It has been a dramatic and challenging year for developers and engineers working in devops organizations. More companies are using AI and automation for both development and IT operations, including for writing requirements, maintaining documentation, and vibe coding. Responsibilities have also increased, as organizations expect devops teams to improve data quality, automate AI agent testing, and drive operational resiliency.
AI is driving new business expectations and technical capabilities, and devops engineers must keep pace with the speed of innovation. At the same time, many organizations are laying off white-collar workers, including more than 120,000 tech layoffs in 2025.
Devops teams are looking for ways to reduce stress and ensure team members remain positive through all the challenges. At a recent event I hosted on how digital trailblazers reduce stress, speakers suggested several stress reduction mechanisms, including limiting work in progress, bringing humor into the day, and building supportive relationships.
As we head into the new year, now is also a good time for engineers and developers to set goals for 2026. I asked tech experts what New Year’s resolutions they would recommend for devops teams and professionals.
1. Fully embrace AI-enabled software development
Developers and automation engineers have had their world rocked over the last two years, with the emergence of AI copilots, code generators, and vibe coding. Developers typically spend time deepening their knowledge of coding languages and broadening their skills to work across different cloud architectures. In 2026, more of this time should be dedicated to learning AI-enabled software development.
“Develop a growth mindset that AI models are not good or bad, but rather a new nondeterministic paradigm in software that can both create new issues and new opportunities,” says Matthew Makai, VP of developer relations at DigitalOcean. “It’s on devops engineers and teams to adapt to how software is created, deployed, and operated.”
Concrete suggestions for this resolution involve shifting both mindset and activities:
- Makai suggests automating code reviews for security issues and technical defects, given the rise in AI coding tools that generate significantly more code and can transfer technical debt across the codebase.
- Nic Benders, chief technical strategist at New Relic, says everyone needs to gain experience with AI coding tools. “For those of us who have been around a while, think of vibe coding as the Perl of today. Go find an itch, then have fun knocking out a quick tool to scratch it.”
- John Capobianco, head of developer relations at Selector, suggests devops teams should strive to embrace vibe-ops. “We can take the principles and the approach that certain software engineers are using with AI to augment software development in vibe-ops and apply those principles, much like devops to net-devops and devops to vibe-ops, getting AI involved in our pipelines and our workflows.”
- Robin Macfarlane, president and CEO of RRMac Associates, suggests engineers begin to rethink their primary role not as code developers but as code orchestrators, whether working on mainframes or in distributed computing. “This New Year, resolve to learn the programming language you want AI to code in, resolve to do your own troubleshooting, and become the developer who teaches AI instead of the other way around.”
Nikhil Mungel, director of AI R&D at Cribl, says the real AI skill is learning to review, challenge, and improve AI-generated work by spotting subtle bugs, security gaps, performance issues, and incorrect assumptions. “Devops engineers who pair frequent AI use with strong review judgment will move faster and deliver more reliable systems than those who simply accept AI suggestions at face value.”
Mungel recommends that devops engineers commit to the following practices:
- Tracing the agent decision graph, not just API calls.
- Building AI-aware security observability around OWASP LLM Top 10 and MCP risks.
- Capturing A-specific lineage and incidents in CI/CD and ops runbooks.
Resolution: Develop the skills required to use AI for solving development and engineering challenges.
2. Strengthen knowledge of outcome-based, resilient operations
While developers focus on AI capabilities, operational engineers should target resolutions focused on resiliency. The more autonomous systems are in responding to and recovering from issues, the fewer priority incidents devops teams will have to manage, which likely means fewer instances where teams have to join bridge calls in the middle of the night.
A good place to start is improving observability across APIs, applications, and automations.
“Developers should adopt an AI-first, prevention-first mindset, using observability and AIops to move from reactive fixes to proactive detection and prevention of issues,” says Alok Uniyal, SVP and head of process consulting at Infosys. “Strengthen your expertise in self-healing systems and platform reliability, where AI-driven root-cause analysis and autonomous remediation will increasingly define how organizations meet demanding SLAs.”
As more businesses become data-driven organizations and invest in AI as part of their future of work strategy, another place to start building resiliency is in dataops and data pipelines.
“In 2026, devops teams should get serious about understanding the systems they automate, especially the data layer,” says Alejandro Duarte, developer relations engineer at MariaDB. “Too many outages still come from pipelines that treat databases as black boxes. Understanding multi-storage-engine capabilities, analytical and AI workload support, native replication, and robust high availability features will make the difference between restful weekends and late-night firefights.”
