Automation works best when agents specialize<\/strong> <\/h2>\n\n\n\nAnother theme we discussed was scale. As organizations move beyond single workflows, they quickly discover that one giant \u201cdo-everything\u201d agent doesn\u2019t hold up, and is likely not the optimal path for impact and scale. <\/p>\n\n\n\n
What does scale is multiagent orchestration. Instead of building one monolithic agent, teams break processes into smaller, specialized agents\u2014each responsible for a specific function. One agent validates data. Another checks records. Another recommends an outcome. Humans oversee the system. <\/p>\n\n\n\n
This approach has two benefits. First, it\u2019s 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. <\/p>\n\n\n\n
That\u2019s 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. <\/p>\n\n\n\n
When these apps, chat and agents work together, work feels simpler\u2014not more complex. This shift creates new opportunities for people. One of the most overlooked impacts of agentic automation is inclusion. <\/p>\n\n\n\n
When systems can summarize meetings, surface the right information at the right time, and reduce cognitive load, more people can contribute effectively\u2014regardless 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\u2019t just increase productivity. It lowers barriers. <\/p>\n\n\n\n
That matters. Not as a side benefit, but as a core outcome of better system design. You don\u2019t plan your way into this\u2014you learn by doing. <\/p>\n\n\n\n
The advice I keep giving customers is straightforward:\u00a0Start deliberately<\/h2>\n\n\n\n You can\u2019t whiteboard every scenario. You can\u2019t predict every edge case. You learn by deploying, observing, adjusting, and scaling\u2014with governance in place from the beginning. <\/p>\n\n\n\n
The organizations that move fastest aren\u2019t reckless. They\u2019re deliberate. They build intelligent apps with clear boundaries, visibility, and accountability\u2014and they evolve from there. <\/p>\n\n\n\n
This shift is already underway. The question isn\u2019t whether intelligent apps and agents will change how work gets done. It\u2019s whether you\u2019ll design for that change\u2014or react to it later. <\/p>\n\n\n\n
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<\/a>. 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. <\/p>\n\n\n\n\n\t\n\t\t<\/universal-media-player>\n\t\t