How Fast Must You Adopt AI in your SDLC?

It depends on how much AI can hurt you.

By now you probably know that I’m passionate about staying at the forefront of new technologies. A lot of what I post is about how to get ahead of the curve, but it’s important to distinguish how fast you could choose to move from how fast you must move to stay competitive as an organization.

When I discuss AI adoption with CTOs, the minimal safe adoption lane on the technology adoption lifecycle curve comes down to their risk of disruption from AI.

How exposed are you?

Think about first-order and second-order effects of AI disruption on your business.

If the majority of your value comes from physical assets, you have a lot of flexibility. If you own a chain of ski resorts, your biggest existential risk is probably climate change, not AI! You might miss some operational efficiencies by not adopting LLMs aggressively, but nobody is going to replace your resorts with an LLM.

If you sell SaaS software, the picture is completely different. AI-native competitors can now build in months what used to take years and tens of millions in funding. Your category could be disrupted by offerings that didn’t exist last quarter. You need to be much closer to the front of the curve.

Second-order effects are harder to see and potentially more dangerous. If you’re in AdTech, what happens when agents replace humans as the primary purchasers online? If you provide news or analysis, what happens when people stop browsing and just ask their agents for outcomes? The raw information is still needed, but there’s no guarantee the current incumbents will own the new delivery model.

Pick a lane

Innovator

You’ll be first to benefit from AI-accelerated development. You’ll attract the most curious, ambitious engineers. You’ll out-deliver and out-innovate traditional competitors.

The cost: you’ll lose team members who aren’t all in. You’ll burn money on experiments that don’t work. You’ll build things that later adopters will just license. You pay the innovator tax.

Choose this if: you have a modern engineering org, you see existential threats or exceptional opportunities, and you’re willing to move fast and break things.

Early Adopter

You’ll out-deliver most competitors and learn from the innovators’ mistakes. You can license tools instead of building them. You’ll still attract engineers excited about AI.

The cost: you’re still adopting before things are proven. You’ll need to replace parts of your stack as winners emerge. You won’t quite keep up with the innovators and AI-native players.

Choose this if: your category is ripe for disruption but you have enough customer loyalty to move fast without being first.

Early Majority

You’ll leverage proven patterns and tools. You can roll out changes deliberately without chaos. You’ll retain organizational wisdom and support existing engineers through the transition.

The cost: you can’t keep pace with innovators and early adopters. You need deep customer loyalty, proprietary data, and/or contractual or other moats to buy time.

Choose this if: AI’s impact on your offering is real but not immediate, and you need time to improve engineering fundamentals first.

Late Majority / Laggard

Your team stays focused on delivering against your current roadmap. No disruption, no retraining, no tool churn. Eventually your existing vendors will ship AI features and you’ll get modest gains.

The cost: no meaningful acceleration. You will not keep up with anyone moving faster, and you risk being overwhelmed if the market shifts.

Choose this if: you see little opportunity or threat from AI in your industry, and you’d rather keep a laser focus on your core business.

The only wrong answer is inconsistency

There is nothing wrong with picking any of these lanes. The problems start when you pick one lane and operate in another.

If your CEO is announcing an AI-forward strategy while your CISO and legal team are blocking every experiment, you’ll make nobody happy. The inconsistencies will show up eventually in the results that your org delivers.

Pick a lane. Align your policies, tool approvals, hiring practices, and incentives to match. Then own it.

Which lane have you picked and why? Not the one in the press release or the board deck. The one your engineers experience every day...