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New data from 200 B2B sales reps reveals how AI is transforming Sales Engineering. Discover why forward-thinking SE leaders are shifting from tactical execution to strategic impact—and how to lead this transformation in your organization.
Modern B2B sellers have more AI firepower than ever. One tool researches accounts. Another sequence of emails. A third prepares roleplay scenarios. AI records the call, transcribes it, and sends follow-up content. Six different tools just to book and run a single meeting.
Here's the problem: By the time that meeting starts, your prospect is already 90% through their own AI-accelerated buying journey. They've consumed your marketing content, taken product tours, consulted internal AI advisors, and diagnosed their problem with precision. They're ready to see your solution in action.
And this is where the modern sales motion collapses.
Under pressure—because the buyer wants to see something now—sellers skip discovery entirely. No context. No validation of pain. No alignment of value to their specific problem. No uncovering of technical requirements or potential mismatches. The call ends the way too many do in 2025: no clear next steps, buyers go silent, pipelines inflate with zombie deals, and forecasts erode.
This is the three-body problem every B2B SaaS company faces: Sellers move faster because of AI. Buyers educate themselves faster because of AI. Products evolve faster because of AI. The motion accelerates. But the mismatch between buyer expectations and seller execution accelerates even faster.
AI was supposed to eliminate sales chaos, not create it. Yet many organizations are seeing faster first meetings with worse qualification, more demos with less relevance, higher outbound volume with lower conversion, and more coaching tools but flat win rates. Revenue leaders don't see acceleration—they see mayhem.
Which brings us to the one role that can actually solve this problem.
In an AI-accelerated world, the strategic leverage of the Sales Engineer has never been higher.
As companies stitch together dozens of AI tools promising "sales execution improvement," one critical question goes unasked: Who is training these models on the products you actually sell, the solutions you actually implement, and the repeatable deal patterns that actually close business?
If Marketing, Enablement, or Sales Operations own that responsibility—they'll do valuable work—but they don't own technical truth. They're not in the trenches diagnosing product-market fit on live calls. They're not mapping solutions to complex buying committees or navigating competitive landmines in real time.
When AI is trained by anyone other than the SE team, organizations get predictable outcomes: Tools teach the wrong patterns, reinforce outdated messages, and optimize for metrics that don't drive revenue. Then leaders wonder why reps are failing faster.
Sales Engineers remain one of the biggest drivers of deal outcomes. But their highest and best use is fundamentally changing—and the best SE leaders in the world are already getting ahead of it.
We recently surveyed 200 Account Executives across B2B sales organizations to understand how their relationship with Sales Engineers is evolving in the age of AI. The findings reveal a profession at an inflection point—and a massive opportunity for SE leaders who act now.
87% of AEs rate Sales Engineers as "somewhat" or "extremely" valuable to closing deals. Even more telling: 76% say they rely more on SEs today to hit quota than they did just one year ago.
Why the increased dependency? Products are getting more technical. 68% of sellers report their solutions became more technically complex in the past 12 months, and 86% want SEs more involved in the sales process, not less.
When we asked sellers about the primary role of a Sales Engineer, here's what they said:
Did you catch that? Demos ranked fifth. The fifth most important thing SEs do.
The most valuable SE contributions happen before and after the demo—understanding requirements, designing tailored solutions, and de-risking implementation. The demo itself is table stakes.
Despite their growing importance, 64% of sellers have had a deal lost or significantly delayed due to waiting for technical selling resources in the past six months. A quarter of respondents said this happened multiple times.
When asked why deals stalled while waiting for SE support:
When we asked what skills sellers most want SEs to develop, two answers dominated:
And when asked about their biggest frustrations working with SEs:
Finally, when asked where SEs should focus their efforts: 33% said focusing on customer-tailored solutions for large, complex deals in later stages.
The message from the field is crystal clear: Sales teams need SEs—not fewer of them, but a different use of their time. They need strategic technical advisors on complex deals, not bottlenecks stuck in repetitive preparation work.
The path forward isn't about doing less—it's about doing what matters most. Here's the shift forward-thinking SE leaders are making:
Tactical, Repeatable Work (Automate This):
↓
Strategic, High-Impact Work (Focus Here):
This isn't about replacing Sales Engineers with AI. It's about liberating SEs from predictable work so they can focus on what actually moves deals forward—the strategic deliverables that influence revenue, not just activity.
Jennifer Jones, VP of Solution Experience at Dayforce, sees the future clearly. She understands that the single most limiting factor for Sales Engineers isn't skill—it's time.
"AI has the potential to shrink pre-planning time, which will allow us to increase the proportion of time we spend in front of prospective customers…in discovery…in solution presentations," Jones explains.
Dayforce isn't trying to trade people for tools. They're using AI to scale technical impact without scaling headcount. By partnering with Ava, Vivun's AI Sales Teammate, Dayforce is automating the repetitive preparation work that used to consume SE capacity.
