Three insights you might have missed from theCUBE’s coverage of Appian World

Agentic AI is already becoming a common part of how enterprises operate. But getting real value from AI depends on how well it fits into a company’s existing governance and compliance processes, especially in highly regulated industries.

Process-centric AI describes an architectural approach in which businesses integrate agentic AI into existing workflows from the ground up, with all the requisite security and governance one would expect of any new enterprise technology. Rather than treating agentic AI as an isolated, experimental technology that’s running separate from standard workflows, process-centric AI creates a stronger foundation to scale AI across the enterprise, while also preserving the predictability and auditability that regulated organizations need.

Enterprise automation company Appian Corp. operates at the center of AI process orchestration, with a platform that incorporates human review and approval to ensure that AI agents cannot build or execute autonomously. It’s a crucial safeguard, explained Matt Calkins (pictured), co-founder and chief executive officer of Appian, in an interview with theCUBE’s Dave Vellante and Alison Kosik at Appian World 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio.

“Agents are an incredibly powerful tool, but they have to be used correctly,” Calkins said. “Agents actually need process more than any other form of AI. They’re the ones that go off the rails the easiest if you don’t have the regulations, the guardrails, the tracking [or] if you don’t have the structure of process. You need [them] when you need adaptive intelligence. You don’t need [them] the rest of the time.”

Here’s the full interview with Matt Calkins:

During interviews at Appian World, Vellante and Kosik spoke with industry experts from Appian, CIBC Mellon, Acclaim Autism and AARP, among others. They explored the architectural framework organizations need to build executive confidence in AI deployments and successfully execute high-impact projects. (* Disclosure below.)

Here are three key insights you may have missed from Appian World:

Insight #1: Stronger guardrails can deepen C-suite confidence in AI and help raise ambitions for bigger projects.

There’s a gap between the value organizations expect to receive from AI and the actual outcomes they experience. That’s partly because most organizations put AI to work on incremental initiatives, and not the major value-driving projects that can have a bigger impact on the enterprise.

“We need to find a way to make AI connect to the most important work in the world,” Calkins told theCUBE in a separate interview. “This is the year where AI hits the enterprise, but it doesn’t happen automatically. We’ve got to make it happen. We have to reassure people that AI can be safe enough to be used in most enterprise cases and do the most valuable work. We’re achieving that with guardrails, with structure, with process.”

It’s clear enterprises want to make AI work: 59% of organizations have AI in production, according to a new study from Harvard Business Review Analytic Services, commissioned by Appian. But Calkins proposes that the C-suite needs more confidence in the safety and efficacy of AI before turning it loose on larger initiatives. That requires strong governance.

“They know what they need. They just haven’t done it yet,” Calkins said. “Ninety-two percent say they know they need rules-based guardrails, but most of them haven’t done it yet. It’s not hopeless. You can look across the economy and realize that people know what they need to make AI reliable.”

Here’s the complete video interview with Matt Calkins:

Appian is working to deliver that trust through AI process orchestration, a tech stack that includes controls for data accuracy and application intelligence, according to Michael Beckley, co-founder and chief technology officer of Appian Corp.

“There’s a great mystery about how to make AI useful for more than just prototyping. How do you actually trust it? How do you make it reliable and repeatable and deterministic so that you can trust it for missions that matter?” Beckley said in an interview with theCUBE. “Building that harness around AI — that’s what companies like Appian are doing, and it’s really remarkable the value that we’re able to unlock.”

Here’s the complete video interview with Michael Beckley:

Insight #2: Smarter AI orchestration can enable data-driven insights while improving AI code output.

A big reason why some enterprises may not fully trust AI is the simple fact that its output often leaves something to be desired. For example, vibe coding may produce software code at lightning-fast speeds, but that code is rarely production-ready. That opens up a whole new set of problems for developers, explained Medhat Galal, senior vice president of engineering at Appian.

“It looks great, and then they run into one problem after the other. If they can make it functional, they can’t make it secure. When they make it secure and the model changes underneath you, then it changes all over again and then you’re back in maintenance mode,” Galal explained in an interview with theCUBE. “You end up being a big maintainer of AI systems instead of building the application.”

Here’s the complete video interview with Medhat Galal:

Enterprises need scaffolding around AI to produce reliable results. For example, AI agents can be used to understand relationships between data in disparate systems. Appian looks to provide this capability through an AI data fabric layer that allows organizations to keep their data in place – no migration necessary – while still driving useful business insights, according to Mark Talbot, director of AI architecture at Appian.

