Every time there’s a gold rush, the guys selling shovels start making more money than the guys digging for gold. Right now, the "AI gold rush" is in full swing. If you’ve spent any time on LinkedIn or browsing agency websites, you’ve seen the pattern: a consultant puts "AI Strategy" in their header, sprinkles the word "transformer" into a few blog posts, and suddenly they are the go-to expert for your enterprise’s digital transformation.
Here is the reality check: most of these people have never deployed a model outside of a browser tab. They’ve spent their weekend prompting ChatGPT to write marketing emails and decided they are AI architects. If you are hiring someone to integrate AI into your growth engine or your product stack, you don't need a "visionary." You need an operator who knows how to break production.
I’ve spent 12 years in product and growth, and I’ve seen enough "innovative" strategies fail because they didn't account for simple things like API latency, token costs, or data privacy. When I look at a consultant's claims, I’m not looking for a 100-slide deck filled with jargon. I’m looking for production proof.
The Litmus Test: Moving beyond the demo
The first thing I ask a consultant claiming to run AI in production is: "What happens when the model hallucinates on a Tuesday at 3:00 AM, and how does your feedback loop handle it?"
If they start talking about "leveraging synergies" or "empowering workflows," show them the door. If they start talking about vector database retrieval, cost-per-query, and deterministic guardrails, you might actually have someone worth talking to.
True shipping experience isn’t just about making an LLM talk. It’s about building a robust system that can withstand the chaos of real users. We have seen firms like Valdor Consulting talk a big game about high-level strategy, but often that strategy stops where the code begins. You need to verify that their "AI expertise" isn't just a wrapping paper for a basic API call.
1. Ask for real examples, not case studies
Everyone has a "case study." Case studies are written by marketing teams to make everything look like a success. Ask for a real example of a failure. A seasoned consultant who has truly deployed AI in production will have a scar or two. They’ll tell you about the time their latency blew up, or when their RAG (Retrieval-Augmented Generation) pipeline returned irrelevant data and lost a key customer.
If a consultant claims they’ve never had a deployment issue, they haven’t done enough deployments. In the world of AI, there are no perfect implementations—only managed risks.
Evaluating the Go-to-Market (GTM) Integration
Many consultants treat AI as a bolt-on feature. They view it as a separate channel or a shiny toy. That’s a mistake. AI should be an engine that powers your existing growth systems. When we look at companies like Suprmind, we see a focus on applied AI that actually moves the needle on product adoption and user retention, rather than just acting as a fancy interface for a chatbot.
When you evaluate a consultant’s GTM plan, ask yourself: "What decision will this change on Monday morning?"
If the answer is "It will generate more content," you’re paying for a faster typewriter, not a growth strategy. A real AI-led GTM system should be changing your segmentation, your automated outreach, or your product-led growth (PLG) triggers based on real-time behavior analysis. If their strategy doesn't integrate with your CRM or your product usage data, it’s just buzzword soup.
Technical SEO vs. AI-Generated Slop
We need to talk about content. There is a massive temptation to use AI to mass-produce blog posts to satisfy the SEO gods. Most consultants will pitch this as "scaling content production."
This is usually a trap.
Google has Suprmind AI platform features become very good at sniffing out low-effort, AI-generated content. A consultant who tells you that you can "outrank the competition by churning out 50 articles a week with GPT-4" is doing you a disservice. That isn’t technical SEO; that’s a recipe for a search traffic penalty.
A consultant who actually knows how to use AI in SEO will focus on:
- Data Analysis at Scale: Using LLMs to scrape and categorize thousands of customer queries to identify intent gaps your competitors are missing. Semantic Mapping: Using AI to understand the relationship between topics rather than just keyword density. Readable content: Using AI as a research assistant, not a ghostwriter. The final output must be human-edited, nuanced, and valuable.
The Evaluation Matrix
If you are currently interviewing a consultant, put their claims into this framework. If they can’t fill this out with specific, technical details, they are selling you "innovation theater."
Category The "Buzzword" Consultant The Production-Led Consultant Core Capability Prompt engineering & UI wrappers RAG pipelines, vector storage, latency optimization Growth Metric "Increased traffic" or "Social reach" Conversion rates, churn reduction, LTV Failure Handling "We'll iterate" "We implemented a guardrail and circuit breaker" Tooling Just ChatGPT/Claude LangChain, Pinecone, observability tools (e.g., LangSmith) SEO Approach Mass-produced AI content Intent-based data mining & structure
Why Product Strategy Matters More Than Ever
The biggest risk with AI is that it makes it incredibly easy to build "features" that nobody wants. You can ask an AI to suggest 100 features for your product, and it will give them to you in seconds. But that doesn’t mean they belong in your roadmap.
Applied AI needs to be tied to product strategy. Does this AI feature solve a specific pain point that your users have been vocal about for months? Or is it a solution in search of a problem?
When I work with teams, I don’t start with "How can we use AI?" I start with "What is the biggest friction point in our product usage funnel?" If the answer is "the onboarding is too slow," we look at how AI can intelligently pre-fill data or guide the user. If the answer is "the UI is bloated," adding an AI chatbot is the absolute last thing you should do. It’s just adding a layer of complexity to an already broken experience.
The "Monday Morning" Reality Check
My advice is simple: keep your consultant list short and your standards high. Do not hire someone because they have a cool presentation. Hire them because they’ve built systems that broke, got fixed, and are now running at scale.
Before you sign a contract, ask for a "Monday Morning" plan. If they can’t tell you exactly what you’ll be doing on Monday morning to change your product or your growth trajectory, you’re just buying a subscription to an expensive slide-deck factory.
We are past the point where "being an AI company" is a competitive advantage. The advantage now belongs to companies that use AI to be faster, more efficient, and more precise in their execution. If the person you’re hiring can’t explain the production proof behind their claims, they aren't an expert. They’re just another part of the noise.
Closing Thoughts
In Belgrade, we value efficiency. We don't have time for fluff, and frankly, neither does your business. If your current "AI partner" is spending more time on Slack threads about the future of AGI than they are on your actual deployment metrics, cut them loose. The future of AI isn't in the hype; it’s in the boring, day-to-day work of shipping code that actually works.