"Ava is 10x better than any sales rep at surfacing the right information in a fraction of a second," Jones notes. "Now our reps show up to calls more prepared and better positioned to engage prospects where they are. When AEs are better prepared, the SE team operates at a higher level—spending time on strategic solution design and competitive differentiation instead of basic prep work."
Want to see how other SE leaders are building the business case for AI teammates? Check out this ROI calculator to determine the capacity and revenue impact for your organization.
Christian Eberle, VP of AI Strategy & Solutions at Gladly (and former Head of Solutions Consulting), brings a different but equally valuable perspective. He's lived the challenges of technical sales and knows that AI hype means nothing without measurable outcomes.
"Roadmaps can no longer be measured in quarters and years. They have to be months and sometimes weeks," Eberle observes, highlighting the velocity demands AI puts on both product and go-to-market organizations.
But Eberle is ruthlessly pragmatic about tooling: "It's really hard to justify soft savings. If you're adding a tool that saves time but not replacing multiple tools at once, you've got to be really sure it delivers tangible value."
Gladly found that tangible value in two specific areas: automating sales-to-implementation handoff documentation and surfacing deal insights for leadership. The handoff automation alone saves Professional Services engineers and Customer Success teams multiple hours per deal—real, auditable ROI that leadership can understand and approve.
Christian's advice to fellow SE leaders? Start with concrete, measurable wins. Prove value in specific workflows before expanding. Make the business case with hard numbers, not soft promises.
If you're a Head of Sales Engineering, VP of Solutions, or Revenue leader responsible for technical selling capacity, here's your practical action plan:
Track a representative sample of your team for two weeks. If more than 30% of SE time goes to repeatable tasks—demo prep, environment setup, basic documentation, knowledge retrieval—you have a massive automation opportunity.
Key question: What percentage of your SE capacity is spent on strategic work that directly influences deal outcomes versus tactical work that could be augmented by AI?
Based on the survey data, sellers flagged three technology investments as highest-impact for SE productivity:
These aren't nice-to-haves. They're the foundation for scaling technical selling without scaling headcount proportionally.
The Ava approach: Vivun's AI Sales Teammate delivers all three through a single platform—powered by a proprietary Sales Reasoning Model trained specifically on B2B technical selling. Ava provides real-time assistance before, during, and after sales calls through text, voice, or avatar, saving reps 6–8 hours per week while improving execution quality.
See how Ava's Sales Reasoning Model differs from general-purpose AI in our e-book.
Move away from activity metrics (number of demos, number of POCs) and toward outcome metrics that tie directly to revenue:
This shift signals to your team—and to leadership—that SE impact is measured by outcomes, not activity.
Don't just deploy AI tools and hope for adoption. Codify exactly how AI should feed SEs so they arrive at every interaction ready to do only what humans do best:
When AI handles the repeatable intelligence work, SEs can focus entirely on strategic insight and relationship building.
Track three metrics religiously as you implement AI augmentation:
Build your business case around capacity gains. If you can free up even 20% of your SE team's time, that's equivalent to adding headcount without the cost, ramp time, or organizational complexity.
Sales Engineers aren't being replaced. They're being elevated. The SEs of 2026 will be strategic advisors, technical architects, and competitive strategists—not demo jockeys and environment troubleshooters.
But this transformation won't happen by accident. It requires SE leaders who can:
Jennifer Jones at Dayforce and Christian Eberle at Gladly are already showing the way. They're not waiting for perfect solutions or complete organizational buy-in. They're starting with concrete wins, proving value with data, and scaling what works.
The question isn't whether AI will transform Sales Engineering—it already is. The question is whether you'll lead that transformation or react to it.
Vivun has partnered with dozens of Sales Engineering leaders to map the practical path from tactical execution to strategic impact. We've captured everything we've learned in a comprehensive resource kit:
A complete guide to reimagining the SE role, building organizational buy-in, and implementing AI augmentation without sacrificing quality or relationships. Access your copy here.
The exact questions our Sales Engineering leaders have used to make the case for a new type of AI Sales Teammate - one who's always available, infinitely scalable, and consistently delivering expert-level outputs. Get the guide here.
Model your specific capacity gains, cost savings, and revenue impact based on your team size, SE:AE ratio, and deal characteristics. Try the calculator.
Hear directly from Jennifer, Christian, and other forward-thinking leaders about what worked, what didn't, and what they'd do differently.
Or schedule a 30-minute strategy session with our team to discuss your specific capacity challenges and build a custom roadmap for your organization: www.vivun.com/book.
The Sales Engineer of 2026 is already emerging. The only question is: will you be leading that evolution, or reacting to it?
This analysis is based on proprietary survey data from 200 B2B Account Executives and interviews with Sales Engineering leaders at high-growth B2B SaaS companies. Vivun delivers Ava, the AI Sales Teammate that helps high-velocity sales teams unlock instant capacity through real-time assistance before, during, and after every customer interaction.
