AI Strategy
I cut through the AI noise and give you a plan built on evidence: where it will genuinely move the needle for you, what it will cost, and where the real risks are.
Everyone is being told they must do something with AI, and most of what gets pitched is a solution looking for a problem. I start from your business, not the technology. I map where AI can genuinely save time, cut cost, or unlock something new, settle the question with evidence rather than impressions, and give you a plan with real costs, real risks, and a sensible order to try things in.
I build AI systems for my own products, so this is grounded in what actually works in production, not slideware.
What it covers
- Where AI genuinely helps your business, and where it does not
- Whether your data is ready to support it, and what to fix if not
- Build, buy, or just use an existing tool, decided honestly
- Rough cost and expected return for each opportunity
- The real risks: accuracy, privacy, cost, and dependence
- A safe, sensible order to pilot and roll things out
- What to keep in-house versus hand to a vendor
- How to measure return on facts rather than vibes
Who it’s for
- Leaders under pressure to have an AI plan
- Companies sitting on data they suspect is valuable
- Teams burned by an AI pilot that went nowhere
What you get
- A written AI strategy tied to business outcomes
- A ranked set of opportunities with cost and expected return
- A clear read on data readiness and what to fix first
- A risk and governance view sized to your business
- A sensible first pilot defined and ready to run
How it works
- 1
Understand the business
We start from where you make and lose money and time, not from the technology.
- 2
Map opportunities
I find where AI could genuinely help and size each one honestly.
- 3
Check readiness
I assess whether your data and systems can actually support it.
- 4
Prioritize
We rank opportunities by return and risk and pick a sensible first move.
- 5
Plan the pilot
I define a first pilot with clear success measures so you learn on facts.
Measuring AI on facts, not hype
Most AI initiatives fail not because the technology does not work but because nobody defined what success would look like before they started. I insist on a baseline first: how long does this task take today, what does it cost, where do errors creep in. Only then does it make sense to ask whether AI moves those numbers enough to be worth the cost and the new risks it introduces. I build AI systems in production for my own products, so the recommendations are grounded in what actually ships, not what demos well.
Strategy and execution are different jobs. Once we know where AI earns its keep, the building is software and data engineering, and if the broader goal is to modernize how the business runs, AI is one lever inside a wider digital transformation. The deliverable is a plan you could hand to a skeptical board: real opportunities, real costs, real risks, and a sensible first pilot.
Why work with me
I’m Matt George, a fractional CTO for SMBs and early-stage startups and the founder of the venture-backed legal-tech company Injuria.ai. I have built and shipped real products end to end, from scrappy MVPs to data and AI infrastructure running in production, so the advice here comes from doing the work rather than talking about it. You get a senior technical partner with nothing to sell you, no vendor kickbacks, and a bias toward shipping.
FAQ
We just use ChatGPT and Copilot. Do we need a strategy?
If that is working, great. A strategy matters once you are about to spend real money or put AI near customers or sensitive data, so you do it deliberately rather than by accident.
How do you measure return on AI?
On facts, not impressions: time saved, cost removed, revenue added, measured against a baseline we set before the pilot rather than guessed after.
Is our data safe with AI models?
It depends entirely on which models and how they are wired up. Part of the strategy is choosing options that keep your data where it should be, and I will be explicit about the trade-offs.
Should we build our own model?
Almost never, early on. For most companies the smart move is to use or fine-tune existing models and spend your effort on the data and the product around them.
Related services
AI & Digital Transformation
Modernizing how your business runs with software, data, and AI, in steps that pay for themselves instead of a risky big-bang project.
Software & Data Engineering
Senior hands-on engineering for the hard problems: data pipelines, scraping infrastructure, integrations, and systems that have to be reliable.
Technology Strategy
A written, costed technology plan that ties every build and buy decision back to where the business is going.
Let’s ship it! 🚀
Whether it’s legal tech, an AI platform, fractional CTO leadership, or a problem you just want a second set of eyes on, grab a time that works.
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