The AI market has developed a familiar problem. A wave of people discovered artificial intelligence recently, learned enough vocabulary to sound convincing, and started selling certainty. They promise transformation, scale, automation, productivity, disruption, and strategy. What they often cannot show is the part that matters most: work that survived contact with reality.
That distinction matters more now than it did a year ago. Search, publishing, software buying, and enterprise adoption are all moving in the same direction. The premium is shifting away from polished output and toward proof. Google’s own guidance is explicit that helpful, reliable, people-first content should demonstrate experience, expertise, authoritativeness, and trust, with trust as the most important element. Google also made “experience” explicit in E-E-A-T for a reason: first-hand familiarity changes the quality of what gets produced.
The same logic applies far beyond publishing. In AI, fluency is cheap. Demos are cheap. Prompt theater is cheap. The expensive thing is getting a system to produce measurable value inside a real business, with real constraints, real users, and real failure modes.
The market has too much AI language and too little AI evidence
One of the easiest ways to look credible in AI is to speak in abstractions. Model orchestration. agentic workflows. transformation roadmaps. multimodal productivity. semantic layers. enterprise copilots. These phrases are not meaningless, but they are easy to borrow and easy to weaponize. They create the impression of depth without the burden of proof.
That is precisely why the current market is so noisy. Generative AI made entry into the conversation dramatically easier. A person can spend a few weeks testing tools, absorb enough platform terminology to sound informed, and start selling “AI strategy” to companies that are even less informed. The gap between sounding current and being capable has rarely been wider.
Official guidance from Google reinforces the same point from another angle. Google does not reward content simply because it is automated or AI-assisted; it rewards content that is genuinely useful. It warns that scaled content without added value can violate spam policies, and its 2025 guidance for AI search experiences tells creators to focus on unique, non-commodity content. That is a direct challenge to the mass production of generic expertise.
In practice, the fake expert usually has one dominant trait: they can describe possibilities far better than they can describe tradeoffs. They know what AI can sound like in a keynote. They do not know what breaks in deployment, what users resist, which workflows should not be automated, how evaluation changes system design, or why a promising prototype quietly dies after three weeks.
Experience matters because AI fails in ordinary ways
The strongest argument for preferring experience is not prestige. It is risk reduction.
AI systems rarely fail in cinematic ways. They fail in ordinary, expensive ways. They save time in a pilot and waste time at scale. They produce impressive drafts that increase review burden. They appear accurate until edge cases arrive. They delight leadership in a demo and frustrate frontline staff in daily use. They speed one step while damaging the workflow around it.
Someone who has shipped AI into production knows this instinctively. They do not just know the tools. They know the friction. They know adoption gaps, change management problems, permissions issues, quality drift, hallucination risk, documentation debt, and the uncomfortable reality that the model is often not the bottleneck. Very often, the bottleneck is process design.
That operational view is increasingly supported by the data. McKinsey’s 2025 State of AI research found that only 39 percent of respondents reported enterprise-level EBIT impact from AI, even while many saw narrower use-case gains. Earlier 2025 reporting from the same survey also said more than 80 percent were not seeing tangible enterprise-level EBIT impact from generative AI. The lesson is not that AI is failing. The lesson is sharper: value is real, but broad enterprise value is harder to produce than AI sales language suggests.
That is why experienced operators sound different from inexperienced sellers. They are usually less absolute, more specific, and more useful. They talk about where AI fits, where it does not, what must be redesigned, what must be measured, and what success actually looks like after the demo ends.
Results are the only language that survives procurement
The market is slowly relearning a basic business truth. Buyers may be seduced by narratives, but they stay loyal to outcomes.
A credible AI adviser should be able to show one or more of the following: reduced turnaround time, lower support burden, better retrieval accuracy, higher conversion on a defined step, fewer repetitive tasks, faster internal research, better content quality under a clear rubric, or stronger operational consistency. Not vague potential. Not a fashionable deck. Not “we help companies unlock AI.” Results.
This shift from evangelism to evaluation is visible in technical standards and platform guidance. NIST’s AI Risk Management Framework emphasizes test, evaluation, verification, and validation across the AI lifecycle. OpenAI’s own documentation makes the same practical point from the builder side: evals are essential for understanding whether an LLM system is actually performing to expectation, especially as models and prompts change over time.
That is a dividing line worth taking seriously. Anyone can sell AI with adjectives. Serious practitioners sell it with measurement. They can tell you what they tested, what failed, what improved after iteration, and what criteria they used to decide the system was good enough to trust.
In other words, results are not just proof of competence. They are proof of contact with reality.
What real AI expertise looks like
Real expertise in AI is less glamorous than the market expected. It is usually built from repetitions that outsiders barely notice.
