How AI is quietly reshaping the creator economy (how brands, agencies and creators actually work)

The Kolsquare MCP, and what it says about the next chapter of influence marketing

Something quiet but structural is happening in our industry.

For the last decade, brands, agencies and creators have been busy figuring out how to work with each other at scale. Contracts, disclosure, measurement, tooling, certifications. That work is not finished, but the foundations are now solid. Europe alone employs tens of thousands of professionals across the creator economy, and the industry is now measured, monitored and, increasingly, regulated.

The next chapter is not about volume. It is about how work actually gets done inside brands, agencies, agents and creator businesses. And AI is quietly rewriting the rules.

At Kolsquare, we shipped a small piece of infrastructure that captures a lot of what this shift really means. Before getting to it, let me describe the problem it is actually solving.

The hidden cost of influence marketing today

If you have ever run an influencer campaign inside a brand or an agency, you know what I am about to describe.

You launch a campaign. Creators publish. Data starts coming in from Kolsquare, from the platforms, from your CRM. You need to make a decision: reallocate budget, brief a new creator, prepare a stakeholder recap, adjust the next wave.

What actually happens?

Someone screenshots the stats. Someone reshapes them in Excel. Someone builds a slide. Someone emails it to the manager. Someone else does the same for a different campaign the next day. Multiply that by hundreds of campaigns per year in a large agency, and thousands across the market, and you start to see the shape of the problem.

The most valuable people in our industry, senior campaign managers, influence strategists, brand leads, agents, spend a significant part of their time doing things that would embarrass a 1998 spreadsheet consultant.

This is the hidden cost of influence marketing today. It has nothing to do with the tools not being good enough. Data platforms have become impressive, our own included. The real issue is that data still sits in one place and decisions get made in another, and the humans in between absorb the friction.

AI, done well, is finally about to close that gap.

What changes when you stop exporting and start asking

For years, the implicit deal between our industry and its software was: we give you a rich interface, you learn where to click, you extract what you need, you translate it yourself into decisions.

That deal is changing.

The new deal is: you tell the system what you want to accomplish, and the system does the extraction, the reshaping, and part of the reasoning for you.

Instead of clicking through five dashboards to find your best-performing campaign of the last quarter, you ask the question in plain language and get a stakeholder-ready answer in seconds.

Instead of exporting a creator profile and manually searching for red flags, you ask the system whether this creator has ever posted content that could compromise your brand.

Instead of manually building a media kit from screenshots, a creator can generate a live, shareable, brand-ready proof of impact directly from their own data.

The interface is still there. But it is no longer the point.

This shift will touch our industry more deeply than most people realise, because the daily reality of influence marketing is far more manual than it looks from the outside. And once conversational access to data becomes the norm, expectations reset very quickly.

What the Kolsquare MCP actually does

In 2026, Kolsquare launched its MCP Server, becoming one of the first influencer marketing platform in the world to ship a native MCP.

For those unfamiliar with the acronym: MCP stands for Model Context Protocol. In plain words, it is a standard way for a professional tool to expose its data to an AI assistant that a customer already uses, without giving up control, security or workflow context.

For a brand or agency using Kolsquare, this changes the mechanics of daily work. Instead of exporting data, they can now ask, in plain language:

« Compare the engagement rate of my last three campaigns and tell me which one performed best per euro spent. »

« Give me a stakeholder-ready one-page recap of the Berlin campaign I launched in May. »

« Based on campaign 48213, which creators would you re-book next quarter, and why? »

These are not hypothetical prompts. They are the questions influence marketing teams already ask themselves every week, and today, they spend hours turning into slides. Now they get answered in seconds, grounded in the customer’s own data, inside the AI tool the customer already uses.

The MCP does not replace Kolsquare‘s interface. It does not replace human judgment. What it replaces is the friction between the data and the decision.

This is less about adding a feature and more about changing how work gets done.

What this shift means for each actor in our industry

Different parts of our ecosystem will feel this shift differently.