At the infrastructure layer, engineers have historically focused on redundancy, auto-scaling, and disaster recovery. Now, engineers should consider incorporating AI agents to improve resiliency and performance.
“For devops engineers, the resolution shouldn’t be about learning another framework, but about mastering the new operating model—AI-driven self-healing infrastructure,” says Simon Margolis, associate CTO AI and ML at SADA. “Your focus must shift from writing imperative scripts to creating robust observability and feedback loops that can enable an AI agent to truly take action. This means investing in skills that help you define intent and outcomes—not steps—which is the only way to unlock true operational efficiency and leadership growth.”
Rather than learning new AI tools, experts suggest reviewing opportunities to develop new AI capabilities within the platforms already used by the organization.
“A sound resolution for the new year is to stop trying to beat the old thing into some new AI solution and start using AI to augment and improve what we already have,” says Brett Smith, distinguished software engineer at SAS. “We need to finally stop chasing the ‘I can solve this with AI’ hype and start focusing on ‘How can AI help me solve this better, faster, cheaper?’”
Resolution: Shift the operating mindset from problem detection, resolution, and root-cause analysis to resilient, self-healing operations.
3. Learn new technology disciplines
It’s one thing to learn a new product or technology, and it’s a whole other level of growth to learn a new discipline. If you’re an application developer, one new area that requires more attention is understanding accessibility requirements and testing methodologies for improving applications for people with disabilities.
“Integrating accessibility into the devops pipeline should be a top resolution, with accessibility tests running alongside security and unit tests in CI as automated testing and AI coding tools mature,” says Navin Thadani, CEO of Evinced. “As AI accelerates development, failing to fix accessibility issues early will only cause teams to generate inaccessible code faster, making shift-left accessibility essential. Engineers should think hard about keeping accessibility in the loop, so the promise of AI-driven coding doesn’t leave inclusion behind.”
Data scientists, architects, and system engineers should also consider learning more about the Model Context Protocol for AI agent-to-agent communications. One place to start is learning the requirements and steps to configure a secure MCP server.
“Devops should focus on mastering MCP, which is set to create an entirely new app development pipeline in 2026,” says Rishi Bhargava, co-founder of Descope. “While it’s still early days for production-ready AI agents, MCP has already seen widespread adoption. Those who learn to build and authenticate MCP-enabled applications now securely will gain a major competitive edge as agentic systems mature over the next six months.”
Resolution: Embrace being a lifelong learner: Study trends and dig into new technologies that are required for compliance or that drive innovation.
4. Develop transformation leadership skills
In my book, Digital Trailblazer, I wrote about the need for transformation leaders, what I call digital trailblazers, “who can lead teams, evolve sustainable ways of working, develop technologies as competitive differentiators, and deliver business outcomes.”
Some may aspire to CTO roles, while others should consider leadership career paths in devops. For engineers, there is tremendous value in developing communication skills and business acumen.
Yaad Oren, managing director of SAP Labs U.S. and global head of research and innovation at SAP, says leadership skills matter just as much as technical fundamentals. “Focus on clear communication with colleagues and customers, and clear instructions with AI agents. Those who combine continuous learning with strong alignment and shared ownership will be ready to lead the next chapter of IT operations.”
For engineers ready to step up into leadership roles but concerned about taking on direct reports, consider mentoring others to build skills and confidence.
“There is high-potential talent everywhere, so aside from learning technical skills, I would challenge devops engineers to also take the time to mentor a junior engineer in 2026,” says Austin Spires, senior director of developer enablement at Fastly. “Guiding engineers early in their career, whether on hard skills like security or soft skills like communication and stakeholder management, is fulfilling and allows them to grow into one of your best colleagues.”
Another option, if you don’t want to manage people, is to take on a leadership role on a strategic initiative. In a complex job market, having agile program leadership skills can open up new opportunities.
Christine Rogers, people and operations leader at Sisense, says the traditional job description is dying. Skills, not titles, will define the workforce, she says. “By 2026, organizations will shift to skills-based models, where employees are hired and promoted based on verifiable capabilities and adaptability, often demonstrated through real projects, not polished resumes.”
Resolution: Find an avenue to develop leadership confidence, even if it’s not at work. There are leadership opportunities at nonprofits, local government committees, and even in following personal interests.
Happy New Year, everyone!
