How Agentic AI Is Changing the Future of Work and AI Use Cases

By now, most of us have had some hands-on experience with artificial intelligence (AI). We've seen how it has transformed the way we work and what some great prompts can accomplish.  And, as the technology advances, AI will take on more complex actions, make decisions based on data and collaborate with human teams to solve real challenges. It will have a particular impact on systems that don’t need human input every step of the way. As AI technology evolves, so should our approach to usage.

We’re in the middle of a massive paradigm shift. AI use cases are decaying fast. AI solutions that once took months to build now come ready-made. That’s great for speed but risky for strategy; by the time you’ve implemented something, it may already be outdated.

The biggest mistake we can make with AI is assuming it fits neatly into traditional frameworks. It doesn’t. To keep up with the speed of AI solutions, organizations need adaptive, modular and interactive approaches that reflect AI’s speed and its uncertainty.

Agentic AI stands at the forefront of this transformation. But what makes it different from the AI we know today, and why is it so exciting for the future of work?

Agentic AI marks a shift from reactive, task-based tools to systems that are autonomous, goal-driven and collaborative. It can interpret intent, take initiative and complete complex, multistep tasks without constant human input. Even more powerful, it can work as part of an agentic team.

Platforms such as Copilot Studio, Google’s AgentSpace and OpenAI’s Operator model are early examples of the agentic approach. Now, we’re starting to see the rise of agentic teams. These are groups of AI agents, each with a distinct role or persona, and tools, collaborating to solve and execute on a problem from multiple angles. Imagine a software engineer agent writing code, a reviewer agent assessing it and the engineer agent iterating on the feedback, all in an autonomous feedback loop. The result? Faster cycles, better output and measurable improvements in quality.

This also changes how we approach use cases. Instead of solving for individual, narrowly defined problems, we can think in terms of problem categories, broader challenges for agentic teams to tackle.

For example, in healthcare, traditional AI might analyze patient data and suggest treatment options based on a query. But agentic AI can proactively monitor patient vitals, recognize when a condition is trending in the wrong direction and trigger follow-ups or initiate parts of a care workflow.

In customer service, it can interpret the intent behind a request, decide the best course of action and even shift gears if the conversation changes. That kind of adaptability means it’s not just following a script but managing the interaction. That means human agents can focus on more complex or sensitive issues, improving both response times, the overall customer experience and orchestration of agents and agentic teams.

The rate of obsolescence is a growing challenge. The more complex a use case, the higher the risk of it becoming outdated before it even reaches production.

Thankfully, there are fewer high complexity use cases and more straightforward use cases that can be tackled quickly and affordably, with minimal risk. But for those more complicated cases, the obsolescence clock is ticking. The goal is to work on getting a backlog that helps prioritize use cases based on current conditions, available tools and both what we know and what we don’t know about the future.

Many organizations have attempted to tackle complex workflows with robotic process automation (RPA). While RPA excels at simple, rule-based tasks, it struggles when complexity increases. As a result, there are many use cases that never made it to production, either because the ROI didn’t quite justify the investment or the right tools weren’t available at the time.

Agentic AI allows us to reconsider and revisit these abandoned use cases and tackle them with AI-driven teams that operate faster, more autonomously and with greater flexibility.

Another growing issue is technical debt. Businesses that once invested thousands in building solutions now face the challenge of being outpaced by widely available tools. These advancements are pushing companies to rethink how they approach long-term development and tool selection.

The speed at which technology advances can make technical debt pile up faster than ever, over a few months, rather than years. The right frameworks can help your organization assess the right use cases and tools that will age appropriately and gracefully. Done right, you may find that some of the tools and strategies you’ve already adopted don’t need to be replaced at all; they just need to be adapted.

This is an exciting time to transform how we think about what’s possible using AI for work. With the introduction of new technology, we’re given the freedom to approach challenges with increasingly inventive solutions, uncovering new layers of creativity and potential in the process.

By embracing agentic AI and re-evaluating your approach to use case design, you can overcome the challenge of obsolescence and get the most out of your AI investments.

The Mastering Operational AI Transformation (MOAT) team at CDW is deeply tuned in to what’s coming next on the AI front. We’re here to help you navigate the fast moving changes in AI, which use cases to prioritize, which tools to use for each use case and how to get the most long-term value out of your investments. This is a chance to build smarter, faster and more creatively than before.