How to Transition Your Career in AI? [7-Step Portfolio Guide]

Building a career in AI requires hard work and a willingness to learn things quickly.

There will always be a fast-mover advantage (not a first-mover).

Google came up with transformers first but OpenAI created it better and faster. You know the story.

Processes, systems, frameworks, and business integrations are changing every day.

Pace is important. Keep it raw and fast.

This will be long so grab your coffee 🍵

The most underrated skill in tech is adaptability.

If you are an engineer and studied 40 subjects in 4 years, you are already used to “change”.

You learned and implemented machine learning, then moved to deep learning and now generative AI, isn’t it amazing?

Table of Contents

• Challenges in AI Implementation

• Career Transition to AI

  • AI Landscape - What’s in it for me?

  • Skills Needed - Hard and Soft both

  • Niche - Integration with your industry

• AI Job Market

• Portfolio [7 Step Guide]

  • 1 - Blogs or Newsletter

  • 2 - Live Projects

  • 3 - Certifications

  • 4 - Talks and Sessions

  • 5 - Research Paper

  • 6 - Share Your Story

  • 7 - Join Paid Communities

• Final Thought

Challenges in AI Implementation

AI can be integrated into any field.

There are 2 major challenges businesses face with AI right now

1. estimating and demonstrating AI value

2. lack of talent/skills

You probably want to solve the first problem as a business leader.

You need to solve the second one if you are a professional or a student.

Or both.

Career Transition to AI

Individually, to transition into AI you must understand 3 things

• AI Landscape - What’s in it for me?

• Skills Needed - Hard and Soft both

• Niche - Integration with your industry

AI Landscape - What’s in it for me?

You can see the below analysis on how GenAI is now used in more business functions than before.

Every year you will see new tech and businesses adopting them to boost profit.

Look at the landscape of AI companies solving problems in different categories.

You have heard about these companies and the impact they are making.

You need engineers to make these and leaders to align business impact—more opportunities.

Now that you know how big the opportunity is, you can take advantage of it and add these skills to your portfolio.

Skills Needed - Hard and Soft both

If you are transitioning into AI don’t consider yourself as a fresher.

Let me explain.

If you don’t have the hard skills (programming, architecture, model building, production environments, AI estimations, cost, ROI, etc.), that doesn’t mean you are a fresher.

What you have are soft skills (work ethics, teamwork, not micromanaging, empathy, problem-solving, etc.) that you have gained over the years through interactions with people.

So you have to learn hard skills and integrate them with your current knowledge.

What you don’t need to learn as a leader?

• Python

• Mathematics

• Neural Network Architecture

• RAG implementation

• LLM from scratch

What do you need to learn as a leader?

• Profit estimation

• Business alignment

• AI strategic systems

• Demonstrating AI business value

• Workforce/talent understanding for a successful AI implementation

Niche - Integration with your industry

Niche is a combination of skills you have where the competition is few.

Example: GenerativeAI expert in Finance for Text data.

It’s good to focus on a niche in the initial years, but if you already have the experience you can be diverse in different domains.

Example: Consultant for Enterprises for AI growth

AI Job Market

The above data shows the growing and declining occupations.

You can check this tool [datanerd.tech] to see what companies are looking for in a candidate and how much the salary they are getting in the job as well as in freelancing.

Portfolio [7 Step Guide]

I have been sharing my knowledge online for 2 years now and one thing that is constant now is getting new opportunities.

Show what you have done.

There are different ways you can show your work if it does not involve coding.

1 - Blogs or Newsletter

There are a lot of options out there. You can use LinkedIn to post the idea of that blog and check if you get any traction.

LinkedIn is the best platform for validating your ideas right away.

If you got the traction go and write the blog on that idea as it is already validated.

I will recommend you create a newsletter because on LinkedIn you cannot own the audience.

You will not have the email address of your audience.

So use a platform like BeeHiiv or any other platform where you have an email list.

2 - Live Projects

Proof of work will put you above your competition.

If you can show what you have built then it will be a star on your resume.

Make sure they are hosted live somewhere.

If you don’t have the coding part, you can write a blog with these sections:

• what problem do you work on

• what was your approach to the problem

• what were the results you got for the business

3 - Certifications

For AI Product Management or any leadership position.

No one can teach you how to lead a successful AI product.

85% of AI development fails because of a variety of reasons.

You work in chaos and certifications will not help in solving the real-world AI mess.

But,

If you still want to go for a certification, I will recommend you to do these 2 things →

1. choose an established individual creator who has credibility in the AI community.

2. whatever you learn make sure you apply the knowledge right away.

Certifications are not for every profile in the tech space.

Now think if it is true: AI Certifications are a waste of Time 

4 - Talks and Sessions

Pick an idea and present it to other people in the space in a live discussion environment.

You will get questions and feedback right away to build your solid understanding.

5 - Research Paper

Pick different ideas in your industry, hire an intern if possible, and write a research paper or create a thesis for your research.

Make sure the idea you choose will be directly proportional to the area of your expertise.

Research takes a lot of time and energy but is worth it as it will make 3 things better:

• business-aligned ideation of your AI idea

• research skills—whether the idea will work or not

• understanding of different innovative solutions available around the same problem

6 - Share Your Story

Once you have all the above parts write your story or the about section for yourself.

People trust people, not machines, not ChatGPT, or any generated text.

Be yourself.

7 - Join Paid Communities

Paid communities are way better than free ones.

Hosts of the communities are more focused and action-driven, can solve your problem, have no spam, and have like-minded knowledge transfer.

Note: I am starting one soon for AI Leaders [product managers, VPs, CEO, AI consultants, Tech Consultants]

Final Thought

Building your career requires hard work.

Smart work is for direction only, and hard work cannot be an option it’s mandatory.

Do one thing at a time, focus on quality.

Again, the time is of fast mover not first mover.

Build a portfolio, and share with the community.

See you next weekend.

Happy AI

Socials

Be part of 50,000+ like-minded AI professionals across the platform

→ LinkedIn → YouTube→ Twitter → Instagram → Medium

→ Telegram → Discord Server → GitHub & Code Resource

→ WhatsApp Community Group → GitHub