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How Will AI Skills Impact Your Career and Salary in 2025?
Discover the top 10 AI skills that can help you earn up to 47% more, according to Coursera and AWS data. Learn why employers are demanding AI-savvy professionals and how you can start building these in-demand skills today.
Published
3 months agoon
By
AIinAsia
L;DR – What You Need to Know in 30 Seconds
- AI Course Demand Is Soaring: There’s been an 866% year-on-year increase in AI skill interest CourseraCourseraCoursera.
- Employers Are Eager: 3 out of 4 companies use GenAI and 62% want employees clued up on AI Coursera.
- Big Salary Boosts: AI-savvy professionals can earn up to 47% more, with the highest gains in marketing, finance, and business ops Indeed, AWS.
- Top 10 Skills: Focus on GenAI, neural networks, computer vision, and ML operations to stay ahead Coursera.
AI Skills are the New Currency for Salary Negotiations in 2025
If you’ve been paying attention to the tech world lately, you’ll know that generative AI (GenAI) is causing quite the stir. In fact, it’s being hailed as the “fastest growing job skill of our time,” and it’s backed by some incredible data.
According to Coursera’s latest Job Skills Report 2025 (Coursera), there’s been an 866% year-on-year surge in demand for learning AI—yes, you read that right, eight hundred and sixty-six percent! That uptick isn’t just a meaningless statistic either, as employers are getting on board faster than you can say “machine learning.”
So if you’re keen on taking your career to the next level—or simply avoiding obsolescence—the question is: Which AI skills should you be investing your precious time in? Stick with me, and I’ll walk you through the top 10 must-learn AI skills, how they can boost your salary by up to 47%, and how you can start learning them (often for free or at a significantly discounted rate).
Why AI Skills Are Worth Your Time
Here’s the skinny on why AI is all the rage. Over the past two years, Coursera found a 1,100% surge in AI course demand from employed professionals, a 500% rise among students, and a whopping 1,600% jump for job-seekers. And that’s not even the whole story:
- Three in four employers are already using GenAI in some shape or form, and 62% expect employees to have a basic familiarity with AI tools.
- 22% of recruiters have even updated their job descriptions to include AI prerequisites, and you can bet that number will grow by the end of the year.
- Another poll, conducted by AWS last year, revealed that 92% of businesses plan to adopt AI-powered solutions by 2028, while 73% consider hiring AI-savvy talent a top priority AWSAWSAWS. The problem? 75% of them claim they “can’t find the talent they need”.
Clearly, the workforce is playing a frantic game of catch-up with the skyrocketing AI demands. And if you’re thinking about what’s in it for you personally, Indeed says AI skills can pad your wallet to the tune of 47% more in salary compared to non-AI roles. Specifically, the biggest pay bumps can be found in:
- Sales and marketing: 43% higher
- Finance: 42%
- Business operations: 41%
- Legal, regulatory, compliance: 37%
- Human resources: 35%
All that to say, the numbers don’t lie—AI isn’t just a fancy buzzword. “There’s an immediate need for professionals to pursue these skills to improve their job readiness,” the Coursera report stated.
AI will impact nearly every job role, necessitating upskilling across all industries… Therefore, understanding AI fluency is essential for all employees and students, regardless of their role, age, or background.
10 Fastest Growing AI Skills to Learn in 2025
If you’re ready to dive in, Coursera analysed over 1,000 skills across five million learners globally to identify the top 10 AI skills professionals should prioritise. Here’s the list in all its glory:
- Generative AI – Use AI to generate text, images, and beyond.
- Artificial Neural Networks – Develop computer systems that mimic the learning processes of the human brain.
- Computer Vision – Teach computers to “see” and understand images or videos.
- PyTorch – A powerful machine learning library for building complex AI applications.
- Machine Learning – Train computers to learn from data (the backbone of all AI).
- Applied Machine Learning – Use machine learning techniques to solve real-world problems.
- Deep Learning – Develop sophisticated AI models for highly complex tasks.
- Supervised Learning – Train AI with labelled data, guiding the model to make accurate predictions.
- Reinforcement Learning – Train AI via trial and error, rewarding correct actions and penalising incorrect ones.
- MLOps (Machine Learning Operations) – Effectively manage, deploy, and monitor machine learning models in production.
