Enterprise AI Platform Blueprint · Regulated-Ready
Your Board Wants an AI Strategy.
Give Them a Platform.
The 8-session Executive Decision Lab where CTOs, CIOs, VPs, and senior architects in regulated enterprises turn AI pilots into a board-ready platform roadmap — vendor-neutral, and anchored to the frameworks your risk team already trusts.
See the Program Overview
Why regulated-enterprise AI stalls — and how this Executive Decision Lab turns pilots into a board-ready roadmap.
Most Enterprise AI Efforts Stall — And It's Rarely the Model
Your board saw the demos and wants an internal copilot, a customer-facing assistant, and automated document processing — all by Q3. But the demo hid the hard parts, and they're leadership decisions, not engineering:
Cost blowout: $500K modeled, $2.1M spent 18 months later — and still nothing in production, because the POC budget priced the technology, not the operating system around it
Compliance block: a production-ready model sat blocked for ~9 months because governance was bolted on after the build, not designed into the platform
Org misalignment: four teams built four pipelines, four security reviews, four cost centers — and no one could answer "who owns AI in production?"
Runaway economics: inference that looked trivial in the POC went from $0 to $500K in three months once every workflow multiplied it at production volume
Pilot sprawl: every function wants its own AI win, so funding spreads across ten pilots and none of them reaches production
No defensible number: your CFO isn't anti-AI — they're anti-unclear economics, and no one walked in with a budget range, assumptions, and guardrails
The question is not "can we build AI?" It's "can we operate AI?"
Enterprise AI failure is usually a maturity problem, not a model problem — it spans strategy, cost, governance, ownership, and architecture. A proof of concept proves possibility; it never proves readiness. The organizations winning at AI aren't the ones with the best models. They're the ones that can operate AI in production — safely, economically, and with a named owner.
Eight Decision Layers. Eight Questions Your Board Will Ask.
One model holds the whole program — from an honest maturity baseline to a funded, board-ready roadmap. Vendor-neutral throughout: criteria, not brands.
Strategy
Which AI use cases deserve investment — and which to kill? Prioritize on value, feasibility, risk, and time-to-value.
Economics
What will AI really cost at production scale? The four cost drivers, scenario ranges, and FinOps guardrails with named owners.
Platform
What shared capabilities do we need to build once and reuse? The golden path that makes autonomy safe.
Vendors
What do we build, buy, or avoid — by platform layer — without locking ourselves into a 12-month migration out?
Operating Model
Who owns what in production? Centralize, federate, or jointly govern — with exactly one accountable owner per decision.
Governance
How do we manage risk and prove compliance without killing delivery? Risk tiers, controls, evidence, and an AI-system registry.
GenAI Architecture
Which technical decisions must we get right early? Hosting, retrieval, data boundaries, and IP protection.
Roadmap
How do we turn all of this into funded execution? A board-ready roadmap and a 90-day plan that passes the Monday-morning test.
Seven Artifacts. One Board-Ready Roadmap.
Not slides. Not theory. One executive-ready artifact per session — assembled into a plan you can defend on Monday morning.
AI Platform Scorecard
A five-level maturity model across eight dimensions — your honest baseline, 12-month target, and #1 priority gap.
Use-Case Prioritization Matrix
Every use case scored on value, feasibility, risk, and time-to-value — with a defensible fund / gate / kill call.
Budget Model + Guardrails
A CFO-readable TCO across Build, Run, Usage, and People — with scenario ranges and FinOps guardrails that have owners.
Vendor Decision Framework
Build / buy / hybrid by platform layer on six weighted criteria — including the exit and portability cost everyone forgets.
RACI Matrix + Team Plan
Exactly one accountable owner per decision, a golden path for delivery, and a hire / upskill / partner / redesign plan.
Governance Blueprint
Risk tiers, controls and evidence by domain, an AI-system registry, and an approval flow — EU AI Act-ready, not delivery-killing.
GenAI Architecture Decision Tree
Hosting, retrieval, data boundaries, and IP protection decided for a real use case — each with a rationale, risk, and mitigation.
