human oversight. The winning organisations will be those that use AI automation to enhance, rather than replace, the judgment that is especially required in sectors like financial services.

See also: Goldman Sachs deploys Anthropic systems with success

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For organizations who are still wedded to the rules and structures of robotic process automation (RPA), then considering agentic AI as the next step for automation may be faintly terrifying. SS&C Blue Prism, however, is here to help, taking customers on the journey from RPA to agentic automation at a pace with which they’re comfortable.

Big as it may be, this move is a necessary one. Modern workflows are at a level of complexity that outlines what traditional RPA was designed to do, according to Steven Colquitt, VP Software Engineering, SS&C Blue Prism. Unstructured data comes from various sources resembling non-deterministic real-world interactions. “Inputs can vary, outcomes can shift and decisions depend on context in real-time,” notes Colquitt.

Brian Halpin, Managing Director, Automation, SS&C Blue Prism, gives the example of a credit agreement where you might need to get 30 or 40 answers from it. He uses the word “answers” deliberately as opposed to data points to account for the level of reasoning that a large language model (LLM) performs.

The element of this being a journey continues to resonate, however. “We’re now saying we’re giving an AI agent the outcome that we want, but we’re not giving it the instructions on how to complete,” says Halpin. “We’re not saying, ‘follow step one, two, three, four, five.’ We’re saying, ‘I want this loan reviewed’ or ‘I want this customer onboarded.’

“Ultimately, I think that’s where the market will go,” adds Halpin. “Is it ready for that? No. Why? Because there’s trust, there’s regulations, there’s auditability […] stability, security. We know LLMs are prone to hallucinations, we know they drift, and [if] you change the underlying model, things change and responses get different.

“There’s an awful lot of learning to happen before I think companies go fully autonomous and real agentic workflows [are] driven from that sort of non-deterministic perspective,” says Halpin. “But then, there will be something else, right? There will be another model. So really, it is all a journey right now.”

SS&C Blue Prism has thousands of customers who have automated processes in place, from centers of excellence (CoEs) to running digital workers in their operations, who they’re hoping to upgrade into the “world of AI”, as Halpin puts it. Sometimes it’s about connecting two separate areas.

“It’s been interesting,” Halpin notes. “As I talk to [our] customers, I see a common thread among companies right now where, in a lot of cases, AI has been established as a separate unit in a company. You go over to the process automation team, and they’re maybe not even allowed to use the AI.

“So, it’s about, ‘How do you help them get that capability and blend it into their process efficiency and allow them to get to the next 20%, 30% of automation, in terms of the end-to-end process?’”

As part of this, SS&C Blue Prism is soon to launch new technology which helps organizations build and embed AI agents within workflows, as well as assist with orchestration. Those who attended TechEx Global, on February 4-5 as part of the Intelligent Automation conference, where SS&C Blue Prism participated, got the full story, as well as understanding the company’s ongoing path.

“[SS&C Technologies] are one of the biggest users of RPA in the world,” adds Halpin. “We have over three and a half thousand digital workers deployed [across the SS&C estate]. We’re saving hundreds of millions in run-rate benefit. We’ve about 35 AI agents in production attached to those digital workers doing […] complex tasks, and really, we just want to share that journey.”

Watch the full interview with Brian Halpin below:

Photo by Patrick Tomasso on Unsplash

American International Group (AIG) has reported faster than expected gains from its use of generative AI, with implications for underwriting capacity, operating cost, and portfolio integration. The company’s recent disclosures at an Investor Day merit attention from AI decision-makers as they contain assertions about measurable throughput and workflow redesign.

AIG has outlined potential benefits from generative AI. Chief executive Peter Zaffino later described the company’s early projections as “aspirational,” yet in a fourth quarter earnings call, he stated that “we see the abilities are much greater.” The change in tone is indicative of positive internal results, and according to Zaffino, “We’re seeing a massive change in our ability to process a submission flow way […] without additional human capital resources. That has been the biggest surprise.”

The company’s claims that generative AI has increased submission processing capacity, the economic impact is direct. AIG reports that in 2025 it “made progress embedding generative AI in our core underwriting and claims processes, and expanding it.” The company’s internal tool, AIG Assist, is implemented in most commercial lines of businesses.

Lexington Insurance, AIG’s excess and surplus unit has targetted reaching 500,000 submissions by 2030. Zaffino reports that Lexington has already surpassed 370,000 submissions in 2025. AIG uses generative models to extract and summarise incoming data, and has developed an orchestration layer in the technology stack “to coordinate AI agents to drive better decision-making and reduce costs in the organisation.” Previous Investor Days, this level of orchestration was not a focus.

The chief executive describes AI agents “as companions that operate with our teams” that provide real-time information, draw on historical cases, and challenge underwriting decisions. The company relies on its ability to manage incoming data “at a fraction of the time” and to orchestrate agents so they can “scale and be able to analyse that information that’s not biased in any way; that’s through the entire workflow.”

AIG links orchestration to compression of what it terms a “front-to-back workflow,” a tighter integration between intake, risk assessment and claims handling. The company states that multiple agents, coordinated through a orchestration layer, streamlines repetitive and previously-lengthy processes.

AIG has applied its generative AI stack in specific transactions. During the conversion of Everest’s retail commercial business, the company reports that accounts were prioritised for renewal “in a fraction of the time.” Management states that it built an ontology of Everest’s portfolio and combined it with its own, which “allowed [the company] to prioritise how the portfolios could blend together.” Ontological alignment is technically demanding and often creates underestimated costs.

The launch of Lloyd’s Syndicate 2479, in partnership with Amwins and Blackstone, extended the ontological approach to a special purpose vehicle. In conjunction with Palantir, AIG used LLMs to assess whether Amwins’ programme portfolio aligned with the syndicate’s stated risk appetite. Zaffino stated that AIG has a “strong pipeline of SPV opportunities.”

For AI decision-makers, the case illustrates the use that orchestration and workflow integration can provide when generative models are embedded in core processes, and the degree to which economic impact depends on measurable changes in capacity and cycle time.

(Image source: “Nagasaki, AIG (Insurance company) building” by Admanchester is licensed under CC BY-NC-ND 2.0. )

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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