AI vs. Hiring Velocity: Why Tech Teams Are Still Stuck

When it comes to AI adoption, tech leaders are moving fast, but not always in the right direction. In a recent LinkedIn Live discussion hosted by Lean Solutions Group, Shahid N. Shah, Fractional CTO and longtime engineer in regulated industries, and Ryan Mann, Marketing Director at Lean Solutions Group, unpacked why so many organizations are struggling to translate AI hype into real hiring and productivity gains.

To watch the full conversation, click here.

The Illusion of Easy AI

Shahid began with a blunt observation: “The thing that looks easy about AI on the outside is exactly what makes it hard. And that it’s deceptively simple.”

He described a cycle of confusion that starts at the top. Executives see flashy demos or quick prototypes and assume similar results are achievable in production. Teams, unsure how to push back, stay quiet rather than admit uncertainty. “We’re all in the same boat,” Shahid said. “We don’t know what we’re all doing.”

The result is what he calls “tomfoolery”: a flood of overpromises that stalls, not assists, real productivity. The first step out of that trap, he suggested, is honesty: acknowledging that every organization is different – and they’re all just beginning to figure out how to integrate AI responsibly.

Leaders Must Use the Tools They Preach About

One recurring theme was credibility. Shahid warned against leaders who promote AI tools they’ve never touched. “Try never to push an AI tool that you’ve never used yourself,” he said.

For technology adoption to stick, leaders need a firsthand understanding. It’s not enough to read about AI copilots or code assistants; leaders must test them, learn their quirks, and only then guide teams. This builds trust and avoids what Shahid called “confused delegation,” where both managers and employees operate from guesswork.

Every Worker Is Now an Individual Contributor

AI’s ripple effect reaches far beyond developers. Shahid noted that across the software lifecycle, from design to QA to DevOps, each person now works as an individual contributor. “Every single role now in the software development lifecycle is an individual contributor,” he said, pointing out that designers, QA testers, and product managers all now interact directly with AI tools.

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That shift demands new literacy. Instead of just writing code or test cases, everyone needs to understand at least the basics of framing prompts, validating AI outputs, and recognizing when automation isn’t the best approach to a given problem. Hiring, therefore, isn’t about replacing people with machines; it’s about finding people who can collaborate with them.

The Rise of “Expectation Engineers”

AI has introduced a new bottleneck: clarity. Shahid coined the term “expectation engineering” to describe the skill of defining what success actually looks like before automation begins. “What the AI will not produce,” he explained, “is what the heck are we expecting?”

He urged teams to involve QA and product teams earlier in defining user expectations, test cases, and data context. This upfront structure ensures that when AI generates artifacts – code, designs, or documentation – they align with business goals.

AI as a Colleague, Not a Tool

“Is AI a tool on the side, or is AI your colleague?” Shahid asked.

For teams to thrive, he argued, AI should be onboarded just like a human colleague, trained in the company context, processes, and priorities. Otherwise, it will produce outputs “out of context,” amplifying errors at scale. As he put it, “it’s one piece of garbage coming in with a thousand pieces of garbage coming out.”

This reframing, from automation to collaboration, demands that organizations rework onboarding, data hygiene, and process design – not just install new software.

The Hidden Cost: AI as a Tech Debt Generator

“AI is our world’s best ‘tech debt generator.” That amazing AI speed, Shahid cautions, can actually become dangerous.

A single bad prompt can create thousands of flawed lines of code or documents. Junior engineers, lacking context or experience, may unwittingly flood systems with unmaintainable output. To counter this, he recommended pairing code generation with AI-driven maintenance: “If the AI generates it, let AI help maintain it.”

Done right, that cycle turns debt into value, yielding fast iteration without long-term decay. But done wrong, it multiplies complexity and burnout.

Building Smarter, Global-Ready Teams

For Lean Solutions Group, the conversation underscored a broader truth: global-ready solutions aren’t about speed alone. There’s about velocity, the sustained flow of skilled, context-aware talent who can adapt as tools evolve.

As Shahid summed up, leaders who expect speed without structure will only “scale the garbage.” But those who treat AI as a partner and begin recruiting for tool fluency rather than just titles, will find that hiring velocity isn’t about moving faster – it’s about moving smarter.

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