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Learn how presales is evolving and howyour team can stay ahead in atech-driven sales landscape.
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- AI is changing presales by compressing preparation, not by replacing the solution engineer (SE). The work that survives is judgment, trust, and reading the room.
- 94% of sales engineers conduct repetitive demos at least sometimes, and only 6% never do .
- A standard demo including prep takes an average of 3.6 hours of SE time, and a custom demo takes about seven hours .
- Every major demo platform shipped an AI agent in 2026. When every vendor can produce the artifact, the artifact stops being the advantage.
- Spryker cut presales involvement in top-of-funnel calls by 95% and saves an average of three hours per SE per week (Demoboost customer story, 2026).
- The opportunity is not more presales output per person. It is more customer understanding per interaction.
AI is changing presales by making preparation fast and cheap, which shifts the value of a solution engineer (SE) from producing material to exercising judgment. Research, first-draft demo scripts, RFP responses, call summaries, and competitive analysis now take minutes instead of hours. What AI cannot do is decide which problem deserves attention, read the politics inside a buying group, or carry the trust a technical buyer extends to a person. In 2026, the SE role is moving from product expert to change guide, and the scarce skill is no longer knowledge. It is discernment.
We asked 25 presales leaders and practitioners how their work is actually changing. Their answers were more consistent than we expected, and they point somewhere more interesting than "AI makes you faster."
The productivity opportunity in AI is not more presales output per person. It is more customer understanding per interaction.
What does a solution engineer actually do in 2026?
Most solution engineers still spend the majority of their week on demos that do not need them. According to the State of Demo Automation 2026 report from Navattic and the Presales Collective, 94% of sales engineers conduct repetitive demos at least sometimes, and only 6% never do. The same survey found that 34% describe their standard demo as having little to no customization.
The arithmetic is unforgiving. SEs run an average of seven demos a week, and a standard demo including prep takes an average of 3.6 hours of SE time. That is a large share of the week spent on work an SE was not hired to do. Custom demos, the work that actually needs an expert, take about seven hours each.
Meanwhile, the support structure is thin. Only 39% of surveyed SE teams have dedicated demo engineering resources, and teams spend an average of three hours per person per week just maintaining demo environments.
This is the backdrop against which AI arrived. It did not land on a role with spare capacity. It landed on a role that was drowning in production work.
Chris Taplin, a presales leader who contributed to our research, put the shift plainly:
His broader argument is that presales used to spend enormous effort understanding the business processes and datasets that would fuel a demo. Much of that preparation now compresses, which leaves the SE free to help customers weigh opportunity against risk, and value against hidden cost.
Cristina Motta described the same movement in six words:
That is the central idea of this article. Technical expertise is table stakes nowadays. What differentiates is the ability to guide a decision.
Is AI replacing the solution engineer, or raising the bar?
AI is raising the bar, and it is raising it for everyone at once, which is why the gap between good and great is widening rather than closing.
Richard Armstrong framed the upside directly:
His point is that AI hands every SE a version of the technical depth, business acumen, and presentation polish that used to belong to a rare few. That is real. It is also incomplete, and Nick Schneider explained why:
His argument is worth stating in full because it inverts the usual assumption. AI makes good SEs better. It does not make them great. Great is still domain expertise, and the gap is about to widen, because the people who are already great will extract the most from what AI gives them. In his own work that looks like agents watching his pipeline and scoring his team's calls overnight, which frees him for the judgment a model cannot fake: reading the room, framing the business case, and knowing what to leave out.
Bruno Prigent arrives at the same place from the buyer's side. AI raises the technical level of presales teams, of clients, and of competitors simultaneously. The benchmark moves from having access to information toward the ability to analyze and decide based on what the model proposes. And in his world, cybersecurity, that decision carries weight a lab experiment does not.
He also names the risk nobody else did. Inaccurate or misaligned AI-generated content sends a poor signal to a prospect and can cause them to disengage. Trust is the primary lever a presales professional builds across a sales cycle, and AI can spend that trust faster than it builds it.
What is every demo platform building right now, and what does that tell you?
Every major demo platform shipped an AI agent in 2026, which means demo production is converging on a commodity and the differentiation is moving elsewhere.
This is worth looking at directly, because it is the clearest available evidence about where the category thinks the value is. Here is what the eight platforms most often evaluated alongside each other announced in the past year.
Read this table and one thing stands out: every one of these is an answer to the same question, which is how to produce a demo faster and with less human involvement.
Sam Clemens, co-founder and CEO of Reprise, described his company's MCP launch in terms of removing the "bottleneck between a seller wanting to show a tailored story" and actually delivering one.
He is right that the bottleneck is dissolving. The strategic consequence is the part the category is quieter about. When every vendor can generate a competent demo on demand, generating a competent demo stops being a competitive position. It becomes the floor.
That is not an argument against demo automation. It is an argument about where the remaining advantage sits, and it lands on exactly the ground our contributors described: governance, seller adoption, buyer understanding, and revenue visibility. Making the artifact is solved. Knowing which artifact to send, to whom, at what moment, and what their behavior means, is not.
