Editorial
Research Report
Mind The Agentic Action Gap: Stop Losing Money With AI Agents
Here’s one of the biggest changes we’re seeing in tech: previously, customers went to vendors with a list of problems they needed to solve, and vendors provided solutions. But today, the order is reversed. Companies are starting with AI as the solution, then figuring out how to use it.
So instead of looking for a tool that reduces help desk tickets or provides better hybrid support, you end up starting with your budget for AI tools, regardless of what specific problems you have in mind.
This isn’t the ideal scenario, but it’s certainly understandable given all the AI hype. There’s a relentless drive from the top to invest in AI. The market, your board and your competitors are all telling you that if you don’t use AI, you’re going to be left behind.
It’s tough to ignore all the pressure. But the good news is that AI has the promise to deliver results. As long as you have the budget for it, investing in AI makes sense.
Table of Contents
- When Budgets Are Tight, ROI Matters Even More
- How to Get More Value From Your AI Budget
- Delivering ROI Builds Trust
When Budgets Are Tight, ROI Matters Even More
You already know that AI has the potential to deliver value across IT (especially in help desk applications), operations, customer service, marketing and other business areas. But there’s “a critical gap between ambition and execution,” according to IBM, given that only 25% of AI initiatives produced the expected ROI.
Organizations are struggling to realize value, especially from all-purpose AI assistants and agents.
When IT operating budgets are tight, it’s even more important to identify areas where you can achieve significant ROI. You’ll have more buy-in from the start. You can build on a solid foundation. And you’ll have one less thing to worry about for your next presentation to the board.
Related Article: What Actual AI Usage Data Tells Leaders About the Work Ahead
How to Get More Value From Your AI Budget
So what do you do once your AI budget is allocated? At that point, the question is simply where to start. Here’s my advice based on where I see the most successful deployments.
Find the Right Use Case
Look for the intersection of what AI is good at and where it can help overloaded teams.
It seems simple, but it’s easy to look past the obvious point — you want to start with AI’s strengths, and find ways to apply these capabilities to help people who are already struggling.
Here’s an example: an insurance team had several employees across multiple offices reporting audio issues on video calls. The IT team gathered input from employees, but the feedback varied based on each user’s experience and each service desk agent’s interpretation. Using AI, the help desk identified an outdated audio driver, fixed the issue for affected users and then proactively updated it on approximately 50,000 other devices.
One strength of AI is that it can parse massive amounts of data to uncover trends and patterns. For example, in help desk applications, AI can solve problems accurately by analyzing data from various sources, including user behavior, changes in the environment (software updates, etc.) and insights from endpoint monitoring.
Build for Human Users
Build for human adoption, not just enterprise deployment.
My pre-teen daughter has a better experience on her iPad than most enterprise users do on their workplace tech. The consumer experience with AI is the benchmark — they put it on their phones, ask questions and it just works. If you want to drive user adoption of AI, you need smoother, more conversational interactions. After all, it doesn’t matter how good IT thinks the tools are if your employees won’t adopt them. Consider establishing an AI guild — a cross-functional team that can offer frontline perspectives from departments throughout your organization.
That said, remember that employee sentiment and surveys can be a tricky way to judge the full impact of tech tools. An employee can have the best experience for seven and a half hours at work, but if something crashes right before they log off, it turns into the worst IT day ever. Human feedback is subjective.
Pinpoint Solvable Problems
Choose problems you can solve with your available, high-quality data.
Good, clean, context-aware data is fuel for AI. And if you don’t have fuel, you probably won’t get very far.
While quantity is important, the quality of your data determines which problems you can actually solve right now. If you work on an IT help desk, you know how important it is to interrogate users and get accurate information about what they were doing when they encountered an issue. This context is still just as important (if not more so) when your help desk is powered by AI instead of humans.
Breadth, depth, validation and transparency make data actionable and provide greater ROI. Contextual data from telemetry and endpoint monitoring supplements any user-provided data, and lets you go beyond surface-level use cases and get more value and insights from standard AI platforms. Just make sure you ask your AI vendor about their safeguards for each type of data, as well as other questions your security team will likely ask.
Be Open to Mistakes
Be willing to accept some mistakes and build trust along the way.
Most enterprises are willing to let AI make mistakes in pursuit of gains they think are achievable. Why? Because, as one Dell executive noted, we’re at the point with AI when “the cost of waiting is higher than the cost of learning.”
The reality is that AI isn’t perfect, and probably won’t be for a few years (at least). So, as you take this journey, you can help mitigate risk by keeping humans in the loop and starting with lower-stakes projects.
Determine How to Measure Success
Think carefully about how you’ll measure success.
If you’re deploying service desk AI agents, for example, you’ll want to know how many tickets they closed and how accurate they were. This is the R in ROI, and these are the types of variables that should guide your deployments.
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If you’re not sure where to start, look at the bottom line. As an IBM exec noted, many leaders have lofty goals for AI, “but the most successful AI implementations often begin with cost savings” because they’re easy to track and lead to buy-in for future initiatives. That’s how you show the board how much value you’ve captured.
Related Article: Why Fear, Not Technology, Is Holding Back Enterprise AI
Delivering ROI Builds Trust
Your AI budget can pay for the platform, but it can’t buy trust — an important consideration, given that trust in fully autonomous agents dropped from 43% to 27% according to Capgemini.
You build trust by creating ROI, one problem at a time. And you do that by being strategic and intentional, including focusing on AI strengths, elevating the user experience and prioritizing high-quality, contextual data. The organizations that get the most out of AI aren’t necessarily the fastest — they’re the ones that know where to start.
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