You’re Not Ready for an AI Platform RFP (Yet) — and That’s Exactly Why You Need This Roadmap
Under pressure to “do AI” but not sure your stack is ready? This is a practitioner’s guide to using the 4+1 model as a gap analysis tool—ship useful work today, and grow into the full RFP when the tim
Most enterprises today feel intense pressure to “deliver AI” — quickly, visibly, and at scale.
Executives want copilots.
Business units want automation.
Boards want AI strategy slides with deadlines.
What they don’t want to hear is:
“You’re not ready to run an AI platform RFP.”
But here’s the truth:
Not being ready for a complex AI platform RFP does not mean you’re not ready to deliver AI.
It means your organization hasn’t yet developed the architectural foundations to choose a platform wisely.
This roadmap is how you build those foundations.
You use it to deliver value early, avoid catastrophic bets, and grow toward the full 4+1 AI Platform RFP at the right moment.
Think of it as a gap-analysis engine framed around the 4+1 Layer AI Infrastructure Model.
It’s designed to be shared — across architecture, data, platform engineering, and leadership — so everyone uses the same vocabulary and understands what it takes to operate AI safely and effectively.
Before the Roadmap: The #1 Failure Pattern in Enterprise AI
Here’s the pattern I’m seeing across the industry:
Enterprises are buying GPUs and DGX clusters before they have governance, pipelines, retrieval standards, or orchestration.
Layer 0 is coming online long before Layers 1 and 2 exist.
This is how organizations end up with:
Expensive paperweights
Stranded GPU islands
Compliance exposure
Shadow inference workloads
Zero visibility into what’s running where
Architectures that collapse under stress
Buying compute doesn’t create an AI platform.
It creates urgency — and risk.
The roadmap below fixes that.
The 4+1 Maturity Roadmap: How Enterprise AI Actually Grows Up
Instead of asking “Which AI platform should we buy?” the real questions are:
Which layers do we have today?
Which layers are emerging?
Which layers must we build or buy next?
And what can we deliver right now without sabotaging ourselves later?
This roadmap follows the natural evolution of the 4+1 model.
At every stage, you can deliver meaningful AI value.
At every stage, the model helps you avoid architectural traps.
The RFP is simply the destination — not the starting line.
Stage 0 — Exploration
“We have a copilot demo somewhere. That’s about it.”
Characteristics
Scattered AI experiments
Business units testing copilots
A random vector DB running under someone’s desk
No central governance or architecture
No shared retrieval or pipeline patterns
Layer Reality
Layer 0 may already be in motion
Layers 1 and 2 essentially do not exist
Layer 3 is just demos
Failure Mode
Buying GPUs without governance.
This is where DGX servers turn into expensive furniture.
What You CAN Ship in the Next 90 Days
A small, governed pilot copilot for an internal domain
Clear “red/yellow/green” rules for PII and AI workflows
A map of where AI-relevant data actually lives
A lightweight working group to own platform evolution
Recommended Actions
Run Stack Builder to visualize the layers you currently have
Share the 4+1 diagram with your teams
Start using the terminology in internal conversations
Time Expectation
1–3 months of foundational work
Stage 1 — Assistant Proliferation
“We built RAG… in five different places.”
Characteristics
RAG everywhere, all different
Pipelines inconsistent or fragile
Embeddings stored with no governance
Success depends on a handful of individuals
Security is beginning to get nervous
Layer Reality
1A exists inconsistently
1B exists everywhere but not coherently
1C is ad hoc
No 2A or 2B
Failure Example (Anonymized)
A financial services company built a RAG system with undocumented embedding logic. When two engineers left, no one knew how it filtered retrieval. Compliance couldn’t determine what data had been used. They had to rebuild everything around a centralized retrieval layer.
What You CAN Ship in the Next 90 Days
A central retrieval service that replaces team-by-team vectors
A standard pipeline that replaces bespoke Python notebooks
A unified classification scheme for AI data
The first version of your AI architecture diagram using 4+1
Recommended Actions
Use Stack Builder to identify your Layer 1 inconsistencies
Introduce central retrieval and pipelines
Align teams around governance metadata (1A)
Time Expectation
3–6 months for stabilization
Stage 2 — Platformization
“We need to standardize this.”