Feel free to tackle these skills one by one or take a pick-and-mix approach. The point is, each skill addresses a distinct facet of AI—whether it’s purely theoretical or intensely practical.
How to Get Started: Learning AI Skills Online
Let’s be honest: not everyone has the time or inclination to enrol in a four-year university programme. Lucky for us, it’s the 21st century, and the internet is brimming with resources to help you learn these skills without quitting your day job. Here are a few popular options:
- IBM SkillsBuild: This platform offers 100% free courses. Yes, you heard that right—free.
- Coursera: Known for its many professional certificates and courses, Coursera has a mix of free offerings and paid programmes. You can also apply for financial aid to potentially access courses at no cost.
- Codecademy: If you prefer a subscription model, Codecademy’s monthly or yearly plans give you access to AI, Python, and machine learning courses (including PyTorch).
Tailoring Your Learning Path
Bear in mind, not all 10 AI skills may be relevant to your current role or future aspirations. Maybe you’re in marketing, so learning Generative AI for content creation could be your top priority. Or perhaps you’re a data analyst, making advanced skills in deep learning or reinforcement learning more crucial. Regardless, continuous learning sets you apart in a job market that’s increasingly demanding AI expertise.
The Power of AI Skills for Your Career
Here’s the thing: your value in the job market really boils down to two aspects—your skill set and your willingness to keep it fresh and relevant. With AI weaving its way into almost every industry, it’s a smart move to upskill now rather than wait till your role is rendered redundant. Plus, the compensation perks are substantial.
Even if you’re not aiming to become a full-blown AI engineer, having a moderate familiarity with AI tools can set you apart during performance reviews or job interviews. You might find yourself at the forefront of new AI initiatives in your organisation, possibly scoring that promotion or raise sooner than you’d think.
AI is transforming business models globally, creating an urgent need for employees who can leverage these tools across diverse job functions.
Whether you’re a project manager, financial analyst, HR professional, or content creator, there’s likely an AI tool that can make your life easier—and your output more valuable.
How AI Skills Can Future-Proof You
Let’s face it, business transformations happen at breakneck speed these days. AI, in particular, is no longer a niche skill—it’s steadily becoming a baseline expectation for a wide range of roles. Recruiters are scouring CVs for AI buzzwords, and if you have them, you’re already leaps and bounds ahead of your competition.
But it’s not just about ticking the box for employers. AI literacy also makes you more adaptable. If your company decides to pivot to new tech, you won’t be left floundering. Instead, you’ll be at the heart of the action, driving change, and potentially shaping the future of your organisation.
Consider how AI intersects with everyday tasks. In HR, data-driven recruitment tools are changing the way we screen candidates. In finance, predictive modelling is helping analysts forecast market trends more accurately. In marketing, AI-driven campaigns are now standard fare rather than futuristic pipe dreams. And let’s not forget how generative AI can whip up blog posts, social media updates, and ad copy in the blink of an eye—freeing up time for more strategic thinking.
Next Steps: Making AI Your Competitive Advantage
So, what are you waiting for? The resources are there, and the world is rapidly moving towards an AI-driven horizon. Whether you fancy yourself a future machine learning guru or just want to keep pace with the times, picking up even a couple of these AI skills will pay off in spades. Here’s the bottom line:
- Identify Your AI Use Cases – Pick the skill that aligns best with your day-to-day responsibilities.
- Select Your Platform – Check out IBM SkillsBuild, Coursera, or Codecademy for online course options.
- Stay Consistent – Allocate a set amount of time each week to practice or study.
- Document Your Progress – Certifications and portfolio projects can make your new skills shine on your CV.
And if you’re still on the fence, remember that hiring managers and senior leadership are actively looking for people who can help them integrate AI into their business strategies. So why not be that person?
Final Thoughts
Generative AI has arrived in a big way, and the job market is scrambling to keep pace. Whether you’re dreaming of a new gig, eyeing a promotion, or just looking to future-proof your career, grabbing hold of AI skills could be the smartest move you make this year. The demand is high, the salaries are higher, and the barrier to entry has never been lower.
So here’s my question to you: Are you ready to seize the AI gold rush, or will you let this once-in-a-generation opportunity pass you by?
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- Coursera Launches Revolutionary Gen AI Skills Training for Teams
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Life
Whose English Is Your AI Speaking?
AI tools default to mainstream American English, excluding global voices. Why it matters and what inclusive language design could look like.
