How to Start a Career in AI?

AI is no longer a futuristic concept, it’s now changing the way how the world works. Whether it’s about the current online shopping experience or companies making big decisions, AI supports everything behind the scenes. It predicts outcomes, fixes problems and improves the overall speed and accuracy of the processes that users want. 

That’s why the demand for AI experts in the IT sector is skyrocketing because now employers are looking for those professionals who have the ability to handle artificial intelligence and its modern algorithms smoothly. Now the question is not only about the AI jobs, but every fresh mind in the crowd thinks, how to start a career in AI, especially if you do not hold prior experience. 

This blog is designed specially to solve this query of yours, and we will provide you with the step-by-step journey to make an ever-evolving career in the field of AI. Whether you are a student, IT employee, Salesforce enthusiast or anyone who has an interest in this domain,  there’s a place for you in AI. 

7 Effective Steps to Make a Career in AI

Breaking into AI may sound complicated, but the process becomes simple when broken into actionable steps. Here’s how to build a successful career in AI from the ground level.

1. Understand What a Career in AI Means

Before jumping to courses or tools, it’s worth recognizing what you’re getting into. AI is not limited to specific roles, there are plenty of job opportunities that you can explore. Roles in AI have evolved and include tech and non tech jobs like:

• AI/ML Engineer
• Data Scientist
• AI Product Manager
• Prompt Engineer
• AI Consultant
• Salesforce AI Specialist

Each of these roles serves different purposes, yet they focus on the same idea of machines performing intelligent behaviour. These roles majorly focus on training models, automating tasks, or working with AI tools to improve.

2. Start with the Basics: Programming and Math

You don’t need to be an expert coder to get a head start in this domain. But you must have some technical expertise before you step out to make an AI career. So, it is better for you to start with:

• Python: This is the most commonly used language in AI. You will need to gain some familiarity with writing simple scripts, manipulating data, and working with libraries like NumPy and Pandas.
• Basic math and stats: You will need a basic understanding of mean, standard deviation, probability, linear regression, and how models assess their performance (i.e., accuracy, precision, F1 score).

This knowledge can all be gained from free resources such as Kaggle courses, YouTube clips, and introductory courses on Udemy. It couldn’t be any easier!

3. Grasp the Fundamentals of Machine Learning and AI

Once your basics are in place, it’s time to dive into core AI and ML concepts. Don’t try to become an expert in everything just get comfortable with the foundation:

• Machine learning algorithms like linear regression, decision trees, and clustering
• Supervised vs. unsupervised learning approaches and when to use each
• Neural networks and deep learning models that mimic how the human brain processes information
• Model training, evaluation, and optimization, including concepts like training/testing splits, accuracy, F1 score, and loss functions

Focus on the concepts, not just code. Understanding how models think will make you far more confident when building or using AI tools.

4. Get Hands-On with Salesforce AI Tools 

Theory is good, but AI is a hands-on field. If you want to become an AI engineer or build a meaningful portfolio, you need to work with the tools professionals use.

These include:

• TensorFlow and PyTorch: For building machine learning and deep learning models
• Scikit-learn: For simpler ML models
• LangChain, OpenAI API, Hugging Face: For generative AI projects
• Vector databases and retrieval-augmented generation (RAG) systems

If you’re more business or platform-oriented, start with Salesforce tools (more on that shortly). Either way, practical exposure is key.

5. Work on Real-World Projects (and Document Everything)

Nothing proves your skill better than working on projects that show how you solve problems with AI. Aim to build at least three solid projects. These don’t need to be complex, but they should be relevant.

Some project examples that you can build are as follows:

• Predicting housing prices with ML
• AI chatbot that answers customer queries using LLMs
• AI-powered lead scoring for sales teams
• Sentiment analysis for product reviews

Document each one in detail on GitHub. Write short LinkedIn posts about what you learned. This will help you to get better opportunities because now recruiters will take you seriously.

6. Start Small with Practical, Everyday AI Use Cases

Before you jump directly into developing sophisticated models, it would be better for you to use AI in daily situations. This step will help you to get more intuition, confidence, and understanding of how AI functions.

