AI-Ready Sales Enablement – The Big Guide

Ask any sales rep if they’re happy with their Enablement program. You’re likely to hear a mix of frustrated remarks such as:

“Customer objections? I can’t easily answer customer questions without asking for help.”

“Training materials? No idea where they are. Probably buried in a Google Drive somewhere.”

“Sales assets? They’re dated and scattered in different locations.”

“Competitive insights? Lol, they’re nowhere to be found.”

That’s because for years, Enablement has been locked into a push-based model of create content, train reps, send them out, repeat. But the reality is that reps don’t have time to dig through static content when they’re in the middle of a deal. The result? They either wing it or waste time hunting for answers.

This is where AI has changed the game.

Large Language Models (LLMs) are flipping enablement on its head. Instead of forcing reps to search for information, you can now bring the right knowledge to them at exactly the right moment. Think of it as more of a pull-based approach.

So how can we get the most out of AI and Automation?

This guide digs into the best practices for getting your people, processes, and content to a state of AI-readiness.

How LLMs Have Changed Enablement

Before we get into how we can effectively embrace AI for sales enablement, let’s look at the changes we’ve seen in recent years.

What was Enablement like before AI?

​​Sales Enablement had remained a (mostly) push-based model up to this point.

Create content. Train Sales Reps. Deploy them to the field.

Identify gaps in your program. Deliver supporting assets.

Repeat.

These are some of the workflow loops of a typical enablement program. The function revolves primarily around a push-based approach to delivering content, training, and support. We can think of those program elements as pillars like this:

Content is created and handed off to reps. Sounds simple enough, right?

The problem is that this model leads to common hurdles + complaints from sales teams:

• “Where do I find that sales asset?”
• “What did the training guide say? I already forgot.”
• “I’m not sure how to respond to this customer objection.”
• “I need to pull in an engineer to answer this question.”
• “How do I do X? We haven’t reviewed since the QBR.”

These concerns primarily stem from the push-based approach. It’s no longer effective or viable at an organization of any size. Reps are overwhelmed with information to the point where they would rather ask a teammate for help than go digging for answers. No amount of content creation will alleviate that pain.

AI’s Shift to Pull-based Enablement

A pull-based approach flips this model upside down by making it easy for reps to get enablement content on demand.

We can no longer force content and training in hopes that reps will use it. Instead, we can make it easy for them to access the knowledge they need when they need it most. Whether it’s for responding to a customer objection, a competitive comparison, or a technical questionnaire – all of this sales knowledge should be automated for instant access.

Yes. The key is to create enablement content that’s easy for a Large Language Model (LLM) to understand.

Thinking back to the pillars of enablement, we can expect user experiences to change dramatically in a pull-based enablement model. For example:

• Self-Service Sales Training: LLM-optimized content can be used to automate sales coaching in real time. This ensures that sales reps can get the right talk track when they need it most – whether that’s in the middle of a sales call or in preparation for a live meeting.
• On-Demand Asset Delivery: Creating sales content is hard enough. Watching it go to waste is even more painful. Rather than searching for content, we should allow reps to ask for it. The demo above shows how easy it is for a user to simply ask for “a good customer case study” and get a response in seconds rather than having to manually search for it.
• Quick Responses for Customer Objection Handling: This is where good playbooks, sales assets, and battle cards come into play. Optimizing this content for LLMs can make objection handling easier for sales teams by automatically generating answers to specific customer questions.
• RFP and Questionnaire Automation: Technical questionnaires are repetitive, tedious, and involve a ton of text-based knowledge. And in the current model, reps need to push RFPs and Questionnaires onto their teammates in hopes of getting a quick turnaround. This makes RFP automation a perfect use case for Large Language Models and furthers the case for a pull-based workflow.

Here’s what that looks like in practice:

How much enablement can we automate?

You’re not going to automate an Enablement team out of their jobs. Forget about it.