Board-Ready Roadmap + 90-Day Plan
Seven artifacts assembled into one funded, governed roadmap and a 90-day plan that passes the Monday-morning test.
8 Sessions. 7 Artifacts. One Board-Ready Roadmap.
An Executive Decision Lab — not a lecture series. Nine frameworks across eight working sessions, each run under the Chatham House Rule and ending with one artifact you take straight into your roadmap.
Session 1
Why Enterprise AI Fails
The three enterprise failure patterns — cost blowout, compliance block, and org misalignment. Score your organization on a five-level AI Platform Maturity Model across eight dimensions, then set your baseline, 12-month target, and #1 priority gap.
Artifact: AI Platform Scorecard
Session 2
The Right-Sized AI Strategy
Turn a list of 15 AI ideas into a funded short list. Score use cases on value, feasibility, risk, and time-to-value, then make a defensible fund / fund-with-gates / kill call — with success metrics that go beyond model accuracy.
Artifact: Use-Case Prioritization Matrix
Session 3
Budget & Economics: What AI Really Costs
Why AI cost structure inverts from POC to production. Build a CFO-readable TCO across the four cost drivers — Build, Run, Usage, People — with conservative/expected/high scenarios and FinOps guardrails that each have a trigger, an action, and an owner.
Artifact: Budget Model + Cost Guardrails
Session 4
Build vs. Buy: Vendor Strategy Without Getting Sold ‘Magic’
Decide build / buy / hybrid by platform layer on six weighted criteria. Spot the commercial traps — consumption pricing, proprietary APIs, the upsell staircase, compliance theater — and score the exit and portability cost before you sign.
Artifact: Vendor Decision Framework
Session 5
Operating Model: Who Owns What
Org misalignment is the #1 non-technical cause of AI failure. Decide what to centralize, federate, and jointly govern; build a RACI with exactly one accountable owner per decision; and close the gaps with a hire / upskill / partner / redesign team plan.
Artifact: RACI Matrix + Team Plan
Session 6
Governance & Compliance: EU AI Act-Ready Without Killing Delivery
Right-sized, risk-based governance built into the lifecycle — not bolted on at the end. Classify use cases by risk tier, assign controls and evidence by domain, stand up an AI-system registry, and design an approval flow your auditors trust.
Artifact: Governance Blueprint
Session 7
GenAI Decisions Leaders Must Get Right
The four GenAI decisions leaders must own: hosting, retrieval (RAG vs fine-tuning), data boundaries, and IP protection. Add the security lens (OWASP Top 10 for LLMs) and model cost as an operating behavior at 10x, 50x, and 100x usage.
Artifact: GenAI Architecture Decision Tree
Session 8
The Roadmap: From Today to 90 Days to 12 Months
Assemble all seven artifacts into one board-ready roadmap and a 90-day action plan. Present it in board-pitch format and survive the five questions every board asks: value, cost, risk, ownership, and timeline.
Artifact: Board-Ready Roadmap + 90-Day Action Plan
Your Competitors Are Already Building AI Platforms
Every week you wait, the gap between AI experimentation and AI production widens. The next cohort has 20 seats. Don't be #21.
This Program Is For You If:
CTO, CIO, VP of Engineering, or Head of AI in regulated enterprises
Senior architects and platform leaders accountable for AI decisions to a board
Leaders with AI pilots — but no approvable, funded plan
Teams drowning in AI vendor pitches who need a neutral decision framework
Organizations where AI prototypes work but production stalls on cost, compliance, or ownership
This Is NOT For You If:
Looking for a coding bootcamp (this is executive decisions, not implementation)
Want a €15 Udemy course (this is a professional investment)
Very early-stage companies with no AI initiatives yet
Want vendor-specific training (this program is deliberately vendor-neutral)
Luca Berton
AI Infrastructure Architect · Author · Educator
Luca has spent his career building the production-grade platforms underneath enterprise AI — securely, at scale, with measurable ROI. He's helped regulated enterprises across Europe move from GenAI prototypes to production systems that pass compliance audits and deliver real business value.
