What is the Future of Sales Enablement?

Revenue enablement platforms have transformed the sales process. There are numerous tools that can help sales reps leverage tailored content, playbooks, and processes to achieve a faster sales cycle.

That wasn’t always the case.

Not even a decade ago, influencers were still writing articles arguing why enablement is important. That’s no longer necessary as the function has become a staple of any mid-sized sales organization. In fact, more than 80% of sales organizations have had an enablement role for more than 2 years.

So how will this already ubiquitous role evolve?

We’re already seeing how AI is being used to help automate sales enablement. We believe this trend will continue with Large Language Models fundamentally changing the way information is delivered to the sales organization. More specifically, we think enablement is moving from push to pull-based delivery.

Create content. Ship 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 an enablement program. The function revolves primarily around a push-based approach to delivering content, training, and support. Those program elements look something like this:

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

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

These concerns primarily stem from the push-based approach. We’re of the view that it’s no longer effective or even viable. 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 can alleviate that pain.

This information overload problem is similar to the issues with knowledge management we previously discussed, but is more acute as it directly impacts the sales process.

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 enable them to ask for information when they need it most; whether it’s for responding to a customer objection, a competitive comparison, or a technical questionnaire.

How do you take enablement from push to pull?

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

Here’s what that looks like in practice:

Thinking back to the pillars of enablement, we can expect some of the user experiences to change dramatically. 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.

Obviously, the first step is to make sure you’re incorporating LLMs and automated workflows into your sales enablement program. There’s a lot for sales leaders to consider here – how they should think about the role, what tools they should buy, etc. Our suggestion is that the solution isn’t necessarily to buy a product, but to change how you think about enablement content.

If there was just one actionable piece of advice we would give enablement managers, it would be to focus on generating content that LLMs find easy to read. Here’s what we mean:

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 graphics and image-based tables are great for humans, but they can 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 where possible. LLMs work well with PDFs, Word docs, Excel sheets and other common formats. This doesn’t mean you shouldn’t create visually appealing graphics, videos, and web-based content at all – just remember that you’ll get more mileage out of LLMs with universally-recognized documents.

It doesn’t matter if you use Seismic, Highspot, Dropbox, or Google Drive to store your sales assets – keep your content in one place and make sure its up-to-date. Enablement leaders who leverage AI need to remember the golden rule: “Garbage in = Garbage out.” We need to think of knowledge sources as a key component of their program and the foundation for all AI-generated content that makes its way to the sales team.

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

Enablement does not work if it’s a one-way street. The best sales leaders know this, which is why reps are encouraged to share feedback on content and how customers receive it. In an AI-first world, this gets turned up to an 11. 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 content. Thumbs up/down, star ratings, even written feedback all go a long way towards 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.