In an industry long dominated by cautious innovation and legacy constraints, Appian is helping financial services companies achieve something rare: agility without compromise. With a platform built on process orchestration, data unification, and now agentic AI, the company is helping traditional firms act more like startups, without abandoning their enterprise rigor.

“We’ve worked with financial services companies since the company was founded 25 years ago. They are our biggest vertical,” said Sanat Joshi, EVP – product and solutions at Appian. “They’ve invested a lot in IT transformation, and we see this as a massive opportunity for them to get a big return on that investment.”

Now, Appian’s latest leap, Agentic AI, is poised to reshape how banks deploy automation and AI. Through Agent Studio, companies can create and deploy intelligent agents in minutes, tightly integrated into enterprise-grade processes.

Turning AI hype into enterprise reality

At this year’s Appian World, Agent Studio was arguably the most talked-about release. It enables customers to build AI agents that do more than just automate, they orchestrate end-to-end processes.

“Literally in a matter of minutes, you can create a production-quality agent and deploy it,” Joshi said. “Early customers are seeing phenomenal results.”

But Appian’s approach differs sharply from the industry norm. Where many vendors treat AI agents as stand-alone bots or chat tools, Appian embeds them within business workflows. The company believes this integration, AI plus process, is the unlock for exponential value.

“Business process plus agents are so much more exponentially powerful,” he added. “That’s the core difference.”

To make that work, Appian provides the structure. The process engine acts as a framework for agents, offering full logging, auditability, and goal setting. The platform’s data fabric supplies agents with the precise, role-secured information they need, “data is the oxygen for agents,” Joshi noted.

This foundation also enables faster development. By layering large language models (LLMs) onto existing infrastructure, teams can create and deploy intelligent systems in record time. And for financial services firms, that speed can mean real revenue.

Real-World Impact in Eight Weeks

One case in point: A customer recently used Appian to launch a new crypto wallet product that integrates both fiat and digital assets. From concept to launch, it took just eight weeks and brought in $30m in customer deposits almost immediately.

“They were able to do this in eight weeks and get the product out,” he said. “That’s just one example of how fast these guys can go.”

Such speed is critical in an industry where competition from fintechs, shifting regulations, and rising customer expectations are driving the need for change.

The Challenge: Siloed Systems and misconceptions

Financial institutions have long been plagued by fragmented systems; transactional silos created through decades of automation projects.

Appian’s goal? Span those silos and unify data, process, and AI under one platform.

Joshi expressed: “That’s really our uber objective or North Star, if you will.”

But tech alone isn’t enough. Many financial services leaders, enchanted by the ease of prototyping tools like OpenAI, assume that building production-grade AI is just as simple.

“There’s a big misperception that it’s easy to prototype AI, and therefore it should be easy to deploy,” he said. “But it’s not. IT raises real concerns, data security, compliance, governance. That’s where we come in.”

Appian addresses these concerns head-on. The platform uses private AI that respects enterprise compliance boundaries: no data is stored, all interactions are logged and audited, and models aren’t trained on customer data. Appian also handles version control for models, ensuring predictable performance even as model providers evolve.

Copilots, command centres, and continuous earning

Appian’s AI Copilot, unlike Microsoft’s similarly named tool, is model-agnostic and deeply embedded in workflows. It selects the best model for each task, balancing speed, accuracy, cost, and regulatory needs.

“Copilot is a generic term suggesting an assistant working side-by-side, without control but with delegated tasks,” the executive explained. “Ours is based on Appian and built for enterprise-grade control.”

To ensure governance, Appian offers Process HQ, a command center that oversees agent behaviour and enables continuous improvement. By marking outcomes as good or bad, organisations create a closed loop learning system. Process mining tools feed into this, identifying bottlenecks and opportunities for further optimisation.

“So yes, process mining, Process HQ, these are still very important,” Joshi emphasised. “In fact, one would say they become exponentially more important now that agents are part of the mix.”

Future-proofing financial services

Looking ahead, Appian plans to enhance Process HQ with generative AI-based summaries and recommendations. Instead of dashboards, business users will receive proactive insights directly helping them stay ahead of risks and opportunities.

For an industry still navigating legacy constraints, this future is tantalising.

“Traditional financial institutions can stay competitive if they move fast and move smart, we give them the agility of a startup with the discipline of an enterprise.”

By integrating AI deeply into processes, rather than treating it as a bolt-on Appian is helping banks and insurers not just modernise, but future-proof.

And perhaps most importantly, it’s helping them finally realise the value of investments made years ago.