AI First is a Hot Topic. AI Second is the Right Way.

Every conference keynote, every vendor pitch, every LinkedIn post — the message is the same: put AI at the center of everything. AI-first products. AI-first roadmaps. AI-first everything.

And look, I get the appeal. AI has the potential to drive tremendous value for organizations that do it right. But "AI First" has never sat right with me — and after a few years of watching companies stumble through adoption, I've got the data to back up that instinct.

The Problem with AI First

The concept may be popular, but organizations rushing into AI without the right strategy, processes, and people in place are consistently coming up short.

Let's start with a number: only 5% of generative AI pilots actually succeed. Isn't that astounding? A recent MIT study found that 95% of company generative AI pilots are failing (Fortune, August 2025).

What's striking isn't the failure rate itself, but the reason behind it. The technology isn't the problem. Organizations are simply not set up to use it effectively. They're skipping the foundational steps that make AI actually work. And those skipped steps are costing them. When you lead with the technology instead of the strategy, you skip the foundational work that makes AI valuable in the first place.

Why Does AI First Keep Failing?

AI has the potential to supercharge your organization. It can help your employees do their jobs faster and easier than ever before. But when companies say "AI first," what they're really skipping are the prerequisites your organization needs to unlock that power. Here's why going AI-first doesn't work:

1. AI Has No Context Until You Give It Some

Most AI solutions are trained on vast, generic datasets, but they lack the specific context of your business, team, and customers. It doesn't know your business. It doesn't know your teams. It doesn't know your customers.

If you want AI to write for your brand, it needs to understand your tone of voice, your products, your target audience, and your buyer personas. If you want AI to surface recommendations or identify the right customers to target, it needs to be grounded in your KPIs, your business objectives, and the metrics that actually matter to your organization.

Giving AI that context isn't a switch you flip. It takes careful planning, clear guidelines, and feeding your technology the right data.

2. Quality In, Quality Out

IT folks love to reuse this old saying, but it's never been more true than when working with AI. If your data is poorly organized, incomplete, or just plain inaccurate, your AI technology will be too. Without well-defined data structures, tagging, analytics, and clear guidelines, you can pump your business full of AI tech and never see the results you're looking for.

Discipline in organizing, tagging, and enriching your data is essential. Before you dive first into AI, make sure your data foundation is as strong as the insights you expect AI to produce.

3. People (and Processes) Matter

AI is not magic, and it doesn't replace people. While AI has the power to produce thousands of images or pieces of content in the blink of an eye, it doesn't know what you're looking for. Without a solid process in place to vet and approve that content, AI isn't making your team more efficient — it has created more overhead for you instead.

When your people and processes are locked down, then you can start thinking about how to let AI do the heavy lifting.

The Bottom Line

AI First may be a hot topic, but the real winners will be the organizations that master AI Second.

Companies rushing to slap AI onto every process are missing the point, and their results (or lack thereof) show it. If you want AI to work for your business, lead with strategy, people, and data. Make AI the accelerator, not the starting line.

When your organization is truly AI ready, that's when AI becomes a real competitive advantage.

Want to be in the 5%? Let's talk about building your AI-ready foundation.