In enablement, we are asked to scale faster, prove ROI, and drive behavior change across hybrid, global, go-to-market teams, all with limited resources.
These expectations can seem impossible when we’re buried in manual content creation, one-off training creation, and anecdotal analysis.
So how can we elevate the way we drive productivity, scale learning, and prove impact?
AI is quickly becoming the go-to answer, and for good reason.
While AI can’t replace human enablement expertise, it can handle the repetitive, time-consuming, and data-heavy tasks that slow us down. Used well, AI frees us to stay focused on what matters most: driving behavior change and business impact.
Below are three key areas where I see AI helping enablement teams shift from reactive execution to strategic leadership:
Streamlining performance insights & impact reporting
Historically, enablement teams have relied on anecdotal feedback or lagging metrics to evaluate program success, which has made it hard to focus efforts and measure impact with confidence.
AI changes that. It enables us to spot performance trends faster, surface gaps proactively, and link our work to business results.
To identify where to focus our time, enablement teams need to start by defining what “good looks like.”
Using AI-powered conversation intelligence benchmarking features to analyze high-performing calls and sequences, enablement teams can identify top-performer behaviors.
To get started, consider comparing a few top and average rep calls using AI scoring and identify one behavior that differentiates them.
Use these insights to create a campaign where top behaviors, such as storytelling or in-depth discovery, are shared weekly via AI-generated clips.
Katie Van Hoomissen
Once teams know what good looks like, AI can help identify field needs.
AI can synthesize CRM and call data to uncover patterns, like common objections or missing assets, before they show up in lost deals.
Even a basic AI-powered win/loss analysis can help you surface the top three challenges reps are facing and include them in a monthly report.
As you build programs to address the identified needs, AI can help you better measure their impact and know what is working.
Go beyond feedback surveys by tying enablement touchpoints (training, coaching, content) to revenue outcomes using AI-enabled attribution models.
If full integration feels too big, start with one program, like onboarding, and compare pre/post-ramp performance using CRM and LMS data. From there, you can scale toward a dashboard that links enablement efforts to metrics like stage conversion or ACV.
Daniel O’Dowd
Tailoring learning & reinforcing behavior change
Once you have identified best practices, uncovered needs, and set up mechanisms to report on your program’s impact, AI can help you move from a one-size-fits-all training approach to contextual, personalized, and scalable learning programs. It can also take some of the burden off managers by automating reinforcement through various tools.
Start by creating personalized learning paths.
Adaptive learning, AI-integrated LMS platforms, can now help enablement teams tailor training content based on rep role, skill gaps, or deal stage.
Even simple tagging of onboarding assets by role (AE, SDR, AM) or region is a great first step towards increased customization. From there, teams can explore systems that auto-recommend modules based on call data or pipeline stage.
AI-powered roleplays offer reps a safe space to practice without needing a manager or peer for support.
These simulations often provide instant, targeted feedback that reinforces broader enablement efforts.
Start by creating one scenario, such as handling pricing objections, and integrate rep practice with AI-generated variations into your enablement plan.
You can also consider adding regular AI-practice requirements for your reps, such as 1 scenario a week or X amount of time per month, depending on your goals.
In addition to training, your enablement team’s content also benefits from AI through contextual content recommendations.
AI can deliver the right asset, battlecards, case studies, and talk tracks, automatically via Slack or CRM based on live deal data to reps. This benefits your enablement team by ensuring that your materials are used when they matter most and saves reps from hunting for content.
The “stickiness” of your enablement programs is also boosted through scalable coaching using AI-powered conversation intelligence behavior tagging.
Conversation Intelligence platforms now use AI to detect behavior trends, like discovery questions or MEDDPICC usage, across hundreds of calls.
Enablement teams can use this to monitor methodology adoption at scale, which can be used to create weekly coaching guides for managers based on AI summaries of team activity and feedback.
When using these tools, just don’t forget to always validate with a manual review to ensure the AI tagging is accurate before scaling.
Daniel O’Dowd
Boosting content creation & delivery efficiency
Beyond improving enablement teams’ focus and enhancing the personalization of the programs they deliver, AI helps us as enablement professionals work faster in our day-to-day activities.
Content creation, communication, and administrative tasks are areas where AI can deliver immediate value, freeing up more of our time for high-impact work.
Generative AI content tools can quickly turn assets into reinforcement materials.
For example, repurposing training decks or recorded calls into flashcards, talk tracks, or bite-sized videos. Start by taking your last workshop recording and use AI to create three microlearning pieces.
Then post them to Slack or your LMS for quick reinforcement. You can also use AI to trigger follow-up nudges or quizzes personalized to each rep’s call performance.
Shun Cheah
Summarization engines tied to call and CRM data can summarize closed-won calls and CRM notes to create searchable win stories.
Using these stories, teams can create a “Win Library” where reps can easily access success stories categorized by industry, objection, or solution, without you manually writing each one.
Finally, AI can help you build field-facing updates in minutes.
Use summarization tools to auto-generate weekly highlights, including content usage stats, call trends, and links to new assets.
If a fully automated newsletter feels out of reach, start by using AI to draft the copy of your next update and insert one data-driven insight, like a common objection trend or training completion rate, before sharing via your usual channels.
Reminder: Progress over perfection
These are just a few of the many ways AI can transform enablement strategies and how enablement teams work.
Let it take care of the repeatable steps, so you can focus on strategy, creativity, and the programs that actually move the needle.
As you start expanding your use of AI in enablement, remember that you do not need to do everything at once.
The key is to start small and identify where your team is spending the most time, or where the stakes are highest, and test one AI-enabled improvement at a time.
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