Tom Godden

In early 2026, the Reimagine research program interviewed senior technology leaders under conditions of candor: off-the-record, non-attributable, no marketing follow-up. What they said contradicts the prevailing conference narrative.

Traditional software development often resembles a manufacturing assembly line, where code passes from one developer to the next, creating idle time and fragmented documentation that fails to meet modern compliance demands.

In Formula 1, victories depend on turning millions of data points into winning decisions. Organizations can apply this same precision to unlock value from underutilized data assets.

As organizations seek to accelerate their cloud migration and transformation, some find themselves in situations where they have to manage IT operations in a multicloud environment, or simply decide whether and how to adopt a multicloud strategy in the first place.

Generative AI is increasingly a point of focus globally. Scalability of the cloud, a massive proliferation of data, and the rapid advancement of ML technologies have positioned AI for wide-scale adoption.

As organizations seek to accelerate cloud migration and transformation, some find themselves in situations where they have to manage IT operations in a multicloud environment, or they simply struggle with whether and how to adopt a multicloud strategy in the first place.

Every decade comes with the introduction of technology that is “going to change the world,” but this leads to disappointment when it becomes too challenging to adopt the new technology. For several decades, this has been the story behind artificial intelligence (AI) and machine learning (ML), and yet we’ve reached a tipping point where some organizations are transforming their businesses and...

Businesses want to unleash the value of data to increase agility, drive innovation, and improve efficiency. While data is abundant and growing rapidly, just producing or storing a lot of it doesn’t automatically create value.