Not by adding AI — by re-architecting around it. The architects' handbook for companies going AI-first — built by the team that ships AI-first foundations across Fortune 500.
In 2025, 42% of organizations scrapped most of their AI initiatives — up from 17% in 2024. The fastest-deteriorating spend category in the enterprise.
Even MIT's most-cited number, its critics admit, captures what every CFO is feeling: 95% of enterprise GenAI pilots produce no measurable P&L impact.
We work on the 5% that compound. Sources: S&P Global Voice of the Enterprise (n=1,006), Oct 2025; MIT Project NANDA, Jul 2025.
Two formats, same arc. Send the long-read to a CEO; bring the deck to an offsite.
Six questions. Two minutes. One number that tells you exactly where your company sits on the AI-first curve — and what it costs to stay there.
Answer honestly. Each "yes" is one of the six anti-patterns we see kill AI rollouts. The lower the score, the closer you are to AI-first. Your score updates live as you answer.
Each question maps to one of the six anti-patterns we've seen kill AI rollouts across 50+ Fortune 500 engagements. Score yourself — then decide if it's worth a 30-minute conversation.
The compounding gap between "added AI" and "AI-first" is now measured in quarters — and widening monthly.
Anonymized, but real. A Fortune 500 international food-and-beverage conglomerate. Their employees reached out to us directly, bypassing their corporate AI rollout. Five verbatim complaints:
Three weeks ago, The Information reported Meta did the same thing publicly: a "Claudeonomics" leaderboard ranking 85,000+ employees by token spend, taken down two days later. Linear's COO on the record: "Don't mistake a high burn rate for a high success rate."
This is what an "AI rollout" looks like inside most Fortune 500s in 2026. Counting prompts. Mandating one tool. No skill layer. No workflow rethink. The 95% pilot-failure rate isn't a tech problem. It's this.
McKinsey, BCG, and Deloitte cannot name this dysfunction — their buyer is the executive whose vanity KPI is the bug. We can.
A sample from production deployments across the practice. Sanitized for NDA compliance — sector, scope, measurable result.
What an AI-first operating model looks like in audited disclosure and CEO-signed memos. Not architecture diagrams — operational rules.
Revenue per employee $1.24M, +3.6× since 2022. Headcount down 49%. AI assistant doing the work of 853 full-time agents; 81% of customer-service chats resolved by AI; CSAT on-par with human agents. Audited disclosure, not a marketing slide.
"Reflexive AI usage is now a baseline expectation at Shopify… Before asking for more headcount and resources, teams must demonstrate why they cannot get what they want done using AI."
That's the operational rule. It cannot be debunked. Most companies don't have one.
The pattern across both: AI-first isn't a tooling claim. It's a measurable operating model — revenue per employee, headcount discipline, written rules at the CEO level.
Named engagements (current and prior portfolio) — the experience underwriting every conversation. References available under MNDA.
12+ years personally leading AI systems for Fortune 500 banks, automotive, and steel/mining majors — through prior roles at Silent Eight (AML), Raiffeisen Bank International (Vienna AI Operations), and Intech Group (acquired Dima's AI/IoT consultancy in 2020). The track record underwriting every engagement.
Engagement details, named references, and outcome calls available under MNDA.
14 verticals, 260+ documented use cases, deep domain knowledge per sector. Engagements spanning Fortune 500 banks to municipal governments.
Also serving: EdTech · Real Estate · Defense · Gambling · B2B Data · Software.
Indicative ranges before scoping. Every engagement starts with a fit call. If we're not the right fit, we'll tell you on the first call and point you somewhere honest.
Fixed-scope pricing per phase, not time-and-materials. References available under MNDA. Higher-touch advisory and AI-First community tier in design — ask on the first call.
Cross-vertical specialty offerings. The architectural patterns we ship inside engagements, packaged for buyers ready to go a layer deeper than the AI-First handbook.
The cognitive substrate for AI agents that remember — across sessions, across languages, across years. Five layers, three-tier formation, hybrid retrieval, locked eval methodology. Not chat history with retrieval. Architecture.
We refused to hire a $5K/mo PM. We built this instead. Three agents — transcript-extraction → product-manager → ticket-file generator — turn a 90-min meeting into 5–10 fully-cited tickets in 20 minutes. Runs three projects in parallel; onboards to any new domain instantly. Pre-commit hook is the safety net. Not a writing assistant. Production.
The handbook is authored by the practice lead based on hundreds of real engagements. Cone Red is co-founded and run by two operators with complementary disciplines.
Dima Levin
Practice Lead · Co-founder · CAIO
Co-founder & Chief AI Officer at Cone Red — the Barcelona-based AI automation lab behind the named engagements above. 150+ enterprise AI projects · €50M+ documented savings · 1 successful exit (Intech Group, 2020).
Also co-founder of GetDeal.AI ($60M+ M&A pipeline) and BankStore.ai ($1.5M raised · 70+ banks · 180+ payment providers). PhD candidate, Smart Cities & AI.
Alex Korobeynikov
CEO · Co-founder · Web3 · Blockchain · AI
4× founder. 8 years in Web3 with $560M+ AUM in prior portfolios. Leads Cone Red's go-to-market and the practice's Web3 / Blockchain / AI convergence work — where on-chain meets on-vault.
Senior practitioners delivering production AI across the engagement portfolio.
Every engineer on the practice has shipped production AI systems — not just prototypes. Extended network of vetted specialists scales the practice for larger engagements.