A very long time ago I was fortunate to learn the usefulness of watching people perform their day-to-day tasks. It was in my beginner, baby days as a CNA insurance company supervisor that I was introduced to the show-me-what-you-do method of observation.
Supervisors were regularly rotated among departments to gain a broad understanding of the company’s overall operations. How did our small department contribute to the big results that were openly shared with everyone?
Our introduction to each new department started with required time spent asking a lot of why-are-you-doing-this questions, documenting each employee’s tasks, and connecting the workflow dots.
The Way Before the Internet Workspace
To peek into the past, imagine every day in your work life where ---
rows and rows of ugly gray metal desks were equipped with
ugly matching ugly gray metal trays to organize your piles of paper
pens and pencils
a stapler
a stack of reference manuals and
a paper calendar
The titles of secretary and insurance policy processor earned the women employees an additional desk accessory – an electric typewriter with durable dustcover.
The Legacy Lesson Learned About Observing and Asking a Lot of Questions
While I didn’t fully (or probably not at all) appreciate the importance of this assignment then, this practice became our problem-solving foundation. After 33 years, the lots-of-questions-before-doing approach is still alive.
The days of manually created work product moving from desk to desk are thankfully long gone. But what hasn’t changed is the importance of efficient, effective workflows. This brings us to uncovering the right uses for AI in our day-to-day activities.
There's little doubt that AI will become an integral part of our everyday complex decision making. We’re just beginning to tap into AI’s ability to find unexpected connections among seemingly random volumes of data.
How these tools can deliver on the everyday things we need to accomplish can feel overwhelming.
Is AI right for us?
Where to even start?
What tools should we consider?
Who will help us navigate the confusion?
Discover Where AI Can Be Useful for Your Company
In AI-speak, we all need to identify practical use cases for our specific company needs.
Let’s start with those last century, no-tech insurance company observation techniques.
While much of our manual workflow has been replaced by some form of automation, the fact remains that every step in the process still moves in a logical 1-2-3-next order. The tremendous value in show-me-what-you-do questions hasn't changed.
Start there.
If you're a solopreneur or small company, all of these tips will work for you too. Your team may be smaller and the implementation less cumbersome but the process is the same.
1. Communicate a clear, non-technical explanation of AI’s strengths and weaknesses
Show relatable examples of AI’s role in your company
Encourage everyone to view AI as a valuable tool that will improve, not threaten, their place in the company
Set the boundaries, expectations, and what comes next
2. Create an AI discovery team
Include both technical and non-technical folks with different business knowledge
Include all levels of the company. Since AI strategy and leadership need to start at the top, this is the opportunity to shape a cohesive approach
3. Create a series of function-specific workshops
Frame these as constructive, idea-sharing forums that will contribute to the company’s long-term strategy
Ask everyone to share their ideas for workflow improvements
Encourage leaders to clearly share their strategic plans that team members are often not privy to
4. Create on online suggestion tool
Encourage everyone to freely contribute their ideas and suggestions
Frame this as a positive platform where problems -- with solutions -- are expected
Distinguish between contribution and complaining
5. Conduct one-on-one task shadowing
Invite AI team members to conduct show-me-what-you-do everyday sessions
Document each workflow and identify opportunities for improvements
Avoid the temptation to approach every opportunity with an AI can solve it mindset. Sometimes the solutions aren’t technical at all
6. Identify data-intensive processes
Look for activities that require significant manual data processing. This is where AI is powerful
Identify where this data contributes to decision-making and strategic insights
Create data security categories and identify data accordingly
7. Identify current AI tools usage throughout the company
Understand who is using each tool and the problems they are trying to solve
Identify potential data and security risk exposure
Add these tools to a master AI tool discovery worksheet
8. Conduct a thorough software inventory
Identify current software and applications in use, by whom, and what specific business objectives are being met
Identify where AI tools can complement or replace long-standing software
Identify where data is stored, who has access to it, considerations for including in AI usage
9. Create use case examples
Create day-in-the life, realistic workflows that illustrate bottlenecks, frustrations, and opportunities
Design AI solutions for each person performing the tasks
Describe how AI improves or changes folks' lives
10. Make regular AI innovation sessions part of the company
Reinforce for everyone from the top down that AI adoption, like all technology, is an ongoing process, not a one-and-done decision
Encourage continuous learning and knowledge sharing by everyone in the company
Schedule regular innovation sessions and reward participation
Saying It In One Sentence
The tools and technologies available to do our best work will continue to surprise us, but sound, step-by-step clarity will never be replaced.
Linda Rolf is a lifelong curious learner who believes a knowledge-first approach builds valuable client relationships. She is fueled by discovering the unexpected connections among technology, data, information, people and process. For more than four decades, Linda and Quest Technology Group have been their clients' trusted advisor and strategic partner. 