Business & Entrepreneurship

Introduction Artificial intelligence has moved from hype to budget line. Across industries, businesses are investing heavily in tools like ChatGPT, expecting faster workflows, reduced costs, and scalable output. On paper, the promise is compelling. In practice, the results have been far less consistent. Recent reporting from Axios highlights a growing disconnect: companies are increasing their AI spending, yet many still struggle to link that investment to tangible returns. This shift has been described as the “AI spending flip”, a moment where spending continues to rise, but confidence in outcomes begins to waver. At the centre of this problem is not the technology itself, but how it is being used. What Is the AI Spending Flip? The AI spending flip reflects a widening gap between investment and impact. Businesses have moved quickly to adopt AI tools, often across multiple departments at once, without fully understanding how those tools fit into existing workflows. The assumption has been straightforward: better tools will naturally lead to better results. But that assumption is proving flawed. AI does not operate effectively in isolation. It depends heavily on the quality of input, the clarity of instruction, and the judgement applied after output is generated. Without those elements, even the most advanced tools produce inconsistent or low-value results. What we are seeing now is a correction in mindset. Companies are beginning to realise that access to AI is not the advantage but effective usage is. Why AI Tools Alone Don’t Deliver Results The expectation that AI can be simply “plugged in” and left to perform is one of the biggest reasons companies are underwhelmed by their returns. In many organisations, employees are using AI without clear guidelines or training. Prompts are written differently from person to person, outputs vary in quality, and time is often spent correcting or rewriting what AI produces. Instead of accelerating work, it creates friction. At the same time, some businesses fall into the opposite trap, over-reliance. They allow AI-generated content or decisions to move forward without proper review. This introduces risk, whether in the form of inaccuracies, poor communication, or misaligned messaging. There is also the issue of under-utilisation. Most teams only scratch the surface of what AI tools are capable of. Without structured training, they default to basic use cases, leaving significant efficiency gains untapped. In all of these scenarios, the problem is not the tool. It is the absence of capability behind it. The Real Solution: Train Employees to Use AI Effectively The businesses seeing meaningful returns from AI are not necessarily the ones spending the most. They are the ones investing in their people. AI performs best when it is part of a system that combines machine efficiency with human judgement. It can generate ideas, draft content, and process information at speed, but it still requires direction, context, and refinement. This is where trained employees make the difference. As Nathan Baws, a public speaker, motivational speaker, business growth motivational speaker, inspirational speaker, keynote speaker and business strategist, explains: “AI is only as powerful as the person using it. The businesses that win won’t be the ones with the most tools, but the ones with the most capable teams behind them.” That distinction is critical. The competitive advantage is no longer tied to access to technology, but to the ability to use it effectively. How to Build AI Capability Inside Your Team Training employees to use AI does not require complex programs or long certifications. What it requires is structure and consistency. The first step is ownership. When AI adoption is left to individuals to figure out on their own, usage becomes fragmented. Assigning responsibility within each team creates clarity and direction, ensuring that knowledge is developed and shared rather than scattered. From there, training must be tied to real work. Teaching features or general functionality has limited value. Employees need to understand how AI applies directly to their daily responsibilities, whether that is communicating with clients, generating leads, or processing information more efficiently. Consistency also plays a major role. Short, focused training sessions delivered regularly are far more effective than one-off initiatives. Over time, these incremental improvements compound into genuine capability. Equally important is the creation of internal systems. When teams document what works, whether in the form of prompts, workflows, or templates, AI usage becomes repeatable. This reduces variability and increases both speed and quality. Finally, human oversight must remain central. AI should support decision-making, not replace it. The most effective workflow is one where AI generates the initial output, and a human refines it to ensure accuracy, relevance, and alignment with business objectives. Why This Approach Works AI tools are becoming increasingly accessible. What was once considered advanced is quickly becoming standard. As a result, the technology itself is no longer a differentiator. What separates businesses now is execution. Companies that prioritise training create teams that can extract more value from the same tools others are underutilising. They reduce errors, improve efficiency, and generate outputs that are both faster and more reliable. In contrast, businesses that continue to rely solely on acquiring tools will find themselves adding cost without improving performance. Conclusion The AI spending boom is not slowing down, but the way businesses approach it must change. The focus can no longer be on acquisition alone. It must shift toward capability. Training employees to use AI effectively transforms it from an expense into an asset. It ensures that tools are used with purpose, outputs are aligned with business goals, and investments translate into measurable results. Ultimately, AI does not replace people. It amplifies them. And the businesses that understand this will be the ones that turn the AI spending flip into a genuine competitive advantage. FAQs What is the AI spending flip? The AI spending flip refers to the gap between rising AI investment and weak business results. Companies are spending more on tools but struggling to see measurable returns. This highlights a shift from buying AI to needing to use it effectively. Why are companies

