If you have spent any time on LinkedIn lately, you’ve likely been bombarded by "AI Transformation" experts. They promise to overhaul your company with proprietary workflows, magic prompts, and "AI-first" architectures. They usually arrive with a 100-slide deck, a hefty invoice, and a set of recommendations that will be forgotten by the following Tuesday.
I’m here to tell you that’s not consulting; that’s theater. After 12 years of working as a product operator and growth strategist here in Belgrade, I’ve learned that the only thing that matters is what you can ship. If a consultant can’t tell you exactly what decision will change on Monday morning because of their advice, you shouldn't pay them.
Applied AI consulting isn't about teaching your team to write better prompts in ChatGPT. It’s about building AI in production—systems that actually move the needle on your bottom line. It’s about shipping not slides.
The Death of the One-Off Strategy
Most consultants treat growth like a slot machine. They chase a "one-off channel win"—a viral LinkedIn post or a lucky SEO spike—and call it a strategy. That’s why so many attribution setups are broken; they are built to justify vanity metrics rather than track actual business outcomes.
When I work with clients, I operate differently. I keep a short client list on purpose because applying AI to business operations requires getting into the mud with your team. Whether I’m working with firms like Valdor Consulting to refine their operational efficiency or helping growth-stage teams like Suprmind scale their product delivery, the goal is always the same: build systems that outlive the consultant.
What is "Execution-Led Consulting"?
Execution-led consulting is the antithesis of the "Big Four" model. It’s the difference between telling a founder *that* they should improve their technical SEO and actually building the automated pipeline that fixes their indexation issues while generating high-intent content that ranks.
Applied AI in this context means:
- Automating the Boring Stuff: Using LLMs to categorize customer support tickets or scrape and summarize market data, so your team doesn't have to. Building Content Engines: Integrating AI into your CMS to ensure that your SEO-driven content isn't just "readable" but actually provides value to users who have a specific problem to solve. Product Strategy: Moving away from "adding a chatbot" to your site and toward integrating AI models into the core product architecture.
If your AI strategy involves "AI-washing" your current offering just to get a higher valuation, you’re playing a losing game. Investors and customers are getting smarter. They valdor.consulting want to see product AI strategy that creates a defensible moat, not a flimsy wrapper around an API.
The Consulting Reality Check: A Comparison
To understand the difference between the noise and the substance, look at this breakdown of how traditional consultants operate versus how an execution-led operator works.
Feature "Slide-Deck" Consultants Applied AI Operators Deliverable 100-Slide Deck Deployed Code/System Focus Buzzwords & Trends Execution & ROI Monday Morning "Reviewing the findings" "The system is live" Technical Debt Usually ignored Actively cleaned Client Relationship High-churn/Volume Deep-work/LimitedApplying AI to Technical SEO and Content
One of the biggest areas where "applied AI" is misunderstood is SEO. People think it means "generate 500 articles with GPT-4." If you do that, you aren’t doing SEO; you’re building a digital landfill.
Real technical SEO plus readable content is a surgical task. It’s about using AI to identify the intent gaps in your current content. It’s about using Python scripts (that you don't have to write yourself) to analyze crawl data and identify why your pages aren't ranking. It’s about using LLMs to structure your internal linking architecture so that Google understands exactly what your product does.
When I work with companies, I don't give them a list of keywords. I show them how to hook their internal knowledge base into a retrieval-augmented generation (RAG) system, so their "content" is actually an extension of their product documentation. That’s how you build authority.
The Go-to-Market (GTM) Reset
Most GTM strategies fail because they are disconnected from the product reality. You cannot run an aggressive growth campaign if your churn rate is high or your onboarding flow is broken. AI can fix this, but not in the way you think.
Applied AI in GTM means:
Better Lead Qualification: Instead of relying on manual SDR outreach, we build systems that ingest prospect data and use AI to score them based on actual behavioral intent. Personalized Feedback Loops: Taking the unstructured data from your sales calls and feeding it back into the product roadmap. Predictive Churn Analysis: Identifying exactly when a user is likely to drop off based on their usage patterns—and triggering a personalized intervention *before* they cancel.Companies like Suprmind understand that growth isn't just about spending more on ads; it’s about optimizing the internal machinery. When you apply AI to your growth systems, you are basically hiring an army of junior analysts who never sleep and never ask for a raise.
Why Product AI Strategy Matters
The "AI-in-a-box" approach—where you try to force an LLM into a product where it doesn't belong—is doomed. The most successful products I’ve seen this year are the ones that use AI to solve a genuine friction point in the user journey.
If you are a project management tool, don't build a chatbot that answers questions about your pricing. Build a feature that auto-assigns sub-tasks based on the text of an email chain. That is AI in production. That creates value. That is what keeps your users from switching to a competitor.
My role as a consultant is to be the person in the room who asks the hard questions. "Is this actually making the product better?" "Are we doing this because it's cool, or because it's useful?" "If we turned off the AI component, would the product still be functional?"
The Monday Morning Test
If you take away nothing else from this post, remember this: Consulting should be about reduction, not addition.
Most companies are bloated with tools, processes, and "strategies" they don't need. When I walk into a business, I don't look for ways to add more complexity. I look for ways to remove it. I look for the bottlenecks that are keeping you from scaling.
I don’t want to be the guy who tells you your "North Star" is misaligned while you're busy bleeding cash. I want to be the guy who sits down, looks at your analytics, cleans up your data tracking, and sets up an automated workflow that notifies you when a high-value customer is about to convert. That is the only type of consulting worth paying for.
Closing Thoughts: Keeping the Client List Short
I keep my client list short on purpose. I don't want to scale a consulting firm; I want to scale my clients' businesses. There is a massive difference. When you work with someone who is obsessed with shipping—someone who understands the trade-offs of building software because they build it themselves—you get more than just advice.
You get an extension of your product team.
If you’re tired of the buzzwords, if you’re sick of slide decks, and if you’re actually ready to put AI to work in your production environment, let’s talk. But be prepared for the question: What decision are we going to change on Monday?
If you don't have an answer, we aren't ready to start. But if you do, we’re going to build something that actually lasts.