Brands. Marketing teams get real-time analytical control without waiting for someone to build a report. Executives get answers to strategic questions in the meeting where the question is asked, not two days later. Reputational due diligence, which today happens partially and manually, becomes systematic. Decisions on creator selection, budget allocation, and measurement all get faster and better documented.

Agencies. The competitive advantage of an agency in the next five years will not just be the roster of creators or the quality of the strategy. It will be the speed at which they deliver insight, adjust in-flight, and produce clean, stakeholder-ready outputs for their clients. Conversational access to data flips that game. Agencies that adopt it early will operate at a very different pace.

Creators. For creators, the shift is even more direct. Today, most of them still report brand collaborations manually: screenshots, spreadsheets, Canva files updated every few months. AI-native tools will let them share live, standardised proof of value with any brand, spend more time on what they actually do best, and be evaluated on cleaner, more transparent data.

Platforms like Kolsquare. Our own role changes too. We are no longer just a place where data lives, or an interface where humans work. We become the strategic intelligence layer that plugs into any AI environment our clients use, while ensuring the data stays clean, complete, isolated per client, compliant, and trustworthy. The moat shifts from richer UI to richer context: deeper data coverage, sharper credibility signals, better handling of nuance across countries and platforms.

Different parts of the industry, same underlying change: work stops being about manipulating outputs and starts being about making decisions.

The questions our industry has to face

This transition is not trivial. It opens a set of questions our industry has not seriously debated yet.

Data trust. As AI increasingly reasons on client data, we need shared, verifiable standards for how that data is isolated, secured and audited. Enterprise brands are already asking hard questions here, and rightly so.

Creator IP. As creator content gets consumed by AI systems, the question of ownership, attribution and consent will only become louder. Value that AI can extract from creator work at scale must, in some form, flow back to those who created it. Our industry cannot leave that question to platforms alone.

Humans in the loop. Removing friction is not the same as removing judgment. The best use of AI in our field will be to give our best professionals leverage, not to replace their responsibility. Explainability, audit trails, and clear ownership of decisions become new baseline expectations.

Compliance and responsibility. As tools get faster, so does the ability to launch questionable campaigns. Responsible influence has to be built into AI workflows from day one, not layered on top after a scandal.

Environmental cost. Reasoning systems consume real energy. Every AI-generated report, every automated benchmark, every conversational query has a carbon footprint that our industry has not yet begun to internalise seriously. We should be building AI workflows that create leverage per watt, not just leverage per minute. This is a conversation our industry has to open, and Kolsquare intends to open it.

Concentration of power. Model providers are few. The more we integrate them, the more we must protect against vendor lock-in and preserve strategic independence at the platform layer.

None of these questions are hypothetical. All of them are already live.

Where Europe fits in

I am biased on this next point.

Europe has, over the last five years, built the most advanced regulatory and self-regulatory infrastructure for influence marketing in the world. National laws, SROs, trade bodies like UMICC and EIMA, certification programmes, monitoring technologies, thousands of hours of standards work. Most of this did not exist five years ago.

The AI shift is arriving on top of that infrastructure, not underneath it.

That gives Europe a rare opportunity: not to be the most regulated creator economy, but to become the global benchmark for a responsible one. AI, deployed thoughtfully, is exactly the layer that lets responsibility scale.

That is the bet we are making at Kolsquare. We do not just want to build AI-powered products. We want to build AI-powered people: brands, agencies, creators, and our own team, operating with dramatically greater leverage than they have today, and doing it responsibly.

The Kolsquare MCP is one brick in that direction. The next chapter of our industry will be written brick by brick, in code, in standards, and in the choices we all make about what we automate and what we still owe to the humans on both sides of the screen.

And you, what do you think about the impact of AI on the Creator Economy?

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I’m Quentin Bordage, the co-founder and CEO of Kolsquare, Europe’s leading influencer marketing intelligence platform, Entreprise à Mission (2020) and B-Corp (2024), part of team.blue group (since 2024). I’m also executive committee member of UMICC, and co-founder of EIMA, the European Influencer Marketing Alliance.