AI Made Everything Feasible. That’s the Problem

A delivery-side guide for Sales Engineers navigating the AI hype cycle

I’m not a Sales Engineer. I build and ship software with AI-native teams. I’m the person on the other side of the handoff, the one who inherits what gets promised during the sales cycle.

And I’m watching a new pattern emerge that should concern every SE reading this: AI has made feasibility trivially easy. Deliverability is still hard. 

Almost every POC now proves something can work. Very few prove it will actually ship, scale, and survive contact with production. 

Before AI, the effort required to build something acted as a natural filter. “That would take two sprints” was often a soft no. That filter is gone.

Today, with Cursor, Claude, Copilot, and agentic tooling, teams can stand up a convincing prototype in hours. The cost of saying “yes” during a POC dropped dramatically. 

The long-term cost didn’t. 

Every “yes” demonstrated during a sales cycle becomes an implicit product commitment in the customer’s mind. The customer never sees a prototype. They see a promise. 

And when the gap between what was demoed and what actually ships becomes visible, that’s no longer an engineering problem. It becomes a trust problem. The data backs this up: DORA’s 2024 research found that a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability, even as AI use was associated with gains in documentation quality, code quality, and code review speed. [1]

“Pilots work because they operate in a controlled reality. Production fails because it has to operate in the real one.” – Nitin Seth, Co-Founder & CEO, Incedo. [2]

The question is no longer: “Can we build this?” 

It’s: “Can we deliver, support, and maintain this at scale?” 

If you can’t answer that confidently, the POC is proving the wrong thing. And you’re not alone in that uncertainty. McKinsey’s 2025 State of AI survey found that most organizations are still in the experimenting or piloting stage, with roughly one-third reporting that their companies have begun to scale AI programs [3]. Gartner later reported that by the end of 2025, at least 50% of generative AI projects had been abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. [4]

Don’t accidentally build a product line

This is the most dangerous version of the feasibility trap, and it’s especially relevant for Sales Engineers. 

SEs have always built lightweight custom work, scripts against APIs, dashboards, integrations, workflow automations. That’s not new. 

What changed is the scale of what can now be built in an afternoon. 

AI-assisted tooling can generate a functioning dashboard, a working integration, or a polished prototype before lunch. It looks like product. It feels like product. The customer assumes it is product. 

Six months later, when it breaks or doesn’t scale, that becomes a support issue, not a “remember that thing the SE hacked together?” conversation. 

And here’s the harder question: 

Who owns this when you move to the next account?

If product management doesn’t know it exists, and engineering isn’t prepared to maintain it, you’re not proving value. You’re creating operational debt someone else will eventually pay for.

Faster code doesn’t automatically mean faster delivery

This is where many AI productivity claims fall apart. 

Code generation is only one slice of software delivery. Shipping still requires reviews, testing, coordination, deployment, monitoring, support, security review, and cross-team alignment. 

AI can absolutely accelerate output. 

That does not automatically accelerate delivery. As the authors of “Revenge of QA” argue, coding has never been the real bottleneck in software delivery. The constraint is usually downstream: code review, integration, system testing, verification, and validation. [5] A related IT Revolution summary makes the operational risk concrete: AI-generated code can increase volume faster than code review, testing, and production systems can absorb it. [6]

“An individual developer or even a team of developers going faster does not necessarily make the system move faster.” – Rachel Stephens, RedMonk, analyzing the 2024 DORA Report. [7] 

Organizations that understand this distinction are building real advantage. Organizations that ignore it are creating larger backlogs, more fragile systems, and unrealistic customer expectations at much higher speed. The 2025 DORA research puts it more carefully: AI acts as an amplifier, magnifying an organization’s existing strengths and weaknesses. [8]

And perhaps most striking, in one early-2025 randomized controlled trial, METR found that 16 experienced open-source developers working on 246 real issues took 19% longer when allowed to use AI tools, while still believing AI had made them 20% faster. The perception gap between what AI feels like and what it delivers is itself a risk factor in any sales conversation. [9]

And there’s another trap hiding underneath all this: 

Just because you can build something doesn’t mean the product should absorb it. Every feature carries hidden weight: 

● UX complexity 

● Maintenance overhead 

● Support burden 

● Strategic drift 

● Long-term architectural cost

SEs may not own the final product decision, but they often surface and validate the requests that trigger those decisions. 

That makes judgment more important than ever.

Five questions worth asking before the deal closes

Here are the questions that expose whether AI acceleration is real operational capability or just demo velocity.

The question that matters most now

AI shifted the constraint. 

The hard part of technical sales is no longer proving something can work. The hard part is judgment: Is this the right thing to build, operationalize, and support?

That question deserves more airtime between Sales Engineering, Product, Engineering, and Customer Success. 

Because the SE role just expanded. 

Great SEs now need product judgment, engineering intuition, and commercial instinct at the same time. They need to weigh maintenance cost alongside demo impact. They need to think about operational ownership, not just technical feasibility. 

The organizations that win with AI won’t be the ones that demo fastest. They’ll be the ones that operationalize responsibly. 

You are still the last honest conversation before the commitment gets made. That just became more important, not less.

Sources 

[1] “Announcing the 2024 DORA report” – Google Cloud / DORA, 2024.
https://cloud.google.com/blog/products/devops-sre/announcing-the-2024-dora-report

[2] “AI Pilot Purgatory: Why Enterprise AI Rollouts Fail to Scale and How to Fix the ROI Trap” – UC Today, 2026.
https://www.uctoday.com/productivity-automation/ai-pilot-purgatory-enterprise-scaling/

[3] “The state of AI in 2025: Agents, innovation, and transformation” – McKinsey / QuantumBlack, 2025.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[4] “Why 50% of GenAI Projects Fail – And How to Beat the Odds” – Gartner, 2026.
https://www.gartner.com/en/articles/genai-project-failure

[5] “Revenge of QA: The Age of AI is the Age of Verification & Validation” – Elisabeth Hendrickson, Jeffrey Fredrick, John Willis, Kamran Kazempour, Joseph Enochs, IT Revolution / Enterprise Technology Leadership Journal, 2025.
https://itrevolution.com/product/revenge-of-qa/

[6] “The Revenge of QA: How AI Code Generation Is Exposing Decades of Process Debt” – Leah Brown, IT Revolution, 2026.
https://itrevolution.com/articles/the-revenge-of-qa-how-ai-code-generation-is-exposing-decades-of-process-debt/

[7] “DORA Report 2024 – A Look at Throughput and Stability” – Rachel Stephens, RedMonk, 2024.
https://redmonk.com/rstephens/2024/11/26/dora2024/

[8] “DORA Research: 2025 – State of AI-assisted Software Development” – DORA / Google Cloud, 2025.
https://dora.dev/dora-report-2025/

[9] “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity” – Joel Becker, Nate Rush, Beth Barnes, David Rein, METR, 2025.
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/