But you are going to be able to do much more with less. When it comes to AI, the question you really want to ask your team is, “With this system in place, are we really doing more with less?” 

At 1up, we like to think it’s even better than that. With the help of GenAI, we’re now doing more, faster. 

We’re starting to work smarter and outsourcing more of the repetitive low-value tasks, to instead surface up insights and elevate the strategic conversation. 

Of course, there are still roadblocks. 

AI isn’t approved for a lot of use cases in large organizations, and we’re still far from AI replacing humans.  

But it was never supposed to replace humans. At least not in sales. After all, our customers are humans, and those customers want to work with other humans when it comes to making purchases, especially big ones, like in the B2B segment. 

No. AI is not a replacement. Instead, AI is a tool to leverage for efficiency, a tool we can embrace to find ways to get faster and smarter about how we work.

Chatbots, Copilots, Agents – What Does Any of This Really Mean for the Practitioner?

We get it. These names sound like a bunch of sales and marketing “fluff.” Some folks think they’re just fancy words for AI assistants.

But there’s more to it. In our deep dive on RFP Agents, Chatbots, and Copilots, we find that your choice of words (and tech) depends heavily on your use case.

When it comes to AI Revenue Enablement, you’ll know what you’re dealing with if you can work backward from the problem. 

• Are you looking for answers to product questions? Then you’re working with a chatbot.
• Does your team need help completing RFPs and questionnaires? Then you need a copilot.
• Do you want to automate the analysis of your best-performing enablement content? It’s usually an agent that provides insight.

These are the kinds of tools that, once you start working with them, you understand more. Before you know it, you’ll have a chatbot, a copilot, and an agent hard at work for you.

Building AI-Optimized Content

In the AI era, the role of enablement content has evolved. Where we once had static fact sheets and product brochures – generic content – we now need personalized, digestible information that is contextually relevant. 

You want to create content that can easily be understood by AI. You also want to ensure your AI tools can search your libraries and knowledge bases to make that content accessible to your team. 

The foundation of any great Enablement program is a Knowledge Base.

In many cases, Chatbots simply regurgitate information they find on the internet without context and often hallucinate information that isn’t true. On their own, mindless chatbots do nothing to help a revenue enablement program. This is why having a strong Knowledge Base of accurate information is table stakes.

You want to make your content accessible to AI and limit its knowledge to that information. That way, you have total control of the AI’s knowledge while delivering it to your sales reps in seconds.

If you want to shift your sales strategy to a model that actually delivers enablement in real time, AI is a must-have component. Just make sure you focus it on solving a specific problem.

Common use cases include personalizing talk tracks, answering technical questions, training day 1 sales reps, or crafting responses to customer objections.

For example, here’s how you might automate answers to product questions in Slack, Teams, or Google Chat:

In this demo, you’re no longer dumping content on reps and hoping they remember to use it. Instead, AI can ensure the right knowledge is delivered exactly when it’s needed, without anyone having to go digging for it.

This is just one example of using AI to automate sales knowledge. The key here is adjusting how you create, store, and manage sales enablement content so it plays well with your AI systems. If your content isn’t structured properly, even the smartest AI won’t be able to make it useful for your team.

Here’s how to make your enablement content AI-ready:

Optimize for Text-based Content

Focus on creating sales assets that are easy for a machine to read. Content that is visually appealing for a human is not necessarily easy for a language model to use. 

For example, high-end visuals, videos, and infographics are great for humans, but they can sometimes be difficult for an LLM to parse. Prioritizing text quality will greatly impact how the models “see” your content and will, in turn, make it easier to deliver that information to the team.

Pro Tip: Use universal file formats. LLMs work really well with PDFs, Word docs, Excel sheets, and other common formats. That doesn’t mean you shouldn’t create visually appealing graphics, videos, and web-based content at all – just remember you’ll get more mileage out of LLMs with universally recognized documents.

