This article is based on expert insights originally published by enterprisersproject.com.
IT automation has no real finish line. Even as the practice matures across software development, infrastructure, and operations, it continues to evolve—technically, strategically, and in the way it affects the people doing the work. Understanding the forces shaping that evolution is essential for IT leaders who want automation to deliver durable business value rather than short-term efficiency gains.
The following four trends, drawn from insights provided by practitioners and industry experts, are likely to define how IT automation expands and how teams and organizations adapt to it.
1. Automation as a Business Tool, Not a Technical End Goal
As automation spreads across the software development lifecycle and deeper into IT operations, the conversation is shifting from what can be automated to why it should be automated. Organizations that treat automation as a destination—something to achieve for its own sake—are likely to be disappointed with the results. Those that treat it as a means to well-defined business outcomes are better positioned to succeed.
“Automation is not a goal in and of itself, but more an approach to take towards technology.”
Ned Bellavance, Director of Cloud Solutions, Anexinet
Reducing deployment time is a common starting point. But the real value emerges when teams articulate what they will do with the time and capacity they recover. If a full development environment that once took two months to provision can now be spun up in two hours, the organization gains bandwidth to invest in security improvements, feature development, or cost optimization. Identifying those downstream opportunities—and tracking progress against them—is what connects automation to business strategy.
“Automation simply for automation’s sake yields no practical results. Automation that provides critical feedback that is actually consumed by the organization is key.”
Wayne Ariola, CMO, Tricentis
IT leaders have a growing opportunity to position automation as a driver of digital transformation rather than a back-office efficiency measure. Framing automation investments in terms of business outcomes—faster time to market, reduced operational risk, reallocation of engineering talent—helps make the case to business stakeholders and keeps automation efforts accountable to real goals.
2. Selective Automation Over Wholesale Transformation
The loudest voices in the automation discussion often advocate for automating everything—leveraging AI, machine learning, and robotics to radically transform how IT operates. In practice, most organizations are not cloud-native startups built from the ground up with that mindset. Fear of risk, skills gaps, and organizational inertia mean that a sweeping automation revolution is rarely achievable in one move.
“Fear—stemming from lack of knowledge, technical skills, or simply aversion to change or risk—will hold businesses back from trying to automate everything. Instead, IT will identify low-risk, smaller-scale projects to get their feet wet with automation.”
Mark Kirstein, VP Products, BitTitan
Selective automation—identifying discrete, lower-risk processes as initial targets—is a more pragmatic path for most IT organizations. This mirrors how many enterprises approached public cloud adoption: starting with non-critical workloads to build confidence, develop internal expertise, and demonstrate value before committing to broader initiatives.
Selective automation also has a strategic upside. Smaller, well-scoped pilots generate concrete data that teams can use to connect automation directly to specific business outcomes early in the adoption curve—reinforcing the case for expanding automation incrementally and deliberately.
3. Aligning People and Processes Before Deploying Automation Tools
Automation tooling is not a magic wand. Dropping a new tool into an existing team without changing the surrounding processes or equipping people to use it effectively is a well-documented recipe for failure. This is as true for test automation as it is for CI/CD pipelines, infrastructure-as-code, or any other form of IT automation.
“To achieve the desired results, the people and associated business processes must be aligned in a way that lets them take advantage of that automation. We need to transform the people and processes so that automated tooling has the desired impact.”
Wayne Ariola, CMO, Tricentis
This principle is foundational to DevOps. Continuous integration and delivery pipelines deliver value only when the team has adopted the practices—shared ownership, fast feedback loops, blameless postmortems—that allow those tools to function as intended. Without that alignment, the tooling surfaces friction rather than eliminating it.
IT organizations that have stumbled with automation often trace the failure not to the technology but to the gap between the tool and the team using it. As automation adoption widens, expect more emphasis on change management, process redesign, and organizational readiness as prerequisites to tool selection and deployment—not afterthoughts.
4. Continuous Learning and Reskilling as a Core IT Discipline
Modern IT has created a paradox for practitioners: they are simultaneously expected to be generalists and deep specialists. In a DevOps environment, a developer may be responsible not only for writing application code but also for deployments, infrastructure configuration, security posture, and observability. That breadth of responsibility requires a wider skill set than traditional siloed roles demanded.
At the same time, that same environment often requires deep expertise in specific domains—container orchestration, CI/CD toolchains, microservices design, and increasingly, automation platforms themselves. The pace at which these disciplines evolve means that skills depreciate quickly.
“This accelerated rate of change in IT has created a skills gap which continuously forces the retraining of the existing workforce. The only way to address this skills gap is through continuous learning.”
Flavio Villanustre, VP Technology and Head of HPCC Systems, LexisNexis Risk Solutions
Continuous learning is no longer a professional nicety—it is a table-stakes requirement. IT leaders have a responsibility to actively support skill development on their teams, not just expect individuals to keep pace on their own time. The good news is that the options for doing so have expanded significantly: structured online courses, vendor certifications, microdegree and nanodegree programs, peer learning groups, and mentoring arrangements all offer accessible and cost-effective paths to keeping skills current.
There is also a direct business return on this investment. Teams that continuously develop their skills are better positioned to identify improvement opportunities that extend beyond their current automation scope.
“Ongoing talent development and training introduce new opportunities to innovate. Professionals learn ways to improve existing processes and systems, which can have a very positive impact—some leading to new revenue-generating opportunities for the organization.”
Flavio Villanustre, LexisNexis Risk Solutions
Putting It Together
These four trends are not independent. Treating automation as a business tool (Trend 1) requires the kind of deliberate, selective approach described in Trend 2. Getting measurable results from automation depends on the people and process alignment outlined in Trend 3. And sustaining all of it over time—as the technology and the business both evolve—requires the continuous learning commitment described in Trend 4.
IT leaders who engage with all four dimensions simultaneously are better positioned to make automation a lasting driver of business value rather than a one-time efficiency project.