86% of companies are already adopting AI tools or planning to do so, and the business benefits are huge. They’re reporting better work quality and scalability, more streamlined workflows and improvements to the customer experience.
Yet only 49% of professionals feel confident in their ability to use AI (Pluralsight AI Skills Report). There’s a major skills gap emerging, and companies who want to stay competitive must proactively address it.
In 2026, it’s no longer a question of whether or not you need AI skills; it’s a question of which skills to prioritise and how to develop them effectively within your organisation.
So what are the most business-critical AI skills in 2026 and beyond?
Let’s take a look.
The most important AI skills for all teams in 2026
1. Practical AI literacy
Practical AI literacy means understanding what AI tools can and can’t do, how they work at a high level and where they can add genuine business value. It’s not about advanced technical skills, but rather, giving teams the confidence to use AI effectively and responsibly in their day-to-day work.
Why this skill matters:
Let’s say your marketing team is using AI to support campaign planning or content creation. With a solid grounding in how AI works, they can decide how to use it strategically (to speed up the research phase, for example, or generate first drafts) and when to prioritise human creativity and decision-making.
2. Prompt engineering
Just as communication is a critical skill for collaborating with your colleagues, it’s also essential for effective collaboration with AI.
To get relevant and usable results from AI tools, you need to clearly communicate the output that you want. In other words: you’ve got to master the art of prompt engineering.
This includes providing relevant context and background information, describing the format or type of output you want, and setting constraints around tone, length, or what to include and avoid.
Why this skill matters:
In practice, strong prompt engineering saves time and improves quality. Teams who know how to prompt well spend less time correcting or reworking AI outputs and more time applying them to real tasks. That’s when AI becomes truly useful and scalable across the business.
3. Ethics and compliance
As AI becomes part of everyday workflows, teams need a clear understanding of how to use it responsibly and in line with legal and regulatory requirements. Ethics and compliance skills focus on issues like data privacy, bias, transparency and accountability, helping employees understand not just what AI can do, but what it should be used for.
Why this skill matters:
Let’s say your HR team is using AI to support candidate recruitment or employee performance reviews. Without clear guardrails, there’s a risk of introducing bias, mishandling sensitive data or relying too heavily on automated outputs. Teams trained in ethical and compliant AI use can apply AI safely, protect the business from risk, and build trust with employees and customers.
This allows you to scale AI with confidence, knowing its use aligns with company values and regulatory expectations.
4. AI data fundamentals
In everyday business use, teams often provide AI tools with data from internal systems, documents, spreadsheets or customer records. But if that data is messy, incomplete, outdated, or biased, the output will also be flawed.
Effective AI use requires an understanding of AI data fundamentals, which means training teams to think critically about the data they’re feeding into AI tools. Where is the data coming from? Is it complete and up to date? Does it show the full picture or only part of it? Are there gaps or biases that could skew the result?
Why this skill matters:
This skill helps teams avoid blind trust in AI outputs. Instead, they can interpret insights in context and make more informed, responsible decisions.
5. Strategic AI adoption and integration
Many organisations start by using AI in an ad-hoc way, with teams experimenting independently and without a clear plan. Strategic AI adoption is about taking a more cohesive and coordinated approach.
This means equipping teams to recognise where AI can genuinely add value, how it fits into existing processes and when human oversight is still needed. It also involves agreeing shared ways of working with AI, so tools are used consistently rather than in isolation across the business.
Why this skill matters:
When teams are trained in strategic AI adoption, organisations can scale AI use more effectively. Instead of fragmented experimentation, AI becomes a practical capability that supports efficiency, collaboration and long-term business value.
Get your team up to speed with the UX Design Institute
AI is now an inevitable part of everyday business, but you can’t leave AI adoption to chance. If you want to remain productive and competitive, you must train your people to use AI confidently, responsibly and with clear purpose.
Structured team training is the most effective way to build that confidence and get teams aligned. The UX Design Institute’s Training for Teams focuses on developing the skills that matter most, from practical AI literacy and prompt engineering to ethical use, data fundamentals and strategic integration. This ensures teams establish a common understanding of how AI should be used across the organisation.
Training is designed around real workplace scenarios, enabling teams to apply new skills directly to their roles and workflows. And, with flexible delivery options, you can upskill your employees without disrupting their day-to-day work.
Don’t fall into the AI skills gap. Invest in structured AI training early on to prepare your team, and your business, for all the challenges and opportunities that AI presents.
Learn more about our team training or get in touch to discuss your needs.
For more insights and perspectives that will benefit your team, take a look at the other articles featured below.
- Benefits of online UX design training for teams
- The top UX design trends in 2026 (and how to leverage them)
- AI in user research: unlocking new opportunities
Emily is a professional writer and content strategist with an MSc in Psychology. She has 8+ years of experience in the tech industry, with a focus on UX and design thinking. A regular contributor to top design publications, she also authored a chapter in The UX Careers Handbook. Emily also holds a BA in French and German and is passionate about languages and continuous learning.
