Artificial intelligence (AI) is everywhere in sales enablement. Nearly every platform now includes it, often with bold claims about transformation and impact. Some of those claims are justified. Many are not.

Regardless, there is no doubt that AI will play a role in sales enablement. The real question is, where will it add the most value?

While it is tempting to sprinkle AI fairy dust on everything, organizations need to be careful not to automate the very behaviors that make sales professionals effective. The opportunity is real, but so is the risk.

Practice Has Always Been the Missing Piece

While sales enablement professionals love a workshop, the uncomfortable truth is that sellers don’t improve by attending workshops alone. They improve by practicing. through practice — repetition, feedback and adjustment.

Research on deliberate practice shows that skill development depends on structured, repeated effort rather than one-time learning events. However, in most cases, structured practice is the first thing that disappears in a busy sales organization. Managers don’t have time, or they simply don’t know how, role-plays feel awkward or are so generic that they add little value, and everyone just says they’ll “do it later.”

This is where AI can make a real impact.

Tools that simulate sales conversations allow sellers to meet virtual buyers, practice objection-handling drills and demonstrate their ability to pitch, in a low-risk environment. They can experiment, fail and try again without pressure or scheduling constraints.

That has real value.

Used properly, this is a major step forward for enablement. It provides scale for something that has traditionally been limited by time and resources.

But practice isn’t coaching — and that distinction matters.

Coaching Is Not Advice

In many organizations, “coaching” is used as a catch-all term for managerial guidance. In practice, most managers default to mentoring: offering opinions, suggestions and personal experience.

Mentoring is valuable. Experience matters. But it isn’t coaching.

Coaching, done properly, doesn’t provide answers. It draws them out. It helps individuals think more clearly and develop their own solutions. Research shows that this approach, focused on questioning and reflection, leads to greater ownership and better performance than simply giving directives.

AI can support elements of this process, but it cannot replicate it.

AI tools can analyze calls, suggest improvements and identify patterns. They cannot interpret emotional nuance, rebuild confidence after a lost deal or adapt in real time to a seller’s mindset. Coaching is inherently relational, and that remains a human responsibility.

Positioning AI as a replacement for coaching creates risk. It can weaken one of the most important drivers of sales performance: capable, engaged managers.

Where AI Adds Value — Without Replacing Judgment

Now, that doesn’t mean AI tools should be avoided. In fact, quite the opposite. They just need boundaries.

In written communication, AI can improve clarity, structure and tone. It can tighten an email, spot jargon and be excellent at suggesting stronger calls to action. In proposal development, it can challenge whether value is explicit or buried under features. That’s something any seller could benefit from.

But AI shouldn’t be doing the thinking for the seller.

If a seller types a vague prompt and sends whatever the AI tool produces directly to a client, the buyer gets AI output, and the seller wonders why the deal stalls. While they may think they have strengthened their commercial judgment, what they’ve actually done is outsourced it.

In contrast, if they draft the message themselves and then ask an AI tool “How can I make this clearer?” or “Does this focus enough on outcomes?” AI becomes a tool to refine; a subtle but critical difference. The same principle applies to call analysis. Data on talk-time ratios or question depth can be highly useful as patterns emerge that managers might miss. But the value is always unlocked in the follow-up conversation.

Instead of saying, “You need to listen more,” a connected manager reviewing those insights might ask, “What do you notice about the balance of that conversation?” This approach prompts reflection and learning. The data informs the discussion, but it does not replace it.

The Risk of Skill Erosion

As AI becomes embedded in daily workflows, organizations must consider its long-term impact on skill development.

Research on automation and cognitive offloading suggests that heavy reliance on intelligent systems can erode critical thinking and decision-making skills over time. For leaders driving enablement, this highlights the need to pair AI adoption with intentional safeguards that ensure these tools enhance human thinking instead of substituting it.

Selling is a thinking profession. One that requires judgment, curiosity, commercial instinct and the ability to understand nuance. At the gym, you wouldn’t expect a muscle to grow if you didn’t lift the weight. The same applies here.

Enablement should build capability, not dependency. If AI performs too much of the thinking, those skills can weaken. There is also a risk that managers disengage from coaching. If AI provides simulations and feedback, some may assume their involvement is less necessary. This is a critical mistake.

Organizations with strong coaching cultures consistently outperform their peers. Manager-led coaching remains one of the most effective levers for improving performance. AI should create more capacity for coaching, not reduce it.

What Sales Enablement Leaders Should Prioritize

To realize the benefits of AI in training without undermining performance, enablement leaders should focus on these key areas:

1. Boundaries

Establish very clear guidelines for how AI can be used. Organizations can use AI to build a volume of practice, sharpen written communication or surface patterns in performance, but they should not position it as a substitute for human development.

2. Coaching

As AI technology becomes more sophisticated, the ability to ask high-quality questions becomes more valuable, not less. Leaders should be training managers to understand the difference between coaching and mentoring and hold them accountable for both.

3. Alignment

AI must be integrated into the existing organizational context. If the organization values consultative selling, then AI scenarios should reinforce discovery depth and commercial insight, NOT just speed and efficiency.

4. Reflection

There must be time and space for intentional reflection. After simulations, after feedback, after analysis, sellers should identify one behavioral insight and one specific action to apply in their next live conversation. Without this, feedback stays purely informational and doesn’t lead to meaningful change.

5. Focus

Avoid being carried away by hype. Regardless of popular opinion about an AI-powered product, it should only be adopted if it promotes the specific behaviors the business wants to see in the field. Decisions should be guided by performance outcomes, not market momentum.

A Balanced View

There is no doubt that AI is reshaping the industry. It provides access to practice at an unprecedented scale, which is a massive plus for every L&D leader. It quickly captures data that was previously difficult to source and helps to sharpen execution in practical ways.

But the essence of selling — understanding people, navigating complexity and building trust — remains human.

The future is not about choosing between people and technology. L&D leaders simply need to set new boundaries (and challenge existing ones) by leveraging technology based on what’s most effective, not what’s most popular.

Use AI to practice. Use it to refine. But — and this is crucial — preserve the human conversations that build confidence, questioning that builds judgement and mentoring that imparts wisdom.

AI can accelerate development, but it cannot replace the craft.