Artificial Intelligence
October 19, 2026
Why Incumbent B2B Media Companies Must Play Offense to Become AI-Native
# AI-native B2B media company
# Market-maker platform
# Proprietary first-party data
# AI-powered human-led
# AI ROI for media and events
The Channel Company CEO Matt Yorke on turning nearly 50 years of shelved proprietary data into a market-maker platform, and the operating discipline it takes to get there.
Heather Holst-Knudsen
The incumbent mistake one CEO refuses to repeat
Matt Yorke remembers what it felt like to be the incumbent the last time a technology shift rewrote B2B media. He was at IDG (now Foundry) when TechTarget (now Informa Techtarget) and CNET arrived as pure-play internet properties. IDG was doing digital work, but it was not a pure play and did not fully lean in. "It was always that sort of defensive approach," he told The Revenue Room™ Podcast .
"Why would we put more over there when we already have this massive sum of revenue over here?"
That hesitation gave the new entrants room to build valuable franchises.
Now CEO of The Channel Company , parent of CRN, Yorke is running the opposite play. The lesson for any AI-native B2B media company in the making is direct: incumbents that treat AI as a feature to defend the current model will repeat the last cycle; incumbents that treat their proprietary data as the foundation of a new platform can lead this one. The difference is not access to models. It is whether leadership is willing to reimagine what the business sells.
The business consequence: data on the shelf is capital you are not using
Most media, events, and data businesses are sitting on more proprietary signal than they monetize. Yorke described The Channel Company's inventory: nearly 50 years of content, awards entries, behavioral data, campaign data, roughly 61 research studies a year, data on every MSP in the world, and an overlay showing which partners can absorb MDF dollars and drive pipeline for vendors.
Much of that data had a single use. Awards entries are a good example. Entering is a rigorous process, so the submissions hold detailed first-party information.
Then, in Yorke's words, "We put all that information on the shelf because it gets covered at the event and it goes into the websites and then that's that."
The economic cost is not obvious on a P&L because it shows up as value never created rather than revenue lost. But the consequence is real. Proprietary data that sits idle cannot improve products, sharpen targeting, inform pricing, or support new revenue lines. Meanwhile, the open web traffic that historically monetized much of that content is under pressure. Yorke noted that many publishers are reporting traffic declines of around 45% or worse in an era of hybrid and AI search. When the distribution model weakens, the data asset becomes the business.
There is a margin consequence too. The Channel Company is PE-backed, and Yorke was candid that margin expansion is always on the agenda. His framing is that AI allows a company "to do those things with a growth mindset and not just a cutting mindset." Historically, every new dollar of revenue pulled human capital along with it. Yorke expects that ratio to change materially: at some point, adding revenue will not require adding headcount in the same way. He does not think The Channel Company is there yet, but says it is getting close.
Operating diagnosis: why most incumbents stay on defense
The defensive posture rarely comes from a lack of ambition. It comes from how the operating system is wired.
Data is owned by the function that produced it.
Editorial owns content, events owns awards and attendance, research owns studies, the agency owns campaign data. Each team uses its data for its own product. No one owns the cross-product signal, so no one builds on it.
AI is deployed as tools, not redesign.
Copilot access and a handful of pilots can produce real productivity gains without changing what the company sells.
Yorke's warning to customers applies internally as well: "I can't ship you a box of AI."
Nor can a leadership team deploy one solution and declare the company AI-native.
Early failures harden into skepticism.
The Channel Company's first in-house AI proposal builder was, by Yorke's account, "a bit clunky, it hallucinates a bit." An agency-side campaign builder was so narrow, and had access to so little data, that it fit only one or two customers. Without a review mechanism, failures like these confirm the suspicion that AI is overhyped and stall adoption.
Governance comes first, or not at all.
Companies holding customer PII (personally identifiable information), campaign data, and MDF-related information cannot scale AI responsibly if governance is an afterthought. At The Channel Company, governance came first, precisely because of the customer data it holds.
Five moves to become an AI-native B2B media company
1. Inventory the shelf, then reframe the business around it
Start with the question Yorke asks: what does AI allow you to do that you could not do before, and how can you reimagine the business to create more value for customers? The answer at The Channel Company is a platform that moves into predictive outcomes, mapping vendor demand to the partners best suited to serve it in order to drive a quantifiable pipeline outcome. Yorke uses the term "market maker" for this role: "It's our content that connects people that drives the adoption of technology."
The compounding mechanism matters more than the label. More participation across content, events, awards, and research produces richer intent and capability data. Better data improves the match between buyers and partners. Better matches produce more measurable pipeline for customers. More measurable outcomes strengthen retention and justify deeper participation, which feeds the data again. Yorke calls it going "back to the future": connecting buyers and sellers as the business once did in print, now dynamically and in real time.
