Five essential skills for building AI-ready teams
AI is developing at a rapid pace and transforming the way global industries operate. As companiesaccelerate AI adoption in order to stay competitive and reap its potential benefits, the urgency forbuilding AI-ready capabilities in the organisation is increasing.
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The true value of AI-based solutions depends on the teams who understand, challenge, embrace and
integrate it wisely. In my new book, Artificial Intelligence For Business, I highlight the impact of AI on
the future of work, specifically the skills gaps and job displacements, as well as future essential skills
required in global organisations.
For business leaders, building AI-ready teams means more than just hiring technical experts or data
scientists. Success in the AI business landscape means upskilling the workforce to develop five
essential capabilities that will enable people to thrive.
AI literacy and understanding
Knowledge and understanding of AI can seem overwhelming, particularly to those in non-technical
roles who may struggle with the constant flood of information about large language models, Python
codes and AI platform functionalities. While not everyone needs to have a deep technical
understanding for how to develop AI-based solutions, they should understand what AI can do and
where its limitations lie.
AI literacy goes beyond a mere understanding of AI technologies. It involves building foundational
understanding of its context and value, as well as the ability to question its design and implementation. AI literacy should be developed across all teams in an organisation, including how AI works, the different types of AI solutions, how data is used, where bias can creep in, and what real world applications look like in the relevant industry. Building AI literacy begins with organisational education and training programs that offer executive- level understanding of AI capabilities, limitations and risks, as well as industry-specific applications.
Additionally, hands-on experience and real-world applications are critical in developing an understanding AI in a business context. The aim is to raise the level of understanding to ensure every AI-related business decision is made with awareness and purpose.
Critical thinking and data scepticism
As we increasingly apply AI-based technologies in our daily business, the outcomes can be quite compelling. The potential productivity gains and scale of benefit are driving organisations to implement AI-based solutions across various business functions. The outputs of AI tools may appear clean and professional, but may not always be rooted in accuracy or truth. In addition, there may be hidden biases that could be detrimental, particularly if the outputs are used in critical decision- making processes. AI-ready teams need to develop critical thinking skills – the ability to analyse AI outputs, identify anomalies or biases, and make well-informed decisions relating to its use. As organisations increasingly use AI-based systems, there is a risk of over-reliance and trust on its output, without truly understanding how the outcomes are derived.
This is where critical thinking becomes indispensable. Building internal capabilities in 'data scepticism', or the ability to challenge assumptions, examine how models are trained, and identify potential errors, anomalies or biases in the output, is critical for organisations. Although a certain level of technical competency may be required to deep dive into the AI-system capabilities, a basic level of confidence to raise concerns and questions across all teams interacting with AI solutions and outputs will be essential for organisations. Deep technical training is not required for this. More importantly, leadership teams should prioritise building an organisational culture where employees are encouraged to question and analyse AI- generated insights. For example, establishing scenario-based exercises, diverse team discussions and formalised feedback loops will help sharpen critical thinking skills across the organisation.
Human-machine collaboration
As the capabilities of AI-based technologies rapidly advance, the question of whether to replace human resources with AI is becoming increasingly dominant in the global business landscape. In recent months, we have seen several global organisations make headlines as the decision to replace laid-off workers with AI and automation takes centre stage. This includes brands such as Klarna, UPS, Duolingo, Google and Salesforce, among many others.
In my experience, the integration of new technologies does not automatically mean replacing people. As we have observed over decades of industrial revolution, technology enables shifts in working environments, taking over tasks and pushing human resources to more complex or different types of work. Albeit AI development is significantly more rapid and its capabilities enable more sophisticated tasks, the cycle of shifting work remains the same. In the AI age, this means creating new kinds of teams where humans and intelligent systems collaborate effectively to deliver cohesive and sophisticated work at an accelerated pace.
To support this, companies should focus on role redesign, process mapping, and experimentation
with AI tools in real workflows. Encourage cross-functional collaboration - between business, tech, and data teams - to break down silos and co-create solutions. The key is to help people see AI as an assistant, not a threat.
Ethical reasoning and responsible innovation
With the rise of AI application in business comes a surge of ethical concerns and risks, including bias, data privacy and over-reliance of AI for critical decision making. To leverage AI-based technologies effectively, organisations cannot afford to overlook these concerns, particularly considering the developing regulatory scrutiny and fragility of consumer trust. Every team should receive education and training on the ethical concerns and challenges of AI application in business, including the ability to recognise biases in data and outputs, understanding explainability requirements, and making inclusive decisions. Responsible use of AI should be a foundational part of the organisational culture.
Realistically, this goes beyond formal training programs to enable successful adoption in organisations. Transparent communication, open dialogue, best practices and use cases are needed to explore potential unintended consequences and ensure responsible use is top of mind for all teams. Ethical reasoning should not be designed to slow innovation, but ensure that it is able to flourish within the space of safe and responsible use for the business.
Adaptive learning and growth mindset
One of the most foundational skills for an AI-ready team is adaptability. Exponential technologies, particularly AI, are developing rapidly and constantly changing. The most valuable skill in an AI-ready organisation is not the knowing everything, but being curious, open to change and continuously willing to learn. Embedding this growth mindset in how teams work and collaborate gives employees permission to explore new capabilities, learn quickly from failure, and experiment with new tools and solutions within a safe environment. In the current AI age, organisations need to prioritise investments in microlearning platforms that are able to encourage continuous rapid learning, knowledge sharing and reward curiosity.
Critically, leadership teams should model this mindset, demonstrating the willingness to evolve and rethink traditional assumptions and limitations. Adaptability will ensure the organisation does not just survive the era of AI transformation, but thrives in it. AI-readiness goes beyond training programs, certifications and tools proficiency. It is truly a team- wide capability that requires sustainable investment in people. The future of work is not only impacted by the rapid development of AI, but how intelligently organisations are able to prepare the workforce to embrace it responsibly.
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