Missing outcome owners and nine-month hardening cycles kill AI pilots, not weak models. Stop funding experiments nobody can be held accountable for.
Via iOpex: Why 95% of AI Pilots Fail: 5 Questions Every CXO Should Ask
Ask most enterprises why their AI program is stuck in pilot mode, and you will hear a story about the model: not accurate enough, not cheap enough, not ready yet. In a July 29, 2026 iOpex fireside with Srikanth Akkiraju--who has run transformation at Philips and ServiceNow--that answer does not hold. Pilots die from missing purpose and missing named outcomes, not from model quality.
If your AI portfolio has activity metrics but no named P and L outcome, you are funding experiments--not operations someone can be held accountable for. Licenses issued and use cases opened feel like progress. They are not the same as a process changed, revenue moved, or cost removed.
Akkiraju's second failure mode is tempo. Nine-month hardening cycles made sense when software barely moved between releases. AI now shifts every quarter. Waiting for a perfect release does not produce a better product; it produces a shelved one. Owner-led firms that still treat AI like a waterfall IT program are already behind the ones testing in production, failing cheap, and compounding fast.
Akkiraju's framing splits enterprise AI into two economies with very different returns.
Everyday AI--better email, faster search, draft help--is popular and roughly a wash. People save five minutes and spend four reviewing the output. It builds goodwill, not P and L. Breakthrough AI is different: three to five curated bets an organization can redesign around, measured like capital investments, not vanity dashboards.
That distinction hits SMEs hard. A 30-person firm can burn a year stacking chatbots onto broken workflows while the competitor redesigns two money-making processes and ships. The moat, Akkiraju argued, is not the platform. "Your moat is how you want to operate as a company." Two firms can buy the same stack Monday; neither can buy the other's judgment, customer habits, or institutional context.
Agent sprawl raises the stakes. Unused SaaS was waste. An ungoverned agent with live system access is liability. If you switched on agent discovery tomorrow, what would you find--and who would be surprised? Activity metrics still miss the point: agents create value only when a governed workflow turns recommendations into completed, accountable work.
Smart operators treat AI like a portfolio with owners--not a science fair with demos.
A lot of people started things because they just needed to start things. Or competition is doing it. But not really thought about the purpose. - Srikanth Akkiraju, via iOpex
This story maps cleanly to two Tier-1 services owner-led firms use when pilots outnumber outcomes.
Workflow ROI Audit finds where AI saves money--and where it does not. Instead of counting use cases, you get a plain-English map of which workflows have named P and L owners, which everyday tools are goodwill theater, and which three-to-five bets deserve capital-grade attention. The goal is not a longer pilot list. It is a shorter, owned portfolio.
Fractional AI Officer puts someone on the operating tempo: outcome ownership, hardening cadence measured in quarters not nine-month waterfall cycles, and an agent control tower before discovery becomes a surprise. Someone has to own whether AI is changing how the firm creates value--or merely decorating broken processes with chatbots.
We stay vendor-neutral. The question is not which model wins this quarter. It is whether leadership can answer Akkiraju's real test: are we using AI to do old things faster, or to change how value is created?
Put the five questions in front of whoever owns finance, revenue, and operations. Where the answers diverge, that is not a soft "alignment" problem--that is where transformation actually starts. If your dashboard counts agents and licenses but cannot name an outcome owner, start there.
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