At its heart, DevOps is a methodology that enables fast, frequent delivery of value to end users. This often happens through cloud-native technologies and a realignment of tools, teams and culture.

To appreciate the benefits of DevOps, remember that traditional IT organizations work with big, bulky applications that can be fragmented among teams. This can lead to handoffs, long lead time and a lack of ownership over the final product. Testing integration may be less of a priority, which creates challenges, because by the time code gets into production it’s hard to pinpoint the source of any problem.

Here’s why I see DevOps as a much better foundation for digital transformation.

The sudden shutdown of physical offices across the country this spring has accelerated many organizations’ journeys down the digital transformation path. Companies that had previously been reluctant to adopt tools such as videoconferencing and cloud collaboration rolled those solutions out to users in a rush as they scrambled to enable remote work. Because they deployed so many solutions in such a short period of time, most organizations will want to undertake some sort of security assessment to be sure they haven’t opened themselves up to new vulnerabilities. But this moment also presents an opportunity for organizations to provide a permanent boost to their cybersecurity practices through ongoing engagements.

Here are three examples of CDW’s high-value cybersecurity offerings.

For organizations to realize the potential value of AI, according to McKinsey, their AI strategy must align with their corporate strategy. They need a “clear data strategy that supports and enables AI,” and they must invest in staff and technical training to ensure they have the skills to implement AI at scale.

I keep those imperatives in mind when I work with business and IT leaders who wonder how AI might help their organizations. My advice to them: To get the most out of the technology — to make AI work for you — it pays to take a phased approach to implementation.

Video surveillance technology is now in the middle of the same transformation that revolutionized business telephones 15 years ago. Organizations are moving from old, inflexible analog camera systems to modern and agile IP-based digital video surveillance technology. At the same time, they’re shifting responsibility for these systems from facilities teams to IT groups who are more familiar with providing reliable and consistent digital services to other business units.

Organizations use data analytics in a variety of ways: A health insurance company may want a way to identify emerging risk factors for disease in its patient populations; a nonprofit that connects bone marrow donors with people requiring transplants may look for ways to slash the time it takes to identify a likely match; a national retailer may seek to better understand the shopping preferences and habits of its target customers.

On the surface, at least, there’s little in common among these organizations and what they hope to achieve, but they each must excel at the same thing to succeed: They have to be adept at organizing and analyzing data, and they need a data strategy that ultimately works for them.

Any manufacturer with a complex production line understands the importance of having a connected factory. By deploying machines that can communicate and share data with a central system, a manufacturer can adapt to operational needs in real time to drive efficiencies and improve the bottom line. But connected factories also face challenges that can hamper success if left unaddressed.