2. Turn trusted content into a product layer
CRN Answers, The Channel Company's generative AI search product built on the CRN archive, is less about search than about learning how audiences will consume content when the website is no longer the main destination. Yorke is not convinced that typing a URL will be how people discover and consume content within five years. CRN Answers gives the company an entry point into LLM-based consumption, shows that trusted content can be a product layer rather than a page, and reveals the questions buyers and partners are actually asking.
The next phase connects answers to a personalized journey, recommending the next best article, video, or rich content, and feeds those signals into the company's intent platform. The principle Yorke emphasized: "We're not giving that away, we're reinforcing." Source authority is the asset. AI should strengthen it, not dilute it.
3. Put AI where it removes friction in existing revenue
The fastest proof points come from revenue workflows that already exist. The Channel Company's agent for its Aurora awards platform guides entrants through category selection and responses. Yorke reported roughly a 400% increase in completion rates versus the pre-agent process. The agent also supports table sales for the awards events, and it frees customer success managers from fielding inbound questions. On the sales side, an AI call-report platform outperformed salespeople's own call reports in a proof of concept and was rolled out.
These are not experiments for their own sake. Each one is tied to a paid product, a sales motion, or a cost of service.
4. Build a learning system, not a launch calendar
The Channel Company now runs 27 AI agents, up from 19 when Yorke presented at RevvedUP in the spring. The growth came with structure. Agents are published through a central studio and reviewed by a small AI committee that Yorke likens to air traffic control. Usage is tracked weekly by department on a Domo dashboard and reported to the board quarterly. Four AI innovation awards of $500 each are given every quarter. Every new hire completes AI training and signs the governance documentation before getting tool access.
The company's current estimate is about six and a half hours of productivity gained per employee per week. Yorke's next step is to put hard dollar figures against that number. That is the right sequence: adoption, then time, then money. Time saved that is not redeployed into revenue or service quality does not show up in enterprise value.
5. Draw the human line explicitly
"AI-powered, human-led" is only useful if employees know where the line sits. Yorke's version is clear: "Do not outsource the decision to AI. Outsource the work to AI to get there faster and better so you can make a better, more informed decision." AI stops where accountability, trust, and judgment come into play. Even the predictive platform he describes will "always work with humans," aligned with the company's partner marketing managers in its concierge revenue line.
The same clarity applies to hiring. The first question for any new role is whether the candidate, or the hiring manager, can prove why AI cannot do it, or how adding headcount alongside AI produces a more efficient outcome. The stated goal is higher productivity and better customer service, not headcount reduction.
90-Day Management Focus
- Days 1-30: Build the data inventory. List every proprietary data asset by source, owner, refresh rate, consent status, and current use. Flag the assets used once and shelved.
- Days 31-60: Pick one compounding loop. Choose a single buyer-seller connection your company is positioned to improve, such as intent to partner, attendee to sponsor, or reader to research buyer. Define the outcome a customer would pay for and the data required to deliver it.
- Days 61-90: Install the operating scaffolding. Stand up a review mechanism for agents, a weekly adoption view by function, and a first attempt at converting time saved into dollars. Name the executive who owns commercial AI value, not just AI tools.
Recheck at day 90: which shelved data asset is now in use, and what customer outcome does it support?
Questions for the leadership team
- Which proprietary data assets do we collect once, use once, and shelve?
- If our website stopped being the primary way customers find our content, what would we be selling?
- Where are we making a defensive investment decision that a new entrant would make offensively?
- Who owns the cross-product signal that connects our content, events, research, and data?
- Where must human judgment remain non-negotiable, and have we told our teams?
Continue the conversation
Matt Yorke sits on the Revenue Room™ CXO Advisory Board, and the questions above are the ones CEOs in our network are working through now. Listen to the full episode of The Revenue Room™ Podcast, and if you lead a media, events, or data business making this shift, request an invitation to a Revenue Room™ CEO Boardroom Exchange, where operators pressure-test these decisions with peers.
Where to Take This Next
Revenue Room™ CXO membership is where this exact question, who owns the decision once AI sits inside the workflow, is being worked through in real time by CEOs, CROs, and RevOps leaders building the next revenue operating model together. The Revenue Room™ AI Value Creation Scorecard is a practical starting point for mapping where decision rights are currently missing inside your own commercial workflows.
About The Revenue Room™
Revenue Room™ CXO is an invite-only executive peer network and year-round decision system for profitable growth, serving CEOs, Presidents, and C-suite operating leaders across media, events, data, information services, marketplaces, subscriptions, and professional communities. Explore Revenue Room™ CXO membership or start with the Growth Readiness Diagnostic.
RevvedUP 2027
This conversation continues on stage at RevvedUP 2027 , The Revenue Room™’s flagship annual conference, March 14–16, 2027, at The Vinoy in St. Petersburg, Florida. Now in its third year, RevvedUP brings together a curated group of CEOs and C-suite revenue leaders around this year’s theme, “Compounding Advantage: AI, Data, and the New Race for Enterprise Value.” The questions raised in this post are the ones operators will pressure-test with peers in the room.
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