It looks like someone who can translate a business problem into a workflow problem instead of reaching for a model first. It looks like someone who understands that knowledge quality often matters more than prompt cleverness. It looks like someone who can define failure conditions before launch rather than inventing success language after launch. It looks like someone who knows that adoption, governance, and review structure are product features, not administrative details.
It also looks like intellectual honesty. A real expert can say, “This use case is weak.” Or, “This is not ready for production.” Or, “You do not need an agent here.” Or, “The cost of review will eat the productivity gain.” That kind of answer often sounds less exciting, but it is far more valuable.
The same standard is increasingly visible in search and content systems. Google’s people-first guidance asks who created the content, how it was created, and why it exists. Those questions map neatly onto AI consulting too. Who is making the recommendation. How did they arrive at it. Why are they proposing it. If the answer is mostly branding, borrowed jargon, and urgency theater, the risk is obvious.
A weak AI expert sells inevitability. A strong AI expert sells fit.
How buyers should separate operators from performers
The fastest way to improve AI buying decisions is to stop asking, “Do they understand AI?” and start asking, “What have they actually made work?”
That question changes everything.
Ask what use cases they implemented, not just advised on. Ask what metrics moved. Ask what the baseline was. Ask what failed and why. Ask how they evaluated outputs. Ask what human review remained necessary after deployment. Ask how they handled stale knowledge, low-quality inputs, security constraints, and user adoption. Ask what they would refuse to automate.
These are not bureaucratic questions. They are filters for seriousness.
If someone cannot explain evaluation, they are not ready to be trusted with production AI. If they cannot distinguish a prototype from an operating system, they are selling theater. If they speak only in futures and never in retrospectives, they probably do not have enough real work behind them.
This is also why experience should be visible in how AI systems are built and documented. OpenAI’s guidance for GPT knowledge emphasizes that uploaded files become working context for the system, and instruction quality determines whether outputs stay grounded. That makes provenance, boundaries, and disciplined knowledge design part of expertise, not an afterthought. The internal E-E-A-T brief used for this article makes the same point particularly well: fluency is not authority, and trust comes from clean sources, clear ownership, revision discipline, and explicit limits.
The market is moving from performance to proof
There is a broader cultural change happening under the surface. The first phase of the AI boom rewarded speed, novelty, and visibility. The next phase will reward reliability, judgment, and evidence.
That transition is already visible in the language of serious institutions. Google’s documentation for succeeding in AI search experiences does not ask creators to flood the web faster. It asks for unique, satisfying, non-commodity work. NIST does not ask organizations to be louder about AI. It asks them to manage risk and evaluate systems. OpenAI does not describe evals as a luxury. It describes them as foundational to reliability.
Those signals matter because they point in the same direction. The era of AI spectacle is not over, but it is losing pricing power. Buyers are getting more skeptical. Teams are getting more selective. Search systems are getting better at identifying thin value. The burden is moving back where it belongs: onto proof.
That is healthy for the market. It favors builders over broadcasters, operators over trend merchants, and professionals over tourists.
The right response is not cynicism about AI. AI is already useful, and in many cases genuinely powerful. The right response is higher standards. Prefer people who can show scar tissue, not just certainty. Prefer people who can discuss constraints, not just opportunities. Prefer people who have moved a metric, improved a workflow, or made a team measurably better.
Because in the end, the fake expert sells possibility.
The real one can point to what changed.
Sources
Creating helpful, reliable, people-first content
Google Search Central guidance on people-first content, E-E-A-T, trust, authorship, and content quality.
https://developers.google.com/search/docs/fundamentals/creating-helpful-content
Our latest update to the quality rater guidelines E-A-T gets an extra E for Experience
Google Search Central Blog post explaining why experience was added to E-E-A-T.
https://developers.google.com/search/blog/2022/12/google-raters-guidelines-e-e-a-t
Google Search’s guidance on using generative AI content
Google’s official guidance on how AI-assisted content is judged and where low-value automation becomes a problem.
https://developers.google.com/search/docs/fundamentals/using-gen-ai-content
Top ways to ensure your content performs well in Google’s AI experiences on Search
Google’s 2025 guidance emphasizing unique, non-commodity content for AI search environments.
https://developers.google.com/search/blog/2025/05/succeeding-in-ai-search
The State of AI Global Survey 2025
McKinsey research on where organizations are actually seeing value from AI and where enterprise-wide impact remains limited.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
AI Risk Management Framework
NIST framework describing trustworthy AI and the importance of test, evaluation, verification, and validation across the lifecycle.
https://www.nist.gov/itl/ai-risk-management-framework
Working with evals
OpenAI documentation explaining why evaluations are essential for testing and improving LLM system performance.
https://developers.openai.com/api/docs/guides/evals/
Evaluation best practices
OpenAI guidance on designing evaluation systems for reliable AI outputs in production settings.
https://developers.openai.com/api/docs/guides/evaluation-best-practices/
Author:
Jan Bielik
CEO & Founder of Webiano Digital & Marketing Agency
