Read the two messages below. Which do you prefer?

Message 1
Customer complaints are up 12% this quarter. An update will be provided as soon as it becomes available. Thank you for your attention to this matter.

Message 2
Customer complaints are up 12% this quarter. The majority of complaints (9.7%) are related to our latest software update. We will discuss how best to address this issue during our team meeting on Monday.

While both messages are clear, correct, and comprehensible, only the second is meaningful. It explains the source of the customer complaints, what caused the rise in complaints, and what needs to happen next to address it.

Now look at the messages in your email inbox. Which type of message is more common in your organization — messages that resemble the first example or the second? Our research shows that the majority of internal communications within multinationals resembles the first message.

Our question to you is this: why do you think so many highly educated and hard-working people communicate so poorly at work? The truth is most people don’t do it deliberately. From an early age, schools emphasize diction, grammar, and style over sense-making. This is especially true in places like Taiwan, where the purpose of learning English is to pass standardized tests. Students are drilled to seek out the one correct answer out of many, as if communication were also arithmetic. However, at work, as in life, what we say and how we say it depends on the specific situation, our purpose, and our relationship. While diction and grammar are necessary, they are insufficient. We prefer the second message because it is more meaningful. 

That’s because people who habitually communicate like Message 1 currently levy an invisible cost on their organizations. Their style of communication forces their readers to choose between two bad options: email back and forth trying to make sense of the writer’s intention, or skip the clarification process and guess. If readers choose the first option, their time cost goes up. If they choose the second, they end up making decisions without shared understanding. When they guess incorrectly, they often need to redo the work — another time cost for the company.

This is all about to change.

Consider what happened to accountants when Microsoft Excel arrived on the scene in the early 1990s. The software did not outright replace accountants. It made it possible for companies to stop incentivizing manual calculation using calculators and start emphasizing data entry, analysis, and interpretation instead.

As the use of Large Language Models (LLMs) becomes more commonplace at work, people who insist on doing work that machines can do cheaper, faster, and more reliably than they can will see their efforts decline in relative value, just as those accountants did who insisted on using calculators even after spreadsheets became the de jure tool of the trade.

Right now, people who communicate like Message 1 are in the majority. Being in the majority has kept them safe. Now that LLMs can demonstrably produce clear, concise, and intelligible messages — often faster, cheaper, and more reliably than people — the current mode of crafting messages is increasingly untenable.

Just as accountants with calculators could not match the speed and accuracy of spreadsheets, most people will struggle to compete with LLMs in producing messages. Employers are likely to expect that the drafting of routine communications be delegated to these systems, much as calculation was once handed over to spreadsheet software. Freed from the time and effort of manual computation, accountants shifted their focus to higher-value financial work.

Now, free of message production tasks, we can instead focus on meaning, including sense-making, problem-framing, perspective-taking, and storytelling. But these are also the areas where LLMs are weak and ineffective.

The challenge for most professionals is that we do not yet know how to divide our work between what can be delegated to machines and what remains best done by humans. Those who learn to make that distinction — whether individuals or organizations — stand to gain an early advantage over their competitors. Learning how to prompt LLMs is offers a first-mover advantage to early adopters, as companies learn to redesign work to take advantage of efficiencies that artificial intelligence offers.

In the Harvard Business Review article “Looking Ahead: Implications of the Present,” renowned management thinker Peter Drucker noted that although it is pointless to try to predict the future, it is possible to “identify and prepare for the future that has already happened.”

In communication at work, the future that has already happened is this: We need to learn how to break down our work into tasks that LLMs can do cheaper, faster, and better than us. With the recovered time, we need to develop mastery in competencies that these LLMs are weak at performing. Such competencies include sense-making, problem-framing, active listening, perspective-taking, judgment, and storytelling.

By combining the strengths of machines with those of human judgment, we can move toward a form of communication that is more purposeful — and more effective — than what most of us achieve today.

In work settings, communication is the fourth of the five competencies that will undergo fundamental change in the age of artificial intelligence. Discussions of the first three — critical thinking, creative thinking, and collaboration — are provided in the links below. In the fifth and final article, we will look at leadership.

—For the last 34 years, William Zyzo has been teaching, training, and coaching executives, senior managers, and physicians throughout Asia Pacific, including Taiwan, Hong Kong, Singapore, Malaysia, Thailand, South Korea, Japan, and Australia. He is the Managing Director of Z&A Knowledge Solutions and serves as the Advisor to AmCham’s Advance Learning Lab.

On critical thinking
Futureproofing Your Career in the 21st Century

On creative thinking
Creativity: A Superpower for Futureproofing Your Career in the 21st Century
How to Think Creatively

On collaboration
Collaboration: An Essential Competency for Futureproofing Careers in the Age of AI