Why CEOs are choosing to own the AI agenda.
Over the past few years, I’ve spent time in leadership rooms across markets, industries and levels of maturity. Different accents, same whiteboards, same question: why isn’t AI delivering the impact we expected?
Most executives assume the risk sits in the technology. Picking the wrong platform. Backing the wrong vendor. Moving too slowly on agents and automation. In reality, the organizations struggling to see value from AI are rarely blocked by technology at all.
They are blocked by adoption.
That is why AI has moved decisively onto the CEO’s agenda. Not because the tools suddenly got harder to use (quite the opposite), but because the organizational challenges around using them properly can no longer be delegated.
From Experiment to Infrastructure
Across regions, AI has crossed a clear threshold. It is no longer treated as an innovation experiment or functional upgrade. Increasingly, it is viewed as core infrastructure—something that reshapes how decisions are made; work is structured; and value is created.
I see this shift consistently, whether organizations are based in North America, Europe or Asia Pacific and whether they operate inside or outside the direct selling channel.
At the same time, ROI expectations have compressed dramatically. Where boards once tolerated a three-to-five-year horizon, many now expect meaningful returns within 12–24 months. This isn’t a local adjustment. It’s a global reset. And it has exposed an uncomfortable truth: many organizations were not as ready to adopt AI as they believed.
Why Adoption (not Technology) Is the Bottleneck
When adoption fails, it rarely does so loudly. It shows up as pilots that never scale. Teams using generic tools for marginal gains. Licenses piling up. AI notetakers outnumbering participants in meetings. Prompt courses rolled out. AI-generated slides everywhere.
Plenty of movement. Very little meaningful change.
In most organizations, AI is technically “in use” but not embedded. Intent exists, but behaviour does not shift. The result is predictable: uneven uptake, modest gains and disappointing ROI.
This matters because the technology is delivering value. Large cross-industry studies show that more than 80 percent of organizations already report positive AI ROI, with most of the remainder expecting it within the next year. But dig one layer deeper and the picture changes.
The majority of gains come from basic efficiency—time saved, tasks accelerated, output marginally improved. Useful, yes, but more vanity than value. Far fewer organizations are seeing improvements in decision quality, revenue growth or the creation of genuinely new capabilities—the areas where long-term value actually compounds.
AI is working. Just mostly at the shallow end of the pool. The gap isn’t technical. It’s human and organizational.
The Human Constraint
I’ve written before about the human economy, where trust, connection and empathy become the real currencies as change accelerates. This isn’t a leadership ideal. It’s a practical requirement for adoption.
AI cannot be forced onto an organization. People need to understand it, trust it, see where they fit and have clarity on what the company will—and will not—do with it. When leaders skip that groundwork, resistance builds quietly; adoption stalls; and progress plateaus without anyone quite noticing.
That’s why AI adoption breaks down when it’s treated as a software rollout rather than an organizational shift that changes how people work, decide and are rewarded.
The Agent Reality Check
Nowhere is the adoption gap clearer than in the current excitement around AI agents. Despite the hype, only a small minority of enterprise AI use cases run with anything resembling true agent-level independence. The vast majority remain assisted or partially automated with humans still initiating, overseeing and correcting the work.
This isn’t failure. It’s realism. But it does highlight a familiar misalignment. Organizations are racing ahead conceptually while lagging operationally. Without redesigned workflows, governance and incentives, more advanced capabilities simply don’t stick.
Direct Selling’s Excuse Has Expired
Historically, the channel has been underserved by enterprise technology. Legacy systems, fragmented data and uneven investment were real constraints—and for a long time, fair ones.
That excuse no longer holds.
AI has leveled the playing field. Capabilities are cloud-based, commoditized and accessible regardless of size, geography or sector. In that world, “we’ve been behind on technology” doesn’t cut it—not in direct selling, not anywhere.
Some of the most effective AI adoption I’ve seen up close hasn’t come from large enterprises. It’s come from smaller, more agile organizations without decades of technical debt. They deploy, iterate and move fast. They’re not experimenting, they’re executing.
Larger players can catch up. But only if they abandon incrementalism. Because the pace these leaner organizations are moving at is real, and it’s already leaving some behind in the rearview mirror.
Why CEOs Are Stepping In
Across industries, the organizations seeing stronger AI returns share a common trait. They treat adoption as organizational transformation, not a technology rollout.
They invest in capability, not just access. They redesign workflows instead of bolting AI onto old habits. They align incentives so people are rewarded for changing how work gets done, not for protecting the status quo.
These are not functional decisions. They cut across governance, risk, talent and operating models. They surface trade-offs between speed and control, autonomy and compliance, efficiency and reinvention.
Only the CEO has the authority to resolve those coherently.
The Real Risk Ahead
This shift is not a reflection of poor leadership or lack of ambition. Most organizations are genuinely trying to move forward. The problem is that meaningful AI adoption requires creating space for change at a time when teams are already stretched. Without explicit executive ownership, that space rarely appears.
Over the next few years, the gap will widen between organizations that treat AI adoption as a strategic discipline and those that treat it as a collection of tools. The former will compound gains as capabilities spread. The latter will remain stuck in cycles of experimentation, wondering why the returns never quite arrive.
For direct selling leaders, the implication is clear. The question is no longer whether AI will matter, or even which technologies to adopt. It’s whether the organization is genuinely prepared to absorb what AI makes possible—and whether leadership is willing to own the structural change adoption requires.
DAN DEBNAM, Founder & CEO, Inovara, is a highly sought-after speaker and trusted expert in digital transformation, AI strategy and innovation within the direct selling and network marketing industry and beyond. Known for his engaging style, humor, practical approach and ability to turn complex technologies into actionable strategies, Dan regularly inspires and equips audiences across major direct selling events in the UK, Europe and the USA.
From the March/April 2026 issue of Direct Selling News magazine.
