Why do AI pilots repeatedly shut down... Appian says "The key lies in the workflow, not the technology"

Companies are accelerating their experiments with generative artificial intelligence (AI), but have repeatedly failed to translate them into actual business results. The American software company Appian diagnoses that the reason is not the AI itself, but the failure to properly integrate AI into “deterministic” business processes.

Appian’s Chief Business Value Engineer Greta Peterman stated at the recent “Appian World 2026” event: “AI itself is like an engine without a car,” and pointed out, “AI must be embedded into workflow processes to operate predictably and effectively.” She then explained, “Otherwise, it will only stay at the opportunity level with unclear goals.”

The core she emphasizes is that “personal productivity” should not be confused with “enterprise innovation.” This means that simply improving employees’ speed in document summarization or drafting is unlikely to cause structural changes at the enterprise level. She believes that, especially in businesses like invoice reconciliation or sales order management that require regulatory compliance and audit trails, systems that can clearly verify results are more important than AI that provides probabilistic answers.

“Demo AI is different from real-world AI”

Peterman pointed out that the AI needed on-site by enterprises is not a system that outputs “seemingly reasonable answers,” but one that can produce results convincing enough for financial officers or regulators. She took invoice reconciliation as an example: “Such processes should not be handled probabilistically; they must produce absolute and auditable results.”

This aligns with the major limitations faced by enterprises when adopting generative AI recently. Because even if impressive results can be demonstrated in demos, in actual business applications, due to the possibility of errors, responsibility attribution, and regulatory issues, the scope of use is often limited. Ultimately, using AI as a standalone tool like pasting it into workflows cannot create real AI business value; it must be integrated into existing business processes and control systems.

ROI reaches 441%… More important than time savings is the “subsequent effects”

Peterman also mentioned a survey commissioned by Appian from market research firm IDC. The survey showed that companies using the Appian platform achieved a 441% return on investment (ROI) over three years, and shortened time-to-market by 59%.

However, she emphasized that these figures are not simply about time savings. She explained that high-performing companies not only focus on how much work time has been reduced but also track the financial impact generated by process changes in subsequent stages.

In fact, Appian revealed that in collaboration with a global medical technology company, they quantified how an AI-assisted sales order workflow could capture millions of dollars in value from subsequent defects. This means that a seemingly minor process anomaly could determine 80% of the subsequent impact.

Peterman stated, “The 20% of processes that appear abnormal may generate 80% of the impact in later stages. If you only focus on doing ‘cool things,’ you won’t solve the real problems that cause customer friction or put you at a disadvantage compared to competitors.”

AI success or failure depends on “internalization” rather than standalone tools

This statement indicates that enterprise AI strategies are shifting from “whether to adopt” to “how to internalize.” With the rapid proliferation of generative AI, although pilot projects are increasing, there are still limited cases that can demonstrate convincing results to the board or management.

Ultimately, it can be interpreted that measurable AI business value depends more on the extent to which controllable workflows, auditability, compliance responsiveness, and subsequent cost reductions can be demonstrated, rather than flashy demos. The competition in enterprise AI is now entering a stage where the depth of actual process innovation, rather than the number of experiments, determines success.

TP AI Notes This article uses a language model based on TokenPost.ai for summarization. The main information in the text may be omitted or differ from facts.