Why Enablement Still Needs Humans in the Age of AI Agents

‍Insights from Rebecca Bell on the future of sales enablement

AI is changing how enablement teams work — but according to Rebecca Bell, a go-to-market enablement and transformation leader with 30 years of experience, it isn't taking their jobs. In a recent conversation on the Sales Enablement Pioneer Podcast, Bell laid out a grounded, experience-tested view of where AI genuinely helps enablement teams, and where it simply can't replace the people doing the work.

No Function Gets "Absorbed" by AI

Asked which parts of enablement — content, coaching, admin, analytics — AI is likely to take over entirely, Bell's answer was blunt: none of them.

"AI will have a way of supporting in all of those areas," she said, but the human role doesn't disappear — it shifts. Humans still have to set the strategy, define the tone, and decide what "good" looks like. AI becomes an accelerator and a contributor, sometimes even a useful challenger of the work — but not the owner of it.

She used communications as an example, drawing on her own background in comms roles. She'll use AI to help draft messages, but she never ships the first draft untouched. AI-generated content, she noted, often fails to speak to the hearts and minds of the actual audience — and AI tends to deliver its output with total confidence, even when it's wrong. Catching that gap is a distinctly human job.

Why Even "Automatable" Work Still Needs a Human Check

The conversation also tackled a more practical question: are there enablement tasks safe to fully automate? Bell's answer centered on reporting and analytics — areas that seem objective on the surface but aren't as stable as they look.

She shared an internal example: her team ran the same prompt against the same dataset using their own individual instances of an AI tool — and got different results each time. Why? Because each person had trained their AI differently to understand their own definitions, audiences, and context. Same question, same data, different answers.

The implication is significant for enablement leaders trying to prove their impact with data: an always-on AI dashboard isn't something you can set and forget. It needs to be reviewed, scrutinized, and spot-checked repeatedly — not trusted after the first (or tenth) confident output. As enablement works to shift from a "cost center" to a "value center," the integrity of the data behind that case matters enormously.

The Rise of the "Super Enabler"

One of the most interesting threads in the conversation was the emergence of what Bell calls the super enabler — a newer organizational model appearing in AI-native startups and scale-ups.

Traditionally, enablement teams are built around individual specialists: someone owns onboarding, someone owns tools and process, someone covers a region. Bell describes this as effective but rigid and hard to scale. Newer, faster-growing companies are experimenting with a different structure: a smaller number of "super enablers," each managing a suite of virtual agents that extend their reach across multiple functions.

But Bell is candid about the risks of this model:

• One person can't own everything. Even with agents doing the legwork, a single super enabler reviewing a large volume of AI output is "a lot of homework to mark" — and mistakes are more likely when oversight is stretched thin.
• Learning suffers in isolation. If a super enabler is only cross-checking their own agents' work, they lose exposure to new ideas from a wider team.
• Speed isn't always the goal. "Just because you can do more doesn't mean you should do more," she said. Enablement exists to support human sales reps — who are, in her words, "slow, messy, complicated" — and no volume of AI agents can make people move faster than they're able to.

Her conclusion: most organizations will still need multiple super enablers, spending real time coordinating and exchanging ideas — not one person running an army of agents alone.

What Happens to Early-Career Enablement Roles?

If senior "super enabler" roles become the norm, where do people entering the field learn the ropes? Bell called this her biggest open question. Fewer specialist roles could mean fewer entry points into the profession — a genuine tension she doesn't pretend to have solved.

Her tentative answer: some functions — like product enablement or certification work, which follow more of a structured formula — could still be a training ground for earlier-career professionals working alongside AI agents, even as more complex, judgment-heavy work (like onboarding or stakeholder alignment) shifts toward the super enabler model.

AI and the Manager: Supplement, Not Substitute

The conversation also turned to sales managers, whose coaching role Bell was firm can't be automated away. AI's real value here, she argued, is in surfacing insight — using conversational intelligence to understand what's really happening on customer calls, or powering AI roleplay so reps can practice more before a live coaching session.

But the danger, she warned, is managers treating AI insight as a replacement for their own coaching time rather than a way to focus it better. "AI supplements, scales and supports where the manager can't be" — it should sharpen a manager's attention on the highest-value conversations, not remove them from the process.

Her Advice: Experiment

Asked for one closing piece of advice for enablement professionals navigating this shift, Bell didn't offer a playbook — because she doesn't think one exists yet.

"I don't think there'll ever be a formula that works everywhere," she said. Different organizations, cultures, and challenges will require different approaches. Her advice is simply to keep experimenting, stay connected to the wider enablement community, and stay open to ideas from outside your own organization — which is exactly where conversations like this one come in.

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