Characteristics
GPU scheduling conflicts appear
Business expects reliability
Architecture starts asking for SLAs
Compliance starts asking for controls
You’re experiencing the limits of “RAG everywhere”
Layer Reality
2A (Control Plane) emerges
2B (Execution Plane) starts becoming real
1A–1C maturing
2C still invisible
Failure Example (Anonymized)
A healthcare enterprise bought what they believed was a full AI platform. Six months later, they realized it didn’t support lineage, residency enforcement, or runtime isolation. Eighteen months of re-architecture followed.
What You CAN Ship in the Next 90 Days
Standard provisioning and isolation for GPU workloads
First operational metrics for inference workloads
Defined model versioning and rollback
A shared retrieval and pipeline layer that supports multiple teams
Recommended Actions
Use Stack Builder to map your target 4+1 architecture
Share the layered architecture with your engineering, platform, and data teams
Start asking vendors: “Which layers of this model do you actually cover?”
Time Expectation
6–12 months to stabilize the platform layer
Stage 3 — Autonomy Emerges
“We finally see the missing middle.”
This is where enterprises discover what hyperscalers have been hiding:
a real reasoning layer.
Not an operator.
Not autoscaling.
Not a set of YAML files.
A reasoning plane.
Characteristics
Hybrid or multi-cloud workloads
Latency/cost/residency trade-offs everywhere
Multi-agent workflows forming
Governance expectations rising
Layer Reality
2A and 2B are real
1A–1C are coherent
2C is now necessary
What You CAN Ship in the Next 90 Days
A policy-as-code framework for workload placement
A basic residency and classification enforcement system
A dry-run mode for reasoning decisions before enforcement
The early design of your reasoning-plane boundaries
Recommended Actions
Once Layers 1 and 2A/2B are reasonably in place, you’re ready to use the 4+1 AI Platform RFP (Open Edition)
Use the RFP to force vendors to declare which layers they cover
Use the strategic risk section to filter out unsafe platforms
Time Expectation
9–18 months for full reasoning-plane maturity
(But valuable autonomy and guardrails can ship far earlier.)
Stage 4 — Unified AI Platform
“AI infrastructure finally feels like the rest of IT: stable, governed, shared.”
Characteristics
Multiple copilots on a shared AI platform
Mature control, execution, and reasoning layers
Shared retrieval and pipeline services
Business semantics modeled at Layer 3
Layer Reality
This is where enterprises stop “doing AI” and start operating an AI platform.
What You CAN Ship in the Next 90 Days
Platform-level SLOs
Reusable agentic patterns
Internal documentation using 4+1 vocabulary
A vendor evaluation process built on the 4+1 RFP
Recommended Actions
Use the 4+1 RFP for all major vendor evaluations
Encourage architecture teams to cite the 4+1 model in internal reference docs
Socialize the maturity roadmap across engineering and leadership
Time Expectation
1–2 years to reach platform stability — but value is delivered at every step.
This Roadmap Is a Gap-Analysis Engine
This is not an academic maturity model.
It’s a practical tool for understanding exactly where you are and what needs to happen next.
Use it to answer:
Which layers do we truly have?
Which are accidental?
Which are missing?
Which decisions are safe to make now?
Which decisions would create lock-in?
Then:
Step 1 — Use Stack Builder
Map your architecture to the 4+1 layers.
This gives you an immediate picture of your strengths, gaps, and risks.
Step 2 — Identify Your Stage
Most enterprises land in Stage 1 or Stage 2.
Step 3 — Use the 4+1 vocabulary internally
Bring the model into architecture reviews.
Label systems by layer.
Ask vendors which layers they live in.
Step 4 — Share the roadmap internally
The more teams that adopt the model, the stronger your alignment becomes.
Step 5 — Use the full 4+1 RFP when you reach Stage 3
That’s when you’re selecting platforms, not just tools.
Download the RFP (When You’re Ready)
If you’re at or approaching Stage 3, you’re ready for the full RFP.
📄 4+1 AI Platform RFP (Open Edition, v1.1)
Final Thought
Most enterprises don’t fail at AI because of model issues.
They fail because they try to buy “AI platforms” before building the layers those platforms rely on.
This roadmap helps you deliver AI now while maturing into the architecture you actually need.
If this post helps your team move forward with more clarity and less chaos, share it widely.
If you want a second set of eyes along the way, I’m here.
But everything in this post — the roadmap, the model, the RFP — is yours to use independently.
Let’s push this standard into the industry together.