Use Our Agile and DevOps Hype Cycle to Guide Tech Investment

Although the AI halo is losing a bit of its luster, AI still figures prominently in the Agile and DevOps Hype Cycle. AI-augmented software engineering and AI code assistants continue to be highly hyped. AI-augmented testing is also emerging, albeit with more realistic expectations. AI also shows up in DevOps platforms. (AI for IT operations platforms is no longer on this Hype Cycle, as it became obsolete before reaching maturity.)

Software engineers should caution stakeholders that AI in agile and DevOps is risky while it remains a speculative technology. Overblown claims and expectations make it difficult to gauge the true benefits, and agile and DevOps have yet to establish best practices for AI.

The scarcity and high cost of software engineering talent has fueled an increased focus on developer productivity. Although AI has been central to that conversation, innovations like platform engineering, which improve developer experience, are also key.

The prominence of value-based metrics in this Hype Cycle reinforces Gartner’s position that productivity must be rooted in value. DevOps Research and Assessment (DORA) is increasingly attracting hype as DevOps practitioners remain focused on lean-oriented metrics of success.

It’s easy to get caught up in the glamour of new innovations, but given adoption and maturity of agile and DevOps are uneven across organizations, software engineering leaders need to nurture their less advanced teams as much as those on the cutting edge. Even if teams grapple with similar challenges, they’re bound to face them in different ways and at different times. There’s value in adapting innovations to suit each team and its specific situation.

It's also wise to consider innovations like supply chain security that get less fanfare. The purpose of this innovation was to ensure the security of the third-party components of software solutions. However, it’s starting to encompass more risks, including those to intellectual property. This will be critical as organizations depend on generative AI tools to source software, often of unknown origin.

In the next two to five years, software engineering leaders will be rewarded by a slew of transformational innovations. 

• Observability functions as a digital enterprise’s central nervous system. Observability platforms and tools reveal data that decodes system behavior and detects anomalies.

• Platform engineering entails building and operating self-service platforms that help teams develop and deliver software. It reduces the burden of infrastructure construction and maintenance so developers can spend more time focusing on customer value and learning.

• Site reliability engineering is a collection of systems and software engineering principles used to design and operate scalable, resilient systems. Site reliability engineers work with platform teams to continuously optimize systems that meet defined service-level objectives. 

• Software supply chain security is a set of processes and tools for curating, creating and consuming software in ways that mitigate attacks against software or its use as an attack vector. Intellectual property is increasingly created and stored on software, so securing the software supply chain is critical.