Keep Your Knowledge Base Fresh

It doesn’t matter if you use 1up, Dropbox, or Google Drive to store your sales assets – keep your content in one place and make sure it’s up-to-date. 

Enablement leaders who leverage AI need to remember the golden rule: Garbage in = Garbage out.

MAKE SURE YOUR CONTENT IS RECENT AND RELEVANT.

We cannot stress this enough. You need to think of knowledge sources as a key component of your program and the foundation for all AI-generated content that makes its way to the sales team.

You should be able to connect knowledge from multiple repositories like this:

Pro Tip: Choose a knowledge base that reps can chat with directly from Slack, Google Chat, or Microsoft Teams. A knowledge base with an integrated answer generator makes it easy for sales teams to get information quickly without having to switch tabs or search for documents.

Should We use AI to Generate Enablement Content?

Depending on your use case, this could be a huge win or a major L. 

AI can be great for ingesting content you’ve created to help you parse out, summarize, and reposition based on audience, but it doesn’t have the ability to understand the voice, brand, or unique experience/context that a human, integrated into the team culture, product, messaging and sales cycle can bring.

This means that while you should use AI to build on or work with your own content, it’s not always a good idea to have AI create original content. Sales content is where you need the human touch. You don’t want it to feel generic and depersonalized.

When to Use AI to Automate Content	When to NOT Use AI for Enablement Content
To craft talk tracks around strengths and features	To evaluate competitive differentiation
To analyze win/loss rates	To create new strategic messaging
To automate knowledge management	To create sales playbooks
To train sales reps	To coach poor performers
To find responses to customer objections	To speak directly to customers

What about Competitive Content?

Do you want to place your competitive enablement in the hands of AI? 

YES, for automating answers and insights based on strategic content you’ve already built out.

NO, for building that content.

An LLM just doesn’t know how to position you against the competition. It won’t understand the many nuances or differentiators you deal with. These elements must come from within your sales team.

For a deeper dive, check out our Super Cool Guide to Competitive Enablement.

For Technical Product Content, AI + Human Collaboration is the New Standard

With AI creeping into just about every software tool companies use, the way businesses create and manage product documentation has changed.

When it comes to product assets and technical docs, here are some best practices for leveraging AI:

• AI for Summarization & Organization
  AI can be incredibly useful for structuring large amounts of technical information, helping teams filter out noise and surface what matters. Surfacing answers to “How does X work” can save humans hours instead of having to dig through API docs.
  
• Keeping Human Expertise for Accuracy & Context
  No matter how smart AI gets, technical product details still need human oversight. Engineers and product specialists bring the real-world knowledge that AI lacks.
  
• Maintaining Strict Data Boundaries
  Keeping AI usage in-house (or within approved platforms) ensures that sensitive company information doesn’t end up in a third-party model. You’re going to want to keep sensitive data safe while using AI.
  
• Using AI as a Writing Assistant, Not a Creator
  While AI can help reword, reformat, and streamline technical documentation, it shouldn’t be the sole author. A human should always have the final cut when it comes to technical documentation.

At the end of the day, AI isn’t replacing product experts – it’s making their jobs easier. But when security and precision are on the line, the human touch still matters more than ever.

Data is Good, Insights Are Where the Magic Happens

With more data flowing through platforms than ever, it’s starting to feel like just another resource – plentiful, easy to access, and, honestly, a little overwhelming.

The real game-changer isn’t just collecting data. It’s knowing what to do with it.

Enablement teams that shift the conversation from “So what?” to “Now what?” bring the most value. The key? Turning raw data into actionable insights that fuel repeatable success. And that doesn’t happen in isolation – it takes a feedback loop that connects AI-generated insights with human decision-making.

– Stephanie White, Senior Director, Sales Enablement

In short, yes, AI can generate data and insights into that data. But you need humans to bring the “Now what?” element. 

AI can provide invaluable insight and even make amazing predictions. Humans take that information and move forward. That’s why it’s imperative that you create a feedback loop for rating AI-generated outputs.

