Why we bet the company on AI in 2026

For seven years, ThoughtLeaders has been a YouTube sponsorship agency. We connect brands to creators, manage the pipeline from first match to live deal, and we have done it across roughly $50M of sponsorships and 25,000+ collaborations. The data we accumulated along the way — every match, every price, every outcome — became the company’s most valuable asset long before AI became useful.

This year, we made the call: rebuild the company around AI. Not a product feature. Not an internal pilot. The entire operating system of the business.

Here is why, and what it has produced.

The trigger

Two things happened at roughly the same time.

The first: agents got good. Not “good for a demo” good. Good enough that an account manager could ask a coding-grade tool to pull a list of channels in a category, cross-reference repeat-deal history, and produce a renewal-opportunity shortlist — and the tool would actually do it, accurately, in under a minute. We watched it happen on real client work and the conclusion was unavoidable: the workflows we had been training people on for years were obsolete.

The second: our data finally outgrew us. Seven years of matched outcome data is the kind of asset that tells you things if you can ask it the right questions. We had been answering maybe ten percent of the questions our data could answer, because the other ninety percent required either custom SQL or a willing data engineer with three free days. Most of those questions never got asked at all.

The combination of the two — agents that can do real work, against a data set that has been waiting for someone to interrogate it — made the bet obvious.

The shape of the bet

We chose Claude Code as the foundation. Two reasons. One: it is general-purpose enough that the same toolchain serves both the engineering team building products and the business team running deals. We did not want to ship a separate tool for each function. Two: the agent paradigm aligns naturally with how a sponsorship operator already works — research a channel, evaluate a deal, pitch a brand, follow up. Each of those is an agent-shaped task.

We then made three commitments:

1. Every employee uses AI for their core work, not as a side experiment. Not “if you want to.” This is the job now.
2. The business team builds its own skills, the same way the engineering team builds its own scripts. We refused to gate AI capability behind a developer queue or a ticketing system.
3. The data layer comes with us. Whatever we built internally, the toolchain had to be able to query the seven years of sponsorship data the same way our analysts do.

What it produced

A quarter of the year in, the results are not subtle.

Account managers run renewal analyses in under a minute that previously required a request, a queue, and a half-day round trip with an engineer. One employee — Emma, on the AM team — built her own agent that scans live deals, identifies channels with strong renewal signals, and drafts pitch emails with the supporting data already embedded. She did this without writing a line of code. The agent has produced a steady stream of qualified rebooking opportunities her team can act on the same day.

Engineering velocity has changed shape. Smaller team, more shipped, fewer mistakes. The boring observation about AI making developers faster turns out to be true; the interesting one is that it made our developers more willing to ship to production, because the verification loop — write, test, run, fix — finally feels short enough to trust.

The most important result is harder to point at, because it is what we did not lose. Every other company we know that tried to add AI added it as a layer on top of their existing process. We replaced the process. Things look different now. The work feels different.

What this is the prelude to

Over the next two weeks, we are going to share, in order:

1. How we ran the training program for both developers and the business team
2. What 100% Claude Code adoption actually looked like inside the company
3. The shared connectors and skills we built so the business team could compose AI workflows without writing code
4. How individual contributors compounded the system on their own — Emma’s case study, and others
5. What our own employees and our pre-release client testers say AI is now unlocking for them
6. And, at the end of the series, what we are opening up to the rest of the industry

The TL;DR of that final piece: every advantage we built internally over the last four months is becoming something our clients — existing and new — can use directly.

If you run a marketing team buying YouTube sponsorships, or you run an agency, or you are a creator pricing yourself in a market that has gotten more sophisticated, the next two weeks are written for you.

We will not pitch theory. We will show the work.