By E2M Global Advisory LLC · Updated August 9, 2026
Whenever a company asks us to review its commercial operation and apply artificial intelligence, one question should be answered before touching a tool: What will the team do with the time it manages to free up?
AI does not eliminate a company’s complexity on its own. It can be wrong, needs context, and requires oversight. But when applied to a specific process, with approved sources and a responsible person, it can reduce repetitive work and return capacity to the team.
The real debate is not only about how much time a technology can save. It is about deciding what the company will do with that time.
Two paths after automation
Imagine—only as a working scenario, not as a promise of results—that a company manages to free up 28 percent of the time devoted to certain commercial tasks. Measuring the real effect on costs, revenue, and quality requires the company’s own data; that hypothetical percentage should not be turned into a universal ROI.
From there, two paths appear.
The first is to cut. The organization interprets the time released solely as an opportunity to reduce capacity. The effect on costs may be immediate, but it does not necessarily improve the customer experience, commercial quality, or the ability to grow.
The second is to multiply. The same people spend less time searching for information, reconstructing proposals, or updating systems, and more time preparing decisions, serving customers, developing opportunities, and improving the process.
This difference is strategic. Automating a task and redesigning a way of working are not the same thing.
What the evidence shows about productivity
A study by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond analyzed the phased deployment of a generative AI assistant among 5,179 customer support agents. Access to the tool increased average productivity by 14 percent, measured as issues resolved per hour. Among people with less experience or lower prior performance, the improvement reached 34 percent; the effect was much smaller among more experienced professionals.
The finding does not show that every tool will produce those results in every company. It does show something important: in a well-defined process, AI can spread useful work practices and accelerate the learning curve for people who have not yet mastered the process.
For commercial leadership, the practical conclusion is not “install AI.” It is to identify where repetitive work, reusable information, clear criteria, and a reviewable output already exist.
The prediction that did not come true in radiology
In 2016, Geoffrey Hinton said that it no longer made sense to keep training radiologists because machine learning would soon outperform their ability to interpret images. Hinton would later receive the 2024 Nobel Prize in Physics for his foundational contributions to machine learning.
A decade later, the profession has not disappeared. The American College of Radiology describes a persistent shortage of professionals and expects demand for imaging studies to grow at least as quickly as the radiology workforce. AI is being incorporated to reduce task burden and improve efficiency, not as an automatic replacement for all clinical work.
The reason is simple: interpreting an image is one part of the process. Clinical context, accountability, communication, oversight, and decisions affecting the patient also matter. When technology accelerates a task, it can increase the system’s capacity to serve demand that previously went unmet.
The business analogy is useful. Preparing a draft faster does not eliminate the need to understand the customer, decide a commercial term, validate a margin, or take responsibility for a proposal.
From salesperson who executes to leader who directs
In many teams, commercial leadership still operates as an additional salesperson: searching for documents, reconstructing account context, chasing updates, and correcting proposals. AI can change how that time is distributed, but only if the company defines what may be delegated, which sources may be used, and what review is required.
The new role is not to leave technology operating without control. It is to direct a work system in which tools, people, and data have clear functions.
- AI prepares a draft; a person validates the content and owns the decision.
- AI summarizes a meeting; the owner confirms agreements and next steps.
- AI retrieves information; the team decides which source is authorized and which one takes precedence.
- AI identifies an incomplete opportunity; leadership defines the criteria for prioritizing it.
That shift transforms the professional from a task operator into the owner of a workflow.
The value is not only in doing the same work faster
The turning point comes when a company uses the time it recovers to do something it could not previously sustain: prepare every meeting better, respond sooner without losing rigor, reuse scattered knowledge, keep the CRM current, or support more customers with the same team.
That is why the useful question is not whether AI will “take” or “create” jobs in the abstract. In a specific company, the question is more demanding:
If you recover some of the time your commercial process consumes today, will you cut it or multiply it?
At E2M Global Advisory, we help small and midsize companies identify a priority commercial process, analyze its sources, define responsibilities, and decide on an initial use case with human control. To assess where it may make sense to begin, learn about the Commercial AI Diagnostic or start by checking fit.
Sources
- Brynjolfsson, E., Li, D., and Raymond, L. R. (2023). Generative AI at Work. NBER Working Paper 31161; subsequently published in The Quarterly Journal of Economics.
- Nobel Prize Outreach. Geoffrey Hinton — Nobel Prize in Physics 2024.
- American College of Radiology. The Radiologist Shortage: A Workforce Update (2026).
- American College of Radiology. Radiologists Will Adapt — Embracing New Technology Is What We Do (2026).