Published
48 minutes agoon
May 10, 2025By
AIinAsia
TL;DR — What You Need To Know
- Most AI tools are trained on mainstream American English, ignoring global Englishes like Singlish or Indian English
- This leads to bias, miscommunication, and exclusion in real-world applications
- To fix it, we need AI that recognises linguistic diversity—not corrects it.
English Bias In AI
Here’s a fun fact that’s not so fun when you think about it: 90% of generative AI training data is in English. But not just any English. Not Nigerian English. Not Indian English. Not the English you’d hear in Singapore’s hawker centres or on the streets of Liverpool. Nope. It’s mostly good ol’ mainstream American English.
That’s the voice most AI systems have learned to mimic, model, and prioritise. Not because it’s better. But because that’s what’s been fed into the system.
So what happens when you build global technology on a single, dominant dialect?
A Monolingual Machine in a Multilingual World
Let’s be clear: English isn’t one language. It’s many. About 1.5 billion people speak it, and almost all of them do so with their own twist. Grammar, vocabulary, intonation, slang—it all varies.
But when your AI tools—from autocorrect to resume scanners—are only trained on one flavour of English (mostly US-centric, polished, white-collar English), a lot of other voices start to disappear. And not quietly.
Speakers of regional or “non-standard” English often find their words flagged as incorrect, their accents ignored, or their syntax marked as a mistake. And that’s not just inconvenient—it’s exclusionary.
Why Mainstream American English Took Over
This dominance didn’t happen by chance. It’s historical, economic, and deeply structural.
The internet was largely developed in the US. Big Tech? Still mostly based there. The datasets used to train AI? Scraped from web content dominated by American media, forums, and publishing.
So, whether you’re chatting with a voice assistant or asking ChatGPT to write your email, what you’re hearing back is often a polished, neutral-sounding, corporate-friendly version of American English. The kind that gets labelled “standard” by systems that were never trained to value anything else.
When AI Gets It Wrong—And Who Pays the Price
Let’s play this out in real life.
- An AI tutor can’t parse a Nigerian English question? The student loses confidence.
- A resume written in Indian English gets rejected by an automated scanner? The applicant misses out.
- Voice transcription software mangles an Australian First Nations story? Cultural heritage gets distorted.
These aren’t small glitches. They’re big failures with real-world consequences. And they’re happening as AI tools are rolled out everywhere—into schools, offices, government services, and creative workspaces.
It’s “Englishes”, Plural
If you’ve grown up being told your English was “wrong,” here’s your reminder: It’s not.
Singlish? Not broken. Just brilliant. Indian English? Full of expressive, efficient, and clever turns of phrase. Aboriginal English? Entirely valid, with its own rules and rich oral traditions.
Language is fluid, social, and fiercely local. And every community that’s been handed English has reshaped it, stretched it, owned it.
But many AI systems still treat these variations as noise. Not worth training on. Not important enough to include in benchmarks. Not profitable to prioritise. So they get left out—and with them, so do their speakers.
Towards Linguistic Justice in AI
Fixing this doesn’t mean rewriting everyone’s grammar. It means rewriting the technology.
We need to stop asking AI to uphold one “correct” form of English, and start asking it to understand the many. That takes:
- More inclusive training data – built on diverse voices, not just dominant ones
- Cross-disciplinary collaboration – between linguists, engineers, educators, and community leaders
- Respect for language rights – including the choice not to digitise certain cultural knowledge
- A mindset shift – from standardising language to supporting expression
Because the goal isn’t to “correct” the speaker. It’s to make the system smarter, fairer, and more reflective of the world it serves.
Ask Yourself: Whose English Is It Anyway?
Next time your AI assistant “fixes” your sentence or flags your phrasing, take a second to pause. Ask: whose English is this system trying to emulate? And more importantly, whose English is it leaving behind?
Language has always been a site of power—but also of play, resistance, and identity. The way forward for AI isn’t more uniformity. It’s more Englishes, embraced on their own terms.
You may also like:
- How Singtel Used AI to Bring Generations Together for Singapore’s SG60
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- Or try out the free version of Claude AI by tapping here.
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Build Your Own Agentic AI — No Coding Required
Want to build a smart AI agent without coding? Here’s how to use ChatGPT and no-code tools to create your own agentic AI — step by step.