📚 Author of 8 technical books
🎥 1M+ YouTube views, 793+ tutorials
🎤 KubeCon EU & Red Hat Summit 2026 speaker
🏢 Founder of Open Empower BV
🔧 Kubernetes, Terraform, MLOps at scale
🏛️ Regulated enterprise specialist (EU)
"Every enterprise I've worked with has a brilliant AI team. What they don't have is the platform to put those models into production. That's what kills AI projects — not bad science, but missing infrastructure. This program fixes that."
Everything You Need to Build Your AI Platform
8 live Executive Decision Lab sessions (recorded)
One executive-ready artifact per session
AI Platform Maturity Scorecard (8 dimensions)
Use-Case Prioritization Matrix + success metrics
AI Budget Model + FinOps cost guardrails
Vendor Decision Framework (6 weighted criteria)
RACI Matrix + Team Plan templates
Governance Blueprint (EU AI Act-ready)
GenAI Architecture Decision Tree + risk checklist
Board-Ready Roadmap + 90-Day Action Plan template
Private peer cohort under the Chatham House Rule
1:1 strategy call with Luca (30 min)
Less Than One Failed AI POC
Most companies spend €50–500K on AI projects that never reach production. This program costs less than a single wasted sprint.
Individual
€2,500
per person
Team
€2,000/seat
3–5 seats
Enterprise
Custom
6+ seats
💡 L&D Budget Friendly: Most companies have €2–5K per person annual training budgets. This qualifies. We provide invoices and certificates of completion. 14-day money-back guarantee — no questions asked.
Frequently Asked Questions
When does the next cohort start?
We run 4 cohorts per year. Join the waitlist to be notified when the next one opens. Cohorts fill fast — the last one sold out in 9 days.
How is this different from AI courses on Coursera or Udemy?
Those teach you how AI works. This is an Executive Decision Lab that produces the decisions — and the board-ready roadmap — that get AI into production. It's leadership strategy, not data science: vendor-neutral, and anchored to NIST AI RMF, ISO/IEC 42001, and the EU AI Act. You leave with a plan, not a certificate.
What if I miss a live session?
All sessions are recorded and available within 24 hours. You can also ask questions asynchronously in the private cohort community.
Is this technical?
It's executive-first — decisions, not implementation detail. You won't write code or configure GPUs. You'll make the hosting, retrieval, vendor, cost, governance, and ownership decisions well enough to lead your team and defend them to your board.
Is the program vendor-neutral?
Yes — completely. The entire program is criteria, not brands. You leave with reusable decision frameworks, not a recommendation to buy a specific vendor.
What is the Chatham House Rule, and why does it matter?
You can use every idea, pattern, and lesson freely — but you never attribute who said what or where they work. It's what lets senior leaders name real blockers honestly, which is where the value is.
Can I expense this?
Yes. We provide invoices and certificates of completion. Most participants expense this through L&D or professional development budgets. At €2,500, it's well within most companies' €2–5K annual training budgets.
What's the time commitment?
Each session is a working session, plus a short assignment that builds one artifact of your actual roadmap — not busywork. Bring a real initiative and you'll leave with a plan you can defend on Monday.
Do you offer refunds?
Full refund within 14 days if the program isn't what you expected. No questions asked.
We already use ChatGPT/Copilot. Why do we need this?
Using AI tools and operating an AI platform are completely different things. When your CEO asks "why can't we build our own copilot?" — this program gives you the decisions, the governance, and the board-ready roadmap to actually do it.
I'm not a CIO/CTO. Can I still join?
Absolutely — if you're involved in AI platform decisions (Platform Lead, ML Engineering Manager, Solutions Architect, senior architect), you'll get massive value.
Stop Experimenting.
Start Building Your AI Platform.
8 sessions. 7 artifacts. One board-ready roadmap that gets your AI initiatives from pilot to funded production. The next cohort won't wait.
14-day money-back guarantee · Invoice available · Questions? luca@lucaberton.it