Becoming AI Ready: How to Creatively Secure Your Future

Are you worried that AI will replace your job? Wondering how to stay relevant as technology rapidly evolves?

In this article, you'll discover how to become AI-ready through a practical three-phase framework that helps you creatively embrace change and secure your professional future.

The Real Competition Isn't Humans Versus AI

The biggest misconception about AI readiness is that it's a human-versus-machine competition. That's not the battle happening right now.

Kyle Shannon explains that the real competition is humans with AI versus humans without AI. The people who learn to work alongside AI tools will outcompete those who resist or ignore them. This isn't about AI replacing you—it's about someone who knows how to leverage AI potentially taking opportunities you could have had.

This shift represents the third major technical revolution Kyle has witnessed in his lifetime. He missed the PC revolution because he was too young to be Steve Jobs or Bill Gates. He caught the World Wide Web revolution in 1994 when he co-founded one of the first digital agencies and created websites for Fortune 500 companies before most people knew what websites were.

When ChatGPT launched, Kyle immediately recognized this as another once-in-a-generation transformation. One of his first thoughts was that the Turing test had been passed. Unlike the previous shifts he'd experienced, this one felt like science fiction becoming reality.

The Upside of Being AI Ready

The benefits of AI readiness are substantial and measurable.

Members of Kyle's AI Salon community who actively practice AI readiness are surviving multiple rounds of layoffs at their companies. They're being invited to serve on AI councils. Some have become heads of AI strategy for their organizations.

These aren't necessarily people with computer science degrees or technical backgrounds. They're artists, educators, and builders who are committed to learning and practicing with AI tools. Their willingness to embrace change positioned them as valuable assets rather than replaceable employees.

The Cycle of AI Readiness

Before implementing AI readiness, you need to understand what Kyle calls “aching gaps”—the fears and concerns about your professional future.

An aching gap is the anxiety you feel when you imagine losing your job. It's the fear that keeps you awake at night, wondering if you'll become obsolete. These gaps represent the distance between where you are now and where you need to be to feel secure in an AI-powered world.

The good news is there's a structured approach to closing these gaps. Kyle developed a three-phase cycle called Play First, Create Excellence, and Generously Lead. This framework provides a clear path forward regardless of your current skill level or industry.

The cycle is designed to be iterative—you continuously move through these phases, each time raising your capabilities and securing your position.

#1: Play First to Build Your Foundation

The first phase focuses on exploration and discovery through purposeful experimentation.

Establish a Daily Practice

A daily AI practice is more than just a habit—it's a centering ritual that helps you clarify what you want and who you want to become.

Kyle recommends starting each day by asking yourself what you want to accomplish and what you want to contribute to the world. This reflection grounds your AI exploration in purpose rather than letting you drift aimlessly through features and capabilities.

The practice should happen consistently at the same time each day. This regularity builds momentum and prevents AI work from getting pushed aside by other priorities.

Play With Purpose

Random experimentation has limited value. Purposeful play means directing your exploration toward specific goals and challenges.

Kyle describes Jim Ross, who works in the self-storage business—not digital storage, but the physical world of cardboard boxes and garage doors. Jim established a daily AI practice using a Pomodoro timer. Every morning over coffee, he sets a one-hour timer and works with AI tools for his business.

One evening, Jim joined Kyle's AI Learning Lab live-stream, where Kyle was teaching how to create songs with Suno, an AI music generation tool. Jim had met with a prospective client just four hours earlier. He knew their name, their business, and what they were trying to accomplish.

While watching the tutorial, Jim had ChatGPT write personalized lyrics incorporating the client's information and goals. He then used Suno to generate a complete song from those lyrics. He sent the song to the client immediately.

Within five minutes, the client responded: “You got the job.”

Jim closed the deal by learning across domains and applying a seemingly frivolous skill—AI-generated song creation—to his self-storage business. The client recognized the intention and effort behind such a personalized gesture. To them, it didn't make sense that someone could write a song that fast and that personalized, which made the gesture even more memorable.

Jim continues this daily practice, sometimes working on specific business needs and sometimes just experimenting and playing. He never knows exactly what he'll do during his hour, but consistent practice continuously builds his AI capabilities.

Learn Across Domains

AI readiness requires borrowing insights and approaches from fields outside your expertise.

Traditional career development often meant deep specialization in a single domain. AI readiness flips this model. The most valuable professionals will be those who can connect ideas across different disciplines and apply insights from one field to solve problems in another.

Kyle encourages deliberately exposing yourself to domains you know nothing about. If you're a marketer, study how scientists approach research. If you're a designer, explore how engineers solve problems. Use AI to help you understand these foreign domains and identify transferable principles.

This cross-domain learning makes you adaptable. When your industry shifts—and it will—you'll have mental models and approaches from other fields to draw upon.

#2: Create Excellence to Raise Your Game

The second phase focuses on producing exceptional work that demonstrates your capabilities.

Recognize and Avoid AI Slop

AI slop refers to the generic, mediocre content flooding the internet—work created by people who treat AI like a vending machine, inserting a prompt and accepting whatever comes out.

Kyle warns that AI slop is becoming recognizable. Audiences can tell when someone has simply prompted AI and published the result without adding their own thinking, taste, or refinement.

