The Sales Development AI gap is coaching

Only 12% of Teams Use It for Coaching?

AI has become table stakes in sales development. Research, enrichment, sequencing, call summaries, and task automation are now common across modern SDR teams. Most revenue leaders can point to at least one AI investment made in the last twelve months, and many can point to several.

Yet the 2026 State of Sales Development Report reveals a striking disconnect between adoption and impact.

While AI usage across sales development is widespread, only 12% of teams report using AI for coaching. That number is not just surprising. It is diagnostic. It explains why many teams feel like they are investing heavily in AI without seeing corresponding improvements in pipeline quality, conversion rates, or rep performance.

The problem is not that sales teams are underinvesting in AI. It is that they are investing in the wrong layer of the problem.

AI Is Everywhere in Sales Development

But Mostly Around the Edges

Most sales AI today is designed to remove friction from the workflow. It helps reps move faster and handle more volume. That includes tools for account research, list building, email generation, CRM updates, and call documentation. These tools save time and reduce administrative overhead, which matters in high-volume environments.

However, the core constraints on outbound sales remain unchanged. Buyers are harder to reach. Attention is fragmented. Phone conversations remain the most effective path to meaningful pipeline, but only when handled well. Activity alone does not solve that. Efficiency alone does not solve that.

AI applied to task automation improves speed. It does not inherently improve skill.

The Real Bottleneck in Outbound Has Always Been Coaching

Outbound success is not determined by how many calls get made. It is determined by what happens once a conversation starts. That includes how a rep opens, how they frame value, how they respond to pushback, how they manage pace and control, and how they create next steps.

Those are coached behaviors. Historically, coaching has been the hardest part of sales development to scale. Managers have limited time. Call reviews are subjective. Feedback is often inconsistent and delayed. As teams grow, coaching quality tends to degrade, even in well-run organizations.

AI had the potential to fundamentally change this dynamic. Instead of listening to a handful of calls, teams could analyze thousands. Instead of anecdotal feedback, coaching could be grounded in patterns. Instead of generic advice, reps could receive targeted guidance based on real conversations.

The fact that only 12% of teams are applying AI here shows how early the market still is.

Why So Few Teams Use AI for Coaching

There are three primary reasons AI coaching adoption has lagged behind other use cases.

Coaching Has Been Framed as a Manager Problem

Most AI tools are sold as rep productivity tools. They promise time savings, efficiency gains, and automation. Coaching, on the other hand, has traditionally been framed as a management responsibility. As a result, AI investments often bypass coaching entirely and focus on individual rep workflows.

That framing is outdated. Coaching is a system problem, not just a management task. It requires consistent inputs, clear standards, and scalable insight. AI is uniquely suited for that, but only if it is designed with coaching as the primary outcome.

Conversation Quality Is Harder to Measure Than Activity

Activity metrics are easy to quantify. Calls, emails, touches, and meetings booked are straightforward. Conversation quality is more nuanced. It requires understanding language, intent, timing, and context.

Many AI tools stop at surface-level call analytics. They summarize conversations but do not translate them into actionable coaching insights. Without clear guidance on what to coach and why, teams struggle to operationalize AI for skill development.

Enablement and AI Have Been Disconnected

In many organizations, enablement owns coaching frameworks, talk tracks, and onboarding. AI tooling lives elsewhere. When these systems are not integrated, AI insights do not map cleanly to how teams train and develop reps.

Closing the coaching gap requires aligning AI insights directly to enablement strategy, not treating AI as a standalone analytics layer.

Orum’s Perspective on AI Coaching

Orum’s approach to AI in sales development starts with a simple premise. The fastest way to improve pipeline is to improve conversations. Everything else is secondary.

This philosophy has shaped Orum’s AI coaching suite and the broader guidance Orum has shared through its AI coaching playbook and related resources.

Rather than using AI to simply summarize calls or track talk ratios, Orum focuses on identifying patterns that separate effective conversations from ineffective ones. That includes how reps open calls, position value, handle objections, and transition to next steps.

The goal is not to overwhelm reps with data. It is to provide them with clear, actionable feedback that directly maps to better outcomes.

What AI Coaching Looks Like When Done Well

When AI is applied correctly to coaching, it changes how teams operate.

Reps receive timely, specific feedback. Instead of waiting weeks for a call review, they can see where conversations break down and why. This accelerates skill development and shortens ramp time.

Managers shift from being call auditors to performance coaches. They spend less time hunting for examples and more time reinforcing behaviors that work. Coaching becomes proactive instead of reactive.

Enablement teams gain visibility into which messages and techniques actually perform in live conversations. This allows training content and playbooks to evolve based on reality rather than assumptions.

Most importantly, pipeline quality improves. Better conversations lead to better qualification, stronger meetings, and more consistent progression through the funnel.

The Competitive Implication of the 12% Gap

The fact that only 12% of teams are using AI for coaching should get every sales leader’s attention.

It means the majority of the market is still using AI to go faster, not to get better. That creates a window for teams willing to invest in skill development at scale.

As outbound becomes more difficult and buyer tolerance for poor conversations continues to drop, coaching will become the primary differentiator between teams that generate durable pipeline and teams that burn out reps chasing activity.

AI will not replace coaching. It will determine who can do it effectively.

Where Sales Leaders Go From Here

The takeaway from the 2026 State of Sales Development Report is not that AI adoption is lacking. It is that AI strategy is incomplete.

Teams that want to see real ROI from AI need to move beyond task automation and apply AI to the moments that actually determine success. That means coaching conversations, reinforcing skills, and creating systems that make improvement continuous rather than episodic.

AI coaching is still early. That is exactly why it matters.

The teams that close this gap now will not just be more efficient. They will be fundamentally better at outbound.