Stop building for stability
The organisations seeing lasting impact with AI are not the ones who made perfect technology bets. They are the ones who built in a way that lets them adapt. Here, we share where to focus investment and how to build for flexibility.
The build, buy or partner decision would not matter much if the landscape were stable. Make a choice, execute, move on.
Take a typical scenario: A leadership team identifies an AI capability they believe differentiates them competitively. They commit to building it. Six months later, they have assembled a team, begun development, and realised the underlying models they planned to use have been surpassed by something announced last week. Their build decision now requires continuous re-evaluation of technology choices, which demands different skills than they hired for.
This is not a failure of strategy. It is what happens when you build for stability in a landscape that does not offer any. The question is where to direct investment so it holds its value regardless of what shifts underneath.
Where to focus investment
The highest-value AI investments are not in the models themselves; they are in the agents and applications built on top of them. Custom AI agents tailored to your workflows deliver outcomes that off-the-shelf tools cannot. They work with your data, follow your business rules, and automate the processes specific to your operations.
This is also where value compounds. When a better model is released, a well-architected agent gets better without being rebuilt. The business logic stays, the integrations stay, the workflow stays, only the underlying capability improves.
How to structure partnerships
Partnerships with AI vendors vary more than most teams expect. Some let you build capability you own outright – code, integrations, and IP you can take forward independently. Others tie you to specific platforms or vendors in ways that limit your ability to switch when better options emerge.
How to build for flexibility
Twelve-month roadmaps assume a stability that doesn’t exist. Building in short cycles of two to four weeks with each iteration delivering measurable commercial value lets you adapt as the technology evolves. You learn what’s working, adjust course early, and avoid discovering halfway through a long project that your assumptions have changed.
This requires people who stay close to what’s shifting: new model releases, emerging capabilities, changes in cost and performance. Most organisations don’t have this expertise in-house yet, which is why who you build with matters as much as what you build.
That’s how we build at Quantium. If you’re working through these trade-offs, reach out to our Chief AI Officer, Ben Chan.