“Upload a photo and the system identifies the right product.”
This is where AI stops being a marketing tool and starts acting like field support.
In the real world, people don’t always have the model number handy. Nameplates are scratched. Documentation is missing. Equipment has been swapped over the years. A technician shows up and realizes they’re looking at something different than expected.
Vision changes the workflow:
- Take a photo of the nameplate (or the equipment itself)
- Upload it
- Let the model extract and interpret what it sees
- Get the correct product match, right documentation, and next steps
Then the system can surface what actually matters in the moment:
- Safety steps
- Maintenance checklists
- Warranty or service details
- Quick troubleshooting guidance
- Links to deeper documentation when needed
This is the shift: your site stops being a brochure and starts behaving like an operational tool. And that’s where adoption follows — because people will keep using what saves them time under pressure.
“Chatbots have annoyed people for years.”
We’ve all been trapped in the old chatbot loop: “Please choose from these options.” “No, not that.” “Here are three unrelated links.”
The problem wasn’t the idea of assistance, it was that the user experience felt interrupted and forced. Marco’s point: the new pattern isn’t a bot that hijacks the journey. It’s assistance that supports it.
A better experience tends to look like this:
- The user navigates normally
- Help appears only when it’s relevant
- The UI stays calm and optional (a side panel, a small nudge, a contextual helper)
- The user can ignore it or use it without friction
And design matters more than most teams think:
- Make it obvious what’s AI-generated
- Show sources or “where this came from” when possible
- Avoid clutter and gimmicks
- Match the helper’s behavior to the page type (product page vs. industry page vs. support page)
If your AI experience feels like a pop-up ad, users treat it like one. If it feels like a quiet expert standing nearby, users trust it.
“Everyone will do mediocre content, so intent becomes the key.”
AI is flattening content. Fast.
When every company can generate 20 blog posts by Friday, the differentiator isn’t volume — it’s usefulness. And usefulness comes from intent: what the user is really trying to do.
To win on intent, Marco’s guidance is refreshingly unglamorous:
- Understand the user’s starting point
- Answer the question directly (no throat-clearing)
- Provide deeper content for users who need more
- Structure pages so both humans and machines can interpret them easily
And in technical markets, credibility is crucial. Content should include:
- Real authorship and expertise signals
- Clear structure and scannable logic
- Sources and references when appropriate
- Niche specificity that proves you actually know the domain
Backlinks still matter, but specificity is a weapon. In a world of AI-generated sameness, the teams that win are the ones who can be precise.
“Take the salary for the next hire and invest it into AI.”
Budgeting for AI can feel like a rabbit hole. Marco offers a pragmatic way to think about it:
If you’re about to hire, consider whether that investment should go into a scalable AI system first.
This works because AI supports:
- Marketing
- Sales
- Service
- Research
- Internal knowledge work
This isn’t about replacing people. It’s about removing the low-level drag that keeps skilled people stuck doing repetitive work.
The other big piece is control: privacy, access, logging, and model choices. Many enterprises will build their own ecosystem because they can’t afford to gamble on shifting platform rules or unclear data boundaries.
Done right, one investment becomes a shared capability across teams instead of a shiny marketing experience that dies in six months.
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