Every Company Has Access to AI — But Not Every Company Has the Foundation to Win With It. Here’s How to Get There.

The way we work today determines so much more than just the output we create. The clichéd “AI helps you work faster and smarter” isn’t fluff, but understanding how and what to change within your organization is less obvious and can be quite overwhelming.

At every scale, startup, mid-size or large enterprise, if you don’t change the operational process of your company and truly embed AI into it, you will not be relevant. This is felt from the top down, across R&D, product, creative, brand and marketing. Every team now has the tools to amplify their output, and the opportunity is enormous as long as you’re willing to make the changes. From my experience, these are the ones that matter most.

The first mistake: Treating AI as a tool upgrade

A lot of companies are approaching AI the wrong way. They’re bolting it onto existing processes — a smarter automation here, a faster export there, and then calling it a transformation. It isn’t.

Real transformation means rethinking the architecture of how your company operates. Not only what your teams use, but how they’re structured, how fast they can move and what they’re actually accountable for. The companies that will succeed are the ones that build the strongest foundation around those tools.

That means getting things right: Vision, product, infrastructure, go-to-market, brand and distribution. Salesforce is a good example of what this shift looks like in practice. Instead of treating AI as an add‑on feature, it has built AI into the core of its platform through Einstein and related capabilities, redefining how customers use the product rather than just accelerating old workflows.

Build structure for speed

Adding AI to legacy processes is like putting a Ferrari engine on a horse-drawn carriage. It might look fast for a second, but the wheels will fall off at the first turn.

A good place to start is reviewing your org chart and, most importantly, how your R&D functions. Traditional technical silos mean that shipping one feature requires coordinating four different teams. That might have been fine in a slower era. In an AI-first landscape, that bottleneck can quietly kill you.

The fix is moving to a product-centric model where each team is organized around a product. Each one is capable of taking a feature from idea to live without external dependencies. Once you have a shared infrastructure for multiple products, you develop faster, move faster, decide faster and build faster.

The lesson isn’t about any one way of doing it. The lesson is this: If your structure isn’t built for speed, your tools won’t save you. Reorganize first, then run.

Why infrastructure is your growth strategy

The tension every leader faces when moving fast is that quality is usually the first thing to break. Be determined not to let that happen. Treat infrastructure as the entire growth enabler. When you consolidate operations under single leadership, establish a dedicated architecture function to define long-term technical direction and build a team whose sole job is to remove friction, you are setting the standards and giving every squad the tools they need to scale without creating chaos.

Stability and speed are not opposites. But you have to build for both deliberately. Optimize only for velocity, and you’ll ship fast and break trust. Optimize only for stability, and you’ll be irrelevant before you’ve finished your QA cycle. The goal is a foundation solid enough to run on.

Build AI with AI

A few months ago, something shifted in the industry that I don’t think enough leaders are paying attention to.

AI tools used to suggest code. Now, AI agents actually build software. The distinction matters enormously. Integrating agentic coding tools into your development workflow is paradigm-shifting. Developer productivity can double, even triple — and that changes the fundamental economics of building software. It changes who can contribute, how fast you can iterate and what’s possible with a team of any given size.

Clarity on updated vision and mission

Technology and structure are useless if the people inside your organization don’t understand what they’re actually building toward. The cultural shift you might need the most is not just enthusiasm for AI; plenty of companies already have that. It is about ownership.

When every person stops thinking about their individual function and starts thinking about outcomes, not just closing tickets, but owning results; not “go ask someone else,” but “let’s make sure you’re unblocked” — the energy will completely shift.

Siloed functions become genuine service models. Teams that used to work in their own bubble become central to everyone else’s transformation. Engineers understand the business impact of what they’re shipping. Content teams are embedded in product decisions. Every department is in service of the whole.

This is the hardest change to make. It is also the most important.

What the winners will look like

AI is inevitably going to get better. The specific tools you’re using today could even be obsolete or commoditized within 18 months. The company that is building around them is what actually matters.

The companies that will come out ahead are the ones that use this moment to do the harder work — stay agile, adopt early, grow their infrastructure and evolve their culture so their people build valuable products for your customers.

The gap between a 10-person team and a 100-person enterprise is disappearing faster than anyone expected. That is genuinely exciting if you’re ready for it. It is genuinely terrifying if you’re not.

You don’t need a perfect AI strategy. You need to build a company that can execute one — and keep executing as the tools change beneath you. Don’t wait for the perfect moment to change; build through this one.