Using my cognition comparison matrices.. Where human and AI meet
When embarking on an artificial intelligence (AI) journey one of the biggest on going challenges businesses face is how to effectively integrate AI into their daily operations.
While technology promises to revolutionize industries, The key to success lies in selecting the right use case—one that aligns with your business goals, solves real problems, and is feasible to implement.
Here we are going to explore how to identify and scale the perfect AI use case, ensuring your strategy not only succeeds in the short term but also sustains long-term growth.
LIke in every journey information is key to our understanding.
Let’s start with the basics:
What is a Use Case?
A use case is how we practically define what AI will be used for within an organization.
It outlines the specific role AI will play and the tasks it will handle, bringing the broader AI vision into day-to-day reality.
By focusing on well-defined use cases, businesses can clearly see how AI fits into their operations and how it will contribute to solving problems or improving processes.
In essence, the use case is where strategy meets execution, ensuring AI’s potential is translated into meaningful, practical applications.
What Defines a ‘Good’ AI Use Case?
A good AI use case is one that is straightforward and simple—but in a way that aligns with your organization’s internal environment.
What’s considered “simple” depends entirely on your company’s unique structure, culture, and processes.
While a use case may seem universally easy to implement, what is simple for one business could be complicated for another, depending on factors like internal workflows, company DNA, market conditions, and regulations. The key is to define simplicity on your own terms.
A good AI use case should integrate seamlessly into your existing operations without unnecessary complexity. It should solve a clear business problem using a straightforward workflow that makes sense in your organizational context. Whether it’s automating a routine task or enhancing a specific process, the best use cases are those that fit naturally within your team’s capabilities and resources.
Remember, simplicity doesn’t mean basic—it means that the AI use case is clear, manageable, and aligned with how your business works.
Why Selecting a ‘Good’ Use Case Matters?
Selecting the right use case matters because it serves as a stepping stone for your AI journey. If a good use case isn’t chosen, two major challenges often arise:
Human Cooperation: AI is a cooperative technology—it’s only as good as the people working alongside it. If people aren’t actively providing feedback, training the AI, or using it regularly, the AI solution is unlikely to succeed.
Building trust through regular feedback and training helps ensure AI becomes a trusted tool rather than an obstacle. Selecting a “good” use case increases the likelihood of collaboration, encouraging people to embrace AI as a tool that complements their work.
Scalability: If the right use case isn’t selected, scaling becomes difficult, and the organization may have to start from scratch the next time it tries to implement AI. Even though I believe that there is no failing AI project—as each experience helps shorten the next AI onboarding and increases the potential for success—selecting a “good” use case significantly improves the likelihood of scaling AI. Whether scaling means expanding across teams, geographic locations, or using AI for more complex applications, a well-chosen use case sets the foundation for future growth.
How to Identify a ‘Good’ Use Case By mapping Human Cognitive efforts ?
AI is designed to mimic human cognitive effort, so it’s useful to identify a good AI use case by comparing tasks based on human cognitive effort required for executing a task
When looking for a straightforward, simple use case, the goal is to find tasks that AI can easily take over and humans would be relieved to stop doing. These typically fall under low-value effort cognitive tasks, which are repetitive, time-consuming, and don’t require creativity or deep critical thinking.
Mapping Tasks to Cognitive effort required
A good AI use case often starts with tasks that require minimal human thought—routine, repetitive tasks. These are where AI shines, allowing people to focus their energy on more engaging work. By identifying the low cognitive tasks within your organization—whether standalone or part of more complex processes—you can define a good AI use cases.
These foundational tasks will enable AI to enhance human efforts at every cognitive level, particularly in areas where people are eager to offload routine or repetitive work.
Low Cognitive effort:
These tasks involve simple, repetitive actions, such as categorization, sending email templates, or answering FAQs. AI is highly effective at taking over these tasks, freeing up people to focus on more engaging or complex work. Starting here makes AI adoption smoother and faster since these processes are already well-understood and widely used.
Mid Cognitive effort:
While these tasks require more understanding and monitoring—like overseeing processes, making basic decisions, or performing quality checks—AI can still support them by handling the low cognitive components. For instance, AI can automate data gathering or flag potential issues for human review, allowing people to concentrate on more complex decision-making.
Creativity:
Although creative tasks, such as generating new ideas and engaging in strategic thinking, are driven by human innovation, AI can assist with the low cognitive tasks embedded within these processes. AI can help by organizing information, analyzing data, or automating routine steps, giving humans more time and mental space for creativity. AI won’t replace human creativity, but it can provide support by organizing the groundwork, giving humans more space to innovate and push boundaries.
Emotions:
At the top of the cognitive spectrum are tasks involving human emotions, empathy, relationship-building, and authentic communication. While AI may not yet fully replicate emotional intelligence, it can handle supportive tasks that aid in these areas. For instance, AI can analyze customer communication and categorize interactions based on an assessment of the customer’s emotional state. By recognizing patterns in tone, language, and sentiment, AI can flag conversations that require more human empathy or deeper engagement. This allows humans to focus on building meaningful relationships while AI takes care of organizing and flagging important emotional cues within the communication.
By mapping tasks to cognitive efforts, you can identify good AI use cases that enable AI to assist at every level of human effort. This ensures AI complements human strengths while handling the routine work that can slow down productivity.
Using mapping cognitive effort is not about humans or AI
Mapping cognitive abilities is not about people or AI its about the organization.
by using this mapping organisation gain full clarity on what AI is responsible for and what humans are accountable for.
When an organization examines a process they want to integrate AI into, breaking down the tasks and understanding the cognitive effort required for each step immediately clarifies who should handle what.
This level of clarity for each individual task along the process not only defines success but also helps identify areas that need improvement if things are off track.
A common reason for AI adoption projects to fail is this very lack of clarity.
We may label the issues as data policies or lack of human engagement, but if you think about it, data issues often stem from not fully understanding what AI needs to succeed, while human disengagement is typically caused by unclear definitions of roles and responsibilities.
By using this simple mechanism of mapping cognitive effort , everything becomes clearer and more manageable.
AI Strategy and Mapping Cognitive Effort
Your AI strategy is about creating a vision for a new working environment where technology and people work together to achieve business goals.
The use cases you select are the building blocks of that vision.
By mapping cognitive effort to identify use cases, we ensure that we have a clear method to continuously grow and break down how AI can be successfully integrated into every task.
This approach simplifies the process, making AI adoption more seamless and scalable.
In essence, this means we’ve achieved the foundation for scale. We’ve built a strong platform for accelerated adoption and sustainable growth.
My Thoughts:
I keep reminding people that AI is a technology that mimics human cognitive abilities.
We can easily make this connection when we are comparing vocal conversations to what we would expect if we were talking with a real person.
However, when it comes to back-end processes, where AI operates as an invisible algorithm, we often fail to make that same connection.
But the reality is—it’s the same. If we view AI as a thinking mind, we should ask ourselves: If we hired a human with the same cognitive abilities (no awareness but advanced cognition) to perform this job, where would it be best to utilize their abilities, and what would we expect of them?
This simple yet profound question provides a new perspective that simplifies what we tend to view as complex about AI.
In this discussion, the challenge is finding a ‘good’ use case.
By breaking down tasks and mapping them to cognitive efforts, we discover a powerful yet simple method to identify the right AI use case and the path to scaling AI within the organization.
Hope this helps!
