Method

Most companies don't fail because they change too slowly; it's because they try to change the wrong way. When they take the evolution approach, it's slow incremental improvements that never move the needle. When it's the revolution strategy, it can often lead to chaos and wasted effort. We explain why the real skill and impact come from balancing both > https://bit.ly/3KkhLud

Due to the complexity of sensitive data, patient privacy, and established processes, successfully adopting AI in the medical field has been a big challenge for a lot of companies. In one of our most popular Tech Talks, Christopher Beeman, Vinay Tikka, and Ben Xavier share their best practices, so your team can have a smooth integration in 2026. Watch for free here > https://lnkd.in/eb3q5mrS

Get the weekend started with a technical breakdown of the mechanics of ROI in the software development lifecycle. Jason Rome, Raj Sethi, Ashwin Gaidhani, and Tapati Bandopadhyay discuss cost, risk-adjusted ROI, and practical wins for legacy modernization during an in-depth Build What's Next episode. Give it a listen here 👇 Spotify: https://bit.ly/48IMER6 Apple: https://bit.ly/4oRdsop Or wherever you get your podcasts.

One of the major DORA limitations is that it measures output but often misses sustainability. With the SPACE framework, you have a broader approach that accounts for important things like satisfaction, well-being, and collaboration. We explain how to use both so your company has a full picture of its engineering health. https://bit.ly/4rcDWCy

Technology doesn't drive success; the people using it do. When it comes to being an AI-powered organization, success depends entirely on strong foundations in data governance and change management. We explain how to invest in both pillars, so your team can turn its AI ambition into sustainable value for your business. https://bit.ly/40BE0QN

In healthcare, new features can often be mistaken for solutions. The end result of that is patients facing frustrations due to disconnected experiences. If you're looking to build a more connected experience, check out Michael Ifkovits's blog on how you can start designing for the entire ecosystem. https://bit.ly/4rV9YCS

There are typically two forces within an education company's product team model: 1. Engineering and product teams who want to move fast and iterate quickly 2. Curriculum teams who often need a more measured approach Travis Barrs and Carol Rego explain how to achieve balance between these two, and how Travis's company is addressing it by sequencing their work. Give it a listen here 👇 Spotify: https://bit.ly/4uactDq Apple: https://bit.ly/4ue6LRe Or wherever you get your podcasts.

Markets require faster product development while maintaining quality and reducing risk. Does that mean it's time to shift away from the waterfall strategy, which has been the preferred approach for over 20 years? See the framework we're using to achieve better and quicker results. https://bit.ly/4hxUXS3

Rather than using AI to develop new features, the smarter long-term business decision is to use it to optimize and increase engagement with the ones your product already has. In one of our most popular Build What's Next episodes, Michael Lewandowski and Andy Vitale discussed the critical role of observability and user research, and how it can make valuable features more easily discoverable. Give it a listen here 👇 Spotify: https://bit.ly/4pZ70N3 Apple: https://bit.ly/4a5Sqyz Or wherever you get your podcasts.

The empowered product team model is often associated with startups, but maintaining that agility at enterprise scale requires a different approach. We look at the six forces that challenge team empowerment in large organizations and provides a framework that can preserve autonomy while ensuring strategic alignment. https://bit.ly/4orHcZR