You’ve been here before.

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Remember Agile transformations? At first,  we renamed job titles,  took a few Agile training classes, and called it a day. For a while, it looked like progress. But under the surface, little changed. Individuals lacked the upskilling needed, outdated decision-making bottlenecks stalled velocity, and leaders at the top of the food chain felt they didn’t need to change much if at all. Over time, most tech execs realized that the keys to lasting impact through Agile involved true upskilling, reorganizing teams, and true company-wide buy-in. .

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Now, here comes Generative AI. Bigger promises. Greater potential. But also, greater potential to get trapped in the cycle of surface level change.

Today’s tech leaders don’t need reminding that Generative AI is reshaping how organizations build, operate, and compete. What they do need is clarity on how to avoid repeating the early Agile missteps—because this time, the stakes are higher. Generative AI doesn’t just demand new tools, it demands a true workforce evolution—a fundamental shift in how we approach AI in the workforce.

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Here are the six most impactful changes you can make today to ensure your team is set up for the Gen AI workforce transformation that is coming– whether we like it or not.

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1. Rethink Core Capabilities  

Getting true ROI with Generative AI doesn’t happen because you bought a license or spun up an innovation pod.

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Gen AI rewires the entire cost structure of how value gets created.  Writing a line of code is trending towards free. So, ask yourself - “What are the new jobs to be done by my software engineers?” And then rework your hiring, firing and promotion process to align to the new capabilities your team will need. Finally, get to work ensuring they are gaining proficiency in these skills.   

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Gen AI is about reshaping intelligence flow across the enterprise. That means your workforce doesn’t just need new tools. It needs new muscles, new skills.

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At Stride, some of the key capabilities we now measure our team against include:

• Systems Design - ability to balance tradeoffs to apply the right design for the problem.
• Business Outcomes - translates user needs into a technical plan that produces measurable business results
• Expectation setting - practices clear and effective communication to negotiate expectations around scope & timelines

These shifts redefine the nature of AI in the workforce, requiring new thinking on team structure, performance expectations, and role progression. Now’s the time to sit down with your team and rethink those performance review criteria and know that it will take time to iterate until it feels right for your team.

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2. Showcase Measurable Leadership Buy-In

One of Agile’s core challenges was leadership misalignment. Boards wanted “agile” results but still demanded fixed roadmaps. Engineering was supposed to be autonomous, but budgeting and compliance structures hadn’t gotten the memo.

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You know how that ended: superficial agility at best, frustrated teams at worst.

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With AI, this tension is magnified. Executive teams want AI because of its promise to reduce costs and boost speed. But if your org is still running on quarterly delivery gates, strict approval hierarchies, and risk-averse culture, you’ll stall out.

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This is your chance to reset—not just how teams operate, but how leadership leads. If the C-suite treats AI like an automation lever instead of a strategic transformation, your people will keep their heads down and do things the old way. You won’t get evolution. You’ll get surface-level adoption with deeply rooted resistance.

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Two quick visible wins leadership teams can do today:

• Ask your team what they’d like to see from you, if anything, and then communicate on the feasibility and timeline of achieving those things. Specifically inquire as to the things they want you to either start doing OR stop doing.
• Incorporate Gen AI into your annual goals in some way. This sends a clear message on the number one measurable outcome that the team can rally behind.

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3. Avoid Meaningless Titles

Everyone wants a “Head of AI,” but many teams aren't quite sure what that person owns—or how they plug into product, engineering, and data.

Layer any new job titles with meaningful role accountabilities:

• Decision boundaries: Where does AI suggest vs. decide vs. act?
• New hybrid roles: Think AI product leads, data translators, AI-augmented analysts.
• Technical fluency across functions: Not just in engineering, but in ops, product, and go-to-market.

In addition, consider soft skills within these new roles, the more code is written by agents, the more the humans need to shift to collaborative, business driven mindsets. 

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You’re not just changing job titles. You’re designing how humans and machines co-create value. That’s not a reorg. It’s a re-architecture. These decisions fundamentally shape the trajectory of your workforce evolution and how well you integrate AI in the workforce in a way that is scalable, ethical, and effective.

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4. Remove  Governance Bottlenecks

AI runs at real-time speed. Most enterprise governance models don’t.

In Agile, we saw massive bottlenecks when teams were told to be autonomous but still had to send everything through three approval layers. You likely fixed some of that over the last decade.

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But AI puts new pressure on decision velocity. Can a team act on an AI insight without waiting two weeks for sign-off? Can a product leader iterate on a model without triggering a procurement fire drill?

If not, your governance is your biggest barrier to ROI.

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You’ll need to rethink compliance, risk management, and performance monitoring—not to slow AI down, but to make it responsible and fast. Speed and safety are not enemies. But old governance models treat them that way.

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5. Remember: Tools Don’t Drive Change. People Do

AI vendors will promise you the moon. But here’s the reality: your outcomes will depend more on enablement than on model performance.

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You already know this. You’ve seen tools fail because the culture wasn’t ready, the workflows didn’t adapt, or the incentives were misaligned. Your workforce doesn’t need hype. It needs fluency. That means training, yes—but also permission to explore, to challenge, and to collaborate with AI in context.

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Treat AI like a co-pilot, and you need to train your pilots. Not just on how to fly faster, but on how to navigate differently. And don’t assume every team will figure it out on their own. As one CIO put it, “We didn’t just need tools. We needed to unlearn decades of habits.”

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6. Final Word: Cultivate, Don’t Slap a Sticker On It

If you’re a tech leader today, you’re in a sweet spot—and a risky one. AI can be your growth engine. Or it can become another stalled initiative buried in the transformation graveyard.

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The difference is how you lead the workforce evolution.

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This isn’t about renaming roles or installing a new tool. It’s about building a culture, structure, and skill set that’s ready to partner with AI, not just adopt it. That takes care, intention, and a willingness to uproot some legacy habits.

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Because in the end, transformation doesn’t happen by labeling the pot. It happens when you cultivate the soil.

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And that starts with you.

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