The Paradox Of Pace: Why AI Acceleration Is Making Your Team Slower

By Drew Naukam, CEO of Gorilla Logic

This article first appeared in Forbes Technology Council.

“Fifty percent productivity increases!” “Coders are 10 times more productive!”

Social media is swamped with content extolling the virtues of AI and its related engineering productivity benefits. Acceleration is the charge of the day—AI will allow engineering teams to ship faster, innovate quicker and drive material increase on R&D spend, right? Then boards and CEOs read these quotes, turn to their CTOs and ask why they aren’t seeing these benefits.

The hype around AI is putting more pressure on engineering leaders to go faster, but the reality is far from the hype. I’d like to share why the gap exists between hype and reality, and how engineering leaders can close it with some very practical steps.

The Paradox Of Pace

Tell me if any of the following sounds familiar:

• “I gave my team the tools. Why aren’t they going faster?”

• “We need to go faster, but we’re also under pressure to get out the next release (and the next release, and the next).”

This is the paradox of pace. You need to go faster, so you can’t afford to slow down to go faster. Most teams plan to capacity, so there’s no slack to learn. Now give your engineers AI tools (tools that are supposed to be miracle accelerants) and voilà! They magically deliver more, right?

But here’s the problem. Did you train them? Did you give your team time in the week to experiment and fail? Without that space, you trigger what’s often called the “hot stove effect.” Teams feel pressure to deliver immediately, yet they’re expected to adopt tools that require time and practice to use effectively.

When deadlines are tight, people fall back on what they know. Engineers, in particular, value predictability—and many AI tools are non-deterministic and, at times, unpredictable. That discomfort leads to avoidance, and the result is a familiar pattern: productivity dips, investments in new tools go underutilized and, despite good intentions, you end up creating resistance instead of adoption.

Three Steps To Close The AI Adoption Gap

So what can be done? First, recognize that AI is just a tool—a very effective tool, but a tool nonetheless. There is a learning curve. It takes time to learn context management and how to prompt, and you’ll likely be slower at first as you navigate it. Proficiency begets speed. Think of it as a three-step playbook:

1. Protect learning time—it’s not optional.

In the short term, engineering leaders have to relax delivery pressure and create a safe environment to learn and fail. Carve out time during sprints (with an honest reduction in commit expectations) and build in retrospectives where teams share learnings.

2. Make early failure safe and shared.

Those failures need to be celebrated. Share with the team so everyone knows it’s fine to stumble—just don’t make the same mistake twice. When experimentation is expected and protected, resistance drops and adoption accelerates.

3. Fund automation sprints, not just licenses.

Once your people are over the adoption curve, you have to think about the workflows your team manages. That requires dedicated AI automation sprints that make you slower in the short term but faster in the long term.

That last point is where the real leverage lives. AI has the ability to review and triage bugs, make recommendations on fixes and ascribe a confidence factor to the solution. Engineering teams are building agents that automate this entire cycle, but it can take months to train an agent on your codebase and get it to consistently deliver an acceptable confidence probability.

Once proven in the right workflows, I’ve worked with engineering leaders who have reduced their bug-fixing efforts by 80%, freeing teams up to focus on innovation and new features. QA is another area ripe for AI (and crucial to address as your teams produce code faster). Both of these require a budget for “infrastructure” and pulling dollars away from new features in the short term. That’s a conversation CTOs need to have with product and finance, clearly and early.

In Conclusion

Acceleration enabled by AI is possible, but it doesn’t just happen. CTOs must create the environment for success and negotiate for the resources and time necessary to automate. Do these things, and productivity gains of 50% are achievable after ramp-up in the right workflows. Ultimately, AI productivity improvement relies on leadership and change management. Not everyone is doing it well yet, but your team, with the right leadership, can.

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