If your AI strategy fits on a slide, it's not a strategy. It's a wish list with a logo on it.
I've lost count of how many companies I've talked to who tell me they "have an AI strategy." When I ask to see it, it's usually a slide deck from a Big Four consultancy with a maturity model, a vendor comparison matrix, and a vague roadmap that says things like "Phase 3: Scale AI across the enterprise." No timelines. No technical specifics. No mention of the data sitting in 47 different spreadsheets that nobody's touched.
That's not a strategy. That's a receipt for a very expensive conversation.
What Most Companies Get Wrong
The most common mistake is treating AI strategy like vendor selection. Someone in leadership reads an article, gets excited, and the directive comes down: "We need to integrate AI." So a team gets assembled, RFPs go out, demos get scheduled, and three months later the company has licensed a platform that nobody knows how to use for problems nobody has clearly defined.
The second most common mistake is going too big. "Become an AI-first company" is not an actionable goal. It's a slogan. Compare that with "automate invoice processing so our AP team stops spending 20 hours a week on manual data entry." One of those you can actually build. The other one goes on a poster.
Real AI strategy doesn't start with technology. It starts with your operations.
What Real AI Discovery Looks Like
When I start an engagement, the first thing I do is map workflows. Not at a whiteboard-and-sticky-notes level — at a "show me exactly what happens when a customer order comes in" level. Where does human judgment currently bottleneck your operations? Where are people doing repetitive cognitive work that follows a pattern? Where are decisions being made slowly because someone has to pull data from three systems and cross-reference it manually?
That's your opportunity map. Not "where could AI theoretically help" but "where is the pain right now, and is it the kind of pain AI is actually good at solving."
Then comes the data landscape. What data do you actually have? What's its quality? What's accessible via API versus locked in PDFs and email threads? I've walked into companies that were convinced they were "data-rich" only to find that their most critical business data lived in a single person's Excel workbook with no backup. You can't build AI on a foundation like that, and telling the client that truth early saves everyone months of wasted effort.
This is why discovery is not a sales exercise. Sometimes the honest answer is "you're not ready for AI yet." Maybe you need to fix your data infrastructure first. Maybe the process you want to automate is so inconsistent that there's nothing for a model to learn. I'd rather tell a client that in week one than let them find out in month four when the budget's gone.
The Build-vs-Buy Decision Framework
One of the highest-value things a real AI strategy delivers is a clear build-vs-buy recommendation. Sometimes the answer is a SaaS tool. If you need document OCR and your use case is standard, there are excellent products that will do it better and cheaper than anything custom. Knowing that and saying it plainly is part of the job.
But sometimes the answer is custom. If your workflow is specific to your industry, if your competitive advantage depends on how you process information, if the off-the-shelf tools get you 70% of the way but that last 30% is where all the value lives — that's when you build. The strategy is knowing which situation you're in and having the technical depth to back up the recommendation.
The worst outcome is building custom when you should have bought, or buying a platform that doesn't actually fit and then spending more to customize it than building from scratch would have cost. I've seen both. They're equally painful.
Why Implementation Beats Strategy Every Time
Here's the thing nobody in consulting wants to admit: a mediocre strategy that gets executed well will outperform a brilliant strategy that sits in a Google Doc. Every single time.
Strategy without implementation is just opinion. I've seen companies spend six figures on AI strategy documents that were never acted on. The strategy was fine. The execution never happened because the people who wrote the strategy didn't build software and the people who build software weren't involved in the strategy.
This is why at BCK Systems, every engagement is structured to move from thinking to building as fast as possible. Our process runs four phases: Discovery (1-2 weeks), Strategy (1-2 weeks), Implementation (4-12 weeks), and Handoff (2 weeks). Total timeline is measured in weeks, not quarters. The strategy phase produces a clear technical plan, and then we immediately start building against it.
And every engagement ends with the client's team owning what was built. Full documentation, knowledge transfer, no proprietary lock-in. If you want to take what we built and never talk to us again, you can. That's by design. If you need ongoing support, we're here — but it should be a choice, not a dependency.
Red Flags to Watch For
If you're evaluating AI consultants, here are the things that should make you nervous:
- Nobody asks about your data. If the conversation is all about models and platforms and nobody has asked what your data looks like, where it lives, and how clean it is, walk away. Data is the foundation. Ignoring it is malpractice.
- Timelines measured in quarters, not weeks. AI projects that take 18 months to deliver initial value are almost always going to fail. The technology moves too fast, requirements change, and organizational patience has a shelf life.
- Deliverables are slide decks, not working software. If what you're paying for is a PDF with recommendations, you're paying for advice, not results. Advice is cheap. Working systems are valuable.
- They can't explain the technical approach in plain language. Complexity isn't a sign of sophistication. If someone can't explain what they're building and why without resorting to jargon, they either don't understand it themselves or they're trying to make you dependent on them to translate.
- There's no plan for handoff. If the engagement doesn't include explicit knowledge transfer and documentation, you're not hiring a consultant — you're hiring a permanent vendor. That might be fine if you want it. It's not fine if you don't realize it until the contract renewal comes.
How We Approach It
At BCK Systems, I've built this process from the ground up based on what I've seen work — and what I've seen fail — across dozens of AI projects. The approach is simple: start small, start specific, and ship working software.
Discovery is real analysis, not a sales pitch. Strategy is a technical plan with specific recommendations, not a maturity model. Implementation produces working systems, not prototypes. And handoff means your team can maintain and extend what was built without calling me.
The best AI projects I've been part of all started the same way: someone had a specific, painful, well-understood problem, and we built a specific solution for it. No grand transformation. No enterprise-wide AI rollout. Just one problem, solved well, proving value, and then expanding from there.
If that sounds like what you need, let's talk. And if you're not sure whether you're ready for AI at all, that's a fine place to start the conversation too. I'd rather spend an hour helping you figure out the right timing than sell you something you don't need yet.