Measurable AI business value starts with process

Most enterprise AI pilots fail to deliver measurable AI business value, but Appian Corp. suggests it’s not the technology that’s to blame.

The gap between AI experimentation and actual corporate transformation is widening as organizations mistake individual productivity gains for structural change. According to Greta Peterman (pictured), principal business value engineer at Appian, embedding AI into deterministic workflows rather than deploying it as a standalone tool is a prerequisite for compliance, auditability, and real return on investment.

“AI by itself is just AI. It’s like an engine without a car,” Peterman said. “For AI to work deterministically and effectively, it must be incorporated into workflow processes, otherwise it becomes just an opportunity without a desired outcome.”

Peterman spoke with theCUBE’s Alison Kosik during Appian World 2026’s exclusive broadcast on SiliconANGLE Media’s live streaming studio, theCUBE. They discussed achieving measurable AI business value, the difference between probabilistic and deterministic AI, and how organizations can move from pilot to board-level results. (*Disclosure below.)

Measurable AI business value beyond time savings

The difference between AI that impresses in demos and AI that satisfies regulators and CFOs comes down to determinism. Enterprise processes such as invoice matching and order management cannot tolerate probabilistic outputs. Peterman explains that these processes require absolute and auditable results.

“[OpenAI] I had paper. “What I really liked about this piece is that it’s kind of like when you were a kid and you went to take a test. People who actually know this piece know that A plus B equals C and that’s deterministic. If you’re talking about a bill reconciliation process, you don’t want it to be probabilistic. You absolutely need to have that.”

A study commissioned by Appian and conducted by International Data Corp. found that organizations using the platform saw a three-year return on investment of 441% and a 59% reduction in time to market, Peterman said. Companies that achieve this level of profitability share common characteristics. It’s not just time savings, it’s measuring the downstream impact of process changes. Peterman’s team worked with a global medical technology provider to quantify how a single AI-assisted sales order workflow detected millions of dollars worth of downstream defects. What appears to be an edge-case process can cause 80% of the downstream impact, hidden from teams focused solely on throughput.

“Twenty percent of what looks like an exception process impacts 80 percent of downstream processes,” she said. “If you’re just thinking about doing cool things, you’re not actually addressing the friction points with your customers and your competitors.”

Here’s the complete video interview, part of SiliconANGLE and theCUBE’s Appian World 2026 coverage:

(*Disclosure: TheCUBE is a paid media partner of Appian World. Neither Appian, the sponsor of theCUBE’s event coverage, nor any other sponsors have editorial control over theCUBE or SiliconANGLE content.)

Photo: SiliconANGLE