Friday, February 27, 2026

You Are Your Career Pilot. AI Is Your Copilot.

What a 25-year IT career transition taught me about AI, conversations, and standing out in a changed job market

A colleague reached out to me recently. After nearly 25 years at a major international humanitarian organization, his position was being eliminated in a restructuring. He is not a junior person. He is a seasoned enterprise architect with PMP, CISA, and ITIL credentials, and a career that spans infrastructure, cloud migration, ERP programs, and data architecture on a global scale.

His question, between the lines, was the same question a lot of experienced IT professionals are sitting with right now: where do I fit and what are my options?

We had a good conversation. Here is what I shared with him.

The Job Market Has Changed. So Has the Competition.

Here is what is actually happening in hiring right now: it is often AI talking to AI. Candidates are using AI to generate resumes and cover letters. HR systems are using AI to screen and rank them. By the time a human being gets involved, you may already be in or out of the pile.[1]

That creates a trap. If your resume looks like an AI wrote it, and the screener is AI, you will score like everyone else who used AI. You will be generic. You will be invisible.

The way out of that trap is the same principle that has always applied in marketing: long copy sells. Tell a story. Not a list of job duties. Not a summary paragraph full of keywords. A story: here is a problem I faced, here is what I did, here is what happened as a result. What did the organization gain? What did you learn? That kind of resume stands out precisely because most submissions do not have it.

The question to ask yourself is: what can only I say? What is in my experience that no one else can claim? Find that, and build your materials around it.

AI Fluency Is Now a Baseline. Build Your Credentials.

Every organization I follow right now is either adopting AI or scrambling to figure out how to. And the ones further along are discovering a surprise: AI takes more supervision than they expected, not less.

In my own experiments, which now number more than 50 projects since last summer, technical work typically takes more than six iterations to get right. The AI is capable. But it needs direction. It needs someone who can frame the problem, evaluate the output, catch the wrong turns, and keep the project on track.

That is the skill I call orchestration. Not coding. Not prompting tricks. The judgment to know when the AI is going in the right direction and when to redirect it. That kind of judgment comes from experience. You have experience.

But here is the practical reality: you also need to signal AI fluency on your resume, because every job description now asks for it. The good news is that credentials are available, many of them free or nearly free.

Where to start:

Google AI Essentials on Coursera is a solid entry point. Microsoft Azure AI Fundamentals (AI-900) is relevant if your background includes infrastructure or cloud. For those in data architecture, Retrieval-Augmented Generation (RAG) architecture is worth understanding: it is the design pattern of combining your own data with an LLM to produce outputs grounded in your specific context, and it is already standard in enterprise AI deployments. A course in prompt engineering also signals that you understand how to direct AI effectively, which is what orchestration is at its most practical.[2]

Take the class. Get the certificate. Put it on your resume and your LinkedIn profile. These credentials tell a hiring manager that you are not just familiar with AI in the abstract; you have done the work to understand it and apply it.

Treat AI as a Conversation, Not a Search Engine.

This is the insight I find myself repeating most often, to my students, to colleagues, and now to anyone navigating a career transition with AI as a tool.

Most people approach AI the way they approach Google: type a query, get an answer, move on. That approach produces mediocre results, because the AI does not have enough context, and you never give it the chance to develop any.

The better approach is a conversation. Pose the problem. See what comes back. Add context. Make comments. Correct what is wrong. Ask a follow-up. Push on the weak spots. The AI gets more useful as the conversation develops, just as a colleague does on a team when you are working through a problem together.

I have applied this directly to my own writing. I loaded samples of my own writing into Claude, Gemini and ChatGPT and said: analyze my voice. What patterns do you see? The AIs identified three distinct writing modes I use depending on the context. Now, when I want to draft something, I can say: write this in my voice, mode one (which is how I write blog posts). The output sounds like my blog posts, not like an AI template.

You can do the same with your resume, your LinkedIn profile, your cover letters. Load your own writing. Ask the AI to model your voice. Then use that voice in everything you send out.

The Shadow IT Problem Is a CIO-Level Insight.

My colleague and I also talked about something that I think is worth naming directly for anyone positioning themselves for a leadership role.

Many organizations believe they have AI governance under control. They have approved tools, usage policies, token limits, data handling rules. And yet employees are regularly working around all of it. Loading data into personal accounts. Using consumer AI tools from home. Emailing themselves results. I know this not from research but from direct experience with my family and friends.

Governance by restriction does not work when the tools are this accessible. The better frame, one I borrowed from an old political saying about keeping people inside the tent, is to build a bigger tent. Get the experimentation inside the organization where you can see it, learn from it, and harvest what works. If people are going to experiment with AI regardless, and they are, the organization is better served by knowing about it and talking about it.

If you are going into an interview for a senior IT or architecture role, having a clear point of view on this question sets you apart. Most candidates do not.

A Practical Plan for the Next 90 Days

If I were in transition right now, here is what I would focus on.

First, get AI credentials on your resume. Pick the one most relevant to your background and complete it. Do not wait until you have several. One, done, visible, is better than several in progress.

Second, load your own writing into an AI tool and ask it to identify your voice. Then practice using it. Your materials should sound like you, not like everyone else who used the same AI tool with no customization.

Third, rewrite your resume as a story. Not a duty list. For each significant role, answer: what was the situation, what did I do, what resulted? Specific outcomes, specific scale, specific impact. The AI screener and the human reader both respond to this.

Fourth, upgrade your LinkedIn profile and consider LinkedIn Premium at least during your active search. Pay attention to where you rank against other applicants for target roles. That is real signal.

Fifth, reach out personally to former colleagues, mentors, and advisors. Not a mass message. Individual notes. The human network still matters once you clear the AI screening stage, and you need someone who will say: I know this person, and I stand behind them.

One More Thing

My team at the Data4Good Center is building a tool called Career Lighthouse, designed specifically for professionals in transition, especially from the nonprofit sector. It does skill matching across industries and will eventually flag where a targeted course or credential closes the gap for a near-match role. We are not live yet, but we are close. Stay tuned.

In the meantime, the principle is the same whether you use Lighthouse, LinkedIn, or just a good conversation with a trusted advisor: your 25-year career has more range than your job title suggests. The skills transfer. The question is whether your materials make that legible.

AI is not replacing experienced IT professionals. But experienced IT professionals who know how to work with AI will replace those who do not. You are the pilot. AI is the copilot. Get comfortable in that seat.

What are you finding in your own job search? What is working, and what is not? I would love to hear from you in the comments.

Full disclosure: I used Claude to help draft this post, drawing from a recent advisory conversation, my own AI project notes, and D4G team discussions. I provided the outline and edited the final copy you are reading. Another collaborative use of AI.

 

Notes

[1] See Nino Paoli, “Trust is at an all-time low for both job seekers and recruiters’: Hiring platform CEO says talent acquisition is in an ‘AI doom loop’,” Fortune, November 18, 2025.

[2] Recommended starting points: Google AI Essentials; Microsoft Azure AI Fundamentals; Prompt Engineering for ChatGPT, Vanderbilt University; Google Career Certificates — AI. See the Resources section below for direct links.

 

Resources

AI Credentials

 RAG Architecture Background

Job Search Tools

Data4Good Center

The postings on this site are my own and don’t necessarily represent positions, strategies or opinions of any of the organizations with which I am associated.
Posted by at

No comments:

Post a Comment

Subscribe to: Post Comments (Atom)