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Download the article PDF: My Ai Journey And Practical Lessons You Can Use Now
Alan A. Ayers, MBA, MAcc
My artificial intelligence (AI) awakening wasn’t a lightning bolt but a slow current—a series of small jolts. I kept encountering people who seemingly talked about AI all the time—fluent in the lingo and referred to by others as “AI gurus.” From their conversations, it was clear they were putting AI into real operations—analyzing data, streamlining tasks, even building custom AI tools and agents. But it was still as if they were harboring a secret.
I realized these weren’t magicians. They simply had been quietly putting in the hours and experimenting with AI the way you’d practice a new language—through immersion and actively conversing with the tool.
Meanwhile, I was still treating AI like an academic topic to speak and write about, not a daily habit I lived. I’d informed the industry on organization-level uses—front desk automation, ambient scribes for charting, revenue cycle intelligence—but when it came to my own leadership habits, the technology wasn’t yet “real” to me.
It finally hit me: if my peers can differentiate themselves by their mastery AI and I haven’t, then I’m the one falling behind.
Every leader is starved for time. I didn’t have hours to wander the internet “playing with prompts.” I needed practicality and structure. To jumpstart my skills and put AI straight to work, I enrolled in a professional education program at Rutgers University. The insights gained from the course were far greater than I had imagined.
Why Building AI Skills Is Critical
In business, credibility comes from results. It’s results that bring value to organizations. And results are tilting toward those who can work with AI.
In a healthcare context, AI may refer to rules engines, optical character recognition, or machine learning algorithms, for example. When I reference AI here, I mean everyday tools that read and write text—like ChatGPT or Google Gemini. You give them instructions, and they produce drafts, summaries, comparisons, and outlines you can edit.
This type of AI has formed a wedge that’s splitting the workplace. The “AI haves” pair their experience and knowledge with AI to deliver smarter, faster results, making themselves indispensable. The “AI have-nots,” however, are stuck in the past—less productive, less creative, and ultimately, less relevant. This growing gap is separating people whose careers will soar from those who may stall and eventually fade.
The data mirrors my prediction. Microsoft’s 2024 Work Trend Index found that three-quarters of global knowledge workers use generative AI at work, and many are bringing their own tools because they don’t want to wait for a corporate plan. At the same time, just over half of employees who use AI say they’re reluctant to tell their managers they’re applying it to important tasks. If usage is surging and disclosure is lagging, the divide between AI haves and have-nots will widen invisibly—and quickly.¹,²
There’s a performance story here, too. A PricewaterhouseCoopers global analysis reported that industries most exposed to AI are already seeing roughly 3 times higher growth in revenue per employee than the least exposed sectors—a directional signal that capability with AI is translating into business outcomes.³ Controlled studies echo this at the task level: When call-center agents were given an AI assistant, productivity rose about 14% overall with the largest gains for less-experienced workers. Also, in professional writing tasks, access to ChatGPT cut task time by around 40% and improved quality by about 18%.⁴,⁵
If you lead in urgent care—running center operations, managing throughput, supervising providers, or overseeing revenue cycle—that’s the difference between keeping up and quietly slipping behind. Past success doesn’t guarantee future relevance.
Quiet Adoption
Quiet adoption is the grassroots trend of employees independently using public AI tools to work faster and smarter without any official company mandate. This is happening for a simple reason: Facing immense pressure to be more productive, employees are using easily accessible AI to speed up their tasks and are no longer waiting for their companies to catch up.
The primary risk of quiet adoption is security vulnerability created when employees input confidential company data or HIPAA-protected patient information into unsecured, public AI platforms without any oversight.
If people are already using AI but not saying so, leaders should do 2 things now:
- Open a dialogue. Acknowledge AI is being used. Invite teams to share what tools they’re trying and how they’re using them, and make it safe to surface both wins and misses.
- Set guardrails. Be clear about what data is off-limits, when human review is required, and how to cite or verify sources. Focus on security and ethics—not bans that drive usage underground.
Bring AI to Every Task
There’s no user’s manual for exactly how AI will help your work. You only discover that by applying it to real tasks—again and again—until you figure out what works and what doesn’t. Some days I spent hours trying to make AI deliver, only to realize the task wasn’t a fit and moved on. That time isn’t wasted. It’s how you learn the terrain.
As an emerging technology, AI’s capabilities are uneven and unpredictable. It’s excellent at some things, shaky at others, and surprisingly helpful in places you don’t expect. For instance, every month I would spend over an hour reconciling my credit card statements to my expense reports. I then learned I could upload PDFs to AI, ask the model to list what transactions are common to both (and which are not), and within minutes, identify any errors or omissions that otherwise could cost me hundreds of dollars.
The only way to learn where it helps in your job is trial, error, and reflection.⁶
Treat each task on your daily list as an experiment: “Where can AI accelerate the thinking I need to do here?” Keep score. Over weeks, you’ll figure out where AI shines—such as idea generation, synthesis, reframing, rewriting—and where it still needs your hands firmly on the wheel—such as exact math and fixed-rule tasks, or anything that requires checking original sources.
AI Is Being Used Incorrectly by Your Peers
Once you start using AI deeply, you may start to realize some of what those “AI gurus” were saying contained a lot of hype. Many treat AI as a search engine with better conversational skills or as a calculator that writes paragraphs. That’s not its advantage.
Large language models excel at language: drafting, structuring, summarizing, critiquing, translating across tones and audiences, and keeping a line of reasoning with you. They help you think about your thinking.