You can do the following things for this practice:

• Use common AI tools like ChatGPT or Gemini for automated tasks like summarising reports, generating code snippets, or developing outlines
• Experiment with no-code AI tools like Notion AI, Canva AI, or Descript and observe how AI enhances productivity
• Try out AI Chrome extensions such as Harpa or Merlin to help with research, writing, or customer support work

Implying these practices provides you with valuable experience to share in interviews about how you’ve already incorporated AI into your workflow, even without an official AI job.

You don’t necessarily have to create an LLM from scratch to show you’re AI-ready. You must prove to employers that you know how to use AI in a meaningful way.

7. Join Communities, Stay Updated, and Build in Public

To stay updated in the field of Artificial Intelligence, it would be best for you to surround yourself with people, conversations, and tools that keep you learning. This doesn’t just keep your knowledge fresh, but it also opens doors to mentorship, jobs, and collaboration.

Here’s what you can do to keep yourself relevant all the time:

• Join communities like Kaggle, Discord AI servers, or LinkedIn groups.
• Follow experts on LinkedIn, Twitter (X), and YouTube who break down complex topics in simple terms.
• Subscribe to weekly AI newsletters.
• Post what you’re learning, what you’re building, or what problems you’re solving with AI (even small things)
• Contribute to open-source AI projects or collaborate with others on simple builds.

Now that you know how to stay active and engaged in the AI space, let’s talk about why it’s worth putting in all this effort in the first place.

Here’s what a career in AI offers:

• Future-proof skills: AI is not disappearing because it’s becoming the norm nowadays.
• High pay: AI job roles always score among the highest-paying tech jobs.
• Work flexibility: Most AI jobs are remote or hybrid.
• Creative freedom: You’ll tackle real-world challenges with real-world effects by unleashing your creativity with the power of AI.
• Cross-industry mobility: AI is applicable across all industries like healthcare, finance, education, retail, and SaaS.

While these benefits apply to AI careers in general, there’s one space where this evolution is especially exciting, and that’s the Salesforce ecosystem. Let’s explore how AI in Salesforce is creating new career opportunities and transforming how people work.

Importance of AI in the Salesforce Ecosystem

Salesforce is now not just a CRM platform but is growing to become an AI ecosystem that aims to support every team, from sales to service, marketing to support. AI is now at the heart of Salesforce’s day-to-day business applications. Whether sending an email for help to a sales representative, preparing summaries to assist a service agent, or providing data-driven insights to marketers in the background, AI in Salesforce helps make things smarter and faster.

This change is providing great value to the users. Salesforce is building easy-to-use, no-code or low-code AI tools that are embedded in the interface you already use. This means that professionals from any background can enhance productivity, experience, and the quality of evidence-based decisions for their customers. As Salesforce is continuously investing in AI, learning how to use these tools not only makes your job easier, but it also increases your value in a competitive job market.

With features like Einstein GPT, Agentforce, and Prompt Studio, AI is now built into the everyday tools people use inside Salesforce. This means the platform can now:

• Automate conversations with customers
• Generate content or insights instantly
• Predict outcomes and trends
• Power AI-driven agents to handle tasks

What’s exciting is that this shift is making it the best career option for those who are engaged in Salesforce. 

Overall, AI isn’t replacing Salesforce careers, but it is making them stronger. 

Final Thought

Lastly, it is better to sum up this blog by saying that making a career in AI seems overwhelming at first, but it’s definitely possible with your dedication and efforts. Whether you are from a tech or non-tech background, it doesn’t matter because the field of artificial intelligence is widely open for learners and newcomers. 

So, it would be better for you to start your AI career journey today because no perfect moment exists on the clock. Start putting in small efforts, follow the steps we mentioned above and align your learning experience to the real-world use cases. 

You can also enter into ecosystems like Salesforce AI, where you don’t need to be an expert from day one; you just need to start, and the Salesforce community will support you. In case you need any consulting regarding your career in AI, don’t forget our experts are just a call away!