Tools of the Future
Introduction
In this guide, we explore essential insights from our Cegos whitepaper, Getting to Grips with AI: Training for Ethical and Effective Use in Business. As AI continues to reshape industries, understanding its applications, especially in the context of Generative AI (Gen AI), has never been more critical. Gen AI refers to systems capable of generating new content — such as text, images, or code — based on data inputs, offering immense potential for business innovation. In this guide, we focus on our key findings from the whitepaper and take a closer look at the supporting tools that will shape our future with AI and help people work with AI ethically, effectively, and confidently.
AI hesitation: Is your organisation stuck in the ‘wait-and-see’ trap?
If you’re finding yourself hesitating when it comes to generative AI, you’re not alone. A staggering 90% of managers are either cautious or only dipping their toes in with small-scale experiments. But staying in ‘wait-andsee’ mode could mean falling behind. To move from curiosity to real impact, organisations need structured training and a clear plan for AI adoption. To help close this gap, our team has developed an AI maturity model for organisations to help navigate the landscape.
A January 2024, Boston Consulting Group survey found that 90% of managers are either taking a cautious stance or only experimenting with AI on a limited scale.
Discussion Question: Where does your organisation fall on the AI maturity scale, from Explorer to Transformer?
Scan to try out our AI maturity test.
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AI
Onboarding: Could this be the missing link between fear and fluency?
The first step in making AI work for your organisation isn’t just handing employees a new tool, it’s shifting their mindset. Many people start in a state of ‘unconscious incompetence’: not knowing what they don’t know. But before they can truly get the most out of AI, they need to move to the next stage: ‘conscious incompetence.’ This is where they begin to realise what they don’t know and how much there is to learn. Without this shift in awareness, you risk a workplace divide with some employees racing ahead while others resist, creating friction instead of progress. Strong AI onboarding should focus on accessibility, clear language, and risk management to create that shift.
The 4 Stages of Learning
Unconscious Incompetent
I don’t know that I don’t know
Conscious Incompetent
I know that I
don’t know
Conscious Competent
I know that I know
Unconscious Competent
I forget that I know
To acknowledge a lack of knowledge is a prerequisite for acquiring new GenAI and integrating them into business processes.
Discussion Question: What steps can your organisation take to ensure that all employees (not just early adopters) are equipped with the knowledge to navigate the risks and opportunities of generative AI?
Workflows first. Tech second.
Is your AI in the right order?
AI isn’t magic; it’s a tool. And like any tool, its impact depends on how well it’s embedded into business processes — not just used in isolation, but integrated — where it can make a real difference. This requires more than just enthusiasm. It demands cross-functional alignment, training, and clear accountability. It means asking: “Where in your workflow can AI save time, boost accuracy, or spark new ideas?” The organisations seeing the biggest benefits from GenAI aren’t necessarily the most advanced tech-wise — they’re the ones that embed AI with purpose.
Companies that take a strategic, processoriented approach to AI integration report up to 30% higher operational efficiency and 20% greater employee satisfaction.
Discussion Question: What core workflows in your organisation could benefit most from intentional AI integration — and what would it take to embed effectively?
The hidden cost of bad prompts: Is your AI wasting time and energy?
Good AI output starts with good input. If your team isn’t crafting effective prompts, they’re not getting the best from GenAI and they’re likely wasting time in the process. Think of it like giving directions: the clearer you are, the faster you get where you need to go. Better prompts not only improve productivity but also reduce the environmental impact of excessive AI queries.
According to the 2024 digital ecodesign barometer, a complex prompt is less costly for the environment than five successive prompts on generative artificial intelligence interfaces.
Discussion Question: What steps can your organisation take to improve prompt writing across teams to maximise both productivity and sustainability when using GenAI?
GenAI is only as smart as its users. Are you thinking critically?
AI might be intelligent, but it’s not infallible. It can generate biased, misleading, or just plain wrong results if users don’t approach it with a critical eye. The best AI users don’t just accept outputs; they challenge, refine, and verify them. Critical thinking allows individuals to identify these discrepancies, assess alternative solutions, and make informed decisions that align with ethical and professional standards. By combining analytical thinking with emotional intelligence, professionals can ensure that their use of GenAI is both thoughtful and responsible, especially when dealing with complex or ambiguous situations.
Companies that invest in critical thinking training see 35% fewer errors in decision-making when using AI.
Discussion Question: How can your organisation integrate critical thinking and emotional intelligence training to support employees in making more informed decisions with GenAI?
AI bias won’t fix itself. So, are your teams trained to spot it?
AI reflects the data it’s trained on, which means it can also reflect and reinforce biases. Biases like fixation bias or automation bias can lead to flawed decision-making and missed opportunities. By fostering awareness of these biases, organisations can help employees recognise and mitigate their influence. This is particularly important when using GenAI, as these tools can sometimes reinforce existing biases in data or human judgment. The good news? Bias awareness and training can significantly improve the quality of AI-driven insights.
Teams trained to recognise and overcome biases in AI decision-making see a 40% improvement in accuracy and a 30% reduction in bias-driven errors.
Discussion Question: What steps can your organisation take to actively address and minimise biases in the use of GenAI across different departments?
Emotional intelligence is still on the human edge. Can AI truly understand emotions?
AI can analyse patterns and detect sentiment, but true emotional intelligence? That’s still uniquely human. While AI can analyse emotions based on data patterns, it cannot replicate the depth of emotional understanding that humans possess. Emotional intelligence includes recognising, understanding, and managing one’s own emotions while also being sensitive to the emotions of others. In organisational settings, emotional intelligence can help leaders and teams navigate complex interpersonal dynamics and build supportive relationships. Without it, AI risks influencing decisions that miss the mark on empathy and human connection.
A study by the World Economic Forum found that 85% of workplace success is attributed to emotional intelligence rather than technical skills.
Discussion Question: How can emotional intelligence training be incorporated into AI implementation strategies to enhance collaboration and decision-making within your team?
The green AI challenge. Can AI innovation and sustainability coexist?
AI is a double-edged sword when it comes to sustainability. On the one hand, it’s helping optimise energy use, predict climate patterns, and reduce waste. On the other, training AI models consumes vast amounts of energy. This presents a challenge when trying to balance technological innovation with ecological responsibility. Eco-design, frugality, and digital sobriety are critical in ensuring that AI development aligns with sustainability goals. The future of AI isn’t just about what it can do; It’s about how responsibly we use it.
The global energy consumption of AI systems, particularly in training large models, is projected to grow by 30% annually, underscoring the need for energy-efficient AI technologies.
Discussion Question: What sustainability practices do you have integrated into your AI development processes to minimise its ecological impact while still leveraging its benefits for progress and innovation?
What’s holding your company back from taking the next step?
To dive deeper into the topic and explore comprehensive recommendations on how to navigate AI and its ethical applications, scan the QR code to download the full whitepaper Getting to Grips with AI: Training for Ethical and Effective Use in Business here.
