As AI moves from a pilot to an adopted platform in our work, a deeper truth is emerging. The workforce has, for years, avoided their agency’s ability to direct, delegate, or deliver effectively. If people struggled to articulate their instructions, structure thinking, or clarify outcomes for others, AI will only reflect that confusion back to them. This human agency gap will be blamed on technology rather than on the person’s readiness to use AI well. A lack of agency capability manifests as vague prompts, unclear expectations, and poor directives. For us to be successful working with intelligent systems, communication skills will become key, alongside collaboration. Whether you’re running a business, leading a team, or managing a project, mastering the basics will determine your impact.
A comprehensive IBM study indicates that artificial intelligence will evolve from a tool for efficiency into a primary driver of business innovation and revenue by 2030. Ultimately, we foresee that future competitive advantage will belong to organizations that successfully embed AI into their core strategic infrastructure. This insight and understanding will require the workforce to prepare. The vantage point will be those who operate below surface level with LLMs, sharpening agency to think in patterns, collaborate with clarity, and work with AI beyond prompts.
Here’s an example. When SharePoint and Teams rolled out, the workforce learned how to navigate portals, digital directories and get by on surface-level features. Now, with advanced technology, we need to implement a ‘digital detox’ due to poor information architecture. These platforms now house multiple versions of the same document because they are layered in digital file folders, and AI can’t discern which version to use. This serves as a reminder that investing in technology must include training initiatives. Because if the workforce does not master the basics, poor information architecture will give people an excuse to blame AI for hallucinations.
Technology exists to extend human intelligence. But if we want to close the gap on administrative complexity, a new way of working needs to be part of the paradigm shift. While Microsoft is developing Copilot to be more personal, useful, and human-centered, there’s still a deep learning curve in adopting the tool.
AI is not a digital encyclopedia or database. It does not store the inputs the way a database does. It reasons by detecting patterns in information and tries to detect the next word or phrase. When people understand how AI operates, they will use it for what the tool is intended for. For instance, instead of asking for answers or facts, we ask it to reason from the complexity we are trying to solve, patterns, and conditions. That is how teams surface insight, explore scenarios, and make better decisions without confusing probability for truth.
Here’s a suggested formula to get your mind to start thinking differently and master the basics. Because we are all unique in our abilities, there’s a sweet spot within any winning formula. Find yours and run with it.
Clarity × Systems × Learning = Exponential Impact
clarity of thought
We are up against a new dynamic where our communication skills are shining a light on the strength of this capability. Clarity is the ability to articulate what matters now. It is the process of breaking a task into a decision and providing precise instructions that help determine the next action or move. While prompting helps to interact with AI, the ability to communicate that clarity through automated systems and high-trust frameworks is what actually scales.
systems
Our view of systems is going to hinge on our ability to turn tasks into systems, a repeatable way of turning intent into action. Clarity is crucial in realizing optimal systems. Part of this is the ability to architect information so our systems can operate at scale and manage volume without risk of interruption. Having clarity on what is under the hood simplifies the entire infrastructure of how things will get done.
learning
When you have clarity and sound confidence in operationalizing systems, then learning at speed takes shape. There’s a misconception that learning at speed is about consuming content faster, but it is really about shortening the distance between when insights are absorbed and when they’re executed.
In this formula, continuous learning, a deep dive into foundational understanding, will allow for quicker application, observation of quality output, and knowing when to adjust or refine system improvements. Try the learning sprint prompt below to master the basics of communicating with clarity.
When we start to realize that going deeper, below the surface of what we do, we understand where AI becomes a multiplier, not a quick fix or answer, but rather amplifying our intelligence to do better.
The future belongs to those who master the basics, communicate with clarity, adopt systems, and execute intentionally.
Learning Sprint Prompt:
You are a masterclass communicator, storyteller, and mentor with deep expertise in AI human-centric collaboration. I need your help mastering the basics of clarity of thought so I can deepen my day-to-day interactions with AI, iterating in natural language to improve my partnership with large language models.
Break this topic into a structured 5-day syllabus where I spend exactly 15 minutes each day completing the lesson. Start with fundamentals and progress to advanced concepts.
For each lesson:
- Explain the concept clearly and concisely.
- Use real-world workforce examples.
- Ask me five Socratic-style questions to assess and deepen my understanding.
- Give me one short exercise or experiment to apply what I learn in my role.
- Before starting the next lesson, ask if I need clarification. If yes, rephrase the explanation with hints to bridge my understanding.
After each lesson, include a mini review quiz to gauge my understanding. End each day by encouraging me to reflect on what I’ve learned and suggesting specific ways to apply this to my role.
