When people talk about AI at work, the conversation usually jumps straight to speed. Faster tasks. Faster decisions. Faster output.
In my recent conversation with Keith Kirkpatrick, President and Research Director with Futurum Group, we spent less time talking about how quickly agents can act—and more time talking about who stays in control, how work actually gets done, and why intelligent apps matter more than ever.
The future of work isn’t about humans racing to keep up with machines. It’s about humans stepping into higher-value roles—guiding, shaping, and supervising intelligent systems that operate at scale.
Intelligent apps change the role of the human
As agents become more capable, the role of the business user changes. People keep working—but they do less manual work.
Humans are increasingly moving away from step-by-step processes to designing the flow, defining the rules, and deciding where judgment matters in their workflows. Intelligent apps become the place where all that happens. Apps are evolving, becoming intelligent, adaptive operating surfaces where people and agents work together.
That’s an important shift. Agents are not replacing apps. Agents and apps are working together. Agents now show up inside apps, embedded in places where work already happens, with the right context, data, and governance.
Human-in-the-loop isn’t a checkbox, it’s an imperative
One of the most practical questions Keith raised was about control: How do organizations decide what agents can do on their own—and when humans need to step in?
The answer is design.
Take something like processing a request for proposal (RFP) or an insurance claim. An organization might decide that transactions under a certain threshold proceed automatically, while higher-risk cases require review. That decision goes beyond technical limits. It reflects business risk, regulatory requirements, and confidence in the process.
The important part is this: those boundaries are intentional, adjustable, and visible. You don’t hardcode them once and walk away. You refine them as conditions change and as confidence grows.
That’s what human-in-the-loop really means: putting judgment where it matters most.
Automation works best when agents specialize
Another theme we discussed was scale. As organizations move beyond single workflows, they quickly discover that one giant “do-everything” agent doesn’t hold up, and is likely not the optimal path for impact and scale.
What does scale is multiagent orchestration. Instead of building one monolithic agent, teams break processes into smaller, specialized agents—each responsible for a specific function. One agent validates data. Another checks records. Another recommends an outcome. Humans oversee the system.
This approach has two benefits. First, it’s more resilient. If something changes, you update one part instead of everything. Second, it creates reuse. An agent built for one process can often support others.
That’s how automation compounds. Apps, agents, and chat each have a role. Automation works best when you match the right tool to the right task. A mobile app with a barcode scanner is faster when speed matters. A background agent is better when no interaction is needed. And chat earns its place when collaboration, clarification, or exploration is involved. Apps, agents, and chat each have a role, the key is to leverage each option where it makes the most sense.
When these apps, chat and agents work together, work feels simpler—not more complex. This shift creates new opportunities for people. One of the most overlooked impacts of agentic automation is inclusion.
When systems can summarize meetings, surface the right information at the right time, and reduce cognitive load, more people can contribute effectively—regardless of working style. For example, meeting transcripts can allow participants to stay fully focused on the discussion, knowing the notes will be available after the meeting. Intelligent assistance doesn’t just increase productivity. It lowers barriers.
That matters. Not as a side benefit, but as a core outcome of better system design. You don’t plan your way into this—you learn by doing.
The advice I keep giving customers is straightforward: Start deliberately
You can’t whiteboard every scenario. You can’t predict every edge case. You learn by deploying, observing, adjusting, and scaling—with governance in place from the beginning.
The organizations that move fastest aren’t reckless. They’re deliberate. They build intelligent apps with clear boundaries, visibility, and accountability—and they evolve from there.
This shift is already underway. The question isn’t whether intelligent apps and agents will change how work gets done. It’s whether you’ll design for that change—or react to it later.
If you want to go deeper into how organizations are putting these ideas into practice and to hear how they are making deliberate choices about when to automate, or assist, or hand things back to people, I encourage you to watch my full conversation with Keith Kirkpatrick. We cover real examples, design choices, and what leaders should be thinking about next. I invite you to explore how intelligent apps, agents, and human judgment come together at work and what this could mean for your team.
