How AI and Data Are Rewriting B2B Growth Craig Dempster, CEO of Trilliad

Nearly all organizations have adopted some sort of AI tool (if not multiple), but many say that the ROI isn’t there. Why is this happening? What’s holding back B2B companies from seeing true success with today’s advanced AI tools?

AI doesn’t fix broken systems. It accelerates them.

That’s the core problem. Most organizations are dropping AI into environments that are already fragmented. Disconnected data. Siloed teams. Inconsistent workflows. Leaders expect AI to clean that up. It doesn’t. It amplifies whatever’s already there. If the foundation is messy, AI makes the mess bigger.

The second issue is intent. Too many companies treat AI as a tool to deploy rather than a decision to make about where it can actually move the business forward. So teams automate content, automate reporting, automate outreach, and then wonder why the numbers don’t improve. Automation without alignment isn’t progress. It’s just faster noise.

The organizations seeing real ROI started differently. They got clear on how marketing, sales, and customer data could work together first. Then they applied AI where it could sharpen a process or improve an outcome. That sequence matters. 

Your 2026 Growth Imperatives suggest that the issues are not with the AI tools themselves, but the underlying data infrastructure. How can teams make their data as actionable as possible to keep up with modern businesses today?

Stop treating data like a reporting function. Start treating it like a decision engine.

Most B2B organizations aren’t short on data. They’re short on shared data. CRM, campaign platforms, sales tools, customer platforms are all collecting signals, but nobody’s looking at the same dashboard. Marketing interprets results one way. Sales interprets them differently. Customer Success is working from something else entirely. That’s not a data problem. That’s an alignment problem.

Making data actionable starts with one question across every team: which signals actually matter? Which accounts are engaging? What topics are moving buyers? Where is momentum building, and where are deals stalling? When teams agree on those answers and pull from the same source, data starts driving real decisions.

That’s the shift. Shared signals. Shared accountability. One view of what’s working.

How should B2B marketers be rethinking their brand-to-demand strategy to stay relevant and visible, given AI’s role in search?

Here’s the reality: many buyers are encountering a brand inside an AI-generated answer before they ever visit their website. That changes everything about how visibility works.

AI answer engines are now shaping first impressions. So the question marketers need to ask isn’t just “where do we rank?” It’s “is our content structured in a way that AI systems can actually interpret and surface?” If the answer is no, you’re invisible at the most important moment in the buyer’s journey.

Content needs to be clear, authoritative, and built around the exact questions buyers are asking. 

And brand and demand can’t keep operating as separate functions. The brands that stay visible are the ones creating consistent, memorable signals over time, so that when a buyer is ready to move, there’s no question about who they think of first. 

Your guide emphasizes that sales enablement should move away from sporadic sales training sessions. What should effective sales enablement training look like today?

One-time training creates short-lived energy and very little lasting change. Most organizations know this, and they keep doing it anyway.

The problem is that a standalone event can’t keep pace with how buyers actually behave. Sellers need continuous reinforcement tied to real conversations and real customer signals, not a slide deck from last quarter. Managers need to coach against what’s happening right now in the field, not what was happening six months ago when the last training was scheduled.

Effective sales enablement today is embedded in the operating system of the business. It’s tied to measurable outcomes. It shows up in how deals are reviewed, how feedback is delivered, and how teams are developed week over week. The strongest organizations don’t treat sales development as an event. They treat it as part of how the business runs.

Traditional lead generation models often prioritize volume over engagement. How is the rise of AI and interactive content changing what effective lead generation looks like in 2026? 

Lead generation is no longer about volume. It’s about intent.

For years, teams measured success by how many contacts they captured, even when most of those contacts showed no real buying signals. That model is breaking down fast. Buyers expect immediate value and more control over how they engage. Handing over an email address in exchange for a gated PDF isn’t the exchange it used to be.

Interactive content and AI are changing the pattern. When a buyer engages with a conversational tool that gives them something useful right away, that’s a different signal than a form fill. AI helps teams read those signals and separate casual interest from real momentum, so the response can be personalized and timed correctly.

What does it actually mean to “operationalize AI” inside a revenue organization? What are some areas where many companies go wrong?

Operationalizing AI means it’s no longer a side project. It’s how the business actually runs.

In a revenue organization, that means AI is embedded across marketing, sales, and customer success, improving decisions, timing, and execution in real workflows, not just in pilot programs or innovation labs.

Most efforts stall for three reasons: poor data quality, fragmented systems, and a lack of operational enablement. Companies implement AI before the foundation is ready. And without a solid foundation, AI has nothing meaningful to build on.

The other common mistake is letting teams operate against different goals with different measurements. If Marketing, Sales, and Customer Success aren’t aligned on what success looks like, AI can’t bridge that gap. It will just automate the misalignment. Get the foundation right first. Shared goals, shared data, shared accountability. Then AI becomes a real accelerator.