There’s a lot of fear about AI taking over our jobs. It’s both true and untrue. While AI is here to help businesses run smarter and solve problems faster, we need people who can use AI fluently to achieve this.
From predicting the next big sales trend, fine-tuning a marketing strategy, or improving production efficiency, AI is used everywhere to make better business decisions and act on them in real-time.
In the true sense then, this is a golden opportunity for you to build a career in AI. This blog gives you insights into how you can build one.
The 4 pillars of AI in Business
When we break it down, AI’s impact in the enterprise world revolves around four key areas: Content, Commerce, Customer, and Operations.
- Content: In today’s digital age, content is everything. AI helps businesses decide what to show their customers—whether it’s product recommendations or targeted ads. It uses data to figure out the best strategies, like which products to promote or which customers to focus on for a new launch.
- Commerce: AI takes on big questions like, “Should we raise prices?” or “What will sales look like next year?” By analysing heaps of sales and market data, AI helps businesses make smarter, high-stakes decisions.
- Customer: Knowing your customers is key, and AI gives businesses a deep understanding of customer behavior, loyalty, and segmentation. From timing a product launch to fine-tuning marketing efforts, AI helps companies stay one step ahead.
- Operations: AI also improves how businesses run. From streamlining production schedules to optimising supply chains, machine learning models help spot inefficiencies and suggest ways to keep operations running smoothly.
The 3 main career roles in the AI
When it comes to having a career in AI, it’s important to consider how building models or writing code bring out scalable solutions that are easy to access and understand. This is where understanding the broader roles of data, product, and engineering teams help.
Data Engineers – the backbone of AI solutions
Data engineers, analysts, and scientists are responsible for collecting and maintaining high-quality data for analysis. Without a solid data foundation, AI models and systems would struggle to function properly. This is where careers in data engineering, data analysis, and data science come into play. Each role contributes to building pipelines and models that ensure data is processed, analysed, and ready for use in problem-solving scenarios.
Programmers – agents of scaling solutions
Once solutions are built, they need to be scaled. This is where engineers step in and take the responsibility of scalability. Think about widely used AI platforms like ChatGPT. These systems need to work at scale, delivering seamless experiences to users around the world. Engineers handle this complexity by building systems that can scale without compromising performance.
If you’re strong in data structures, algorithms, or system thinking, this is where you can truly make an impact. Engineering ensures that the technical backbone of AI is strong enough to support real-world applications.
Product managers – bridge between tech and business goals
Product managers and strategists focus on using AI and machine learning to drive meaningful business outcomes. If you’re more inclined towards problem-solving, strategy, and understanding business needs, this could be the right path for you. The goal in product management is to harness AI’s capabilities to solve real-world problems, whether they are industrial or social in nature.
Steps to solve problems using AI: From definition to solution
Defining the problem
The first and most crucial step is defining the problem. Solid evidence is needed to prove it’s worth solving—whether that’s increasing revenue, expanding the user base, or improving the user experience. Business and product teams analyse data to assess the impact, using clear business metrics to guide the process.
Gathering and processing data
Once the problem is defined, the next step is gathering and preparing the right data. Data engineering teams ensure the data is cleaned, processed, and ready for analysis. Since the quality of the data directly impacts the model’s success, this step is critical to laying the foundation for effective model development.
Building Models
With quality data in place, the next step is model development. Data scientists build custom models tailored to the specific business problem, not just off-the-shelf solutions. They fine-tune algorithms to ensure the model performs well with real-world data, and once ready, it’s passed to the machine learning (ML) engineering team for deployment.
Deployment
Once the model is built, the focus moves to deployment. The ML engineering team ensures a smooth transition from development to production, integrating the model into business systems. Whether for internal teams or customers, software engineers make sure the AI solution is ready for real-world use.
What do companies expect from you as an AI professional
Strong foundations in mathematics: A deep understanding of calculus, statistics, and the theory behind algorithms is essential. It’s not simply about applying a model; you need to understand why a particular algorithm fits a specific business problem.
Informed decision-making: Companies expect you to know how to choose the right algorithm for the task at hand. Each business challenge is different, and it’s your responsibility to match the solution accordingly. For instance, if the goal is to predict customer loyalty, discriminative AI might be your best tool for analyzing behavior.
Curiosity and a commitment to learning: AI is constantly evolving, and companies seek candidates who stay ahead by continually learning. They’re looking for individuals who go beyond standard projects, explore new approaches, contribute to open-source initiatives, and dig deeper into the fundamentals of AI.
Taking the first step toward a career in AI
Starting your journey in AI depends a lot on your strengths and interests. Whether you’re drawn to coding, mathematics, or problem-solving in business, there’s a path for you.
Coding: If you’re technically inclined, learning a programming language is key. Python is an excellent choice for AI, as it’s widely used in data science and machine learning. Other languages like Java or Scala also open doors, especially if you’re working with larger-scale systems. The real magic happens when you start using open-source APIs, allowing you to build complete projects and connect the dots between different tools and technologies.
Mathematics: Some of you may have a strong grasp of calculus, algebra, or probability. These skills are incredibly valuable in AI, as they form the backbone of machine learning models and algorithms. If math is your strong suit, focusing on how mathematical concepts translate into AI applications will set you apart.
Business Problem-Solving: AI isn’t limited to tech and numbers, it’s a dynamic tool for addressing real-world business challenges that shape outcomes. If you’re skilled at understanding business dynamics and strategy, AI offers countless opportunities to solve complex problems and drive impactful results.
Go explore a career in AI – now is the time
Contrary to belief, the rise of AI offers exciting career opportunities, making it a thrilling field for new graduates to explore.
Whether you’re interested in coding, data science, or business applications, there’s a role waiting for you that let’s you solve any business problem using AI.
Stay curious, keep experimenting, and remember—continuous learning is your greatest asset. The future of AI is exciting, and your journey starts now.