Human Feedback Keeps AI Responses Accurate and Prevents Hallucination

AI-generated content is only as good as the input it learns from. That’s why the best enablement strategies aren’t just about pushing out information – they create a two-way street where feedback actively improves what AI delivers.

Instruct Sales Reps to Upvote & Downvote AI-Generated Content

Great sales leaders already know this: Enablement isn’t effective without rep feedback. If sales teams aren’t actively sharing what’s working (and what’s not), content can quickly become stale, irrelevant, or just plain wrong. In an AI-first world, this kind of feedback isn’t just helpful – it’s essential.

Why?

Because AI models learn from every interaction. When users upvote helpful content, it reinforces what’s valuable. When they downvote or flag irrelevant answers, it helps AI course-correct.

The smartest revenue teams make this dead simple:

• Add thumbs up/down ratings to AI-generated sales knowledge 👍
  
• Use star ratings for more nuanced feedback ⭐
  
• Encourage users to write corrections to clarify what’s missing or off-base ✍️

That’s because LLM performance depends greatly on a constant user feedback loop. Upvoting content reinforces its quality and influences how future information is generated. To achieve this, revenue teams should make it easy for both buyers and sellers to rate the quality of AI-generated sales knowledge. Thumbs up/down, star ratings, and even written feedback all go a long way toward this goal.

Pro Tip: If you’re using a tool that allows you to monitor user queries, incorporate that into your feedback process. A user might not leave a negative review for a piece of content, but you might notice them arguing with the AI if they’re unsatisfied with its output. We’ve seen countless examples of users who are hesitant to share feedback but seem to have no trouble yelling at their chatbot.

AI-Ready People and Processes

How do you get your sales team ready to work with AI?

The good news is you won’t need to change much. Here’s how you want to think about workflows and processes in an AI-native Enablement program.

Focus People on What Only People Can Do

We’ve all heard the directive to do more with less, but that tone is shifting slightly. 

Now it’s about doing more with our most valuable (and expensive) resources – people.  

To do that, we need to free our people up from the repetitive, the slow, and the redundant.  

AI technology is a major component here to not just automate, but also to enable, giving enablers the ability to quickly act, assess, and drive their work forward.  

The mindset of focusing people on things that only people can do with more intentionality is also extending to the teams we support.  

We’re seeing more SDRs shifting from cold calling/email en masse to deeper account research that can uncover relationships. SDRs are now seeking opportunities and digging into market trends more than before.  

Stop Spray-and-Pray Outbound

Outbound isn’t dead. It’s different.

Of course, we still need to be making outbound efforts. But when it comes to AI, teams have leaned too heavily into automation. If potential clients were already reluctant to respond to outreach, AI makes the tenuous relationship between sales rep and client so much worse. 

Jason Lemkin has a great take on how AI is negatively impacting outbound.

A less obvious second order effect of AI Sales Reps (or whatever you want to call them) is that they’re making it harder for real humans to do the job.

The explosion of AI-generated outbounds makes: 

• Email providers more focused on Spam reduction 
• Recipients more cautious of opening unsolicited emails
• Everything more noisy, so the authentic emails written by humans get lost in that noise

The bottom line: 

AI-generated spam is hurting human email deliverability.

What’s more, everyone knows that “spray and pray” email campaigns are a terrible practice.

But with such a low bar for personalization, can we expect subject lines to be a little more differentiated?

Yeah, right. 🙄

Does your inbox look like this nightmarish hellscape?

The worst part is that these email pitches now sound exactly the same across ALL industries.

Our theory is that somehow, somewhere, someone wrote a blog post about high-performing subject lines that spawned an outbreak of emails that look identical and refuse to die.

To get over this, we need to get back to emails that were written by a human and aren’t sourced from a list of examples that every other sales rep is using.

So what does all this tell us?

Supporting strategic outbound is the way forward. That means using AI for:

• Deep account research
• Personalizing pitches and responses

AI killed the spray-and-pray approach to outbound.

