AI Made Friendly HERE

10 Ways To Get Started In AI — Without Being Technical

10 ways to get started in AI (without being technical)

Allie K. Miller

It’s easy to feel behind with artificial intelligence. If you haven’t figured out how to integrate tools like ChatGPT into your business, if you don’t know the difference between ML and NLP, or if you feel overwhelmed with all the breaking AI news, you might think you’ve missed the boat. Luckily, that’s not the case. AI is just getting started. Business breakthroughs, huge progress, and a whole new level of efficiency are waiting for professionals just like you to discover today.

Allie K. Miller believes that, “AI isn’t the future, it’s now.” As an AI leader, advisor and investor, Miller has worked with hundreds of companies on AI product development, implementation and deployment. Previously, Miller was Amazon Web Services’ global head of machine learning for startups and venture capital, founding the group and growing it into a multi-billion dollar business line. She was also a lead product manager for AI products at IBM
. As the most followed voice in AI business, Miller shares practical resources on building an AI-first company to her 1.5 million followers. For her work in AI business and education, she has also been named AI Summit’s AI Innovator of the Year and LinkedIn’s Top Voice for Technology 2019–2023.

“One of the biggest myths of working in AI is that it’s only for engineers,” said Miller. “That couldn’t be further from the truth.” She added, “AI companies and AI projects need everyone, including marketing specialists, HR leaders, salespeople, operations managers, finance and legal professionals. The tools are more accessible than ever, and this is the opportunity of a lifetime.”

Miller is helping others build a career in the AI age, starting with this list of 10 ways to become more familiar with AI technology and the value it offers, ranked from easiest to hardest.

10 ways to build a business and career in AI: start your transition

1. Read AI trends and test tools

“Begin by enriching your knowledge,” advised Miller. “Subscribe to AI-focused newsletters like The Rundown AI or Ben’s Bites, and follow leading voices such as former Google
chief decision scientist Cassie Kozyrkov, Wharton professor Ethan Mollick, or futurist Sinead Bovell.” Listen to people who have been in the game for a while and turn insights into action by actually testing out tools. Make sure theory leads to practice.

“Start with AI tools like ChatGPT and Perplexity AI,” added Miller. “Make learning AI a habit and stay informed. It will pay dividends when it comes to getting value out of AI.” It’s never too late to begin learning how to apply this technology to your business and career.

Time required: A few hours per week. Cost: $0.

2. Forecast your job’s evolution

Predict the future of your specific role by assessing where you’re at. “Go on LinkedIn or Indeed and look up AI-specific versions of your current role,” advised Miller. “If you’re in sales today, look at sales roles in AI. Then do a gap analysis between the roles you find and your current skills and work to close the gap.”

The next version of your existing role may require proficiency with a handful of tools. “A designer might have to know Adobe Firefly, or an account manager might have to know Salesforce’s
AI tools,” added Miller, who recommends you spend a few hours learning those tools, consult YouTube for step-by-step tutorials, and update your resume when you’ve mastered them for real. “Understanding and demoing these tools will give you that significant edge” to clients and employers.

Time required: 10-20 hours. Cost: Tool subscriptions vary.

3. Find an AI mentor

For the next level up, find someone who has successfully made the transition from your current role to an AI-centric one (or several). “This could be an ex-colleague, someone who went to your college, or a stranger on social media,” suggested Miller. Ask their advice on entering the AI space.

Entrepreneurs can find the AI version of their current business by searching online for tools in their space, and get ideas on how their offering could adapt. Either way, make use of other people’s experience to shape your own. “Get concrete, role-specific advice and act on it,” added Miller.

Time required: 10+ hours of search, outreach, scheduling, networking. Cost: $0. Resource: Another person.

4. Attend an AI conference or event

You don’t know what you don’t know. Go deeper in your research by attending a conference and having your eyes opened to new and exciting technological concepts you didn’t realize existed. Meet new people and understand their work. “Conferences can be a huge leg up for networking, meeting hiring managers, and learning where this tech is headed,” explained Miller.

“If you’re more technically inclined, attend events like NeurIPS or CVPR. For less technical fields, consider industry-specific events like ScaleUp AI for investing or MAICON for marketing.” Turn up with an open mind, take loads of notes, and follow up with everyone whose work piques your interest.

Time required: 3-5 days. Cost: Conference entrance and travel.

5. Enroll in AI courses

Even though AI is rapidly changing formal education, Miller believes “credentials can open doors.” Don’t just rely on building your own knowledge, find courses tailored to your career goals and “formalize with certificates as required.”