Why is judgment becoming a scarce skill in presales?
Judgment is becoming scarce because everything adjacent to it got cheap, and scarcity follows whatever cannot be automated.
Taiwo Odeyemi gave us the sharpest version of the thesis:
His own workflow shows what that looks like in practice. He uses AI in three ways: synthesizing a prospect's public materials, past notes, and industry context into a tight brief before a call, so he walks in knowing the likely objections. Producing first drafts of demo scripts, ROI narratives, and follow-up emails that he then reshapes with what he actually heard. And pattern-spotting across deals, looking at which messaging or demo flow correlated with deals that moved versus deals that stalled. His conclusion is the one that should be pinned above every presales team's desk:
"Less time producing artifacts, more time actually listening and adapting in the room - which, ironically, makes presales more human, not less."
Ahmed Fouad added the commercial dimension:
And Ed Sarney explained why the technical depth still matters even as the drafting gets automated. As AI innovation changes how customers operate, it becomes more important to understand how the technology actually works, not less.
Are the best solution engineers becoming builders?
Yes, and this is the most concrete change in day-to-day work. The SEs getting the most from AI are not using it to write better emails. They are using it to build things.
Lokesh Shetty put it best:
He uses AI daily to turn raw product knowledge into reusable tools. Peter Antoniou provided the evidence behind the idea at team scale:
That capability has let his team extend technical processes, build reporting and dashboards, and develop new integrations at a pace that was previously out of reach.
Malik Nir gave the clearest example of what building looks like on a single deal. Presales teams send buyers a lot of flat documents: spec sheets, pricing spreadsheets, comparison tables. A buyer has to read the whole thing to find the part that applies to them. Malik now takes that same document and, with one prompt, turns it into a small web page the prospect can click through, choosing the options that match their situation and seeing only what is relevant.
The document did not get better. It stopped being a document. That is a change an SE can make on a Tuesday afternoon for one deal, without a designer, a developer, or a budget line.
Malik brings it straight back to the human in the loop, though. Somebody still has to understand what the customer is actually trying to achieve, recognize the constraints they are working within, and help them find the right route forward. The tool is new. The thinking behind it is not.
Jeroen de Haas offers the section's necessary warning. He feeds Claude pre-demo calls, RFPs, and sample data to build sharper narratives, and it digests information better than he can alone. Then:
What work should AI take off a solution engineer's plate?
AI should take the work that happens around the customer conversation, not the conversation itself.
Our contributors were close to unanimous on where the line sits. Patrique Gaillard:
Rishi Kapoor described the same division across a wider set of tasks, using AI to accelerate research, refine messaging, build proposals, and prepare for partner and customer meetings:
Mohammed Ali Khan made the operational case. Administrative tasks, compliance checks, and RFx drafting move off the SE's plate, and the effort redirects toward relationships, value engineering, and genuinely hard problems.
Jenis Sheth gave us the one quantified figure in the research:
The number matters less than what he does with the reclaimed hours: strategic thinking, customer engagement, and solution innovation.
Prakash Somaiya named the consequence nobody wants to say out loud. If preparation is free, being unprepared is now a choice.
His teams at Centelon and Sibiliti embed AI across RFP and proposal drafting, deck creation, demo script writing, prospect research, and synthetic data generation. At the same time, he is clear that AI still cannot run a live demo, lead an in-person workshop, or build the trust that complex B2B decisions require. His advice to every presales professional is worth repeating:
Artam Künzner offers a useful corrective on sequencing. As a one-person presales team, he uses Claude to turn complex RFPs around in an hour, sharpen scripts and narrative, summarize calls, and coach himself after meetings. He is still skeptical of AI-first thinking:
Deciding what to hand off is easier with a map
The Interactive Demo Playbook covers how presales and GTM teams use interactive demos across the buyer journey, from first touch to adoption.
Get the Playbook
What stays human when preparation gets cheap?
Trust, storytelling, and the judgment behind a recommendation stay human, because they are the parts of the job that depend on being accountable for an outcome.
Kirk Taylor drew the cleanest line of anyone we spoke to:
Put that next to Bruno Prigent's observation about trust and an argument forms that is bigger than either quote. Once information becomes cheap, credibility becomes expensive.
Abdallah C. reaches the same conclusion by a different route. AI accelerates research, tailors demos, and anticipates customer needs, which gives him more time for the questions that matter:
Sean Campbell adds a warning that presales leaders should take seriously when they think about hiring and ramping. He uses agents as executive assistance for daily briefings, research, and pulling out insights. He also hopes the profession keeps building the skills to succeed without them:
AI fluency is becoming necessary. It is not a substitute for knowing how to do the underlying work.
Several contributors reached for the same metaphor to describe what AI has become to them, and it was never a "tool." One SE described it as the buddy every SE has always wanted, taking on the repetitive and less glamorous work so he can concentrate on the conversation, the story, and the strategy. Biju Radhakrishnan was more concise:
Andrés García-Baquero León describes it as a thinking partner used to research industries, prepare customer conversations, summarize information, and challenge his own ideas:
And one contributor, approaching a decade in the profession, described AI as a collaborative thought partner in terms that summarize the whole argument:
Where does the line between AI and the SE actually fall?
