Which AI Skills Does Your Business Need?

Artificial intelligence (AI) is no longer a futuristic concept; it’s an essential tool for businesses striving to remain competitive in a rapidly evolving world. But as organisations integrate AI into their operations, a critical challenge emerges: determining which skills are necessary to fully harness its potential. From technical expertise to ethical considerations, the answer lies in a balanced approach to skill development.

Recent research highlights the urgency of the matter. O’Reilly’s 2024 survey revealed that 79% of UK employers have witnessed employees requesting upskilling opportunities in AI, machine learning, and data analysis. This trend reflects a growing recognition that AI is transforming every corner of the workplace, from IT departments to marketing teams.

Chris Chen, CTO at GoodHabitz, emphasises this shift: “The surge in workplace AI use is fuelling demand for technical skills like deep learning, data analysis, and programming. But the real need is for the ability to integrate AI effectively into business operations.”

Balancing Technical and Soft Skills

The technical competencies required for AI adoption are extensive. Key skills include machine learning, data engineering, and proficiency in programming languages like Python and SQL. These capabilities allow businesses to build and deploy AI systems tailored to specific needs. Additionally, cloud computing expertise is vital for scaling AI solutions effectively.

However, focusing solely on technical skills overlooks the human side of AI. Soft skills such as problem-solving, creativity, and collaboration are equally important in ensuring AI tools are used effectively. According to GoodHabitz, 48% of employees believe AI adoption has increased the demand for these skills, with problem-solving and critical thinking taking precedence.

“Soft skills, such as creativity and collaboration, enable teams to articulate challenges, design solutions, and interpret AI insights,” says Chen. This balance of technical and human capabilities ensures that AI enhances, rather than replaces, human roles.

Lewis North, CTO at WorkBuzz, adds: “Once you’ve established the right framework and guardrails, let your teams explore freely—that’s when the AI magic happens.” This creative freedom encourages innovation and allows employees to discover new ways to integrate AI into their workflows.

Ethics and Governance: A Non-Negotiable Priority

As businesses adopt AI, they must address pressing ethical and governance challenges. Issues such as data privacy, bias, and job displacement are top concerns for employees and regulators alike. Vera Matser, Head of Skills at the Alan Turing Institute, stresses that “ethical considerations and governance can’t be an afterthought. Businesses with strong ethical frameworks not only build more trustworthy AI systems but also gain a competitive advantage.”

A framework like the AI Skills for Business Competency Framework helps organisations evaluate their readiness across dimensions such as privacy stewardship and problem-solving. By embedding ethics into AI strategies, businesses not only mitigate risks but also foster trust among stakeholders.

This approach is especially crucial as employees grapple with the implications of AI. Transparent communication and comprehensive training can alleviate fears about job security while highlighting the efficiency gains and reduced repetitive tasks AI can bring.

Upskilling the Workforce for an AI Future

With the pace of AI development accelerating, upskilling the workforce is more critical than ever. O’Reilly’s research found that 81% of UK employers see reskilling as a more cost-effective solution than hiring new talent. This sentiment reflects a broader industry shift towards continuous learning.

“Businesses absolutely need to upskill their existing teams to make the most of AI,” says North. “Especially around data security, governance, and privacy.” By equipping employees with the skills to use AI responsibly, companies can ensure a smooth transition into an AI-driven workplace.

Strategies for effective upskilling include replacing one-off training sessions with ongoing education, creating internal communities of practice, and aligning training programs with specific business use cases. Ethical training is also paramount, as it ensures employees understand the broader implications of their work with AI.

The Role of External Expertise

Not all businesses have the resources to build in-house AI teams, particularly smaller organisations. For these companies, outsourcing certain tasks can provide a practical solution. Adam Cleaver of Collective Agency highlights the benefits of working with startups or consultants for specialised AI needs. “Smaller firms should consider contracting startups or consultants, especially for highly specialised tasks,” he advises.

However, relying entirely on external expertise comes with risks. As AI becomes central to business strategy, in-house knowledge will increasingly determine competitive advantage. Companies must balance short-term needs with long-term goals, ensuring they retain internal capabilities to adapt and innovate.

Crafting a Comprehensive AI Strategy

At the core of successful AI adoption is a well-rounded strategy. This means aligning AI integration with broader business objectives while fostering a culture that values technical and ethical considerations. Alexia Pedersen, SVP at O’Reilly, explains: “Successful integration of digital technologies requires more than just deploying cutting-edge tools. It requires executive support for and investment in a culture of continuous learning.”

Businesses can position themselves as leaders in the AI-driven economy by prioritising upskilling, addressing ethical challenges, and fostering creativity. Whether through in-house expertise or external partnerships, the focus should remain on leveraging AI to complement human capabilities rather than replace them.

Upskilling for an AI Future

With AI advancing at an unprecedented pace, companies face a stark choice: upskill their existing workforce or risk falling behind. Upskilling is not just cost-effective; it’s essential. O’Reilly’s research found that 81% of UK employers see reskilling as a smarter investment than hiring new talent.