If you’re an aspiring machine learning engineer, “try Python courses from Free Code Camp, ML crash courses from Google and, or Andrew Ng’s ML courses,” recommended Miller. If you’re a non-engineer, look into courses like “What is Generative AI?” on LinkedIn Learning, “AI for Everyone” by Andrew Ng, or enroll in Miller’s in-depth “AI for Business Leaders” on Maven. There’s also AI for Non Techies by Heather Murray.

Time required: 2 weeks to 6 months. Cost: Course price.

6. Implement AI in your company

With the basics covered, you can build up a portfolio and begin creating. “Start meaningfully applying AI in your current role,” recommended Miller. “For example, a data analyst could suggest AI tools for data transformation, or learn a no-code platform to build an ML forecasting model. A customer support manager could lead their team to adopt customer service tools like Zendesk
or create a prompt repo for common support needs.” This is AI-ification in action. Apply resourcefulness to find the tools, then monitor the impact (both qualitative and quantitative) on your team’s output.

If you run your own company, empower your team to multiply their output with AI, and figure out solutions together. Prioritize solutions that bring meaningful and positive results, such as freeing up time to work on new ideas that will grow the entire business. “Take initiative and carve out an opportunity for real, hands-on project work,” said Miller, whether you’re building within your role or business.

Time required: Weeks to months. Cost: Enterprise-grade AI tool subscriptions vary. Resource commitment: Team and/or manager alignment.

7. Join a machine learning (ML) side project

If you want to transition into AI but you’re not ready to take the leap in your current business, “engage in an AI collaboration on the side.” Miller suggested you “join Slack or Discord groups, contribute to open-source communities through GitHub, or tap into LinkedIn groups.”

Be deliberate, not haphazard. “Choose projects that resonate with your interests, like sports forecasting models if you’re a basketball fan or botany web apps if you’re a nature lover. I know hundreds of people building AI apps for friends or family,” she added. Pitch your skills and meet new people. You never know what you could co-create.

Time required: Nights and weekends for months. Cost: API usage or tool subscription. Resource required: outside team.

8. Start your own machine learning project

“For the self-motivated, initiating your own AI project can be incredibly rewarding and a great outlet for creativity,” said Miller, who recommended leveraging whatever spare moments you have, emphasizing quality of time over quantity. Learning from the ground up is a great way to understand machine learning, so you can build out your AI-first business or incorporate AI into your role with the fundamentals covered.

“This option is often for engineers,” explained Miller, but “non-technical folks can check out no-code platforms like,, or MindStudio, or bring on a development partner to collaborate and bring ideas to life.” Execution is key, but getting started is the hardest part.

Time required: Nights and weekends for months. Cost: API usage or tool subscription, talent.

9. Work with an AI startup

“Nothing beats hands-on learning, and working at an AI startup is like navigating a jungle on a unicycle. Unpredictable but exhilarating.” said Miller. Collaborating with a startup is a great way to understand the intricacies of working in an AI-driven company.

“Ensure there’s no conflict of interest with your current role and do your due diligence to look into the team and investors,” Miller suggested, then roll up your sleeves and don’t hold back. She added that this approach could initially involve reduced rates until you prove yourself and build up your resume.

Time required: Potential for part-time or full-time role.

10. Start an AI company

“This is the most challenging and ambitious path, and it’s one that demands dedication, risk-taking, and resilience,” explained Miller. You need to fully commit for any hope of success. “90% of startups fail, so be sure your motivation is not just financial reward,” she added.

Even if your AI company doesn’t succeed, you’ll have learned a ton and picked up valuable connections. Even “the potential for impact and fulfillment alone can make it worth it,” said Miller. If you think you’ve got what it takes, find a core audience and solve their problem in an AI-driven way. Listen to their feedback and hit product-market fit.

Time required: Full-time and beyond. Cost: Varies widely (thousands to millions of dollars) depending on product and team.

How to get started in AI: from an expert in artificial intelligence

“AI roles are some of the fastest growing on LinkedIn,” explained Miller, who describes these 10 options above as not one-size-fits-all solutions, but “stepping stones to a more AI-first career.” If every business will one day be an AI-first company, you can future-proof yours by getting a head start.

Whichever step you pursue requires curiosity, passion and determination. “Your ability to learn, engage, adapt, and practice is going to come down to your initiative. So take the first step now to build a stronger career in the AI age,” added Miller. Put the work in now, and you’ll be forever grateful that you got ahead.

Originally Appeared Here

You May Also Like

About the Author:

Early Bird