What Agentforce Missed About Agentic AI for Customer-Facing Teams

This article is based on a conversation between Vivun CEO, Matthew Darrow and Product Leader, Russell Witham , on The Unexpected Lever podcast.

GenAI advancements are fundamentally changing how go-to-market teams will operate in 2025.?

CROs are actively looking to AI to give their sellers new competitive advantages (2024 Gartner Generative AI Planning Survey), whether by scaling customized buyer interactions, automating administrative tasks, augmenting revenue and business intelligence, or other popular applications (Gartner ).

Market leaders, like Salesforce, are betting big on agentic AI, primarily focusing on the bookends of the customer journey, SDR agents to help find prospects and bring them to your door and Support agents help resolve customer tickets.

And these are critical applications, don't get us wrong. They're just not the only applications.

Don't let the technical sales process be a blind spot

Most of revenue generation actually happens between the bookends - between when someone is interested, has shown up on your doorstep, but has not yet taken the next step to becoming a customer or expanding further.?

Technical validation represents the majority of the sales cycle but the minority of RevTech strategy.?

This is the realm of technical sales, where AEs and SEs work together to drive revenue.?

This is the work to be done that wasn’t covered at Agentforce. And it all has to do with the work Sales Engineers focus on every single day.

So what would it look like for agentic AI to move the needle on the technical selling work that represents the majority of buyer time and seller efforts in the sales cycle??

How are companies trying to use AI for Sales Engineering today?

In general, a lot of companies are starting to experiment with the foundational LLMs, and we’re seeing small vendors emerge with chat bots to answer questions and fill out RFP documents.

While helpful, this doesn’t get at the root of the core strategic work of what SEs do.

This approach really misses the mark on the essence of what makes Sales Engineering work so valuable, what makes companies spend hundreds of thousands of dollars per year on individual SEs, and why buyers don't trust anyone else on your selling team.

What is the essence of the SE role that would need to be replicated by AI?

The essence of the SE role is what we call “solutioning.”

The technical sales process is highly complex - it’s not just about answering questions or completing RFPs.?

At the core, it’s about designing solutions for customers based on very complex and variable parameters of their unique needs and the capabilities of your offerings. It takes a lot of time to develop expertise in this through experience, often many years, but that’s what makes this skillset so valuable.

The value add from PreSales expertise lies in connecting the dots between a potential or current customer’s goals and problems with your product’s capabilities and differentiators.

Where do SEs spend their time?

We have studied tens of millions of real technical selling activities across our Vivun customers. For example, we know that answering technical questions and responding to RFPs represent less than 8% of SE time.?

Using AI to focus there is not going to move the needle on revenue.?

The top 2 areas where SEs spend time, by far, is creating a solution for the customer and then showing them that solution via a product demonstration.

So this must be the intervention point for AI with the greatest impact. But it begs the question, can foundational models do this work??

The short answer is no.

Where do foundational LLMs fall short??

You can’t just ask ChatGPT or Claude to create a demo or customer solution and expect a quality output, because foundational models are not useful for replicating the complexity of hybrid human roles.?

Why? There are 2 major problems:

1. Absence of a top-down understanding of Sales Engineering?

These LLMs operate via a “bottom-up” approach, processing vast amounts of data and generating responses based on statistical patterns. This works well for general queries but lacks the structured, top-down knowledge framework that sales engineering demands. Without a holistic understanding of SE objectives, AI’s responses often seem generic and lack the strategic depth that an experienced SE provides. It’s like reading all the words in a book without understanding the plot or the characters’ motivations—AI might know facts but misses the underlying story.

2. Deficient Contextual Knowledge and Relationship Mapping

These LLMs lack the context of how to structure and integrate the information they intake. ChatGPT can pull facts without any understanding of how to link them meaningfully. Even the latest chain-of-thought models lack the appropriate context for a field as complex and nuanced as sales engineering. This often requires a real person with actual experience to understand how to relate variables like customer needs, product capabilities, technical criteria, and the competitive landscape into a cohesive solution. ?????????

Foundational models will get better and better, but the best way to get leverage across the entire team (and not just rely on one person being enterprising), you need a system that does this en mass.

Conclusion

We are in the middle of an incredibly exciting transformation moment for not only B2B selling and buying broadly but also specifically for the Sales Engineering profession.

We believe it’s feasible to create an AI agent that will actually work as a strategic partner to SEs, AEs, CSMs, etc., and not just a knowledge retrieval engine.?

But you’re not going to get leverage from just an LLM with bottom-up knowledge. You’re only going to get a great outcome from a system that incorporates deep domain expertise in Sales Engineering.

The current AI approaches “for” Sales Engineers miss the mark because they have a reductive understanding of SE work. Until they appreciate the complexity and true value of the function, they can’t be expected to “do the work” in a meaningful way that maximizes value to your customers and the internal teams with whom they work.

For a deeper dive, watch the full conversation here . For more discussions like this about the future of GTM, follow The Unexpected Lever to hear not only from Vivun experts but also leading voices across B2B.