“Businesses absolutely need to upskill their existing teams, especially around data security, governance, and privacy,” says North. “AI is only going in one direction, and quickly.”

How to Reskill for Success:

• Continuous Learning: Replace traditional training with ongoing education and knowledge sharing. Internal communities of practice and external partnerships can keep employees up to date.
• Real-World Applications: Prioritise practical training tailored to business goals. From improving efficiency to enhancing customer experience, employees need tools they can apply immediately.
• Ethical Training: Ensure employees understand the implications of AI on privacy and fairness. Bridging these gaps can prevent missteps and build trust.

The question of which AI skills your business needs isn’t just technology—it’s about vision. From mastering machine learning to fostering creativity, companies must embrace a broad approach to skill development.

Equipping employees with the right tools and training, prioritising ethics, and fostering an adaptive mindset will ensure businesses are not just participants in the AI revolution but pioneers of it.

Vitor Monteiro is the CTO at Unflow, a Software and AI innovation studio. He’s also a lecturer and invited teacher at the largest private university in Portugal, Universidade Lusófona.

How can a business assess its current AI capabilities and identify skill gaps?

“To effectively assess AI capabilities and spot skill gaps, businesses should start by examining their current infrastructure and tools. This means cataloguing all AI models, tools, and applications already in use, whether they’re in customer service, operations, or product development.

“Equally important is evaluating how these AI tools perform in practice—are they integrated smoothly into workflows and delivering results that match the business’s goals? Pay particular attention to data quality and accessibility, as the backbone of any AI initiative is a reliable stream of structured data. Without this, even the best AI models can fall short.

“Beyond infrastructure, assessing the team’s technical and domain skills is essential. AI projects require not only technical expertise in areas like machine learning and data science but also a strong grasp of the business’s unique challenges and needs. AI isn’t just about technology; it’s also about applying that technology within the right context.

“Therefore, evaluating how well team members understand the business’s core processes and goals is as critical as their technical knowledge. Successful AI initiatives typically rely on interdisciplinary collaboration, so it’s beneficial to gauge the team’s ability to work across departments, especially since AI projects often require input from departments like marketing, finance, and HR.

“Once these elements are evaluated, the next step is to align current capabilities with AI objectives. This gap analysis reveals whether you need to enhance technical expertise, boost domain knowledge, or improve cross-functional collaboration. Gaps can then be addressed through focused training, strategic hiring, or targeted outsourcing for specific AI functions.

“Building AI literacy across the organization also ensures non-technical roles understand and support AI initiatives. Finally, this assessment shouldn’t be a one-time process. AI and technology evolve rapidly, so set up periodic reviews to ensure that skills and tools remain aligned with both industry advancements and evolving business objectives. This ongoing approach builds a resilient AI capability within the business, ready to adapt to future opportunities.”

Should businesses focus on hiring AI specialists, or is it more effective to upskill their existing workforce?

“Deciding whether to hire AI specialists or upskill the existing workforce depends largely on a business’s immediate goals, available resources, and the complexity of its AI ambitions. Both approaches have distinct advantages.

“Hiring AI specialists can be a fast-track solution, especially if a business requires advanced technical expertise—like building custom AI models, developing machine learning algorithms, or implementing data-heavy projects. AI specialists bring a deep, often niche understanding of AI applications and can add immediate value by designing sophisticated solutions or optimizing existing ones. This approach is especially beneficial for businesses with clear, high-impact AI goals that demand precise, expert execution. However, it’s essential to consider the costs and challenges of attracting top AI talent in a competitive job market, particularly if the organization lacks an established AI culture.

“On the other hand, upskilling the existing workforce can foster a more sustainable AI culture. Training employees who already know the business’s unique challenges and industry nuances enables them to apply AI in ways directly relevant to organizational goals. Upskilling also empowers teams across departments, creating a shared AI literacy that encourages collaboration and buy-in from non-technical roles.

“While upskilling is generally a slower process, it builds a more adaptable workforce and can often be more cost-effective than hiring multiple specialists.

“In many cases, a hybrid approach proves most effective. Bringing in a few AI specialists to handle complex tasks while training existing employees on foundational AI principles creates a balanced, resilient team. The specialists can lead, mentor, and drive innovation, while upskilled team members bridge the gap between technical execution and practical, business-oriented applications.”

What are the core technical skills (e.g., machine learning, data analysis) are essential for implementing AI solutions?

“Implementing AI solutions goes beyond the standard skills of machine learning, data analysis, and programming. It requires a blend of foundational and often-overlooked competencies that enable AI to succeed in real-world settings.

One critical skill is systems thinking, which allows AI to be embedded effectively within complex business ecosystems.

“Data engineering and architecture are also essential since AI models rely on clean, structured data flows. Business process mapping ensures that AI aligns seamlessly with existing workflows, identifying precisely where it can add value without disrupting operations.

“Human-centred design and UX expertise make AI accessible and trustworthy, building user confidence and ease of interaction. Skills in ethics and bias management help prevent pitfalls and ensure fair, transparent AI decisions. Advanced mathematics and statistical intuition allow practitioners to go beyond libraries, giving them insight into the workings of algorithms to optimize and innovate when needed.

