Your 2026 Challenge: Cutting Costs Without Causing Disruption

“Use AI to deliver $10 million in cost reductions across all major business units, but don’t break anything along the way.”

Sound familiar? It should, because that’s the message most CIOs are hearing from their CEO and CFO these days.

The expectation is that the cost savings are there just waiting to be unlocked, and that you have the key. The dollar amounts may change, but the expectations remain the same: use AI to save money without disrupting business operations.

As CIO, your success as a leader depends on how well you can walk a very fine line: strategically deploying AI to save costs without over-rotating into AI and causing preventable failures.

It’s not an easy task, especially when you’re in the middle of the AI hype storm. But it’s certainly possible, especially if you use your perspective and skills to determine the actual cost impact of new AI platforms.

Lead by Example with IT Use Cases

Thanks to AI, we’re in the middle of a digital transformation. IT spending is expected to increase nearly 10% next year, primarily fueled by spending on data centers and software, according to Gartner.

At the same time, CIOs are saving millions of dollars by leveraging AI to cut their service desk spend.

Specifically, they’re deploying AI agents, implementing automated workflows, and promoting AI-powered self-service tools. By fueling context-aware support with high-quality data, they’re accelerating self-healing, advancing contextual problem analysis (rather than relying on generic checklists), and generally shifting left with support desk service.

In my conversations with enterprise customers, many are targeting a 40% reduction in tickets and 50% improvement in MTTR, among other goals. In large organizations, these two achievements alone could result in millions of dollars in savings and a better end-user experience.

Beyond the help desk, CIOs are using endpoint data to optimize hardware refreshes and software licenses based on actual usage and performance. As IT environments become even more complex, identifying these inefficiencies and waste becomes even more crucial.

In one recent case, a global technology and consulting company used endpoint data to save over $80 million in hardware costs. While their original refresh cycle was every three years, analysis showed most devices were still usable for much longer. Extending the lifecycle to five years saved hundreds per employee, which quickly added up.

Think about it: How much would you save by extending the life of your laptops by an extra year or two, or by eliminating any unclaimed software licenses?

These types of data-driven optimizations can quickly reduce recurring operational costs in IT. But your responsibility for the entire organization requires a broader perspective.

Collaborate with Business Units to Identify and Minimize Disruption

As CIO, you should be asking business units to bring you proposals for how they want to use AI to optimize their respective silos. After all, as unit leaders, they should be up to date on how AI can potentially serve their group, whether it’s marketing, HR, or operations.

With AI, it’s your job as CIO to consider each use case individually and manage each unit’s digital transformation based on the expected cost savings versus the potential for disruption. This is where your expertise is invaluable, because you understand on a technical and human level the impact and consequences of AI.

Consider the hardware refresh example:

• Using AI and endpoint data to determine which employees truly need a new laptop is a relatively low-risk endeavor. If you get it wrong, the worst-case scenario is that you’ve either spent too much money on unneeded devices (which you would have spent anyway before the AI analysis) or you didn’t buy enough new devices, which is an easily fixable problem. In other words, there’s minimal disruption from using AI to solve this problem.

Now, think about using AI to optimize and streamline your in-house application development resources:

• One of our customers is currently working through this process, and expects to save millions of dollars by combining more than a dozen DevOps shops into a singular unit. It’s a worthy endeavor, but it’s clearly more complex, with the potential for significantly more organizational-wide disruption, compared to optimizing a hardware refresh.

Utilize Leadership Skills to Minimize Disruption

As I wrote about previously, CIOs are defined by their investment decisions, and by how you use your leadership skills to shape your budget.

Similarly, implementing AI initiatives requires more than just AI expertise. Your success depends on your communication skills as you gather information from unit leaders, your discipline in strategically deploying AI to build trust and small wins, and your plan for managing the inevitable disruption.

There is no one-size-fits-all solution.

Perhaps your organization’s hiring workflow is built on a collection of underpowered platforms, making it ripe for AI-driven optimization. Or maybe it’s already a highly efficient process, and any additional cost savings would be largely offset by potential AI bias and other risks. The discovery process is crucial, and requires a strong leader to determine the best path forward.

So when your CEO asks how AI is going to cut costs, the big question isn’t just what can AI actually do. It’s also what can’t AI do without disrupting your business. Finding the answers to both of these questions is critical as you identify the most appropriate ways to achieve AI-driven cost savings.