MIT NANDA▼ Zero return
95% of enterprise GenAI pilots deliver no measurable return.
$30–40B spent. MIT’s verdict: the divide is approach and integration, not model quality.
State of AI in Business 2025 ↗The Reality
Businesses poured tens of billions into AI last year. Almost none of it paid off. Not because the models are bad, but because generic tools and slide decks never learn how your business actually runs. That gap is exactly what we close.
Source: MIT, State of AI in Business 2025 (NANDA), 300+ AI deployments analyzed.
We’re the partner on the right side of that number. We embed in your business, build AI into the workflows you actually run, prove the return, then keep it on the frontier as the models change. Not a deck. Not a pilot that quietly dies. A system that pays for itself.
Put AI to Work, For Real →The Deployment Gap
The technology isn’t the variable. Two companies buy the same models — one deploys them into the actual work and prints money, the other runs a pilot that quietly dies. The difference is the deployment layer. Flip the switch and watch the headlines change.
of enterprise AI pilots return nothing. It’s not a model problem — it’s a deployment problem. Off-the-shelf tools never learn how the business actually runs, so they stall before they ever touch revenue.
succeed when AI is deployed by a specialized partner — roughly triple the success rate of a do-it-yourself build. That partner, embedded in your operation until the system pays for itself, is exactly what Vesca is.
$30–40B spent. MIT’s verdict: the divide is approach and integration, not model quality.
State of AI in Business 2025 ↗The average organization killed 46% of its AI proof-of-concepts before they ever reached production.
Voice of the Enterprise ↗Escalating cost, unclear value, no real path to production. Hype without deployment doesn’t survive.
Gartner forecast ↗The data is blunt: deployment expertise is the dividing line between the 5% who win and the 95% who don’t.
State of AI in Business 2025 ↗CEO Brian Armstrong mandated AI across engineering. Deployed into the real workflow, it ships faster and leaner.
via The Block ↗Two-thirds of support chats handled, resolution time 11→2 min, ~$40M profit impact in year one.
OpenAI case study ↗Fraud detection, KYC, cash-flow analysis — AI deployed into the core operation, not a sandbox.
via Markets Media ↗The picks-and-shovels proof: when AI is actually deployed at scale, it’s where the money goes.
Q4 FY25 results ↗Without deployment, AI fails.
With Vesca, revenue soars.
Sources: MIT NANDA State of AI in Business 2025, S&P Global Voice of the Enterprise, Gartner, Coinbase, Klarna & OpenAI, JPMorgan, and NVIDIA. Figures reflect each company’s public reporting; outcomes vary by business.
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Based on McKinsey, Deloitte, and industry research on AI automation potential. Figures are directional estimates, not a guarantee.