Published
1 day agoon
May 9, 2025By
AIinAsia
TL;DR — What You Need to Know About Agentic AI
- Anyone can now build a powerful AI agent using ChatGPT — no technical skills needed.
- Tools like Custom GPTs and Make.com make it easy to create agents that do more than chat — they take action.
- The key is to start with a clear purpose, test it in real-world conditions, and expand as your needs grow.
Anyone Can Build One — And That Includes You
Not too long ago, building a truly capable AI agent felt like something only Silicon Valley engineers could pull off. But the landscape has changed. You don’t need a background in programming or data science anymore — you just need a clear idea of what you want your AI to do, and access to a few easy-to-use tools.
Whether you’re a startup founder looking to automate support, a marketer wanting to build a digital assistant, or simply someone curious about AI, creating your own agent is now well within reach.
What Does ‘Agentic’ Mean, Exactly?
Think of an agentic AI as something far more capable than a standard chatbot. It’s an AI that doesn’t just reply to questions — it can actually do things. That might mean sending emails, pulling information from the web, updating spreadsheets, or interacting with third-party tools and systems.
The difference lies in autonomy. A typical chatbot might respond with a script or FAQ-style answer. An agentic AI, on the other hand, understands the user’s intent, takes appropriate action, and adapts based on ongoing feedback and instructions. It behaves more like a digital team member than a digital toy.
Step 1: Define What You Want It to Do
Before you dive into building anything, it’s important to get crystal clear on what role your agent will play.
Ask yourself:
- Who is going to use this agent?
- What specific tasks should it be responsible for?
- Are there repetitive processes it can take off your plate?
For instance, if you run an online business, you might want an agent that handles frequently asked questions, helps users track their orders, and flags complex queries for human follow-up. If you’re in consulting, your agent could be designed to book meetings, answer basic service questions, or even pre-qualify leads.
Be practical. Focus on solving one or two real problems. You can always expand its capabilities later.
Step 2: Pick a No-Code Platform to Build On
Now comes the fun part: choosing the right platform. If you’re new to this, I recommend starting with OpenAI’s Custom GPTs — it’s the most accessible option and designed for non-coders.
Custom GPTs allow you to build your own version of ChatGPT by simply describing what you want it to do. No technical setup required. You’ll need a ChatGPT Plus or Team subscription to access this feature, but once inside, the process is remarkably straightforward.
If you’re aiming for more complex automation — such as integrating your agent with email systems, customer databases, or CRMs — you may want to explore other no-code platforms like Make.com (formerly Integromat), Dialogflow, or Bubble.io. These offer visual builders where you can map out flows, connect apps, and define logic — all without needing to write a single line of code.
Step 3: Use ChatGPT’s Custom GPT Builder
Let’s say you’ve opted for the Custom GPT route — here’s how to get started.
First, log in to your ChatGPT account and select “Explore GPTs” from the sidebar. Click on “Create,” and you’ll be prompted to describe your agent in natural language. That’s it — just describe what the agent should do, how it should behave, and what tone it should take. For example:
“You are a friendly and professional assistant for my online skincare shop. You help customers with questions about product ingredients, delivery options, and how to track their order status.”
Once you’ve set the description, you can go further by uploading reference materials such as product catalogues, FAQs, or policies. These will give your agent deeper knowledge to draw from. You can also choose to enable additional tools like web browsing or code interpretation, depending on your needs.
Then, test it. Interact with your agent just like a customer would. If it stumbles, refine your instructions. Think of it like coaching — the more clearly you guide it, the better the output becomes.
Step 4: Go Further with Visual Builders
If you’re looking to connect your agent to the outside world — such as pulling data from a spreadsheet, triggering a workflow in your CRM, or sending a Slack message — that’s where tools like Make.com come in.
These platforms allow you to visually design workflows by dragging and dropping different actions and services into a flowchart-style builder. You can set up scenarios like:
- A user asks the agent, “Where’s my order?”
- The agent extracts key info (e.g. email or order number)
- It looks up the order via an API or database
- It responds with the latest shipping status, all in real time
The experience feels a bit like setting up rules in Zapier, but with more control over logic and branching paths. These platforms open up serious possibilities without requiring a developer on your team.
Step 5: Train It, Test It, Then Launch
Once your agent is built, don’t stop there. Test it with real people — ideally your target users. Watch how they interact with it. Are there questions it can’t answer? Instructions it misinterprets? Fix those, and iterate as you go.
