Key takeaways

• AI has not necessarily reached its “Excel moment”—the point where its value becomes clear, broad, and measurable.
• More AI does not automatically mean better decisions or stronger financial results.
• Pricing teams need to challenge system outputs instead of accepting them as fact.
• AI is highly useful for data-heavy work, but pricing still requires judgment, context, communication, and execution.
• Every AI initiative should connect to a clearly defined business problem and measurable return.

Has AI reached its Excel moment?

AI is often described as the next Industrial Revolution. That is a major claim. If it is true, businesses should eventually see broad productivity gains, stronger growth, and clear economic returns.

That is where the conversation becomes more practical. AI tools are everywhere, and almost every provider seems to have an AI solution. Yet the presence of a tool does not prove that it is creating value. The same thing happened with earlier business trends, from analytics to blockchain. The language became popular long before many companies had a clear plan for using it.

The real question for leadership teams is simple: What has AI changed, and how much has it contributed to performance?

At Revenue Management Labs, this is an important distinction. AI can be embedded into customized pricing models to speed up analysis and identify patterns. But it still needs to support a specific commercial goal, such as improving price realization, finding margin opportunities, or making decisions faster.

The danger of accepting AI output without question

One concern raised in the discussion is the loss of critical thinking. When people use AI to write code, analyze data, or recommend prices, the output may look reasonable. That does not mean it is correct, useful, or suited to the business.

This becomes risky when employees cannot explain how a result was created or adjust it when conditions change. “That is what the AI recommended” is not a business rationale.

The same issue appears in pricing. A company may invest millions in a pricing system, receive a recommended price, and then assume the system has done the hard work. In practice, the output may be ignored by sales teams, passed along without review, or implemented even when it does not make commercial sense.

A strong pricing process needs a gut check. Leaders should ask:

1. Does the recommendation fit the market?
2. Does it make sense for this customer or segment?
3. Can the sales team explain and defend it?
4. Is it possible to implement consistently?
5. What evidence shows that it will improve results?

These questions do not weaken AI. They make it more useful.

Pricing is both science and judgment

Pricing has a clear analytical side. Teams can calculate elasticity, compare market prices, analyze customer behavior, and model the impact of changes across a portfolio. AI can help perform this work faster, especially when the business has thousands of products, customers, channels, and different market conditions.

But pricing also has a human side. Someone still needs to decide which customers the company wants to win, how the offer should be positioned, and how to explain a price change. A salesperson may need to negotiate. A product leader may need to balance growth with margin. An executive team may need to choose between competing strategic priorities.

That is where a purely automated answer falls short. A good strategy on paper is worthless if the organization cannot execute it in the market.

Revenue Management Labs combines AI-enabled analysis with practitioner-led pricing expertise for this reason. The right answer is rarely a system output on one side or informal sales judgment on the other. The opportunity is in bringing structured intelligence and real market knowledge together.

Start with the problem, not the technology

AI is a broad term. It can refer to sophisticated optimization models, automated workflows, forecasting tools, or a system that helps draft an email. These applications are not interchangeable, and they should not be evaluated in the same way.

Before investing, leadership teams should define what they are trying to improve:

• Efficiency: Can repetitive manual work be reduced?
• Decision quality: Can teams see patterns they previously missed?
• Strategy: Can the business make better choices about customers, products, or markets?
• Execution: Can recommendations be adopted more consistently?
• Financial performance: Can the impact be measured in revenue, margin, or cost savings?

For example, automating a routine customer-service process may produce a clear and measurable return. A pricing opportunity engine may also be valuable, but only if the organization can validate the recommendations and act on them.

Customization matters here. The right approach depends on the company’s industry, data quality, commercial model, team capabilities, and available margin levers. A generic AI tool cannot replace that context.

Measure the return on AI investment

One of the strongest challenges in the episode is the gap between AI enthusiasm and business planning. Senior leaders may discuss AI constantly, but still fail to connect it to their growth plan or financial outlook.

A useful test is to ask: What percentage of next year’s growth or profit improvement is expected to come from this AI initiative?

If the answer is unclear, the business may be investing in activity rather than outcomes. That does not mean every benefit must be known immediately. It does mean leaders should establish a baseline, define expected results, and track whether the initiative is changing performance.

AI should be treated as embedded pricing intelligence—not as a substitute for expertise. The companies most likely to benefit will be the ones that combine faster analysis with sound judgment, clear ownership, and hands-on implementation.

The AI journey is still developing. It may yet have its Excel moment. For now, pricing leaders should stay open to the technology while remaining willing to challenge it. That balance is how businesses protect their edge instead of handing it over to a system.