Enterprise IT Leaders | Are you Using AI to Automate the Wrong Problem?

In 2025, every enterprise is rushing to test out AI to automate everything they do with the hope of increasing customer satisfaction, improving developer experience, and driving business outcomes. Or at least that's the goal. Right?  

However, simply accelerating the use of AI to automate tasks does not necessarily mean you're improving DevOps and meeting your business goals. If the automation requires constant updates, fails to empower developers, or produces errors that impact the customer experience, it's no longer a benefit. It's simply a new problem just to check AI off your to-do list. 

So, before you run off and go all-in — please don't do that — let's break down how you, as a leader, can guide your team and business to use AI for automation strategically.  

Where AI & Automation Should Focus 

You’re likely aiming to move faster, reduce risk, enhance collaboration, and accelerate innovation without sacrificing quality. But remember that AI and automation are only as effective as the data and governance behind them, and they only work if the existing process isn’t broken to begin with. Automating broken processes or even automating without a thoughtful plan can introduce risks you may have never even considered. Just look at the testing field, and you’ll get it.

AI and automation aren’t destinations; they’re accelerators. The goal isn’t to replace people or patch broken processes but to build on a strong foundation and drive smarter, more sustainable outcomes. That means focusing AI automation on high-impact areas: reducing manual toil in development and testing, improving the quality and speed of decision-making, strengthening compliance through intelligent monitoring, and enabling faster, safer delivery of digital services.  

Instead of looking at AI as a whole, which can be daunting, overwhelming, and scary, look at the first singular area in front of you that you can tackle. Once you’ve identified it, follow the steps below to approach AI in an actionable way. 

How IT Leaders Can Approach AI for Automation More Strategically 

Step 1: Start with a Measurable Business Outcome-NOT a tool  

Have a clear outcome. Before adopting any AI solution, define the business problem you're solving and what success looks like. AI automation should truly drive business outcomes, not just efficiency. And don't get stuck in the endless maze of automating inefficient processes; instead, rethink the workflows that will get you to that business outcome that moves the needle. Once you know where you want to go and the impact you want to make, it's time to look at the details.  

Step 2: Map & Start Small   

Begin by mapping out the process end-to-end and understanding the data that flows through it. Look for both repetitive tasks that are ideal for automation and redundant steps that can be eliminated entirely. Removing unnecessary steps streamlines the workflow while automating the rest increases efficiency. Once refined, test your automation in a limited scope to validate results, reduce risk, and build confidence among stakeholders and team members before expanding further.  

Step 3: Find the Right Tool 

There are numerous tools available. And many tools claim to do the same thing or promise the same results. This is why you will be happy that you mapped out your process and aligned it to a defined business outcome. Now, as you search for tools and meet with sales reps, you have a blueprint for exactly what you need, allowing you to ask detailed questions, understand what may not work for you, and open your eyes to the right tools for you (they might not be what you initially thought!)

Step 4: Human in the Loop is Essential 

While AI automation can handle repetitive and data-driven tasks with speed and precision, human oversight remains essential. People provide context, make judgment calls, and navigate edge cases that technology alone can't fully address. Keeping humans in the loop ensures better decision-making, ethical guardrails, and strategic alignment. True innovation arises from the collaboration among humans, intelligent systems, and well-designed processes, each playing a distinct yet complementary role in driving meaningful outcomes. 

Step 5: Measure Impact   

Don’t fall into the trap of tracking task completion. We’re here for business outcomes, and that’s where you should focus. Measure improvements in speed, quality, efficiency, and risk reduction. Use these insights to iterate and optimize continuously. AI isn’t a one-time project; it’s an evolving capability that requires ongoing refinement, feedback loops, and human oversight.  

Step 6: Build for Scalability 

Don’t treat early wins as the finish line. As you prove value, build with an eye toward scale from infrastructure to governance. Ensure your architecture, data pipelines, and teams are ready to support broader use cases. Establish clear roles, guardrails, and ownership models to sustain momentum. Strategic AI adoption for automation doesn’t just solve isolated problems; it can transform how your organization operates at scale. 

The IT Leaders Who Win 

AI is a shift in how businesses operate, compete, and innovate. As models learn and systems mature, so must your strategy. Continuous refinement, thoughtful integration, and human oversight are key to long-term success. The most successful leaders don’t stop at implementation; they operationalize AI. With a clear vision, agile execution, and a focus on business outcomes, IT leaders can turn AI from a buzzword into a business advantage. 

Get an Edge With These Additional Resources:

• Blog: AI Ambition Meets Reality
• Check out our YouTube Channel: https://www.youtube.com/@PerforceSoftware