The 10 AI Skills You Need to Thrive in Today’s Job Market

The era of artificial intelligence (AI) has arrived in the workplace. Our research shows 84% of CIOs think AI will be as important to their business as the Internet.

But challenges remain, with 95% of leaders saying integration is a hurdle for effective AI implementation on several fronts. While infrastructure and cybersecurity top the list of perceived barriers, organisations say finding people who have the necessary skills to implement AI is their third-biggest problem.

The result is a growing need for AI skills that help companies make the most of their investment in technology for the future. In this piece, we'll explore the top 10 technical and nontechnical skills employers are looking for in 2025 and what you can do to develop them.

AI skills help anyone understand, build, apply and interact with AI tools. From engineers and developers to product managers and operations leads, people need both knowledge and practical experience to take full advantage of digital labour. For instance, you may need to be able to grasp how agentic AI is used to power autonomous systems, but you'll also need to be hands-on with AI agents like Agentforce so you know the possibilities and bounds of what they can do.

The first generation of AI skills focused on how to build and iterate within intelligent frameworks. AI solutions gradually became easier for people to adopt and explore in everyday life and business, until all at once, they're now integral across the digital workforce. There's a growing need for AI fluency not just in specialist departments, like data science , but in general business areas — marketing, sales, commerce, customer service and more.

Developing AI skills today involves digging deeper into understanding the way AI tools "think," such as considering what data they're using to reach conclusions and how they make certain decisions. For some roles, this means knowing how to improve the groundwork of AI, but for others, it's a matter of learning how to refine and evolve the tools that already exist.

For this reason, AI skills can be split into two broad categories: technical skills and non-technical AI literacy.

• Technical AI skills are those required to build and maintain AI systems. These may include creating algorithms, creating and running testing environments and implementing tools across business networks. Some of these skills require knowledge of programming languages such as Python or Java.
• Non-technical AI literacy is what you need to work effectively with AI. The focus here is mastering how to interact with AI. Today, this is important for almost every type of role and will become even more essential in the future. One example of non-technical AI literacy is prompt engineering: You aren't building the code that runs complicated AI architecture, but you are stretching AI's capabilities by crafting the right questions and phrases to get more accurate answers.

You don't need to know everything about AI to start honing valuable skills, but certain areas are fundamental, such as knowing how AI "learns" and how to use it ethically. The rapid growth of AI use from AI chatbots to digital workers across businesses and industries means AI-related jobs will demand both technical and nontechnical AI skills.

These are the top 10 AI skills to focus on in 2025:

There are many AI-related roles that require technical skills to get the job done. Here are 5:

Machine learning is the creation and testing of algorithms that underpin AI tools. Key ML skills include creating learning models and mastery of multiple training formats such as supervised learning, unsupervised learning and reinforcement learning.

This skillset is all about what sources your AI tools are using, what decision-making models they're applying, what outcomes they report and how accurate the results are. It's a crucial area for the future of business — our research finds 92% of analytics and IT leaders feel the need for trustworthy data is higher than ever. Data experts play a key role in developing accurate and reliable AI models that can improve AI transparency.

Building AI tools requires a mastery of programming languages. While Java and Python remain the most popular, some tools are coded in C++, Julia or Scala. AI programming skills also include the ability to use and integrate open-source natural language processing (NLP) and large language models (LLMs) in addition to using these models as a jumping-off point to build proprietary tools.

Capturing statistics and running mathematical models are both critical components of effective AI. While AI outputs are the end goal, companies need to know how often tools arrive at the right conclusions and what factors led to these conclusions.

By design, no two AI models are identical. Their purposes, programming and operational pathways will always be different. This creates both opportunities and challenges. Given the wide-open playing field, companies can create AI tools for almost any purpose — if the model is up to the task. AI modelling skills focus on building, testing and revising AI models to meet business needs.

While some AI jobs require know-how related to the technical aspects of the technology, there are many other AI skills that nontechnical workers should know in order to make the most of their tools. Here are 5:

Anyone can use AI tools, but it takes time and practice to know how to maximise and refine the results. Getting familiar with multiple tools can make you a versatile asset as organisations increasingly integrate AI into their operations. At this point, almost any tool used in business has an AI component. Some of these include research, analytics, customer service, writing, design and more.

AI excels at content but often struggles with context. Clear, concise and unambiguous prompts help get the best and most accurate answers from AI. Prompt engineering is the practice of refining AI queries to get better answers more quickly.

As AI standards and regulations evolve, AI ethics and responsible use are at the forefront. Companies need experts to help them limit the risk of bias, ensure data sources are accurate and prioritise data privacy.

AI remains a fundamentally human experience. Tools are modelled on human decision-making, meaning human insight is essential to help understand AI behaviour. This creates the need for critical thinking skills — staff who can examine AI practices and piece together why it makes certain choices.

AI tools can help break down data silos and automate key tasks. For example, AI agents benefit from robotic process automation (RPA) that enables the collection and analysis of data from both cloud-based and legacy data sources. Staff with the skills to connect the dots on AI functions are now in demand.

Skills that can help businesses create AI strategies are in high demand. But how do you get started on your AI skills journey? Depending on your learning style and skills preference, several paths are available.

• Taking courses and certifications: These approaches let you tackle new skills at your own pace and pick the learning platform you prefer. With Salesforce, you can explore the free Trailhead learning platform, which has modules on AI agents, analytics, development, ethics and more so you can gain the AI skills you need to succeed in evolving business environments. Or if you're looking to become a certified expert in implementing Agentforce for your organisation, you can become an Agentblazer specialist.
• Hands-on practice: Going hands-on is a great way to learn more about the various applications of AI. This could be as simple as exploring popular online AI tools and apps to better understand generative AI or experimenting with how AI automation can improve your current workflow. This approach can give you a feel for where AI tools excel and struggle and how everyday user inputs affect outputs.
• Upskilling at work: With many businesses adopting AI at scale, you may be able to learn on the job. It can be more cost-effective for an organisation to train existing staff on AI skills that are relevant to their role rather than onboarding an entirely new hire for a specialised position. It's a win-win: You get AI experience that's valuable for your career development and the company has a low-stakes way to start implementing AI in day-to-day work.
• Join AI communities: Salesforce’s Trailblazer Community is a place for anyone — from seasoned pros to those who are just getting started — to meet and engage around AI online and at in-person meetups. Other popular online groups include the OpenAI Community , which focuses on GPT models and AI integrations and Hugging Face , which offers access to thousands of NLP models. Others include Global AI , Data Science Beginners and Machine Hack , as well as a host of AI-focused discussion boards you can drop in on.
• Stay informed: As digital labour continues to grow, it's important to stay up to date with AI developments. Following news stories, policy changes and industry announcements helps keep your AI skillset aligned with ‌real-world demand.

The growing adoption of AI will continue to drive demand for technical and practical AI skills across sectors. From building better models to creating more effective prompts to ensuring ethical use, there's no shortage of opportunities to shape how AI is developed and applied in your industry. AI learning is also more accessible than ever, thanks to the development of low- to no-cost online resources and practical tools that anyone can try.

Now is the time to learn and improve your AI skills . It’s easy and free to get started with Trailhead . After all, people with the technical and practical knowledge to build, manage and enhance AI tools will be well-positioned to lead the next wave of innovation.