Editorial
You already know that AI can improve operations, reduce mean time to resolution, and deliver a wealth of other benefits. But how do you make sure that the results are worth the effort?
Recently, I worked on an AI implementation that required a fairly extensive process, including a presentation to the company’s AI governance board, meetings with their security team and submitting documentation — not to mention ongoing audits and compliance requirements. While these are all prudent steps, it’s still a lot of time and money to spend.
As we’ve seen with other disruptive technologies, AI has led to significant productivity gains, especially on an individual basis. Employees can save hours by using AI to summarize meetings, draft reports, analyze spreadsheets and perform other common tasks. But we’re at the point in the AI hype cycle where people are asking more questions about the benefits versus the cost.
As Gartner says, we’re in the "Trough of Disillusionment” for AI, with more than 70% of CEOs reportedly unhappy with their return on AI investment.
A New Set of Problems for Organizations
While the productivity gains are fairly easy to see on an individual employee level, they’re not as clear from an organizational standpoint. Because AI can displace low-level work, it creates the need for more high-level employees who can oversee AI outputs and ensure accuracy.
Here’s an example: Consider a company that uses AI to detect anomalies in its IT systems and flag potential outages. AI can surface patterns and trends at scale, but it doesn’t automatically know which alerts matter most or how to respond in the context of a complex, real-world environment. That’s where senior-level IT personnel and high-quality data make all the difference — the people interpret the signals (based on trusted data), understand dependencies across systems and make decisions that keep the business running smoothly.
The lesson is clear: AI can amplify productivity, but it doesn’t replace the need for skilled people and contextual understanding. (In fact, AI often creates more demand for high-level engineers.) The true gains to productivity — and your bottom line — come from combining intelligent systems with deep expertise.
So, how do you achieve these results?
How to Really Boost Productivity & ROI With AI
1. Be Strategic About Implementation
It’s tempting to consider AI as a one-size-fits-all solution for improving productivity, but the reality is much more complex. You need to match the type of AI to the task (generative AI vs. agentic AI, for example) and look beyond the splashy use cases.
An MIT study found that half of genAI budgets are spent on sales and marketing, even though the highest ROI is often in back-office automation. As Forbes contributor and Hill Management Group CEO Andrea Hill put it: “Companies are playing in the shallow end while ignoring deeper value pools.”
2. Set Expectations for AI Governance
Ideally, your AI Governance Committee (you do have one, right?) should focus more on AI-driven productivity and less on policing the use of AI.
For a balanced approach, make sure you include representatives from throughout your organization, not just from risk-averse departments. For the best results, give your committee members regular data about the actual impact of AI throughout the organization, so they can use this information to make smarter decisions in the future.
Related Article: Do AI Coding Tools Really Increase Developer Productivity? Studies Say No
3. Conduct a Cost-Benefit Analysis
Even a relatively basic project can easily take 100+ hours in meetings and coordination time, in addition to the technical work required. Be realistic with your expectations and understand that this approach takes more resources up-front — but is crucial if you want to avoid the AI hype and understand the potential value that AI will deliver.
4. Upskill Employees (and Consider External Partners)
While I’ve already written about the need to upskill your IT staff for AI, maximizing ROI requires investing in employees throughout your organization.
A McKinsey survey found nearly half of employees want more AI training, which highlights the existing training gap. Don’t forget to support your AI governance team with best practices for regulatory compliance and other issues. And remember that external partners can bring additional perspective and insights — as well as higher ROI. As the MIT study noted, external partnerships see twice the success rate of internal builds.
5. Consider the Employee Experience
An AI that’s easy to use improves Digital Employee Experience (DEX) scores, while also freeing employees to handle more interesting and rewarding tasks. We typically include DEX as a secondary benefit when piloting an AI project, because it’s an often-overlooked factor that can make a surprising difference in the day-to-day lives of your employees.
Related Article: AI Skills Training: Strategies for Technical Teams vs. End-Users
6. Use High-Quality Data
Data is the foundation for AI, which means the quality of your results depends largely on the quality of your data. Investing in your data collection practices (including IT visibility and telemetry) will inherently improve the ROI of your AI implementations.
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As AI becomes more and more ingrained in your organization, your bottom line will reflect your commitment to AI — and your ability to use it effectively. An intentional approach lets you capture the productivity gains while continuing to raise the ROI.
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