Consulting firms charge $2M+ for AI roadmaps. I built mine in 48 hours using No Code Tools.
Here is a masterclass on how to DIY enterprise-level AI strategy — for free.
Enterprise AI roadmaps from McKinsey, Bain, and Accenture can run from $500K to over $2 million.
And most of what you’re paying for is “structure”. Slide decks, frameworks, and market research.
Just for the roadmap.
That’s not even the build. That’s the thinking.
And it usually follows a predictable process based on publicly available frameworks.
So instead of paying for advice, I looked at that and thought:
“I can build this myself if I have the right data and the right AI tools.”
I followed Accenture’s AI Roadmap – The Journey to Live and used a stack of free AI tools to build my own.
So I opened up NotebookLM, pulled in The Economist’s “World Ahead” report, and went to work.
In 48 hours, I had:
Strategy
Business case
Data foundation
Responsible AI checkpoints
DevOps plan
Reuse + value realization framework
And it was all mapped to Accenture’s official 6-checkpoint methodology.
Here’s how I did it:
Checkpoint #1: Define Value + Strategy
Tools: NotebookLM + The Economist’s “World Ahead” Notebook
I opened Google’s new version of NotebookLM, pulled up The Economist’s 2025 global trends notebook, and asked:
“Which sectors will be most impacted by AI?”
“What macro risks should we account for in strategic planning?”
I used those answers to define my value hypothesis. Ex: which lines of business to focus on, where AI could reduce cost or create revenue, and which markets to deprioritize based on risk.
I also uploaded my own docs to NotebookLM to ask:
“How do our current goals align with global AI trends?”
This gave me an executive briefing in minutes.
Checkpoint #2: Curate the Right Data
Tools: Google Sheets + ChatGPT (free)
Accenture’s roadmap says: “Curate the right data to deliver your desired outcome”.
So I listed samples of our non-sensitive datasets in Google Sheets. Then I used ChatGPT to:
Identify gaps
Recommend enrichment sources
Assess if our data could scale to other use cases
This helped me lock in the minimum data required to move forward and surface what’s missing.
Checkpoint #3: Update Product Roadmap + Enable Support Teams
Tools: Airtable (free) + Whimsical (freemium)
I created a cross-functional capability tracker in Airtable:
AI readiness by team
Key dependencies (legal, risk, data privacy)
Feature impact zones
Then used Whimsical to draft a lightweight operating model. I followed Accenture’s prompt:
“Are there adjustments needed in your operating model to optimize how specialists work together?”
The goal: No surprises down the line.
Checkpoint #4: Validate Team + Vendor Fit
Tool: NotebookLM
Before committing to tools or partners, I asked NotebookLM:
“What feedback loops should we build into AI production?”
“Which types of vendors are most critical for scalable AI delivery?”
The answers helped me vet our current stack and think critically about build vs. buy decisions (something consulting firms typically charge 6 figures to do).
Checkpoint #5: Update Risk + Governance
Tools: NotebookLM + Internal Docs
Accenture asks:
“Have you updated your risk frameworks for incorrect AI outcomes?”
I uploaded risk docs into NotebookLM and asked:
“Where are the gaps based on global AI governance best practices?”
NotebookLM cited its sources and gave a checklist I could hand off to legal, IT security, and compliance.
Checkpoint #6A + 6B: Realize Value + Reuse Features
Tools: Gamma.app + NotebookLM
After walking through the roadmap, I used Gamma.app to create a board-ready deck:
What value we expect to achieve
How we’re measuring ROI
Where we can reuse this work for new use cases
Then I went back into NotebookLM and asked:
“Can this roadmap apply to our next two product initiatives?”
It helped me uncover quick wins. More importantly, it provided opportunities to reuse what we’d already built (data, features, processes, or models) for new use cases without starting from scratch.
That’s how you create a real AI flywheel: each success powers the next.
Final Thoughts
Hiring a top-tier consulting firm isn’t a bad move. In fact, it can be the right one when you’re dealing with complex org structures, regulatory pressure, or enterprise-wide transformation that spans multiple geographies or business units.
But here’s the truth most teams overlook:
You don’t need to outsource your thinking just to get started.
If you’re in the early to mid-stage of your AI journey (i.e. identifying use cases, mapping capabilities, and aligning internal teams), you already have access to 80% of what consulting firms use:
Proven frameworks
Public research
AI-powered tools
Your own data
By combining tools like NotebookLM, The Economist’s trend reports, and a few no-code platforms, you can get clear on direction, identify quick wins, and build internal confidence…before bringing in external partners.
Consultants should accelerate your momentum, not create it for you.
So whether you’re leading a startup, a growth-stage business, or an enterprise team, you have more power at your fingertips than ever before.
You only need to use it.
#AIstrategy #NoCodeTools #AccentureFrameworks #EnterpriseAI
What’s the first use case you’d tackle if you had a clear roadmap?
Drop it in the comments ⬇️
Let’s build smarter.
– Angela
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