AI, Data, and Revenue Growth: Takeaways from an Executive Roundtable

What telecom and data center revenue leaders shared with each other about AI, revenue data, and adoption.

RevGen recently hosted a closed-door roundtable for revenue leaders from across telecom and data centers to talk about AI in their businesses. The idea was simple: instead of one-on-one conversations with us, put the executives in a room together and let them compare notes with each other. Three topics anchored the discussion: AI and automation in the revenue lifecycle, revenue data as a strategic asset, and the bottleneck around AI adoption. The companies were different. The diagnosis was the same: the technology is moving faster than the organizational discipline needed to run it.

AI and Automation in the Revenue Lifecycle

On automation, the clearest theme was that AI has moved from a productivity tool to something closer to an operating layer for the business. A telecom CMO described mapping AI across every stage of his buyer journey: shop, buy, get, use, pay, and renew.

“This isn’t about replacing you. This is about making you much better,” he said of his marketing team’s early resistance to generative tools. AI-generated headlines, he added, routinely outperform what a human copywriter would choose. On the bigger picture, he was blunter: “This is a big machine. The whole business is a machine. And so who better to run it than, well, the machine?”

An SVP of Revenue Operations at a data center company showed what that looks like in practice. His team pulls CRM, service, and intent data together into daily seller recommendations. The result: the uncovered share of its customer base fell from 40% to 5%, with fewer sellers than the team had three years ago. “Sellers are happier than they’ve ever been,” he said, “but it was really around: if we can show them and tell them where to focus, it’s just made a monumental difference.”

Ian Foley, Vice President Data & Analytics Services, helped facilitate the discussion. He observed that most organizations are still in “the efficiency gain space.” The next level of maturity is moving from decision support to actual decision automation.

The optimism came with a warning about cost. One executive put it plainly: “the sprawl is real” once AI use cases multiply faster than finance teams can evaluate them. His FP&A partners are “in hell” trying to assess dozens of business cases without the staff to do it. His advice: “Go slow at first and make sure use cases actually prove out, and then slow build from there.” Foley added that token economics has become one of the top AI topics clients raise. SaaS-plus-token pricing is replacing flat subscription costs, and capital is now flowing toward workflow routing and smaller, cheaper models to manage that exposure.

Revenue Data as a Strategic Asset

By consensus, data was the most humbling topic in the room. A data center executive admitted that basic issues like unbilled subscriptions, uncharged overages, and missed late fees have gone unresolved for years, stuck between finance-owned and IT-owned systems. “I don’t even know how much money we’re missing,” he said, “but I know we’re missing money.”

A telecom CMO’s frustration was with something that sounds basic: contact data. “Contact information is the bane of my existence,” he said. No vendor has solved it, he argued, because people and their information move around too much.

Foley reframed the problem for the room: AI hasn’t fixed data quality. It’s obscured it. Where a dashboard once made an anomaly visible, a language model will now give a confident answer that’s wrong. He described a hallucinated result generated for a client that experienced staff recognized as invented right away. “The data issues aren’t highlighted by AI anymore,” he said. “If anything, they’re varied or spread out.”

Clearing the Adoption Bottleneck

On adoption, the group landed in the same place: people follow proof, not mandates. One executive runs a monthly “Innovation Lab” where employees swap stories about what AI is doing across the business, backed by a cash bounty for ideas that get adopted. “Suddenly the fear dissipates,” he said. “It’s theirs and not ours, the executive team.”

Foley described a similar dynamic inside RevGen. His conclusion: adoption isn’t really a technology problem. “I think humans need to see their peers being successful doing this.”

Another executive took a more operational route: pair a small, provable set of use cases with real ROI tracking before scaling access, rather than opening every tool to everyone at once.

Three things came up again and again: cost discipline, data governance, and a culture that rewards proof over mandate. None of them is a technology purchase. All of them are the harder, more human work still ahead.

If your team is working through the same questions, RevGen can help. Contact us to schedule a conversation with one of our Data & AI or RevGeneration experts.