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 Answer To Everything
Forward-Deployed AI Engineers
Custom software for your problems is the FUTURE.
Stop renting tools that own your data. We embed, build the system around how your business actually runs, and you keep the IP.
Platforms
Two surfaces. One operating idea.
Build the system. Own the data. Stop renting dashboards that never learn your business.
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Valspark
One-stop software for everything the shop runs. Replaces rented tools, cuts unnecessary cost, and recovers leads that used to slip so revenue climbs.
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Forward-deployed builds
Engineers embedded in your operation. We watch how it runs, ship the system, and stay until it runs without us.
Get startedThe Deployment Gap
Same AI. Opposite outcomes.
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.
AI without
deployment. AI with
deployment.
deployment. AI with
deployment.
95%
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.
67%
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 FTR42 is.
S&P Global▼ Abandoned
42% of companies scrapped most AI projects in 2025 — up from 17%.
The average organization killed 46% of its AI proof-of-concepts before they ever reached production.
Voice of the Enterprise ↗Gartner▼ Canceled
Over 40% of agentic AI projects will be canceled by 2027.
Escalating cost, unclear value, no real path to production. Hype without deployment doesn’t survive.
Gartner forecast ↗MIT NANDA▼ Falls short
DIY AI builds succeed one-third as often as bringing in a partner.
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 ↗Klarna × OpenAI▲ ~$40M profit
AI deployed into support does the work of 700 full-time agents.
Two-thirds of all chats handled, resolution time cut from 11 min to under 2, and an estimated ~$40M profit lift in year one.
OpenAI case study ↗Morgan Stanley × OpenAI▲ 98% adoption
98% of advisor teams run on a deployed GPT-4 assistant.
Wired into the real workflow, advisors went from reaching ~20% of the firm’s knowledge to instant answers across 100,000+ research documents.
OpenAI case study ↗Lumen × Microsoft▲ $50M / year
AI copilot deployed to 3,000+ sellers saves ~$50M a year.
Prospect research that used to take four hours now takes fifteen minutes — ~4 hours a week returned per seller and reinvested into customers.
Microsoft Source ↗JPMorgan▲ $1.5B+ value
AI deployed across the bank now targets $1.5B+ in value.
Fraud detection, KYC, cash-flow analysis, and coding — AI wired into the core operation, not a sandbox, and the payoff keeps climbing.
via American Banker ↗Without deployment, AI fails.
With FTR42, revenue soars.
Sources: MIT NANDA State of AI in Business 2025, S&P Global Voice of the Enterprise, Gartner, Klarna & OpenAI, Morgan Stanley & OpenAI, Lumen & Microsoft, and JPMorgan. Figures reflect each company’s public reporting; outcomes vary by business.
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