Most lab leaders have experienced the frustration that comes from a technology investment gone wrong. You invest in a solution with strong reviews, a polished demo, and plenty of external validation, only to find it falls short in your environment. The confidence you felt in signing the contract quickly fades and leaves you with a nagging question.
What went wrong?
It turns out that the answer is often determined before your first conversation with the vendor. Here are some of the most common reasons why technology decisions go sideways and how to avoid them.
Starting with solutions before defining success
Teams often begin their searches by looking into the solutions available to them. While this may seem like a natural starting point, defining success must come first. Otherwise, evaluations default to feature lists and market narratives, neither of which tells you how a solution will deliver against your specific goals.
Labs that avoid this trap understand what problem they are solving, what success looks like six months after go-live, and how they’ll measure if they’ve gotten there. From there, vendor conversations are about confirming fit rather than rewarding whoever puts on the best show.
Waiting too long to build buy-in
It’s rare to see technology investments fail because no one wants them, but they often struggle because those who need to support the decision were never really on board.
In the lab, decision-makers and end users are usually not the same people. And end users can't move things forward on their own since finance, administration, and IT all have a hand in whether something gets approved, funded, implemented, and actually used.
When these groups come together too late, friction builds and the investment stalls. Leadership signs off on something users push back on, or users get behind a change the organization isn't set up to fund or operationalize.
The labs that get this right start building alignment before the search is fully underway and put a clear owner in place to stay accountable through go-live and beyond. This will likely speed up approval and give the technology a much better chance of succeeding.
Assuming integration will work
One of the most predictable sources of regret post-go-live is treating a compatibility claim the same as a proven integration.
Digital pathology offers one of the clearest examples of this pitfall. Adopting at scale depends on seamless integration between a platform and the LIS. Epic Beaker has become the dominant system for US labs, and while many platforms claim interoperability with it, far fewer have bidirectional integration running in live clinical production.
A solution might look complete in a demo but still introduce manual workarounds, duplicate data entry, or breaks in the chain of custody once the rubber meets the road, leading to serious headaches.
Mistaking AI capabilities for real value
The rise of AI has introduced a new pitfall. As compelling as AI capabilities may be, they only add value when they are embedded in the workflow.
Digital pathology makes the difference (and resulting impact) easy to spot. Pathologists either benefit from an experience that elevates their expertise, within the platform where they are already making diagnoses, or they’re left toggling among screens and clicking through multiple steps that add friction.
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AI tends to shine in a controlled demo environment, which makes it easy to overweigh during a demo. Before you sit down with a vendor, understand where AI might actually help and what that help could look like in practice.
Like all the reasons why technology investments go sideways, the AI trap may be common. It’s not inevitable.
The labs that make the right technology investments take the time to align and gain clarity so that they know exactly what to look for. This may not always feel like the fastest path and can be tempting to skip for teams eager to move quickly. But the upfront work and grounded evaluation are what prevent the bad decisions that hamper your team, and ultimately your patients.
