There’s no shortage of AI conversations happening across the B2B industry, but one thing became abundantly clear during our recent B2B Marketing x Marcel Digital roundtable: while almost everyone is doing AI, very few organizations have figured out how to turn that experimentation into meaningful enterprise value.
Hosted by Richard O’Connor, CEO of B2B Marketing, alongside Kyle Brigham, Chief Strategy Officer at Marcel Digital, the discussion brought together senior marketing leaders in Chicago to share honest experiences of what’s working, what isn’t, and what it really takes to operationalize AI at scale. The result wasn’t another conversation about the latest tools or prompts, but a candid look at the foundations organizations need if they want AI to become a genuine growth driver.
AI has moved beyond experimentation but NOT beyond fragmentation
One of the strongest themes to emerge was that AI isn’t a technology problem: it’s an operating model problem. Across the room, organizations described similar experiences: isolated experiments, enthusiastic early adopters, and growing pressure from leadership to “use AI” without a shared understanding of what success should actually look like. In many cases, businesses had swung to one of two extremes.
Either AI access was tightly controlled through enterprise platforms that proved too restrictive to be useful, or employees had unrestricted access to tools with little guidance, governance or training. Neither approach creates long-term value. Instead, participants agreed that success comes from embedding AI into the way teams work, with clear ownership, practical use cases and a strategy that connects experimentation to business outcomes.
Competitive advantage comes from strategy, not the AI model
The discussion also challenged one of the biggest misconceptions surrounding AI: that simply adopting new tools creates competitive advantage. In reality, the technology itself is becoming increasingly accessible. What differentiates organizations isn’t the model they use, but the quality of their data, the strength of their governance and the decisions they make around implementation. The businesses seeing the greatest success aren’t asking which AI platform to buy next; they’re asking who owns AI strategy, how data is managed, and how the time saved through automation can be reinvested into higher-value work.
Efficiency means nothing without commercial impact
While productivity gains are easy to celebrate, proving that AI is influencing pipeline, accelerating deals remains far more difficult. Participants agreed that marketers risk undermining AI’s credibility if they continue to focus solely on efficiency metrics. Instead, marketing leaders have an opportunity to reposition themselves by speaking the language of commercial outcomes. Demonstrating how AI contributes to things like deal velocity, account growth or revenue generation is ultimately what earns credibility with both the C-suite.
The roundtable also challenged the assumption that efficiency should automatically lead to cost cutting. Research shared during the talk found that 75% of CEOs have already reduced marketing investment or headcount because of AI. Yet, the examples shared around the table painted a very different picture. The most successful AI initiatives weren’t replacing marketers; they were removing repetitive, low-value tasks so teams could focus on strategic work that had previously been squeezed out. Automating proposal creation, briefing documents or sales enablement content wasn’t about reducing headcount; it was about giving experienced marketers more capacity to create and collaborate.
AEO is becoming a strategic investment, not just another trend
Answer Engine Optimization (AEO) and AI-driven discovery sparked one of the most forward-looking discussions of the session. While traditional search remains a core part of the marketing mix, there was broad agreement that buyer behavior is evolving, and organizations need to start preparing for a future where AI-powered search and discovery play a much bigger role. Rather than questioning whether AEO is worth investing in, the conversation focused on how marketers can build an effective strategy through AI-informed content, subject matter expert-led thought leadership, structured data and diversified distribution.
At the same time, participants recognized that success won’t come from investment alone. Building a robust measurement framework that combines traditional analytics with emerging AI visibility tools will be key to understanding ROI and refining strategy over time. As the space continues to evolve, organizations should also be evaluating their current media mix to create room for these emerging initiatives. The consensus was clear: those that start investing in and developing their AEO capabilities today will be best positioned to capitalize on the opportunities of tomorrow.
Marketing has an opportunity to lead the AI agenda
Perhaps the most encouraging takeaway from the session was the role marketing can play in shaping AI adoption across the organization. Rather than being the function expected to simply “do more with AI”, marketing is uniquely positioned to orchestrate how AI creates value across sales, customer insight and revenue generation. That means moving beyond prompts and productivity hacks to focus on the bigger picture: aligning data, connecting workflows and ensuring AI is solving genuine business problems rather than creating more activity for activity’s sake.
Ultimately, the discussion reinforced that AI maturity isn’t defined by how many tools an organization has deployed or how many pilots it has launched. It’s defined by whether AI has become part of a repeatable operating model that supports commercial outcomes. Strategy, data quality and human judgement remain the real differentiators.
Organizations that invest in those foundations won’t just become more efficient, they’ll be the ones that successfully transform experimentation into lasting enterprise value.