AI has raised the floor of quality. Anyone can now generate output that would have been considered decent historically. But that's still the floor—it's still the bottom. It's still garbage compared to what's possible when you apply human judgment and craft.

If you're creating slop, you're not demonstrating value. You're showing that you can push a button, which doesn't secure your professional future.

Raise Your Game Through Consistent Output

Excellence requires volume. You can't produce exceptional work occasionally and expect it to secure your future.

Kyle points to Kelly Boesch, co-host of the AI Salon. Kelly creates a video every single day. Not once a week. Not when she feels inspired. Every day.

She's maintained this practice for three years. Every morning between 7:00 and 8:00 AM, she publishes a new video without fail.

Kelly uses Midjourney to generate original images, then animates them. She uses Suno to create original music for her videos. The result is music videos that, in Kyle's words, are “staggeringly beautiful.”

Here's what makes Kelly's work remarkable: she uses the exact same tools available to everyone else—Midjourney and Suno. The difference isn't access to better tools. The difference is the volume of practice and the refinement that comes from creating daily for three years.

This level of commitment accomplishes multiple things. First, it forces you to work faster and more efficiently. Second, it builds skills through repetition. Third, it creates a body of work that demonstrates your capabilities at a level most people will never reach.

Professionalize Your Practice

Professional practice means understanding the full craft behind your work—what Kyle calls the “chain of craft.”

Many people use AI to generate an image or write a draft and consider the work complete. Professionals recognize that AI output is a starting point, not a finished product. You might spend three hours on a single email, going back and forth with AI sixty times to get it right.

Chain of craft is the collaboration between human activity, human input, AI output, and response—an iterative back-and-forth process. Often it involves multiple tools. You might use ChatGPT for initial content, switch to Claude because it handles tone better, then move to another tool for visual elements. Each tool contributes to the chain, but you maintain the fidelity of your original idea throughout.

Liz Miller Gershfeld, co-host of the AI Salon and a producer in the advertising business for twenty-two years, developed the concept of professionalizing your practice. This means understanding which tools to use and their limitations.

When working with clients, professionalizing your practice looks like understanding the legal limitations of different AI tools. You discuss these limitations with your client upfront. You establish agreements: “We're going to use these four specific tools. If I need to use anything outside of that, I'll come back for another agreement.”

These steps seem obvious in a broader professional context, but they're not standard practice with AI yet. Many people rush into AI without treating it professionally because the technology is new and exciting. This creates risk.

The whole idea of creating excellence isn't about letting AI do things and assuming the outputs are fine. It's about being conscious and conscientious about what it means to level up your work through careful, professional application of these tools.

#3: Generously Lead to Secure Your Position

The third phase focuses on contribution and community engagement.

Create and Contribute Publicly

Generous leadership means putting your work and insights into the world where others can benefit from them.

This is uncomfortable for many people. Sharing work publicly means accepting vulnerability. You might make mistakes. People might criticize you. Your work might not be as good as you hoped.

Kyle insists you must do it anyway. Putting yourself out there establishes you as someone actively engaged with AI rather than someone passively watching from the sidelines.

Share what you're learning. Document your experiments. Show your process, including the failures. This transparency builds trust and positions you as someone with practical experience rather than theoretical knowledge.

Practice in Community

Isolated practice has limits. Community practice accelerates learning and creates opportunities.

Kyle describes AI Salon members who initially felt annoyed by AI discussions. Their spouses asked them to stop talking about AI at dinner. Their coworkers groaned when they brought up AI tools.

These members kept sharing anyway. When layoffs came, those same people who were tired of hearing about AI suddenly needed help. The AI Salon members who generously shared their knowledge became the obvious people to turn to for guidance.

Practicing in a community also means you're surrounded by people at different skill levels. You learn from those ahead of you while helping those behind you, which reinforces your own understanding.

Ask for Help

Generous leadership isn't just about giving—it's also about receiving.

Kyle emphasizes that asking for help is itself a generous act. When you ask someone for assistance, you give them the opportunity to contribute and demonstrate their expertise.

Many professionals hesitate to ask for help because they fear looking weak or incompetent. This mindset prevents growth and isolates you from your community's collective intelligence.

Being willing to say “I'm really good at this, but I'm not good at that” establishes you as someone who thinks critically, has self-awareness, and values collaboration over ego.

Think Critically and Act Ethically

The final component of generous leadership is maintaining standards for yourself and your community.

As AI capabilities expand, ethical questions become more complex. What uses of AI serve people well? What uses exploit or manipulate? How do you balance efficiency with integrity?

Kyle explains that generous leaders don't just adopt every AI capability without thinking. They consider implications. They establish boundaries. They call out concerning practices while celebrating responsible innovation.

This critical thinking distinguishes you from people who simply chase the latest AI features without considering broader impacts. It positions you as someone with judgment and values, not just technical facility.

Members of the AI Salon who practice generous leadership are the ones who survive layoffs and advance into leadership positions. Companies recognize them as people who can guide AI adoption responsibly, not just use AI tools mechanically.

Other Notes From This Episode

• Connect with Michael Stelzner @Stelzner on Facebook and @Mike_Stelzner on X.
• Watch this interview and other exclusive content from Social Media Examiner on YouTube.

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