Training doesn’t mean coding — it just means improving the agent’s understanding and behaviour by updating your descriptions, feeding it more examples, or adjusting its structure in the visual builder.
Over time, your agent will become more capable, confident, and useful. Think of it as a digital intern that never sleeps — but needs a bit of initial training to perform well.
Why Build One?
The most obvious reason is time. An AI agent can handle repetitive questions, assist users around the clock, and reduce the strain on your support or operations team.
But there’s also the strategic edge. As more companies move towards automation and AI-led support, offering a smart, responsive agent isn’t just a nice-to-have — it’s quickly becoming an expectation.
And here’s the kicker: you don’t need a big team or budget to get started. You just need clarity, curiosity, and a bit of time to explore.
Where to Begin
If you’ve got a ChatGPT Plus account, start by building a Custom GPT. You’ll get an immediate sense of what’s possible. Then, if you need more, look at integrating Make.com or another builder that fits your workflow.
The world of agentic AI is no longer reserved for the technically gifted. It’s now open to creators, business owners, educators, and anyone else with a problem to solve and a bit of imagination.
What kind of AI agent would you build — and what would you have it do for you first? Let us know in the comments below!
You may also like:
- How To Start Using AI Agents To Transform Your Business
- Revolution Ahead: Microsoft’s AI Agents Set to Transform Asian Workplaces
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- Or tap here to try this out now at ChatGPT by tapping here.
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Which ChatGPT Model Should You Choose?
Confused about the ChatGPT model options? This guide clarifies how to choose the right model for your tasks.
Published
1 day agoon
May 9, 2025By
AIinAsia
TL;DR — What You Need to Know:
- GPT-4o is ideal for summarising, brainstorming, and real-time data analysis, with multimodal capabilities.
- GPT-4.5 is the go-to for creativity, emotional intelligence, and communication-based tasks.
- o4-mini is designed for speed and technical queries, while o4-mini-high excels at detailed tasks like advanced coding and scientific explanations.
Navigating the Maze of ChatGPT Models
OpenAI’s ChatGPT has come a long way, but its multitude of models has left many users scratching their heads. If you’re still confused about which version of ChatGPT to use for what task, you’re not alone! Luckily, OpenAI has stepped in with a handy guide that outlines when to choose one model over another. Whether you’re an enterprise user or just getting started, this breakdown will help you make sense of the options at your fingertips.
So, Which ChatGPT Model Makes Sense For You?
Currently, ChatGPT offers five models, each suited to different tasks. They are:
- GPT-4o – the “omni model”
- GPT-4.5 – the creative powerhouse
- o4-mini – the speedster for technical tasks
- o4-mini-high – the heavy lifter for detailed work
- o3 – the analytical thinker for complex, multi-step problems
Which model should you use?
Here’s what OpenAI has to say:
- GPT-4o: If you’re looking for a reliable all-rounder, this is your best bet. It’s perfect for tasks like summarising long texts, brainstorming emails, or generating content on the fly. With its multimodal features, it supports text, images, audio, and even advanced data analysis.
- GPT-4.5: If creativity is your priority, then GPT-4.5 is your go-to. This version shines with emotional intelligence and excels in communication-based tasks. Whether you’re crafting engaging narratives or brainstorming innovative ideas, GPT-4.5 brings a more human-like touch.
- o4-mini: For those in need of speed and precision, o4-mini is the way to go. It handles technical queries like STEM problems and programming tasks swiftly, making it a strong contender for quick problem-solving.
- o4-mini-high: If you’re dealing with intricate, detailed tasks like advanced coding or complex mathematical equations, o4-mini-high delivers the extra horsepower you need. It’s designed for accuracy and higher-level technical work.
- o3: When the task requires multi-step reasoning or strategic planning, o3 is the model you want. It’s designed for deep analysis, complex coding, and problem-solving across multiple stages.
Which one should you pick?
For $20/month with ChatGPT Plus, you’ll have access to all these models and can easily switch between them depending on your task.
But here’s the big question: Which model are you most likely to use? Could OpenAI’s new model options finally streamline your workflow, or will you still be bouncing between versions? Let me know your thoughts!
You may also like:
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- ChatGPT Plus and Copilot Pro – both powered by OpenAI – which is right for you?
- Or try the free ChatGPT models by tapping here.
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