Effective communication is key to productivity. Studies show that 86% of employees and executives cite poor communication and collaboration as the primary cause of workplace failures. Additionally, employees spend an average of 3.2 hours per week clarifying unclear communication, leading to wasted time and frustration. The Grammarly report, 2024 State of Business Communication, highlights the staggering impact of poor communication, with 51% leading the way in workplace stress—see additional data insights below. The communication challenges we face today will persist unless we actively improve our collaboration now.
For executives, the challenge lies in conveying complex ideas succinctly. Due to time constraints, executives often provide fragmented instructions, leaving executive assistants and staff to fill in the gaps. This results in email overload, prolonged decision-making, and misaligned expectations.
To bridge this gap, consider these communication best practices:
Be clear and concise: Articulate main points without unnecessary jargon. A well-structured message minimizes confusion and follow-up questions.
Structure thoughts effectively: Use frameworks like the Five P’s, outlined in TIME’s article ‘How to Sound Smarter When You Communicate,’ to organize ideas efficiently.
Encourage two-way dialogue: Active listening and feedback loops ensure the message is received as intended, reducing miscommunication.
Leverage technology: Utilize tools that streamline messaging, documentation, and project management to enhance clarity and efficiency.
By implementing these strategies, executives can ensure their vision is effectively translated into actionable tasks, reducing errors and improving organizational efficiency.
applying communication practices to other tools
Now that we’ve established the importance of clear communication, we can extend these principles to interacting with Large Language Models [LLMs]. AI tools are powerful, but they rely entirely on how well we articulate our thoughts. If human-to-human communication requires clarity, structure, and intent, then effective human-to-AI communication demands even greater precision to ensure accurate outputs.
LLMs are pattern-based systems that generate responses based on input structure, context, and clarity. Vague emails create back-and-forth confusion, while poorly framed prompts lead to inaccurate, generic, or incomplete AI-generated outputs. Instead of expecting AI to figure it out, we must apply the same disciplined communication approach used in business to shape more effective interactions with digital tools.
think before typing or writing
To get valuable outputs from our communication methods, we must reframe our approach, starting with clear and structured thinking.
Identifying the end goal: What outcome do you need from the tool? Are you looking for insights, content generation, or structured recommendations?
Assessing context and key details: What essential information should be included to guide the output toward a relevant response?
Breaking down complexity: In everyday interactions, executives must structure their instructions clearly for their executive assistants and teams. All of their collaboration, regardless of medium, should be refined into digestible components when action or execution on bodies of work is being given.
Iterating for precision: The first output isn’t always the best. As human communication improves through feedback, all of our interactions require refinement and iteration for better results.
The ability to communicate effectively is no longer about speaking well—it’s about thinking well, organizing ideas, and expressing them in a way that gets the right outcome. Mastering these foundational principles will ensure that we interact with all mediums, shaping it into a valuable method for decision-making, strategy, and efficiency. Before defining the structure for communicating, this shift in mindset must come first. What happens when thoughts aren’t articulated clearly regardless of the medium we use?
Misalignment: Humans and digital tools misinterpret the request and produces incorrect or generic outputs.
Wasted time: The user spends more time refining and fixing outputs than actually benefiting from technology.
Missed opportunities: Digital tools can enhance creativity, decision-making, and workflow automation, but only when given precise input.
Executives who expect everyone to “figure it out” are missing the point. We use technology and tools that responds to our instructions with patterns, structure, and clarity. EAs who understand this can help their executives become better thinkers, communicators, and decision-makers. If you are not convinced yet, read more about the impact in the Gallup findings—there’s a science based impact when we can say what we mean, convey empathy in our messaging when conversations are difficult, and overall be a good human being.
translating executive thinking for all forms of collaboration
The way an EA structures emails, IMs, chat messages, organizes workflows, and refines instructions directly applies to working with all forms of collaboration. In particular, how EAs can help refine the prompts for AI interactions using their voice or their executive.
Encourage clarity: Help executives refine their thoughts before speaking or writing them down. If they can’t clearly explain their request, we won’t either.
Break down complexity: Structure instructions step by step rather than overloading a single message with excessive details.
Use persona-based thinking: In AI, you can act as a specific expert [e.g. “Act as a CFO and outline cost-saving strategies”]. To protect identities, you don’t have to use the names of the executives or employees, hypothetical scenarios will work too.
Experiment and refine: Once you nail the type of communication that aligns with your executive’s style, tone and character, save it. Our interactions with digital tools LLM tools, are an iterative process, it is memory based and will start to frame up the output based on what the executive assistant feeds it.
Example:
Weak instruction: “Write a report on our latest marketing strategy.”
Refined instruction: “Act as [executive name]. Summarize the performance of our latest campaign, focusing on engagement metrics and conversion rates. Suggest two improvements based on the data in [executive’s name] voice.”
thinking in patterns
Great communication follows patterns, and AI is no different. Understanding how AI processes language allows us to engineer our thoughts. Here are a few simple but powerful techniques EAs can use to optimize AI interactions:
Question Refinement: AI suggests better versions of vague questions. Example: Whenever I ask a question, suggest a clearer version first.
Flipped Interaction: AI asks the user questions to build a better response. Example: Ask me step-by-step questions before creating my report.
Cognitive Verifier: AI generates sub-questions to improve accuracy. Example: Before answering, generate three clarifying questions to ensure precision.
There’s no denying that business performance hinges on effective communication. Strong communication drives productivity and morale, while miscommunication leads to inefficiencies and financial losses. With increased importance based on the findings in science on how we collaborate, professionals must navigate multiple channels, manage constant notifications, and efficiently summarize information to maintain clarity and collaboration. By applying these patterns, we can train both humans and AI tools to produce more accurate and useful outputs—ultimately meeting expectations and enhancing performance.
EAs who master clear communication and structured thinking will play a pivotal role in shaping the future of collaboration. Those who can articulate their thoughts, refine their inputs, and master precision in communication will be the ultimate game changers, redefining how corporate America works smarter.
Statistics and findings from the 2024 State of Business Communication report published by Grammarly
[IMAGE SOURCE: https://go.grammarly.com/2024-state-of-business-communication-report]
