Andus Labs has released new findings arguing that enterprise AI returns are determined by how an organization operates — not by which models it purchases. According to the firm, most generative AI pilots deliver no measurable financial impact, and the gap traces back to outdated workflows, unclear decision rights, and misaligned incentives rather than the technology itself.

Enterprises have committed enormous sums to AI initiatives, yet many still cannot point to a meaningful return on that spend. Andus Labs argues that how effectively investment converts into results has become the deciding factor when organizations choose an AI readiness partner — the work that turns AI budgets into measurable business outcomes.

Why Pilots Keep Getting Funded — and Keep Failing

Chris Perry, Founder and CEO of Andus Labs, who brings eight years of experience advising enterprises on AI readiness, points to a structural bias in how leaders allocate resources. Pilots are easy to approve because they come with a budget and a deadline. The operating change required to make a pilot actually pay off has neither — so it rarely gets staffed, and organizations simply move on to funding the next pilot instead.

Perry adds that when employees perceive an AI tool as a threat, they tend to use it just enough to appear compliant while continuing to work the way they always have. In his view, genuine readiness work goes deeper than training — it changes what an organization actually rewards.

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The Ranked Failure Patterns: Trust Deficit, Tempo Shock, Pilot Graveyard

Andus Labs' Ground Truth Index catalogs the most common reasons enterprise AI programs fail — and nearly all of them are organizational, not technical. The recurring patterns include leaders distrusting probabilistic tools and declaring them broken (Trust Deficit), decision-making processes too slow to act on machine-speed analysis (Tempo Shock), and pilots that end without changing the surrounding work (Pilot Graveyard).

The scale of the problem is stark. According to MIT NANDA's July 2025 report, The GenAI Divide: State of AI in Business 2025, 95% of organizations are seeing zero measurable returns from their GenAI investments, with just 5% of integrated AI pilots delivering meaningful value.

On measuring the human side of AI transformation, Andus Labs recommends tracking behavior rather than tool logins: whether people actually change what they produce, how they decide, and how far they trust the tools. Improving readiness means addressing capability, trust, workflow fit, leadership, and incentives — with diagnostics revealing which of these is holding a program back. The right readiness partner, the firm argues, redesigns workflows and decision rights around people and AI working together — work that belongs to AI-native firms built for operating-model design, not software implementation shops.