“Finally, strong communication and storytelling help translate complex AI concepts into terms stakeholders can engage with, securing their buy-in. An agile approach, using CI/CD processes, also ensures that AI models remain dynamic, allowing teams to iterate and adapt quickly as data and business needs evolve. Combining these skills creates a resilient AI capability that is not only technically sound but aligned with business and user needs.”

What role do soft skills, such as problem-solving and creativity, play in the practical application of AI within a business?

“In the effective application of AI within a business, soft skills like problem-solving and creativity don’t just enhance technical outcomes—they bridge the gap between AI systems and the people who ultimately drive and benefit from them. AI may rely on algorithms and data, but its success hinges on the insights and adaptability of the people behind the technology.

“Problem-solving skills enable teams to not only address technical challenges but also to tailor AI to real-world contexts, ensuring that models align with business goals and add genuine value. Creative thinkers on the team can envision unique applications of AI that resonate with users, often uncovering opportunities for AI to streamline workflows, elevate customer experiences, or generate new forms of insight. This creativity ensures that AI applications are not static or purely technical but dynamically responsive to evolving business needs and user expectations.

“Integrating people into this process also requires strong collaboration and communication. AI specialists need to work alongside department leaders, end-users, and stakeholders to design solutions that are practical and user-friendly. This partnership helps technical teams deeply understand the problems AI is solving and the human factors influencing its use. Additionally, those with strong communication skills can translate complex AI concepts into accessible language, fostering trust and buy-in across departments.

“Ultimately, the success of AI within a business is a people-centred effort. The technology may execute tasks, but it’s people—armed with problem-solving, creativity, and empathy—who shape AI’s direction, refine its applications, and integrate it into a business in a way that’s meaningful and impactful.”

How can non-technical employees contribute to a business’s AI strategy, and what AI-related skills should they develop?

“Non-technical employees play a vital role in shaping a business’s AI strategy, often providing the business insights, contextual knowledge, and user-centric perspectives that guide AI’s direction. They understand the daily workflows, customer needs, and operational challenges that AI aims to improve, helping technical teams prioritize the right use cases. Their input ensures that AI initiatives address real pain points and opportunities rather than abstract goals. This collaborative, grounded approach often leads to solutions that are not only technically sound but also highly practical and impactful.

“For non-technical employees to contribute effectively, they should develop a few key AI-related skills, starting with data literacy. Understanding basic data concepts, such as data quality, privacy, and how data flows through AI models, enables them to support AI projects with meaningful, reliable input. Familiarity with AI’s capabilities and limitations is also essential. Employees should know what AI can realistically achieve within their field, allowing them to set reasonable expectations and communicate them to others, ensuring that project goals stay grounded.

“A critical skill for non-technical team members is AI ethics and bias awareness. This knowledge allows them to recognize and mitigate biases in datasets, helping maintain fairness and accountability in AI-driven decisions. As they engage with AI outcomes, they can also provide feedback from a user’s perspective, flagging any biases or unintended effects early on.

“Finally, communication and cross-departmental collaboration skills enable non-technical employees to effectively liaise between AI teams and other departments. By learning how to articulate the business implications of AI insights, they can champion AI initiatives within the organization, securing buy-in and aligning teams around a shared strategy. In short, non-technical employees bring an essential, human-centred balance to AI strategy, guiding it in ways that resonate with the organization’s unique goals and values.”

How can businesses ensure that their AI talent remains up to date with the rapid advancements in AI technologies?

“To keep their AI talent current with the rapid pace of advancements in AI, businesses need a proactive approach that emphasizes continuous learning, cross-industry exposure, and hands-on experimentation.

“First, fostering a culture of continuous learning is essential. This can include providing access to online courses, certifications, and workshops from leading AI institutions and platforms. Companies that prioritize ongoing education—offering resources, time, and incentives for employees to engage in training—ensure that their teams stay informed on the latest tools, techniques, and frameworks.

“Encouraging AI teams to participate in industry events, conferences, and meetups also keeps them attuned to real-world applications and emerging trends. Exposure to peers and thought leaders in AI not only expands their knowledge but can also spark new ideas relevant to their projects. Another option is cross-industry collaboration or partnerships with universities and research organizations, allowing AI teams to work on joint projects and research initiatives. This involvement with broader AI communities and experimental work ensures teams remain flexible and open to innovations outside their immediate industry.

“Additionally, businesses should encourage hands-on experimentation within their AI teams. Providing the freedom and resources to pilot new ideas or test cutting-edge algorithms keeps skills sharp and fosters a mindset of innovation. Some companies find it effective to set aside “innovation time” specifically for employees to explore advancements in AI without the pressure of project timelines.

“Finally, regular internal knowledge-sharing sessions, where team members present their learnings or discuss recent projects, are invaluable for reinforcing AI skills across the organization. By building this ecosystem of learning, exposure, and experimentation, businesses ensure that their AI talent remains not only up-to-date but also strategically prepared for future advances.”