John Doe, a middle manager at a Fortune 100 company, is late for work because his daughter’s school opening was delayed. On arrival at the office, he is told he needs to pull a report for the VP of Sales to be presented at a ten o’clock executive team meeting. He has just over an hour.
What follows is the all-too-familiar dance through enterprise friction: six levels of nested ERP menus, missing fields, re-exporting to CSV, cleansing in Excel, pivot tables, formatting PowerPoint charts—all before a final mad dash to assemble something “executive ready.”
This scenario has played out in organizations for decades. But it’s not just a workflow problem. It’s a latency problem—and latency, in this context, is organizational drag in disguise.
Latency is the time gap between intent and action. In human systems, that gap is widened by clunky interfaces, redundant steps, context switching, and brittle toolchains. It’s the delay that happens when systems require people to do what machines could—clicking, navigating, copying, reconciling—because the software doesn’t understand what the person is actually trying to do.
Today’s SaaS architectures, for all their promise of transformation, are still built to manage transactions, not intentions. They reflect a design philosophy optimized for auditability and control—not agility, autonomy, or adaptability.
Michael Carroll captures this beautifully in his recent writing: “SaaS didn’t fail. It succeeded perfectly at something we no longer need.”
These systems, he says, “treat every action as a transaction to be recorded, not a decision to be understood.”
This is where Agentic AI enters—not just as a new tool, but as a paradigm shift.
Agentic systems aren’t just conversational. They perceive goals, plan steps, use tools, and act. They are designed around decisions, not just data or dashboards. That shift is radical: away from treating people as the glue between siloed systems toward a model where software itself reasons, learns, and executes.
The implications are profound:
Instead of navigating menus, John Doe simply prompts an agent: “Show me last quarter’s qualified leads by region, matched to pipeline status and flagged anomalies.” The agent connects systems, runs logic, and presents a ready-made interactive dashboard and presentation—in seconds.
We’ve been approaching this shift for decades, from Minsky’s “Society of Mind” and early expert systems, through supervised learning, and now to transformers and LLMs with tools and memory. But only recently have we crossed the threshold where software can intelligently orchestrate workflows on our behalf.
This is the end of latency as we know it.
We won’t eliminate UIs entirely—exploration, edge cases, and trust will still matter. But the center of gravity is shifting. Instead of humans navigating software, agents will increasingly navigate it for us.
As data, decision, and action converge into a single continuum, we’ll design systems that treat decisions—not transactions—as the atomic unit of work.
This isn’t just transformation. It’s a redesign of the digital workplace around how humans think, not how systems operate.
To explore how reducing organizational latency becomes a strategic advantage in the era of agentic AI, read Winning in the Age of Intelligent Autonomy. This white paper is a deeper dive into how leading enterprises are redesigning for speed, adaptability, and autonomous decision-making at scale.
¹ Carroll, Michael. “The Latency Trap: SaaS’s Silent Sabotage,” LinkedIn, July 8, 2025.