I’ve noticed an ongoing trend toward over-specialization in IT departments. However, one of my key lessons learned over the years is the negative impact of this siloed specialization.

While it’s primarily an organizational issue, the trend towards the mindless embrace of specialized platform offerings from vendors has also led to significant overlap of functions in our enterprise architectures.

If your business is the provision of specialized IT solution platforms, you can of course benefit from razor-sharp specialization.

For all other businesses, I think this needs to be corrected.

The shift from silos to better collaboration

Traditional software application engineering, data engineering and artificial intelligence / machine learning (AI/ML) form large silos today.

While the different IT tasks were assumed to be largely distinct and the objectives different, the business actually demands seamless data exchange and integration between applications and AI/ML models.

We need to shift from isolated tasks to integrated systems.

Engineers in each domain are actually dependent on many shared practices, requiring a common language and methodology. Data pipelines must now support real-time model inference; application software must handle data streams dynamically; and AI/ML models must fit seamlessly into live applications.

These cross-domain interactions should redefine the siloed role of engineers in each area, making it clear that we must think beyond the boundaries of traditional disciplines.

While I worked for the healthcare industry, I observed the same problem of over-specialization. Doctors also have a one-sided focus on specific organs or systems (e.g., cardiologists, neurologists).