McKinsey estimates that knowledge workers spend around 41% of their time on repetitive activities that could potentially be automated.

Take a company with 1,000 employees earning an average salary of $75,000 per year. The annual payroll is $75 million. If 41% of that working time is spent on tasks that could be automated, more than $30 million of labour capacity is theoretically involved.

If that amount were converted directly into additional operating profit and the company were valued at an 8x multiple, the theoretical increase in enterprise value would be around $240 million.

It is an attractive calculation. But there is a problem: saving time is not the same as saving money, and saving money is not automatically the same as creating value.

That distinction is where a serious AI strategy begins.

The Missing Link in AI ROI

Imagine an engineer earning $75,000 per year. An AI system saves that person two hours per day by reducing repetitive work.

Has the company saved 25% of the engineer’s salary? No.

The salary is still paid. What the company has created is not immediate profit. It has created capacity.

If the engineer simply finishes the same workload earlier, the financial impact may be small. But if the engineer can now manage more projects, support more customers, shorten delivery times or avoid the need for an additional hire, the economics change completely.

A more useful ROI equation is:

Time saved → capacity created → additional output → additional margin → enterprise value

Where the Real Opportunity Sits

Companies often look for the most impressive AI application instead of the most valuable one.

The highest-return use cases are often boring: finding the correct technical document, comparing a new customer specification with a previous project, identifying a similar component designed years earlier, or preparing the first draft of a quotation.

None of these applications will create a spectacular demo. But if 30 or 50 engineers repeat these activities every week, the accumulated economic impact can be substantial.

The right question is not:

“What can AI automate?”

It is:

“Where are we repeatedly using expensive human time for work that does not require expensive human judgement?”.

Why AI Agents Matter

This is where AI agents become more interesting than conventional chatbots.

A chatbot answers questions. An AI agent can interact with tools and systems: search databases, retrieve documents, compare information, call APIs, analyse data and execute parts of a workflow.

A chatbot can help an employee write something faster. An agent can help a company execute a process faster.

For example, an agent connected to engineering documentation could retrieve relevant drawings, identify similar past projects, summarise key differences and prepare information for a technical review.

It is not replacing the engineer. It is removing friction around the engineer.

Engineering Is a Natural Candidate

Engineering companies already possess enormous amounts of valuable knowledge: CAD models, drawings, calculations, standards, bills of materials, customer specifications, test reports, quotations, and project histories.

The problem is rarely that the information does not exist. The problem is finding it quickly, understanding its relevance and connecting it to the current task.

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The objective is not to create another generic AI assistant. The more interesting challenge is to connect AI with real engineering knowledge and company workflows, so people can use the experience the organization has already accumulated.

A general-purpose AI may know a great deal about mechanical engineering. But an engineering company needs systems that increasingly understand its products, machines, drawings, standards, customers and previous design decisions.

Growth Without Proportional Headcount

The largest financial benefit of AI may not come from reducing staff.

It may come from allowing a company to grow without increasing headcount at the same rate.

Suppose an engineering department has 100 people and business grows by 15%. Traditionally, management may assume that headcount must grow accordingly.

But if AI removes enough repetitive work, the department may absorb much of that growth with only a few additional hires.

That creates operating leverage: revenue grows faster than organizational cost.

This is an important difference because AI is frequently presented as a cost-cutting technology. In many engineering businesses, its greater value may actually be its ability to support growth without making the organization proportionally larger and more complex.

Speed Can Be More Valuable Than Labor Savings

Simple ROI calculations also miss speed.

If AI helps an engineering team complete a project four weeks earlier, the value may be much greater than four weeks of labor.

The customer can be invoiced earlier. A quotation can reach the customer before a competitor’s. A product can reach the market sooner.

In many companies, the hidden cost is organizational latency: the time required to move from information to decision, from RFQ to quotation, from problem to solution.

Reducing that latency can be extremely valuable.

Consider quotation speed alone.

Two competitors may have almost identical technical capabilities and similar prices. One takes ten days to analyze an RFQ because engineers must manually search previous projects, drawings and cost data. The other uses AI-supported processes and produces a technically sound quotation in three days.

The second company has not simply saved seven days of labor. It has created a commercial advantage.

That difference is much harder to capture in a traditional ROI spreadsheet, but it may ultimately be worth more than the direct cost saving.

Human Attention Is the Scarce Resource

Experienced engineers and managers are expensive because their judgment is valuable, yet organizations routinely use them for low-value tasks such as searching documents, copying information and reconstructing old decisions.

AI does not need to replace them. It needs to remove enough low-value friction so that more of their time is spent on judgment, creativity, problem solving and customer relationships.

This is also why measuring AI success only through headcount reduction is shortsighted.

If a senior engineer spends 30% less time searching for information and uses that capacity to solve a difficult customer problem, improve a design or prevent a costly engineering mistake, the financial return may be considerably higher than the theoretical salary saving.

The Real Question

Can AI create hundreds of millions of dollars in enterprise value? Potentially, yes.

But there is no automatic bridge between “41% of work can be automated” and “41% more profit.”

Management has to build that bridge.

The companies that obtain the best returns will not necessarily be those buying the most AI licenses. They will be the ones who understand where valuable human capacity is being wasted and know how to convert recovered capacity into more output, faster execution, lower structural costs, or higher margins.

The real question is not:

“How much work can AI automate?”

It is:

“What will we do with the capacity that AI gives back to us?”

That is the point where AI stops being an interesting technology experiment and starts becoming a genuine business advantage.

Source: McKinsey Global Institute, The Economic Potential of Generative AI: The Next Productivity Frontier, 2023.

At Innovation Code, we are working on this exact challenge: connecting AI with real engineering knowledge and real industrial workflows. If you want to understand where AI can create measurable productivity and economic value inside your engineering organization, visit www.innovationcode.com.