If I walk into a boardroom in Belgrade—or anywhere else, for that matter—and see a 100-slide deck titled "AI Strategy 2025," I know immediately that the company is burning cash on vaporware. In my 12 years of helping teams move from chaotic startups to stable growth engines, I’ve learned one immutable truth: most "AI readiness" plans are just expensive distractions from the work that actually moves the needle.
As an independent consultant, I keep my client list short. I do this because I refuse to be a "strategic advisor" who flies in, drops a pile of vague recommendations, and flies out while the team struggles to implement anything. I prefer to be in the trenches, doing the execution-led consulting that actually builds systems.
When you approach Applied AI, you need to stop thinking about "AI" as a magical monolith and start thinking about it as a series of specific, narrow engineering and product tradeoffs. My first question is always: What decision will this change on Monday morning? If the answer is "nothing," stop. If the answer is "it helps us automate our lead qualification process," then we’re finally ready to have a conversation.
Beyond the Hype: The "Monday Test" for AI Governance
Most AI governance initiatives fail because they look like legal departments trying to multi-model AI orchestration guide write a constitution for a software feature. They are slow, restrictive, and disconnected from the product roadmap. Real AI governance isn’t about stopping innovation; it’s about creating guardrails that allow your team to ship faster without breaking the bank or leaking data.
When we look at building product strategy with applied AI, we aren't asking "How can we use ChatGPT to replace our support team?" That’s a lazy, one-off win that usually results in disgruntled customers. Instead, we look at the entire data lifecycle. Who owns the data? What is the cost-to-benefit ratio of the API calls? Does this model actually solve the user's intent, or is it just a fancy text generator?
The Governance Matrix
To implement real governance, your product team needs to grade every proposed AI feature on a simple matrix before a single line of code is written.
Feature Concept User Value Impact Data Privacy Risk Decision Changed Customer Support Chatbot High Medium (PII leakage) Reduces ticket backlog Content SEO Generator Low Low Writes blog posts (SEO impact?) Decision Support for Sales High High (Proprietary data) Improves GTM alignmentUse Case Selection: Avoiding the "Hammer and Nail" Trap
I see many teams obsessing over technical SEO or AI-generated content because it feels like a "growth hack." Don't fall for it. Generating 500 mediocre blog posts using ChatGPT is not a content strategy; it’s a long-term liability. Google’s algorithms are smart enough to spot content that lacks a human point of view, and your users are smart enough to leave when they smell the generic AI "flavor."
At Valdor Consulting, we focus on use case selection that solves specific bottlenecks. I look for processes where human effort is wasted on rote pattern matching. For example, if your team is spending hours manually categorizing feedback in a spreadsheet, that is a prime candidate for an applied AI solution. If your team is struggling to understand why churn is happening, AI-driven sentiment analysis can be a force multiplier.
Consider the approach taken by companies like Suprmind. They don't just dump LLMs onto existing products. They integrate AI in a way that respects the user's workflow, focusing on high-utility interactions rather than "wow-factor" demos. That’s the kind of product strategy that lasts.
Running Risk Checks: The Reality of "Lived Tradeoffs"
If you're shipping AI features, you are taking on technical debt that you likely don't understand yet. I’ve helped teams perform "risk checks" that usually reveal that their current infrastructure is fundamentally ill-equipped for AI scaling. You need to be asking these questions:
- Latency and Cost: Does this model perform well enough for the user experience, and at what cost per request? If your API spend grows faster than your user base, your growth engine is broken. Hallucinations: If your product gives the wrong answer, what is the impact on your user? If it’s just a funny image, fine. If it’s financial advice or technical specs, you need a deterministic fallback layer. Data Sovereignty: Are you feeding your enterprise customers' data into a model that might train on it? If you aren't checking your terms of service with the model providers, you are inviting a catastrophic security event.
I’ve seen too many attribution setups that nobody trusts, and when you add AI to the mix, those blind spots become massive chasms. If you can’t measure the impact of an AI feature—if you can’t see the lift in retention or the reduction in support costs—you are effectively flying blind.
Building a Growth System, Not a Feature List
When I consult on GTM and growth systems, I don't look for the next "channel hack." I look for where technology can accelerate a proven human-led strategy. Technical SEO, for instance, isn't about AI-generated keywords. It’s about ensuring your site architecture is clean, fast, and structured so that human-written, high-quality content can actually be found.
If you use AI to build your product, use it to build better systems, not just *more output.* Use it to clean your CRM data. Use it to analyze competitive search landscapes. Use it to build better internal tooling that allows your team founder marketing help to spend more time talking to customers and less time fighting with broken analytics tags.
The Roadmap to Applied AI Success
Audit your bottlenecks: Map out where your team is doing repetitive, high-cognitive-load work. Define the 'Monday' impact: If this feature works perfectly, what decision does it change on Monday? Build the 'Human-in-the-Loop': Never ship AI features that run in a vacuum. Always have a human verification layer for anything that touches the customer. Monitor costs relentlessly: Treat AI API costs like you treat server costs—they should be optimized, monitored, and capped.Final Thoughts: The Independent Advantage
I stay independent because I hate the "all-hands-on-deck" churn of large agencies. When you hire an advisor, you should be hiring for someone who has felt the sting of a failed launch. I’ve shipped products that didn’t scale, I’ve cleaned up tracking setups that were giving us 40% error rates, and I’ve rebuilt SEO strategies from the ground up because someone tried to "hack" the system with low-quality content.
Applied AI is just another tool in the box. It’s not the destination. If you want to prepare your team, stop asking them to learn "AI" and start asking them to identify the biggest friction point in your current product loop. Then, and only then, look at how AI can help you crush that friction.
If you find yourself buried in a 100-slide deck about "AI transformation," put it in the trash. Call someone who will look at your product, look at your growth numbers, and tell you exactly what you need to fix by Monday morning.
Looking for an honest audit of your AI strategy? I keep my client list intentionally short to ensure every partner gets deep, hands-on execution. Let's make sure your "innovation" isn't just a cost center.