Become a Master in Tools That Connect

In B2B SaaS in particular, we started to see a lot of companies scaling up their tech stacks 2-3 years ago. They introduced new dialers, content management platforms, call recorders, and beyond.  

Now in the mid 2020’s, with those contracts coming up for renewal,  some enablement and ops teams are facing ongoing management, training, and adoption for 15-20 tools. Managing those areas alone can become separate jobs in themselves.  The decision to renew is no longer about budget and ROI. Rather, it considers the resource cost of ongoing management. 

Gone are the days of having a single “tech stack” resource, often charged with the CRM.

Now, we’re seeing an increasing need for more enablers to be administrators of tools throughout the GTM teams.  As more of these platforms try to gain increased market share, new functionality releases are increasingly complex with integrations and built-in functions that overlap with other tools, making renewal decisions more complex.  

With all the newfound data and more members of our GTM teams utilizing the tools, getting to a single source of truth is key.  

Integrations with standard tech? Table stakes. 

Higher touch or self-service customer success partners? Non-negotiable.  

Whether they hold the budget or not, more enablers are taking ownership of the integration, training, and adoption of sales tech.  

AI has Made Tool Bloat and Consolidation a Priority

So many tools, with only so much budget to go around.

It was inevitable that businesses would start downsizing their sales tech stacks.

Over the past decade, the Sales tech stack has consisted of stacking one tool on top of another. This was effective for a while, but now we all just have way too many tools, with lots of overlap.

Sales reps are bouncing from tool to tool. They have dozens of tabs open at any given moment. Tool fatigue has become a distraction for many teams.

It’s no surprise that one of this year’s biggest trends is how businesses are consolidating their GTM tech stack.

An average of 40% of SaaS licenses are still going unused, with low adoption rates persisting.

Productiv State of SaaS 2024 report

That is an absolutely bonkers statistic. It gets even more interesting when you look at how tool bloat has grown over the years:

According to BetterCloud, the trend towards consolidation is not only driven by budget concerns but also by a desire to simplify operations across an increasingly complex IT environment.

Our advice? If your team is downsizing your tech stack this year, focus on keeping enablement tools that emphasize automation. Those are the products that are most likely to play well with the other trends described here.

Where does AI fit into the sales training process?

Do we abandon the human element in sales training? 

No. 

No matter how advanced AI has come thus far, there are big advantages to leveraging both live sessions (individual and group) and async, self-guided learning. The former provides much deeper and more collaborative learning experiences, while the latter provides increased scalability and convenience. 

When used alone, there are challenges for each. Used together, sales enablement teams can provide high touch and high frequency to meet learner needs.

Do chatbots replace sales coaches? 

No. Instead, AI coaches can be incredibly helpful in providing more opportunities for sellers and sales leaders to practice. They apply learning and help sales reps prepare for customer conversations on a larger scale.  

Just as every buyer is different, so are sellers.  They often truly shine when coached intentionally and individually to their unique strengths and weaknesses in different scenarios. We need humans for that.

Do we replace the big drawn out sales trainings? 

Yes.

Your team is not going to come out of the gate on their first day on the job with all your company information memorized. It doesn’t matter how much training you provide at the QBR. Most reps are going to forget what they learned in a few days. 

It’s human nature. 

They’re salespeople. Not robots (yet). And that’s a good thing! 

Instead of trying to force more information into your sales team members’ heads, try leveraging AI to do the tedious information gathering.

By automating knowledge management with AI, you leave the fun parts of training to the humans. Here are a few ways to do that: 

Gamify your sales training 

You can make training fun. There are plenty of opportunities to turn training and upskilling into a chance for employees to compete with each other, learn new information, and win awards. 

You can use LMS tools like Docebo to gamify learning. Docebo provides points, badges, and leaderboards and integrates social learning elements like user-generated content and peer feedback.

