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The best 2025 career combo? Deep AI knowledge and soft skills

AI skills are becoming more common, but deep knowledge about adopting AI at scale, and having the interpersonal skills to do so, are still rare and valued.

Feb 21, 2025 • 7 Minute Read

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  • Upskilling
  • AI & Data

Right now, most organizations are trying to get their employees AI literate. Why? It’s simple math. Four in five AI projects fail, mainly because of a lack of AI skills or desire among employees. Naturally, upskilling your staff is a way to fix this problem. Many people can now proudly add “skilled in AI” to their resume, and sleep a little easier.

But how skilled are they, really? And are they skilled enough

It’s a surprisingly important question. As a professional, you need to make sure you’re not learning too much or too little, since your time is already a precious commodity. As a leader, you don’t want to kick off an AI project only to realise you don’t have the right people with the right skills to make it happen.

Only a small number of IT professionals have “significant experience” working with AI

If you asked an everyday IT professional if they had AI skills, they’d likely answer in the affirmative. After all, they’ve likely used ChatGPT and GitHub Copilot, and might have been following the latest tech news. However, foundational knowledge is different from deep knowledge, and the latter is crucial for key players in AI adoption.

Only 12% of IT professionals have significant experience working with AI, according to Pluralsight’s latest Tech Forecast report. At first, that might seem like a huge bucket (“12%! That’s got to be all the AI professionals, right?”) But remember this includes everyone who has significant experience with any kind of AI: using chatbots, AI APIs and SDKs, AI cloud services, AI powered threat detection, and more. Suddenly, that bucket is looking a lot smaller. Meanwhile, 95% of executives believe AI initiatives will fail without staff like these.

Again, it's simple math. If you're a tech professional, low supply and high demand equals opportunity.

Deeper AI skills are needed in every branch of the tech sector

AI adoption is a team effort. It’s not just something that an AI professional needs to know about. There are stakeholders in cybersecurity, data, cloud and on-prem infrastructure, software engineering, legal, and at the executive level. There’s literally no area a project like this doesn’t touch. On top of that, 75% of organizations have plans to deploy AI, while 20% have already deployed it, according to Pluralsight’s 2025 Tech Forecast

These two factors combine to create a scenario where it’s valuable to know more about AI regardless of your role and organization.

Significant AI experience also means knowing the pitfalls to avoid

One of the single biggest and most common mistakes people make when implementing AI is to think of the technology rather than the business problem they’re trying to solve. I was speaking to a consultancy, and they shared a case study of a client who wanted to implement an AI chatbot, even though they had no notable customer service function. “We need AI!” was the justification.

AI adoption is littered with issues like this: pitfalls that people who have deeper AI knowledge know to avoid. Businesses desperately want someone to guide and inform this journey, and so people who have this skills tend to stand out professionally.

AI is where the IT budget is going right now

Organizations seeking to adopt AI (Spoiler: 75% of them) are planning to allocate an additional 17% of budget to AI in the next twelve months. I’ve written about this trend at a wider scale, particularly how $1 trillion in capex is being invested in AI, and how it’s the most popular skill among tech learners right now. 

All of that money equals career opportunities—after all, someone’s got to be getting it, since it just doesn’t evaporate. The most likely recipients are those critical to delivering on AI adoption projects.

To quote All the President’s Men, “Follow the money.”

Soft skills are the other half of the AI equation

Just because you know everything there is to know about AI, it doesn’t mean you’ll be effective at championing its adoption. That comes down to your soft skills—interprersonal skills like communication, holistic thinking, empathy, critical thinking, leadership, and teamwork. 

As I mentioned earlier, AI adoption is a team effort, requiring you to work with stakeholders across the business. All the business areas will also need to be on board, and you’ll need to sell them on the vision. 

Where possible, there’s an AI Center of Excellence (CoE) establishing frameworks, policies, and processes that guide AI initiatives, which means a lot of communication and cooperation. Soft skills are positively essential.

I’ve talked about how in the tech sector, your soft skills are typically more important than your hard skills, like programming or platform knowledge. Dial this up to eleven for AI adoption projects. Soft skills are also relevant for job security in a world where companies are using AI to abstract away technical tasks. 

How much AI knowledge do I need in my role to succeed?

Also see “How long is a piece of string?” Every role is different, and so the level of AI skills you need is going to differ. However, I would recommend looking into your domain-specific applications of AI, rather than the superficial use of generative AI tools. Look into how your field would specifically implement AI, which may include things like:

  • All roles: Familiarity with AI ethics and bias, understanding of model evaluation metrics, AI trends and best practices, explaining AI concepts to stakeholders.

  • Software development: AI APIs and SDKs, AI workflows, model deployment in microservices, AI in frontend and UX, AI and DevOps.

  • Cloud: AI cloud services, containerization and orchestration for AI, MLOps basics, serverless AI, cost optimization for AI workloads.

  • Data: Data preparation for AI, ETL for AI pipelines, big data and AI, vector databases, DataOps for AI, AI augmented analytics.

  • Cybersecurity: AI powered threat detection, AI for behavioral analysis and fraud detection, Adversarial machine learning and model security, automated threat intelligence, the OWASP API and LLM Security top 10 vulnerabilities.

This list is hardly definitive, and has likely changed by the time you’ve reading this, so be sure to keep your eyes open for AI learning opportunities. 

Remember, learning is never a waste of time. Even if you don’t practically implement AI, improving your knowledge will allow you to easily speak to what’s possible (or not) with this technology, where you can apply it, discuss best practices and common pitfalls, and work with others to support their AI initiatives.

Deep experience and soft skills comes with learning, hands-on practice, and getting involved

“Well, significant experience working with AI will only come with time.” That’s only a quarter true. The rest of the equation is taking the time to learn about AI in the first place, carving out time to practically practice those skills, and then seeking opportunities to apply your new talents. 

Meanwhile, soft skills are not something you’re born with, contrary to popular belief. Again, this is something you can learn about (with courses), practice (in the workplace), and seek opportunities to apply (taking on roles and responsibilities where soft skills are key).

Conclusion: If you want a career edge this year, work on your deeper AI knowledge and soft skills.

I’d highly recommend reading my article on the top soft skills in IT to have in 2025. For AI, I’d jump onto a skills development platform like Pluralsight that has learning materials by experts, hands-on labs, sandboxes, and other learning tools, and just start searching for AI courses that relate to your field of interest. 

For a more research-based angle, check out Pluralsight’s 2025 Tech Forecast report. It’s got expert predictions and research from over 50,000 tech learners and leaders broken down into different tech sector areas, and may help you focus your skill development into currently trending areas for career success.

Adam Ipsen

Adam I.

Adam is a Lead Content Strategist at Pluralsight, with over 13 years of experience writing about technology. An award-winning game developer, Adam has also designed software for controlling airfield lighting at major airports. He has a keen interest in AI and cybersecurity, and is passionate about making technical content and subjects accessible to everyone. In his spare time, Adam enjoys writing science fiction that explores future tech advancements.

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