Over the past year, we’ve noticed a shift in the conversations we’re having with clients.
Twelve months ago, most businesses wanted to know whether AI was something they should be paying attention to. Today, the questions are much more specific.
Should we use ChatGPT or Claude? Should we invest in AI agents? Is Microsoft Copilot enough? Should we pay for premium AI subscriptions?
They’re all sensible questions, but they have one thing in common. They’re focused on choosing the right technology instead of building the right business.
At The Penguin Group, we think that’s the wrong way around.
The AI landscape is changing too quickly to build a long-term strategy around today’s tools. Models improve, pricing evolves, new capabilities are released, and new competitors continue to enter the market. Rather than trying to predict where AI is heading, businesses should be asking a different question:
How do we build a business that can adapt, regardless of how the technology changes?
The Problem Isn’t AI. It’s Building for Today Instead of Tomorrow.
Recently, we spoke with someone who had built a custom inventory management system using AI.
The system worked well. The client was happy, and on the surface it looked like a successful project. But as we talked through the solution, we started asking a few simple questions.
- Who fixes it if something breaks?
- Where is everything actually stored?
- How do you make changes as the business grows?
- What happens if the person who built it isn’t available?
There wasn’t an obvious problem with the system itself. The challenge was that nobody had really thought about what happened after launch.
Every change depended on one person. There wasn’t a clear support model, very little documentation, and no real plan for how the business would maintain or evolve the solution over time. Nothing was broken—but you could already see where it would become fragile.
We think this is one of the biggest blind spots in AI adoption today.
Businesses are so focused on getting something working that they forget to ask what happens when that solution becomes business-critical. Building something is one thing. Relying on it every day is something else entirely.
That conversation had very little to do with AI itself. It was about business strategy.
Don’t Build Around Today’s Technology
One of the biggest mistakes we see is businesses trying to optimise for today’s AI.
Teams spend weeks comparing models, testing platforms and debating which provider they should commit to. By the time they’ve finished their evaluation, another announcement has been made, a new capability has been released, or the commercial model has changed.
That doesn’t mean businesses shouldn’t evaluate technology. They absolutely should. It does mean that technology shouldn’t become the foundation of the strategy.
If your customer service process only works because you’re using one specific AI provider, you’ve built a fragile system. If changing platforms means redesigning the way your business operates, you’ve tied your success to technology that you don’t control.
As Nesanel Moeller, Co-Founder of The Penguin Group, puts it: “If your AI strategy needs to change every time a new model is released, you don’t have an AI strategy—you have a technology preference.”
It’s a simple idea, but an important one. Technology should support your business strategy. It should never become your business strategy.
AI Doesn’t Behave Like Traditional Software
For decades, businesses bought software in a fairly predictable way. You selected a platform, implemented it, trained your team and expected to use it for years. Updates happened occasionally, but they rarely changed how the software was purchased or consumed.
AI is different.
Providers are continually introducing new pricing models, capabilities and licensing options as the market matures. Anthropic, for example, recently introduced Claude Max to better support customers with heavier AI usage, while OpenAI continues to evolve its pricing across consumer subscriptions, business plans and API usage. These aren’t isolated changes—they’re a reflection of how quickly the market is evolving and how vendors are adapting to different customer needs.
The lesson isn’t that AI is becoming more expensive or less expensive.
The lesson is that it isn’t standing still.
If your strategy depends on today’s pricing or today’s feature set, you’ll constantly find yourself revisiting decisions. If your strategy is built around improving customer experience, reducing manual work or increasing operational efficiency, then changing technology simply becomes another way of achieving the same business outcome.
Start With the Business Problem
When we begin working with clients, we rarely start by discussing AI.
Instead, we spend time understanding how the business operates. We look at where work slows down, where information gets duplicated, where people spend time on repetitive tasks and where better systems could remove unnecessary effort. Only once we understand those challenges do we start talking about technology.
That’s because business problems tend to remain remarkably consistent.
Businesses want to reduce administration. They want faster customer response times. They want better visibility across their operations. They want to eliminate duplicate work and give their teams more time to focus on valuable activities.
Those objectives don’t change every time a new AI model is released.
The technology you use to achieve them might.
That’s why we encourage businesses to think about AI as a capability rather than a product. Products will come and go. The capability you’re building into your business is what creates long-term value.
Build a Business That Can Adapt
The organisations getting the most value from AI aren’t necessarily the ones with the biggest budgets or the newest tools.
They’re the ones that have built strong operational foundations.
Their processes are documented. Their data is organised. Their teams understand how work flows through the business. When a better AI capability becomes available, they can adopt it without redesigning everything they’ve already built.
That thinking is reflected in McKinsey’s latest State of AI report, which found that organizations generating the most value from AI are focusing on scaling initiatives that deliver measurable business outcomes rather than simply increasing AI adoption. In other words, the most successful businesses aren’t trying to use more AI—they’re using AI to become better businesses.
We couldn’t agree more.
Technology is one of the tools that helps a business improve. It isn’t the strategy itself.
Build for Change, Not Certainty
There will always be another model, another feature and another announcement promising to change everything.
If your strategy depends on predicting which platform will win, you’ll constantly be chasing the next release.
Instead, build a business that’s ready to benefit from whichever technology proves to be the best fit tomorrow. Focus on creating clear processes, reliable data and systems that are flexible enough to evolve. If those foundations are in place, changing AI providers should feel like upgrading a tool—not rebuilding the business.
The businesses that will get the most value from AI won’t necessarily be the ones using the newest models or the latest features. They’ll be the ones building systems that can be maintained, improved and adapted over time.
Before you invest in your next AI project, ask yourself one simple question: If this becomes critical to the business, what does day 100 look like—not just day one?
That’s the difference between building something impressive and building something sustainable.