Last July, McKinsey published the report Seizing the Agentic AI Advantage. The points in this report also match what we see in the companies we advise. There are two common mistakes in AI integration. These mistakes stop companies from getting large-scale benefits from AI, and sometimes even create new risks.
Mistake #1: Staying with Small Pilots
CEOs and CFOs, in the name of budget control, approve only small AI projects. This is understandable, but it keeps the technology always in a “beta level.”
- The company does not build scalable learning and ROI.
- Employees don’t experience AI in real work, so change momentum is missing.
- Especially in business processes, where Agentic AI can bring the strongest impact, the value is lost.
Mistake #2: Putting Agents on Top of Inefficient Processes
Technical teams and business units try to integrate agentic AI directly on top of existing processes, even if these processes are inefficient.
- Inefficient process + agent = faster but still wrong cycles.
- Risks appear: decisions without context, steps that cannot be tracked, actions that are hard to reverse.
- Instead of fixing processes, agents create bigger loops of problems.
The Right Way: Process-First Thinking
Technology integration (cloud, AI, or agentic AI, it doesn’t matter) must start with understanding and redesigning the business process from the beginning to the end.
- Value stream should be mapped, bottlenecks identified.
- The best areas for agentic AI must be chosen.
- Processes should be redesigned end-to-end, not only patched at the edge.
- Human-in-the-loop (approval from people) should not be forgotten.
With this way, agents do not only automate tasks, but bring real efficiency and innovation.
90 Days Roadmap: A Company Story
Think of a company. The management team wants to use AI, but they don’t know where to start. At this moment, a simple “90 days roadmap” can help.
First 30 days: Take the picture
The first month is discovery. The company asks: “Which of our processes really create value? Where are the bottlenecks? Is our data ready for this journey?”
Different business units and IT come together. An AI opportunity map is created. Risks and priorities are clear. At the end of 30 days, everyone knows where to focus.
31–60 days: Design a small but meaningful pilot
In the second month, action starts. The goal is not many small tests, but to take a small but complete slice of one process and run it with AI.
For example, from receiving a customer request until giving the first answer. Decisions that agents can make are defined, and points where human approval is needed are also clear. Data flow is organized, and security rules are put in place. At the end of this phase, the company has a pilot ready for real life.
61–90 days: Learn and expand
In the third month, the pilot goes live. Now real results are visible: are cycle times shorter, is error rate going down, how do employees react?
Metrics are followed and small wins are shared inside the company. These small successes build trust and give energy for the next step. At the end of 90 days, the company not only has a working pilot, but also a clear view of where to continue and where to stop.
Note:
1.This article is not an official McKinsey summary. It is my personal view as a consultant, combining insights from the McKinsey report with what I see in real client projects.
2.This 90-day approach also matches with Project Management Institute (PMI) principles: first assess, then plan and start small execution, and finally measure and scale.
