Your Sales Enablement Stack Was Built for a Pre-AI World. What Should Change?

Most sales enablement teams have added AI somewhere in their workflow.

Sales reps use ChatGPT to prepare for meetings. Product marketers use it to get a first draft started. Content teams use it for research. Companies have rolled out Copilot licenses and added AI features to the tools already in their tech stack.

But take a step back and look at how the work actually gets done, and much of it still looks remarkably familiar.

Product marketing creates a competitive battlecard in PowerPoint. Someone turns it into a PDF. It gets uploaded to an enablement platform or shared drive. Sales is told where to find it. Three months later, a competitor changes its pricing and someone needs to remember to update the deck.

AI gives us an opportunity to rethink that entire workflow.

Start with the battlecard

Battlecards exist for a good reason. Salespeople need to understand how competitors are positioned, where your product is stronger, which objections they’re likely to hear, and how to respond.

The format, however, was designed for a world where the easiest way to distribute that knowledge was through a document.

Today, imagine a rep preparing for a meeting with a prospect and asking:

“I’m meeting with a healthcare company tomorrow that is also evaluating Competitor X. What should I know?”

Instead of searching through folders for the latest battlecard, the rep interacts with an agent that understands your approved positioning, competitive intelligence, product capabilities and sales methodology.

It can surface the most relevant differentiators, likely objections, proof points and questions to ask based on that specific sales situation.

The expertise behind the battlecard still matters. Product marketing needs to determine how the company competes and validate the information being used.

The way that expertise reaches sales can be completely different.

Look beyond content creation

This is where many companies are still thinking too narrowly about AI.

Using AI to write the first draft of a blog post faster is useful. But if that draft still moves through multiple disconnected reviews, gets manually rewritten for different channels, requires someone to check it against messaging guidelines, and eventually gets copied into a publishing platform, you’ve accelerated one step in a much larger process.

What happens when you redesign the process itself?

An editorial agent can understand your audience, brand voice, positioning, SEO priorities, editorial strategy and existing content library.

The content marketer brings the idea, insight and point of view. The agent can help with research, development, editing, optimization and repurposing while keeping the work grounded in the company’s established strategy.

The marketer remains responsible for what the company has to say. They simply spend less time moving that idea through a manual production process.

Think about where your team spends time finding information

Another opportunity sits inside the everyday work nobody really tracks.

How much time does a salesperson spend looking through the CRM, call recordings, email threads, enablement platforms and shared drives before an important meeting?

How much time does product marketing spend listening to sales calls to identify recurring objections?

How often does someone conduct competitive research only to discover that another person completed a similar analysis three months ago?

AI agents can become a layer between your teams and the knowledge scattered across your GTM organization.

Instead of expecting people to know where every piece of information lives, an agent can help bring the right context to the person who needs it.

For sales, that might mean preparing an account brief before a meeting.

For product marketing, it could mean surfacing a pattern in competitive objections across recent calls.

For a content marketer, it might mean identifying customer questions that repeatedly appear in sales conversations and should become part of the editorial calendar.

The value comes from making the knowledge your company already has easier to use.

Move from static enablement to enablement in the moment

Traditional sales enablement has often focused on creating assets.

Pitch decks. Battlecards. Playbooks. One-pagers. Discovery guides. Competitive matrices.

Those assets still have a place, but AI allows enablement to become much more responsive to the situation a salesperson is actually facing.

A rep preparing for a competitive renewal conversation needs different information than someone running first discovery with a new prospect.

A rep selling into financial services may need different proof points than someone speaking with a technology company.

The more context an enablement agent has about the account, opportunity and approved company knowledge, the more useful that support can become.

Instead of asking sales to translate a generic asset into their specific situation, we can increasingly bring the relevant knowledge directly into that situation.

The goal isn’t to remove the expert

There’s an important distinction in all of this.

AI can make product marketing, content and sales enablement significantly more efficient. That doesn’t mean the expertise behind those functions becomes less important.

Someone still needs to decide how you position against a competitor.

Someone still needs to understand the customer well enough to recognize which insight is worth building content around.

Someone still needs to determine what sales should say, what the company believes, and which information can be trusted.

Agents work best when they have strong expertise and clear source material behind them.

If your positioning is unclear, your competitive intelligence is outdated, or your CRM data is unreliable, adding an agent on top of it will not magically create a strong enablement program.

The foundation still matters.

Audit the workflow, not just the tech stack

If you’re thinking about how AI should change your sales enablement organization, start by mapping how work gets done today.

Where are people repeatedly searching for information? Where does work move through multiple rounds of manual review? Which assets require constant updating? What knowledge exists inside the organization but is difficult for sales to access? Where are highly skilled people spending time on repetitive tasks?

Those are often better places to start than asking which AI tool you should buy next.

Your first AI use case might be a competitive intelligence agent. It might be an editorial studio that helps your content team move from idea to published asset faster. It could be an account intelligence agent that prepares sales before every customer conversation.

The right answer depends on where your team is losing time today.

The opportunity with AI isn’t to ask the same team to produce twice as many PowerPoints. It’s to question why the PowerPoint needs to exist in the first place.

Your sales enablement stack was built around the tools and workflows available at the time. AI gives us the chance to redesign both.

The companies that get the most value from it will look beyond adding AI to their existing processes and start asking a bigger question: if we were building this workflow today, how would we design it?