With practice, you can tell the AI how to think through a problem, the role it’s playing, the steps to take, and how you’ll judge the result. The more explicit you are about how you would approach the problem—“first do X, then check Y against Z, then draft options A/B/C with pros and cons”—the better the model aligns to your intent.
Over time, you start noticing how you think. You ask better questions, give clearer instructions, and catch vague requests sooner. Share a few writing samples, and it can write in your voice. Used that way, AI isn’t a toy; it’s a thinking partner that lifts the quality of your work by forcing clarity in your process.
AI Is Super-Intelligence on Your Team
What sets the AI wave apart from past tech shifts? EHRs and workflow automation mostly targeted mechanical or transactional work. Generative AI helps you think and write faster.
I’ve learned that to work with AI effectively, I must treat it as a “superintelligent colleague.” That involves telling AI who it is, who I am, defining what I want it to do, how to do it, and what the outcome should look like. The more detailed my instructions, the better AI serves my needs.
In practice, I work with AI in 2 simple ways:
- Divide and conquer. I keep the parts that rely on my industry and subject matter expertise and hand off the parts that benefit from speed and synthesis. For instance, if I need to know the requirements for taking an x-ray in a certain state, AI can far more efficiently conduct research and provide a report than if I had to familiarize myself with individual state statutes.
- Work back-and-forth. AI and I iterate on the same task. For instance, after researching x-ray laws, I can ask AI to “act as a compliance officer and draft a 1-page summary of these requirements for training new urgent care staff.” I then fact-check, ask it to list objections or opportunities for improvement, then I address each and finalize the draft.
None of this is “letting AI do my job.” Used well, AI forces discipline. I’ve found that it causes me to think more, not less.
Not Everyone Will Share Your Excitement
As I’ve leaned into AI, reactions from others are split. Some reached out with genuine curiosity—“can Alan’s AI tool help with that?”—and wanted to apply AI to new problems. Others seemed threatened or dismissive. They’d label outputs “inauthentic,” “hallucinated,” or “not real work.” Some tried to stonewall. How I made something mattered more to them than what I made.
The irony is that people figure out how to use AI to make their jobs easier and better whether their organizations formally bless it or not. Microsoft’s global study reported that over half of AI users hesitate to admit they’re using AI for important tasks—a quiet, rational response when the culture doesn’t feel safe.²
In other words, even as usage rises, discussions go underground. That’s precisely why leaders should normalize open conversation and set guardrails—so learning spreads instead of hiding in corners.
We’ve seen this play out. Early in 2023, high-profile employers restricted or banned employee use of ChatGPT over privacy and compliance concerns. Some later pivoted to controlled internal tools. The signal isn’t “AI is dangerous,” but it’s “leadership and governance are catching up to reality.” 7,8
For those of us leading teams in urgent care, the takeaway is simpler: Some colleagues will cheer your progress; others will minimize it. Keep building capability anyway. You don’t need everyone’s approval to do better work.
Conclusion
If there’s a single lesson my journey has reinforced, it’s that tools don’t transform your leadership—habits do. When I finally stopped treating AI as a subject of interest and started treating it as a colleague, my work changed. I became more analytical and detail-oriented. And I had more time for the parts of leadership only a human can do—choosing what matters, developing people, and earning trust.
Past success doesn’t guarantee future relevance. We’re heading toward a workplace where expertise may be defined more in terms of how you apply AI to your role than your tenure and experience in a field.
If you’re reading this and thinking, “I haven’t made AI real in my day-to-day yet,” that was me just a few short months ago. Start now. Treat AI as a super-intelligent co-worker. Assign the role. Set the rules. Inspect the work. Keep your name on the line.
Curiosity becomes capability one task at a time.
References
- Microsoft WorkLab. AI at Work Is Here. Now Comes the Hard Part. May 8, 2024. Accessed August 13, 2025. https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
- McGregor J. Workers Don’t Want Bosses Knowing They Use AI—Even as They BYOAI to Work. Forbes. May 8, 2024. Accessed August 13, 2025. https://www.forbes.com/sites/jenamcgregor/2024/05/08/workers-dont-want-bosses-knowing-they-use-ai-even-as-they-byoai-to-work/
- PwC. 2025 Global AI Jobs Barometer: AI-linked to a four-fold increase in productivity growth. June 3, 2025. Accessed August 13, 2025. https://www.pwc.com/gx/en/news-room/press-releases/2025/ai-linked-to-a-fourfold-increase-in-productivity-growth.html
- Brynjolfsson E, Li D, Raymond LR. Generative AI at Work. NBER Working Paper No. 31161; 2023. Accessed August 13, 2025. https://www.nber.org/papers/w31161
- Noy S, Zhang W. Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence. Science. 2023;381(6654):187-192. doi:10.1126/science.adh2586. Accessed August 13, 2025. https://www.science.org/doi/10.1126/science.adh2586
- Dell’Acqua F, Korinek A, Korytkowski P, et al. Navigating the Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper; 2023. Accessed August 13, 2025. https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf
- Ray S. JPMorgan restricts staff from using ChatGPT. Axios. February 22, 2023. Accessed August 13, 2025. https://www.axios.com/2023/02/22/chatgpt-jpmorgan-chase-restricts-ai
- Holmes A. 14 Companies That Issued Bans or Restrictions on ChatGPT. Business Insider. July 11, 2023. Accessed August 13, 2025. https://www.businessinsider.com/chatgpt-companies-issued-bans-restrictions-openai-ai-amazon-apple-2023-7
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