Keep gamification engaging and just challenging enough that your sales reps have to work to win but easy enough that everyone can win. 

Recognize and celebrate your sales reps’ successes with a leaderboard, and don’t forget to show up and play as well. Modeling is the best way to get your team excited and fully bought in. 

Make it easy for reps to learn something new

Even after you train your sales reps, you’ll want to make upskilling an essential part of the job. This is a natural part of career growth. Have a list of ongoing exercises or courses your reps can use to continue to get better at their jobs. 

For example, you can use tools like HyperBound to help reps role-play. Hyperbound enhances sales team upskilling through AI-driven role-play simulations, personalized training scenarios, performance analytics, and accelerated onboarding to improve skills, confidence, and conversion rates.

Make training a self-service experience

You want your teammates to know they’re constantly supported and guided without needing to ask for help. 

The more self-service support you provide, the better they’ll get at their jobs. 

One of the best ways to get into this flow of employee growth and opportunity is to automate sales coaching. Make it easy for them to have immediate access to all the information they need. You can do this by ensuring you have a centralized knowledge base with a tool like 1up that automatically generates accurate responses to your sales reps’ questions. 

Users have told us that their reps might feel embarrassed to ask teammates a basic question, but are never embarrassed to ask an AI. That’s a huge improvement in how knowledge flows through an organization.

Manoj Abraham, COO @ 1up

Less Worrying About Participation, More Focus on Certification

A couple of years ago, many of us in the Enablement space were stretching ourselves to think how to hire, train, and ramp sellers as fast as possible, often with large onboarding cohorts, and super-stretched Enablement teams covering global audiences with a “ship it” approach.  

The goal was to move as fast as possible with as few Enablement team resources as possible.

Success was showing completion of onboarding programs by large volumes of new hires in the fastest time possible.

Now that hiring has slowed in many companies, the success of every new hire matters more than ever.  Teams are being increasingly intentional about building out proper ramping plans to guide new hires through deeper learning as they get into the field and on customer calls, to ensure they don’t just know the messaging, but they truly understand it and can convey customer value in real conversations.

Rather than reporting on participation or completion with quantitative data, success is now about sharing insights with sales leadership about how their new hires are progressing, their early strengths, and opportunities for immediate coaching.  

The qualitative value an Enablement team can add through not just in-depth training, but true certification is the key to success. 

We’re seeing less focus on onboarding and more focus on ramping to full attainment across GTM roles. 

From sellers to customer success, we’re seeing consolidation to one tool and one team to hold the responsibility and deliver insights for both. The Enablement teams that get their sellers to full quota faster will be most successful in earning more seats at the table.

Preparing for Enablement in 2030

There’s no shortage of things to consider as we gear up for what may be one of our biggest evolutions yet.  

In 2023, we heard a lot about the shift to “Revenue Enablement,” focusing on wider GTM teams.  

Then, in 2024, it was all about #proveit, leveraging data to calculate ROIs of everything from new tool purchases to new hires and to impact.  

And while both of those shifts brought us closer to the “table,” with companies stabilizing, we’re seeing yet another shift.

More and more often, we’re hearing “Enablement” used across business as both a noun, as something that is being done by multiple functions in a wide variety of ways, and a verb, referring specifically to a Sales or Revenue Enablement team.

While this can create confusion, it also represents an opportunity for more Enablers to think bigger about who their audiences are, what the needs of the business may be, and how they can make an impact now that there is an opportunity for a bigger stage.

Now, in 2025 and beyond, we can expect to see more businesses shift to GenAI as an assistant and essential tool in sales enablement. It can become a core function that:

• Allows sales teams to personalize interactions
• Empowers sales teams with insights in real time, so they can make intelligent decisions
• Drives sales teams to focus on building long-term relationships by delivering value-driven experiences

Thinking ahead to 2030? Enablement is changing fast, and so is the role of the people behind it. The ones who thrive will be those who stay curious, get creative with AI, and stay close to what the business really needs.