The pattern across 25 contributors is consistent enough to write down as an operating model.
How do presales teams turn this into an operating model?
They turn it into an operating model by treating the demo as a system rather than an artifact, which means governance, distribution, and engagement data have to be designed alongside creation.
Demo creation is getting easier. Demo governance, seller adoption, and revenue visibility are still hard. Three things follow from that.
Take repetitive demos off the SE entirely, not just make them faster. The goal is not a quicker intro demo. It is an intro demo the SE never touches. Spryker reduced presales involvement in top-of-funnel calls by 95%, and every first call now includes a product demo, with an average of three hours saved per SE per week (Spryker customer story). That is a structural change, not an efficiency gain. It works because presales owns the master templates, speaker notes, and approved flows, and sellers personalize from that library per deal.
Make maintenance cheap or the library rots. A demo that falls behind the product costs more trust than it ever saved in time. Radiant Security's demos were six months behind their product on a previous platform. After rebuilding modularly, they cut demo maintenance time by 75% and reclaimed 10 to 20 hours per update cycle (Radiant Security customer story). Voucherify reduced demo prep time by 80%, taking custom builds from around three hours to about 30 minutes (Voucherify customer story).
Feed engagement back into the deal. This is where most teams stop, and it is the half that AI does not solve for you. Knowing which screens a buyer viewed, where they stopped, what they clicked, and who they forwarded the demo to is what turns a demo from a presentation into an intent signal. Demoboost Revenue Intelligence surfaces this at the lead level, including engagement scoring, activity timelines per demo, drop-off and CTA screens, and visibility into who a lead shared the demo with. That is the data that tells an SE which deals deserve their judgment.
The evidence that this changes outcomes is not just ours. Deals that include automated demo touches close 19 days faster and show a six-point higher win rate, and win rates reach 72% when the first demo is sent within 14 days of deal creation, compared with 59% for deals with no demo touch.
Read that alongside the human argument in this article and the two halves fit. Automation buys the SE time. Engagement data tells them where to spend it.
What does this mean for presales in 2027?
It means the profession stops competing on production and starts competing on discernment.
AI can make every solution engineer faster. It can make them better researched. It can help them build. It can draft, analyze, and prepare. But it cannot decide which problem deserves attention. It cannot fully understand the politics inside a buying group. It cannot know with certainty when an apparently technical objection is really about trust. It cannot read every room. And it cannot own the judgment behind a recommendation.
That is why the future of presales is not a contest between humans and AI. As Prakash Somaiya puts it, partner with AI do not compete against it.
Less production. More judgment. Less presenting. More guiding. Less preparation around the customer. More time with the customer.
More human work, not less.
Frequently asked questions
Will AI replace solution engineers? No. AI replaces parts of the preparation around a customer conversation, not the conversation itself. It drafts demo scripts, synthesizes research, and summarizes calls, but it cannot own a recommendation, read a buying group's internal politics, or carry the trust a technical buyer places in a person. The role is shifting from producing material toward guiding decisions.
What presales tasks is AI best at in 2026? AI performs best on prospect and industry research, first-draft demo scripts and narratives, RFP and RFx response drafting, call summaries, follow-up emails, and post-meeting coaching notes. These are high-volume, low-judgment tasks where a strong first draft saves hours and a human review catches what matters.
How much time do sales engineers spend on repetitive demos? A lot. 94% of sales engineers conduct repetitive demos at least sometimes and only 6% never do, according to the State of Demo Automation 2026 report from Navattic and the Presales Collective. SEs run an average of seven demos a week, and a standard demo including prep takes an average of 3.6 hours of SE time.
What is the difference between demo automation and an AI demo agent? Demo automation gives buyers structured, self-guided access to the product so repetitive intro demos do not need an SE in the room. An AI demo agent goes further and attempts to run the session conversationally, answering questions in real time. Both reduce production work. Neither replaces the judgment involved in deciding what to show a specific buying group and what to leave out.
What skills should a solution engineer build in 2026? Domain expertise, commercial framing, and discovery remain the foundation. On top of that, the differentiating skills are evaluating AI output rather than accepting it, building reusable tools and assets, and reading engagement data to decide where to spend time. AI fluency is becoming necessary, but so is the ability to do the underlying work without it.
Does demo automation actually shorten the sales cycle? The available data says yes. Deals that include automated demo touches close 19 days faster and show a six-point higher win rate, and win rates reach 72% when the first demo is sent within 14 days of deal creation, compared with 59% for deals with no demo touch (Navattic and Presales Collective, State of Demo Automation, 2026).
How should presales measure the impact of AI on their team? Do not measure output. Measure the share of demos that reach a buyer without an SE in the room, hours reclaimed per SE per week, demo maintenance time per update cycle, and how much demo engagement data reaches the deal record. Output volume will rise for everyone in the category, which makes it a poor indicator of advantage.
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