“With your AI architecture, the salient components are first the data — the context that you provide to the AI — and that all exists in our data fabric,” Talbot told theCUBE during Appian World. “With our data fabric, you can draw out relations between your support cases [or] between your knowledge base articles that you have for a support application. You also have your tools that are in place. Agents need tools to do work. Your tools are your existing actions and your existing processes.”

Here’s the complete video interview with Mark Talbot:

From a development perspective, Appian is trying to address a problem it identifies as “cognitive debt” – when the velocity of AI code production exceeds developers’ ability to understand and manage the code it produces. The next phase of AI-assisted code development will account for cognitive debt and code quality by creating governed processes that constrain what agents can and can’t do and remove some of the rework, prompt engineering and code maintenance that bogs down development work.

“I think the future software is going to be healthy insofar that we’re enabling the accountability and the responsibility and the ethical use of software,” Galal added in another interview with theCUBE. “AI, at the end of the day, is a piece of software. If we enable that to be the mechanism, the robust way to allow businesses to be assured in how they deploy software, it’s going to be a great future for everybody — companies and customers alike.”

Here’s the complete video interview with Medhat Galal:

Insight #3: Done right, AI-driven workflow automation can substantially accelerate impact.

No business can tolerate mistakes, but the stakes are even higher in the highly regulated financial services industry. That’s something the leaders at CIBC Mellon, a joint venture between The Bank of New York Mellon and Canadian Imperial Bank of Commerce, had to think about as they considered AI deployment, according to Mal Cullen, CEO of CIBC Mellon.

“The risk appetite that we have to live with, with the regulators … if we had an ungoverned or irresponsible AI deployment, it is very, very high,” Cullen said in an interview with theCUBE. “The only place we’re actually using [AI] in a production mode is with Appian right now. The reason for that is because they have a very controlled governance model where I can provide transparency to how it’s being used. I can audit that. I can have documentation and it’s not impacting a client yet — it’s being delivered for an internal purpose.”

As one example, Cullen explained that CIBC Mellon’s development team used the Appian platform to accelerate the development of a client-facing application needed to meet a crucial regulatory deadline.

“If I had gone to the global BNY platforms and asked for changes to support a Canadian regulatory cost reporting challenge, the likelihood of getting that done in time would’ve been low,” Cullen said. “My team built that on Appian. The clients now have a client-facing application to deliver that. The team told me they would never have made that date if we didn’t do this on Appian.”

Here’s the complete video interview with Mal Cullen:

In a best-case scenario, AI-driven workflows shorten manual processes from months or weeks to days or even hours. Acclaim Autism, which helps families navigate the long wait times for behavioral health services, relies on Appian to automate the review of unstructured clinical documents that are needed for insurance payouts. This was previously a manual process relying on guesswork and judgment calls, leading to rejections and rework, explained Jamie Turner, founder and president of Acclaim Autism.

Through AI automation, Acclaim Autism was able to shorten the typical documentation review from six months down to four days, Turner explained. That eliminated service bottlenecks and allowed staff to focus on higher-value work.

“You’re making better use of [your] staff members. You’re getting started with services earlier for kids,” Turner said. “Pick that bottleneck and fix it.”

Here’s the complete video interview with Jamie Turner:

AARP, the U.S. nonprofit serving Americans 50 and older, had a similar experience. Rather than simply licensing an AI model and asking workers to make the best of it, AARP focused on using AI to solve a specific challenge with high-volume manual processes. In this case, it was AARP’s invoice approval workflow, explained Tom Cavanaugh, vice president of platform management at AARP.

It’s an ideal proof of concept for AI modernization: dozens of employees across AARP are involved with invoicing, meaning it’s a highly visible use case. On top of that, it’s a task that requires a high degree of security, auditability and accuracy. A successful deployment could build trust in AI.

“One of the beauties of this tool is that I can sit in front of a business partner and say, ‘What is the problem that you’re having?’” Cavanaugh said. “I can … actively show them what works. Once they get over that initial hurdle of, ‘Oh, I can see what the problem is and I can see how this can help,’ you have a start.”

Here’s the complete video interview with Tom Cavanaugh:

Catch up on our complete video coverage of Appian World:

(* Disclosure: TheCUBE is a paid media partner for Appian World. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE