Any good strategy starts with a clear understanding of what you’re trying to accomplish, such as adding a new tool to boost productivity or reduce MTTR. When introducing and implementing AI and automation to your IT organization, the first step is to think small and strategically.

“Be definitive and prescriptive with the strategy that you want to employ. When you add an IT automation solution, understand that it’s a collaborative effort across teams. A new piece of software that streamlines processes will impact not just the team it’s directly under but also cross-functional teams and beyond. Use this new implementation to foster a culture of automation adoption and allow people to see that this is an augmentation, not a replacement, and a way to enhance your capabilities as a worker.” —Tim Cronin, Product Marketing Manager, IBM Automation

Start with a documented set of processes or functions that the organization performs on a regular basis. Then choose one that touches large parts of the business. That normally means there’s a lot of manual work involved, which means it’s a process that’s ripe for automation. For example, the process to onboard a new customer may require interactions with 10 different people or departments. Look at the effort required at each step and determine which of those 10 interactions represent the highest-value opportunity for your organization—in terms of resources or time—if you sped them up.

“Never eat the elephant in one bite. If you try and do things too fast, you fail because you missed parts of the process. Instead, pick something that’s manageable, reasonable, and that has a lot of manual effort associated to it. Then build from there. Because several small successes end up leading to big wins.” —Melissa Long Dolson

Once you’ve identified a starting point, you need to define how you’ll measure success. When it comes to automation, there are some standard key performance indicators (KPIs) to consider. Mean time to repair and mean time to respond (MTTR) and application uptime are common measurements. Using the onboarding example, you may set a goal of 99.99% availability for an online customer request form.

Beyond these standard metrics, other measurements are important but often harder to calculate. For example, how satisfied are customers with the new online form? As you infuse AI and automation into the mix, you may also consider pulling in metrics such as net promoter scores (NPS) or online reviews. If you’re trying to measure employee productivity or morale, consider using information from employee engagement surveys.

Change management is another area that’s crucial but may be difficult to measure. For example, how much did you save by releasing a new feature ahead of schedule? How many new customers did you attract with those releases? Again, sources of data such as brand health or NPS will help you get a more accurate picture of the success of your IT automation investments.

“I talk to a lot of customers about the cost of change management. When you make a mistake and need to back out code, or when the business comes to you and says, ‘I need you to adjust this because this particular set of functions doesn’t support my business the way it needs to,’ the cost of change management is high. The ability to either lower that cost or avoid change management altogether because you’re doing a better job of aligning to the business—those are the things that are really being measured.” —Melissa Long Dolson

It’s not an exaggeration to say that data is at the heart of any automation effort. The rise of gen AI has put a spotlight on just how critical the quality of that data is.¹

As you implement AI into your IT operations, keep these organizational questions top of mind:


• What do we define as mission-critical data?
• What governance and guardrails do we have in place for using data?
• How trustworthy is our data?
• How do we measure the health of our data?
• How do we manage intellectual property (IP) concerns?
• How do we address compliance guidelines, particularly in highly regulated industries?
• What education and training do we have in place to guide employees on the appropriate use of data?

As with most things related to IT—especially as you consider adding AI to the mix—cost is always a consideration. According to a 2023 report, among those surveyed, IT executives expect their gen AI budgets to be 3.4x greater than they anticipated as recently as four months ago. But these same executives believe that investment will pay off. Almost three quarters—74%—of gen AI spending will go to HR, finance, customer service, sales and marketing, and IT, where investments are expected to cut costs.²

“Organizations are always looking for ways to reduce cost. To start an IT automation project, you are talking about adopting new tooling and acquiring new licenses or new SaaS services, and there is a cost associated with that. But the opportunity to ultimately reduce costs by using IT automation is real, with real returns on your investment.” —Keri Olson

Most of all, though, as a leader, you need to think more deeply about how technology is impacting the way you work, the way your employees work and the way your organization interacts with customers. Implementing IT automation is really about understanding organizational goals and then creating a strategy to best meet them. It’s also about choosing the right technology and solutions and developing a plan for implementation. It will require more and different skills within your IT teams as well, such as AI prompt engineers, ML engineers, AI data scientists, AI trainers, and AI ethicists.

“Look for IT automation tools that are easy to implement, like SaaS tools. The winning solutions in this space are the ones that are the easiest to implement and the easiest to get up and running. Find a tool that can meet you where you’re at and grow as your business grows.” —Keri Olson