Most enterprise AI strategies are stuck optimizing tasks—while competitors are eliminating entire workflows.
AI investments are accelerating across industries, but the returns are uneven. Many enterprises are still asking: “Which tasks should we automate?” That question made sense five years ago. Today, it’s the wrong starting point.
The real payoff from AI isn’t in speeding up isolated steps. It’s in coordinating entire systems—replacing workflows, not just improving them. That shift is already reshaping industries. And it’s catching many enterprise leaders off guard.
Three examples—SHEIN, Uber Freight, and Figma—show how AI coordination is rewriting the rules. Not by making tasks faster, but by making them irrelevant.
1. AI Coordination Replaces Planning, Not Just Speeds It Up
Most fashion retailers still plan collections months ahead. SHEIN retired that model. It uses AI to test micro-batches of 100–200 units, reads customer signals in real time, and scales only what works. The platform synchronizes over 5,000 suppliers, marketers, and logistics partners into one responsive system.
The result: trend to product in 10 days. Not because design got faster—but because traditional design planning disappeared. The workflow itself was replaced.
For enterprise leaders, the lesson is clear: if your AI roadmap focuses on speeding up planning, you’re solving yesterday’s problem. The real question is whether planning should exist at all in its current form.
2. Eliminating Roles by Eliminating the Workflow
Uber Freight didn’t just automate dispatching. It eliminated the dispatcher role entirely. The company uses over 30 AI agents across planning, procurement, execution, tracking, and payments. That system underpins $20 billion in managed freight.
This isn’t a toolbelt—it’s a coordinated operating system. The dispatcher didn’t get more efficient. The workflow that required dispatchers was removed.
Many enterprises still invest in task-level automation: invoice processing, scheduling, routing. But when competitors coordinate the full system, those tasks—and the roles around them—become obsolete.
3. Workflow Control Beats Tool Optimization
Adobe spent decades refining individual creative tools. Figma bundled ideation, prototyping, and publishing into one coordinated platform. Adobe tried to buy Figma for $20 billion. Regulators blocked it. Now Figma is expanding into AI-powered prototyping and site publishing, positioning against Adobe, Canva, and Webflow.
The battleground isn’t “better tools.” It’s “who controls the workflow.” Adobe’s strength became a weakness when the workflow shifted.
Enterprises often invest in best-of-breed tools for each function. But when a competitor coordinates the entire workflow, tool-level optimization loses relevance. The value moves upstream—to system control.
4. Why Most AI Strategies Are Already Obsolete
Many AI roadmaps still read like feature lists: automate this task, improve that function, reduce time here. These strategies assume the current workflow is fixed. They aim to optimize what exists.
But the winners aren’t optimizing—they’re redesigning. They ask: “Which workflows should stop existing?” That’s a different mindset. It leads to different investments, different metrics, and different outcomes.
Enterprises that miss this shift risk building faster horses while competitors build highways.
5. The Cost of Fragmented Automation
When AI is deployed task-by-task, it creates fragmentation. Each tool may deliver local gains, but the system remains brittle. Data silos persist. Coordination suffers. And the ROI plateaus.
Coordinated AI systems, by contrast, create compounding returns. They reduce handoffs, eliminate latency, and unlock new business models. The gains aren’t just in speed—they’re in system-level responsiveness.
Fragmented automation is easy to justify. Coordinated redesign requires leadership. But only one delivers durable ROI.
6. Rethinking Metrics: From Efficiency to Elimination
Traditional automation metrics focus on time saved, cost reduced, throughput increased. These are useful—but incomplete.
The more telling metric is workflow elimination. How many steps no longer exist? How many roles are no longer needed? How many decisions are now made by the system?
Enterprises that track elimination—not just efficiency—see faster payback and deeper transformation. They move from incremental gains to structural shifts.
7. Where to Start: Map the System, Not the Tasks
The shift from task automation to system coordination starts with a different lens. Instead of listing tasks to automate, map the entire workflow. Identify dependencies, handoffs, delays, and decision points.
Then ask: what if this workflow didn’t exist? What if the system could coordinate itself?
This isn’t a theoretical exercise. It’s how SHEIN, Uber Freight, and Figma built their models. And it’s how enterprise leaders can unlock real ROI from AI.
Lead the Redesign, Not the Optimization
The next wave of AI ROI won’t come from faster tasks. It will come from fewer and reimagined workflows. Leaders who redesign systems—rather than optimize steps—will control the pace, the margins, and the market.
This shift requires more than technology. It demands a change in mindset, metrics, and investment logic. But the payoff is clear: coordinated systems outperform fragmented tools. And the enterprises that lead this shift will define the next decade.
We’d love to hear from you: what’s the biggest challenge—or breakthrough—you’ve seen when redesigning workflows with AI?