Introduction AI is no longer a future disruption, it is already actively reshaping how work is structured. Across industries, companies are using automation and machine learning to increase productivity, reduce costs, and streamline tasks that once required entire teams. While major financial research from institutions like Goldman Sachs and Morgan Stanley suggests that the overall unemployment impact is still relatively modest, the deeper shift is structural rather than statistical. Jobs are not simply disappearing, they are being redesigned. Routine tasks are being automated. Entry-level roles are shrinking. And traditional career pathways are becoming less predictable. This creates a critical question: if the job market is changing this quickly, why is education still preparing students for a static version of it? The answer is clear – it shouldn’t be. AI Is Reshaping Work, Not Just Removing It AI does not operate as a simple replacement mechanism. Instead, it redistributes work across three areas: First, it automates repetitive and structured tasks that previously required human labour. Second, it enhances existing roles by increasing productivity and reducing time spent on basic execution. Third, it creates entirely new categories of work tied to AI systems, infrastructure, and oversight. Research shows the net effect on employment is still relatively small in aggregate terms, but the internal composition of jobs is shifting rapidly. In particular, industries exposed to AI are already seeing changes in how entry-level work is structured. The result is not collapse – it is reorganisation. But reorganisation still creates friction, especially for those entering the workforce. The Entry-Level Problem No One Is Addressing Properly For decades, entry-level jobs acted as the foundation of career development. They provided experience, exposure, and progression into more advanced roles. AI is now absorbing many of those foundational tasks. Activities such as basic reporting, administrative coordination, research summarisation, and data processing are increasingly automated or AI-assisted. These were once the training ground for new workers. This means fewer traditional “starting points” into careers. And that creates a deeper issue: if the first rung of the ladder is disappearing, the entire structure of career progression needs to be reconsidered. Education Has Not Kept Up With Economic Change Most education systems were designed for a world where: That world no longer exists in the same form. Today, industries can change in months, not decades. Skills become outdated quickly. And employers expect immediate productivity from new hires. Yet education is still heavily focused on: This creates a mismatch between how people are trained and how the modern economy actually functions. The Solution: Entrepreneurial Thinking in Schools The solution is not to abandon education or traditional careers – it is to expand the definition of preparation. Schools must begin teaching entrepreneurial thinking, which focuses on the ability to create value, solve problems, and adapt in uncertain environments. This does not mean every student must become an entrepreneur. It means every student should learn how opportunity is created, not just how employment is applied for. Entrepreneurial thinking develops: In an AI-driven economy, these are not optional skills- they are essential survival skills. Real-World Education Already Moving in This Direction This shift is already being introduced in parts of Australia through school-based entrepreneurship programs and incursions. One example is Nathan Baws, an entrepreneur, public speaker, motivational speaker, business growth motivational speaker, inspirational speaker and a keynote speaker who delivers school incursions focused on entrepreneurship, financial literacy, and real-world business thinking. He also draws on his experience as a business builder and his Shark Tank Australia background to engage students in practical discussions about opportunity creation and mindset. In his work with schools, he focuses on exposing students to how businesses are built in practice and how ideas become real-world outcomes. He also contributes to broader efforts around financial literacy education for young people, reinforcing the idea that understanding value creation is as important as traditional academic learning. This type of exposure helps bridge the gap between classroom theory and real-world economic behaviour. Why This Matters More in an AI Economy AI dramatically reduces the cost of execution. Tasks that once required teams can now be completed by individuals using AI tools. This shifts the economy in three important ways: In this environment, relying solely on structured employment becomes increasingly risky. Those who can identify opportunities, build solutions, and adapt quickly to change will be significantly more resilient. Entrepreneurial thinking is what enables that shift. Conclusion AI is not simply removing jobs – it is reshaping how work is created, distributed, and valued. While the overall employment impact is gradual, the structural changes are already visible, particularly at the entry level. The real challenge is not technological change – it is educational alignment. If schools continue preparing students only for employment in its traditional form, they risk preparing them for a system that no longer exists in the same way. The solution is clear: educamajor financial research on AI employmeAI employment impact researchnt impacttion must evolve toward entrepreneurial thinking, where students learn how to create value, adapt quickly, and operate effectively in uncertainty. In an AI-driven economy, the most important skill is no longer just employability. It is adaptability and creation. FAQs Is AI currently causing major job losses? Not on a large-scale statistical level. Most research shows AI’s overall unemployment impact is still modest, though changes are visible in specific roles and industries. Which jobs are most affected by AI? Roles involving repetitive, structured, or administrative tasks are most exposed, particularly entry-level positions. Why are entry-level jobs shrinking? Because many entry-level tasks are now being automated or accelerated by AI systems, reducing the need for traditional training-ground roles. What is entrepreneurial thinking? It is the ability to identify opportunities, solve problems, and create value independently rather than relying only on predefined job roles. Do students need to become entrepreneurs? No. The goal is not to force business creation, but to develop mindset skills that allow independence, adaptability, and value creation. How does Nathan Baws contribute to this space? Nathan Baws is an entrepreneur