Are Presales Humans Redundant or More Valuable Than Ever?

Buyers now spend just 17% of their buying journey with sellers.

That stat from Gartner has been shared so often that it’s practically gospel in B2B. But most teams interpret it in the same narrow way:

We need to show up strong in the 17%.

True, but incomplete.

Here’s the real tension: 83% of your buyer’s journey happens without you, and most of that experience is shaped by content created by humans. Humans who are building demos, workflows, AI automations, emails, value stories, and self-guided content that scale without any real-time buyer feedback.

Meanwhile, despite a historic wave of investment, most AI projects still fail to deliver meaningful business value, and our recent Demofest London survey (partnered with HomeRun) shows why. Most people are using AI for personal productivity: summarizing calls, writing cleaner emails, doing account research. Helpful? Sure. Transformative? Not even close.

And that’s the paradox we’re living in: AI is reshaping how we work, but not yet improving how we perform. The answer isn’t choosing between humans and AI. It’s rethinking the relationship between the two.

It’s Both/And:

Humans Create What AI Scales

When buyers spend 83% of their time consuming content without you, everything they watch, read, or click becomes a proxy for your team’s expertise. And the uncomfortable truth is this:

AI scales whatever you give it, including unclear messaging and weak strategy.

In a live demo, you get something precious: immediate feedback. You can adapt your narrative, redirect energy, repair a misunderstanding, or dig deeper into a moment of interest.

But when you scale automated content? There’s no “read the room.” No recalibration. No rescue. That’s why the human element matters more now, not less.

Every piece of content—whether human-built, AI-supported, or fully automated—needs three fundamentals AI can’t decide for you:

• Audience: Who is this for, really?

• Intent: What do you need them to think, feel, or do?

• Message: What’s the clearest, simplest, most emotionally resonant way to communicate that?

This is where the “human-in-the-loop” principle becomes a non-negotiable. Humans provide objective observation, an ability to step out of the creator seat and ask:

If I were the buyer, would this create the reaction I want?

If the answer isn’t a confident yes, you have two options:

1. Fix it before you scale it

2. Get market feedback before you scale it

Because once AI amplifies it, the damage is done.

From Personal Productivity to Organizational Performance

Our Demofest London survey revealed something telling: The vast majority of professionals use AI for individual efficiency, not team performance.

But efficiency ≠ effectiveness.

Summaries, rewritten emails, and auto-generated insights feel good in the moment. But they rarely move pipeline, shift buyer confidence, or improve win-rates. And that is why most AI initiatives stall.

Three reasons most AI projects don’t deliver value

1. They focus on tasks, not systems. Lots of automation. Very little transformation.

2. They lack orchestration. No connective tissue across tools, teams, or workflows.

3. They aren’t tied to performance outcomes. Productivity ≠ revenue. Efficiency ≠ effectiveness.

This gap is why two new roles are emerging - fast:

Forward Deployed Engineers (FDEs)

AI-ready technical specialists who embed with customers during a sales cycle to rapidly build proofs of concept and demonstrate capability early. Customer-facing. Impact-driven.

GTM Engineers

Internally-facing operators who sit inside RevOps and orchestrate AI workflows, infrastructure, and systems so the entire sales organization benefits, not just individuals.

Both roles exist for the same reason:

To turn scattered AI usage into scalable, performance-driving systems.

And right now? Enterprise teams are far ahead of the mid-market in building these systems. The rest of the market is trying to figure out how to fund it, organize it, and operationalize it.

Success Has Nothing to Do with Technology

(And Everything to Do with Humans)

There’s a through-line in all the research we’re reviewing with partners like Brian Oehling:

The biggest predictors of AI success are not technical - they’re human.

Business acumen. Change management. Process redesign. Leadership clarity.

AI transformation isn’t:

• Replacing the current state

• Making old processes faster

• Dropping an LLM on top of messy workflows

AI transformation is:

• Rethinking how work fundamentally gets done

• Re-engineering processes around agentic AI

• Determining what should (and shouldn’t) be automated

• Establishing the role humans play at each step

That is why “human in the loop” isn’t a limitation. It’s the entire strategy.

The best AI programs don’t automate bad processes - They reimagine good ones.

The Close:

The 83% Is Where You Win or Lose

Buyers are spending the overwhelming majority of their journey without you. What they encounter in that time determines whether the 17% feels valuable - or forgettable.

So, ask yourself:

Are you prioritizing speed? Or effectiveness?

Personal productivity feels good. Organizational performance wins deals.

If you want to close the gap:

• Invest in GTM Engineers who can orchestrate AI across the revenue engine

• Apply human-in-the-loop evaluation before scaling anything

• Re-engineer your processes, don’t just accelerate them

• Elevate the moments buyers do spend with you — because the bar has never been higher

Your buyers are already forming opinions long before they meet you.

Make sure the 83% works for you, not against you.

AI will scale whatever you give it. Mediocre prompts mean mediocre results.

If you’re ready to elevate your demos, strengthen your message, and build AI-enabled workflows that boost performance (not just productivity), we can help.

👉 Book a meeting or save your seat in a Demo2Win Public Workshop.