By Chris Onslow, Go Courses CEO Generative AI continues to be one of the most talked-about topics in the world. Hardly a day goes by without a new story about AI – from increasingly capable models to incidents in which AI agents have escaped controlled testing environments and accessed external systems. Scroll through LinkedIn and you’ll see AI-generated presentations, alongside posts debating the merits of using large language models (LLMs) to write content. And around dinner tables and boardrooms, you’ll hear a range of different conversations about AI – from fears that it will replace jobs to excitement about how it is making us more productive. As someone who spends much of my time helping organisations deliver AI learning, I’ve noticed the same queries coming up time and again from employees and the learning teams supporting them. In this article, I answer five questions I hear most often – from choosing the right AI platform and whether answers can be trusted to why Claude, CoPilot and the other AI assistants won’t eliminate the need for human judgement any time soon. 1. What can generative AI actually help me do? This is probably the question we’re asked most often. And the answer is usually, “much more than you think.” While hesitation around using AI at work has reduced significantly, many people still underestimate just how capable today’s generative AI tools have become. For most organisations today, generative AI means using tools such as Microsoft Copilot, Google Gemini, Claude and ChatGPT to write emails and summarise meeting notes. While these tasks are certainly proven use cases, they only scratch the surface of what generative AI can now do in the workplace. What makes generative AI different from previous incarnations of AI is its versatility. Unlike traditional AI systems that were designed to perform a specific task, generative AI can support a huge range of activities across almost every business function. So, whether you’re in HR, finance, software development, marketing or customer service, there are opportunities to use AI to reduce repetitive work and accelerate everyday tasks. For learning leaders, helping employees understand how AI applies to their day-to-day work is one of the fastest ways to accelerate adoption at scale. We consistently find one of the aspects of AI training that learners find most valuable is exploring how platforms can be used in the context of their own role. Through practical demonstrations and hands-on exercises, instructors show how generative AI can help create presentations, draft business plans, analyse spreadsheets, produce reports, generate code, summarise meetings, compare documents and even act as a brainstorming partner. When employees understand how AI fits into their own role, organisations are able to unlock far greater value from their AI investment. Our advice for learning leaders: Foundational training provides a useful benchmark for understanding AI capability across your organisation. Establishing a common baseline creates a clearer picture over time of how AI capability is developing across different teams and where further investment in learning is needed. 2. Which AI platform should I use? This is another question that comes up in almost every AI training session. Should we use Microsoft Copilot? Google Gemini? Claude? ChatGPT? In reality, each platform has its own strengths and the right choice for an organisation will depend on: How you intend to use AI Your existing technology estate Security requirements Integrations Governance policies For example, organisations already invested in Microsoft 365 often choose Copilot because it integrates with applications employees are already using – including Outlook, Teams, Excel and Word. Businesses using Google Workspace may find Gemini fits more naturally into their existing ways of working. Other organisations may choose Claude or ChatGPT for particular use cases or specialist capabilities. The good news is that the underlying skills are largely transferable. So, by investing in AI training you are helping employees build the confidence and judgement to use whichever tools become part of their workplace. Our advice for learning leaders: Choosing a platform doesn’t have to be a purely top-down decision. Giving a group of users hands-on exposure to two or three platforms during training can help organisations understand which tools work best for different roles and use cases. 3. Will AI replace my job? When organisations announce they’re introducing AI to automate tasks, not all employees will embrace the change enthusiastically. Many will. But for others, an unspoken fear lingers – “but wasn’t that my job?” As AI becomes more capable, it’s understandable that some employees will worry about what this means for their own role and future career. So, while they might not dare to ask this question directly, it’s often the elephant in the room when conversations turn to AI in the workplace. In our experience, this concern often reduces once people begin to understand how AI changes their role rather than simply focusing on the tasks it automates. Training plays an important role in this process. We’ve found that effective training doesn’t focus purely on what AI can do. Instead, it demonstrates what people can achieve with AI, helping employees see how they fit into a technology-enhanced future. By taking this human-centred approach, employees begin to see not only how AI can take over repetitive administrative tasks, but also how it could improve productivity, expanding what they’re capable of achieving day-to-day. Our advice for learning leaders: Scaling AI adoption depends as much on employee confidence as technical capability. Help people understand how their role evolves alongside AI, not just how the technology works. 4. Can I trust the answers? One of the biggest misconceptions about generative AI is that it’s either completely reliable or completely unreliable. The reality sits somewhere in the middle. AI is incredibly effective at accelerating many routine tasks and there are certain tasks that generative AI now performs exceptionally well – summarising lengthy reports or generating a first version of a presentation. But it can also make mistakes, sometimes delivering inaccurate information with convincing confidence. In these situations, human intervention and oversight remain essential. For example, if you’re drafting a legal contract or preparing financial forecasts, AI can provide a helpful starting point, but the final responsibility for its accuracy remains with you. Ultimately, an essential part of AI enablement is giving employees the confidence and judgement to know when AI is the right tool and when its outputs need human validation. To support this, organisations should establish clear guardrails around how tools are introduced and used. For example, rather than giving everyone unrestricted access from day one, organisations can phase in AI adoption. This might mean starting with defined use cases or user groups and expanding AI adoption as employees build confidence and demonstrate capability. Our advice for learning leaders: Foundational training can provide a useful baseline for a phased AI roll-out, helping organisations understand where AI capability sits across different teams and identifying where and when employees are ready for more advanced applications. 5. How do I keep up when AI changes so quickly? AI platforms will continue to evolve. New models will emerge and existing platforms will gain new capabilities. Keeping pace with AI requires employees to build both a strong understanding of core AI principles and the ability to keep developing those skills as technology evolves. This requires: Solid foundational knowledge – for example, how to structure a prompt, provide context, evaluate outputs and collaborate with the technology. Ongoing, role-dependent learning – to understand how new capabilities can be applied to their specific responsibilities. In many ways, this is nothing new. Take the Microsoft 365 suite as an example. Learning Word makes it much easier to learn PowerPoint or Excel because the underlying principles are familiar. Similarly, once you understand the core principles of working with AI, expanding use cases and moving between platforms becomes much easier. That’s why effective AI foundational learning focuses on building transferable capability rather than expertise in a single platform. Because once employees understand how to collaborate with AI, they’re well equipped to take advantage of whatever comes next – whether that’s autonomous AI agents, multimodal tools or capabilities that haven’t yet even been conceived. Our advice for learning leaders: Treat AI learning as an ongoing capability. The organisations that realise the greatest long-term value build adaptable skills that evolve alongside the technology. From questions to capability Although these five questions each have a distinct focus, they all point towards the same conclusion – successfully scaling AI adoption across a workforce depends as much on building capability as it does technology. For all organisations, the challenge isn’t simply choosing the right AI platform. It’s delivering consistent learning experiences across multiple roles, regions and languages, all while the technology continues to evolve at pace. At Go Courses, we help organisations build that capability by making it easier to access and deliver high-quality AI training from leading technology providers, including Google, AWS, VMware and Broadcom. Through role-based learning pathways and our global instructor network, we help organisations equip employees with the knowledge, confidence and judgement needed to integrate AI into everyday work. Talk to us about scaling AI training
