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 is it actually replacing the people behind them?

For Rebecca Bell, the answer is no.

With 30 years of experience across go-to-market enablement, transformation, communications and strategic planning, Bell has spent much of her career helping organisations rethink how teams work and scale. In a recent conversation on the Sales Enablement Pioneer Podcast, she explored what happens when AI starts taking on more of the work — from content and analytics to coaching, administration and even entire enablement processes.

Her view is nuanced: AI will fundamentally change how enablement work gets done, but the human role doesn't disappear. Instead, it becomes more focused on strategy, judgement, oversight, alignment and the decisions that determine what good enablement actually looks like.

And as organisations experiment with new AI-native structures, one question becomes increasingly important: if fewer people are needed to do the work, where does that leave the humans who are still responsible for it?

AI Won't Absorb the Enablement Function

When asked which parts of enablement AI might completely absorb — content, coaching, administration or analytics — Bell's answer was straightforward: none of them.

AI can support every one of those areas, and it is already changing how work gets done. But Bell doesn't see a future where the human disappears from the process.

The reason is simple: enablement isn't just about producing an output.

Someone still needs to establish the strategy, understand the audience, define the tone and decide what "good" looks like. AI can accelerate the work, contribute ideas and even challenge the person doing it. But it doesn't automatically become the owner of the outcome.

Bell sees this particularly clearly in communications. AI can help her draft a message, but she wouldn't simply take the first version and send it.

The problem isn't necessarily that the content is badly written. It's that AI doesn't always understand the hearts and minds of the people you're actually trying to reach. And because AI can deliver an answer with complete confidence, the person using it still needs to recognise when something isn't quite right.

That judgement remains human.

Even the "Objective" Work Needs Oversight

Analytics and reporting might seem like the areas where human involvement should matter least. After all, if the data is the same, shouldn't the answer be the same?

Bell shared an example that challenges that assumption.

Her team ran the same prompt against the same dataset using individual instances of an AI tool — and received different results.

The reason was that each person's AI had been trained differently. Their individual definitions, audiences and context influenced how the tool interpreted the same question and data.

That creates an important consideration for enablement teams increasingly relying on AI-generated reporting.

An AI dashboard may be able to produce an answer instantly, but that doesn't necessarily make the answer reliable enough to act on without review.

For enablement teams trying to demonstrate their value and move from being viewed as a cost centre towards becoming a value centre, that distinction matters. If the data is being used to demonstrate business impact, someone still needs to question it, scrutinise it and make sure the conclusions hold up.

AI can accelerate analysis.

It doesn't remove the responsibility for understanding whether the analysis is right.

The Rise of the Super Enabler

This shift becomes even more interesting when you look at how enablement teams themselves could be structured.

Traditionally, enablement teams have often been organised around specialists. One person might focus on onboarding, another on tools and processes, another on product enablement or certification, and others on coaching, skills or specific programmes.

Bell has seen a different model emerging among AI-native startups and scale-ups.

It's the super enabler.

Instead of having a large team of people each responsible for one area, a smaller number of highly experienced enablement professionals could use virtual agents to extend their reach across multiple functions.

The idea is compelling.

A single person could have a whole collection of virtual team members working across different areas of enablement, allowing them to accomplish far more than one person traditionally could.

But Bell also sees some significant challenges.

One is the amount of oversight required.

A super enabler isn't simply delegating work to an AI agent and walking away. They still need to review the output, correct it, adjust programmes and make decisions about what happens next.

Bell describes this as being like "marking their homework."

And that creates a second problem: learning.

If one person is constantly managing their own virtual agents, they may have less exposure to the ideas and perspectives that come from working alongside other humans.

There is also a limit to how much one person can realistically own.

For a large organisation supporting hundreds of sales reps, Bell doesn't see one super enabler being able to manage everything — from sales skills and product enablement to certification, onboarding, tools and processes.

AI may reduce the number of people required.

It doesn't necessarily reduce the need for collaboration between experienced people.

More AI Doesn't Automatically Mean More Speed

There's another important tension in the super enabler model: productivity isn't the same thing as speed.

AI can allow an enablement professional to produce more, faster. But that doesn't necessarily mean the organisation should always do more.

The people enablement is ultimately supporting are sales reps — and humans don't suddenly become faster simply because the systems around them become faster.

People learn differently. They change at different speeds. They need time to understand, practise and adapt.

So the question isn't simply:

How much more can AI help us produce?

It's also:

What work actually deserves to be done?

That distinction becomes increasingly important as AI makes it easier to create more content, more programmes, more analysis and more interventions than ever before.

What Happens to Early-Career Enablement Professionals?

Perhaps the biggest unanswered question is what happens to people entering the profession.

If organisations move towards smaller teams of highly experienced super enablers supported by AI agents, there could be fewer traditional specialist roles available as entry points.

And without those early-career roles, where do future enablement professionals learn?

Bell doesn't pretend to have a definitive answer.

One possibility is that more structured areas of enablement — such as product enablement or certification — could provide opportunities for people earlier in their careers to work alongside AI and build their experience.

More complex areas, such as onboarding, stakeholder alignment and skills-based enablement, may require more experience and could increasingly sit with super enablers.

Bell also sees the possibility of an earlier-career version of the super enabler: ambitious self-starters who use AI to take ownership of smaller, defined areas and gradually build their expertise.

The model is still evolving.

AI Won't Replace the Sales Manager

The human element becomes even more important when the conversation moves from enablement teams to sales managers.

Managers still play a critical role in coaching, supporting and developing their teams. Bell doesn't see that relationship disappearing.

Instead, she sees AI helping managers focus their limited time where it matters most.

Conversational intelligence can surface what's actually happening during customer calls. AI roleplay can give reps an opportunity to practise before speaking to a real customer. Other AI-generated insights can help managers understand where individual team members may need additional support.

That creates leverage.

A manager can't personally coach every rep before every call. AI can help scale some of that preparation and surface insights that might otherwise be missed.

But Bell warns against interpreting that as AI taking another responsibility away from the manager.

The goal isn't for managers to stop coaching.

It's for AI to supplement, scale and support where the manager can't be.

The manager still needs to provide the human judgement, review and coaching that happens after the insight has been surfaced.

AI can help identify where attention is needed.

The manager still has to give that attention.

The Future Isn't a Formula

So where does all of this leave enablement professionals?

Bell doesn't believe there is a single playbook for navigating the AI transition.

And perhaps that's the most important point of the conversation.

Different organisations have different cultures, challenges, structures and levels of AI maturity. What works for one enablement team may not work for another.

When asked for one piece of advice for existing or aspiring super enablers, Bell's immediate answer was simple:

Experiment.

She acknowledges that even people working deeply in this space don't have all the answers yet. The future is still being worked out, and organisations are learning by trying different approaches.

There may never be one formula that works everywhere.

Instead, enablement professionals need to keep testing what works for their organisation, learning from others and staying open to new ideas as the technology and the role continue to evolve.

Because AI may change how enablement works.

But figuring out how it should work for your organisation is still a human job.

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