AI strategy
& compliance

The problem

Everyone in your company has an opinion about AI. The board wants a strategy. Product wants to ship something. Legal is worried about risk. Engineering is skeptical because they've seen hype cycles before. And somewhere in the middle, someone is supposed to make a decision about what to actually do.

The information out there doesn't help. Vendors say AI can do everything. Regulators say it's dangerous. LinkedIn is full of people who automated their entire company in a weekend. And the EU AI Act is 400 pages that your legal team is still trying to interpret.

Here's the thing most people won't tell you: the companies that are winning with AI right now aren't the ones with the biggest budgets or the most advanced technology. They're the ones that figured out which problems are actually worth solving with AI, scoped them tightly, and built something real — instead of running five pilots that never went anywhere.

That's what we help you do. Not a strategy that lives in a slide deck. A plan you can actually execute.

What we deliver

AI readiness assessment

Before you build anything, you need to know what you're working with. We look at four things:

Your data. What do you have, where does it live, how clean is it, and can you legally use it for the things you're considering? Most AI projects don't fail because of bad models — they fail because nobody looked at the data honestly before starting.

Your infrastructure. Can your systems support AI workloads? Do you have the compute, the storage, the networking? Do you need to run things on-premise because of data residency, or can you use cloud APIs? We figure out what's realistic without buying new hardware.

Your team. Do you have people who can build and maintain AI systems, or do you need external help? How much AI literacy exists across the organization? What's the appetite for change? Technology doesn't fail — adoption does.

Your regulatory landscape. Which regulations apply to you? Which of your planned AI use cases fall under the EU AI Act, and at what risk level? What documentation and governance do you need? We sort this out before it becomes a problem, not after.

Use case prioritization

This is where most AI strategies go wrong. Companies brainstorm 30 potential AI use cases, get excited about all of them, start five, finish none.

We help you prioritize ruthlessly. For each potential use case, we assess: How much value does it create? How feasible is it with your current data and infrastructure? What's the regulatory risk? How complex is the implementation? How likely is your team to actually adopt it?

The output is a short list — usually three to five use cases — ranked by impact and feasibility. With clear reasoning for why these made the cut and the others didn't. Some of the best advice we give is telling you which ideas to park.

EU AI Act risk classification

The EU AI Act categorizes AI systems by risk level: unacceptable, high, limited, and minimal. Where your planned use cases land on that spectrum determines what obligations you have — transparency requirements, documentation standards, human oversight, conformity assessments.

We classify each of your planned or existing AI use cases, explain what the requirements are, and tell you what you need to do to comply. In plain language, not legal jargon. The goal is to make compliance a design input, not an afterthought.

For most enterprise use cases — internal productivity tools, document processing, analytics — the requirements are manageable. For higher-risk applications like HR screening or credit scoring, you need to plan more carefully. Either way, knowing upfront is better than finding out later.

Implementation roadmap

You don't need a 50-page strategy document. You need a plan for the next 90 days that answers: what are we building first, who's building it, what do they need, and how do we know it's working?

Our roadmap includes:

30 days: Quick wins and foundations. Set up the data infrastructure for your first use case. Run a proof of concept. Establish governance basics — who owns AI decisions, how do you evaluate results, what's the escalation path when something goes wrong.

60 days: First production deployment. Take the highest-priority use case from proof of concept to something real people are using. Measure results. Adjust.

90 days: Expand and learn. Start the second use case. Apply what you learned from the first. Begin building internal capability so you're less dependent on external help over time.

Beyond 90 days, the roadmap gets less specific on purpose. You'll know more in three months than you know now, and the plan should reflect that reality instead of pretending you can predict a year out.

How we work

Week 1: Discovery

We interview stakeholders across your organization — leadership, product, engineering, legal, operations. Everyone has a different perspective on what AI should do, and understanding those perspectives is as important as understanding your technology.

We also do a data and infrastructure audit. Not a six-month assessment — a focused look at what's relevant to the AI use cases you're considering.

Week 2–3: Analysis and design

We synthesize what we found into a clear picture: here's what you have, here's what's possible, here's what makes sense, here's what doesn't. We build the use case prioritization, the risk classifications, and the draft roadmap.

This is also where we do the uncomfortable work of killing ideas. Some use cases that sound great in a brainstorm don't survive contact with the data, the budget, or the regulations. Better to find out now.

Week 4: Deliverable and alignment

You get a written document — not a deck — covering everything: readiness assessment, prioritized use cases, risk classifications, implementation roadmap, resource requirements, and governance recommendations.

We present it to your team, walk through the reasoning, and work through the inevitable questions: "Why not this use case first?" "Can we really do this in 90 days?" "What if the regulation changes?" These are good questions, and we'd rather answer them before you start executing than after.

Our take on AI in the enterprise

We have opinions. Some of them might save you time:

Start with boring problems. The most valuable AI applications in most companies aren't chatbots or content generators — they're document classification, data extraction, search, and workflow automation. The stuff that saves someone two hours a day but never makes a LinkedIn post.

Compliance is a filter, not a blocker. The EU AI Act sounds scary, but for most internal enterprise use cases, the requirements are straightforward. The companies that figure this out early move faster than the ones who're paralyzed by uncertainty. Compliance tells you which projects to do first and how to structure them — that's useful information, not an obstacle.

Don't build a model. Build a system. The model is 10% of the work. The other 90% is data pipelines, integration, monitoring, error handling, user experience, and change management. If your strategy is about which LLM to use but doesn't address how users will interact with it and what happens when it's wrong, you don't have a strategy.

Pilots that aren't designed to become production are a waste of money. If the pilot runs on a separate dataset, with a separate team, on separate infrastructure, and has no plan for how it gets into the hands of real users — it's a demo, not a pilot. We design pilots that are built to scale from day one.

You need less data than you think, but it needs to be better. "The you need massive datasets" narrative comes from the model training world. For enterprise RAG and automation use cases, you need relevant, clean, accessible data — not necessarily a lot of it. A hundred well-structured documents are worth more than ten thousand messy ones.

Who this is for

This works well when:

• Your leadership is asking "what's our AI strategy?" and nobody has a clear answer yet
• You've run a few AI experiments that didn't go anywhere, and you want to be more deliberate about what's next
• Your legal or compliance team is concerned about the EU AI Act and you need a practical interpretation, not more ambiguity
• You know AI could help but you're not sure which use cases are realistic given your data, team, and budget
• You want to avoid the pattern of buying a platform and then looking for problems it can solve

It's less of a fit when:

• You already know exactly what you want to build and just need a team to build it — that's our AI & intelligent automation offering
• You're looking for someone to build an AI strategy deck for the board with no intention of executing — we're not interested in shelf-ware