AI

AI, strategy, and the future of work: Oxford economist Jean-Paul Carvalho

Professor Jean-Paul Carvalho explores how AI is reshaping cognitive work, organizations, and where business leaders can capture value at scale. Jean-Paul Carvalho is a professor of political economy in the Department of Economics at the University of Oxford and director of Oxford Elevate, the department’s executive education portfolio. In this episode of the Inside the Strategy Room podcast, he speaks with McKinsey Partner Robin Nuttall about what makes AI different from past waves of technology and shares the latest data on its implications for the nature of work, organisations, and business leadership. Audio Robin Nuttall: How has your own interest in AI developed as the technology has advanced, and how did the topic become a part of your programs at Oxford? Jean-Paul Carvalho: I’m naturally interested in AI because technology is the main engine of economic change. This began in the 1700s, with the Industrial Revolution bringing the ability to manufacture goods cheaply and at scale. That revolution led to cars, suburbs, advertising, consumerism, and much of what we associate with modern life today. With that, most of us have lived through an unusually stable environment. That’s changing now, and it’s taken a while for people to come to grips with it. In April 2023, Pew [Research Center] did a survey1 that showed that while 62 percent of respondents believed AI would majorly disrupt the workforce, only 28 percent believed their own job would be affected. So there has been this kind of trepidation with AI; we were forced to pay attention to AI in November 2022 when ChatGPT was released, but some heads remained in the sand. If we go back further, in 2012, the transition from symbolic AI to the deep neural nets we see today was starting to happen: Driverless cars were being trialed on the roads, AlexNet was out, and software engineers […]

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Lawsuit dismissed

A nine-member federal jury in Oakland, California took less than two hours to dismiss Elon Musk’s lawsuit against OpenAI and its chief executive Sam Altman. Crucially, the jury did not rule on the core claims of the case. These included whether OpenAI, the company behind the popular artificial intelligence (AI) chatbot ChatGPT, strayed from its founding mission and whether Altman and OpenAI’s co-founder Greg Brockman enriched themselves at the expense of a charitable purpose. It decided only that Musk had waited too long to sue in relation to his core claims about breaches of a founding contract or breach of charitable trust. A victory for Musk could have neutered OpenAI, which in turn would have probably sent shockwaves through the entire AI sector given the company’s dominant position developing the technology. Now, however, OpenAI has a clear path to take its next big step in the AI race, even though the key question at the core of the case remains unanswered: is OpenAI a nonprofit dedicated to humanity or a corporation dedicated to its shareholders? How it all started OpenAI was founded in December 2015 as a nonprofit entity – an AI research lab. Musk and a group of prominent entrepreneurs pledged US$1 billion to develop AI for the benefit of humanity, free of commercial pressure. Alongside Musk, the founding group included Altman, Brockman and computer scientist Ilya Sutskever. The organisation’s charter committed to two key principles. First, developing AI safely and for the benefit of all of humanity. Second, developing the technology openly, meaning it would be open source. This would allow others to use their underlying models, code, and research freely. This was the deal Musk says he signed up for. And OpenAI claims it continues to honour this deal even today, despite more than US$20 billion in revenue in 2025. […]

Before you buy AI: What manufacturers need to get right first

From NZ Manufacturer magazine – May issue By Sean Doherty, Manufacturing Commentator | NZ Industry Trends There is no doubt that AI will be a game changer for manufacturers, and early adoption will give many firms a competitive advantage because manufacturing is both data-rich and operationally complex. But beware: AI only creates value when it is applied to the right problems. Too many manufacturers get distracted by the demo and find out too late that their data is poor, the tool does not fit the workflow, or ownership of the outcome is unclear. In 2026, the manufacturers most likely to succeed with AI will be those that treat it like any other investment decision. Define the need, test the assumptions, manage the risks, and measure the potential return. Before buying any AI tool, there are four practical basics to get right: the problem, the data, governance, and the pilot. Start with the problem Start by defining the problem in plain language. A manufacturer should be able to say exactly what it wants to improve: machine uptime, planning speed, forecast accuracy, scrap rates, or quality checks. “We need AI” is not a business case. It is noise. Before speaking to vendors, define success. What metric needs to move, and by how much? If the goal is less downtime, what is the baseline, and what improvement would justify the investment? If the issue is quality, is the target fewer defects, faster inspection, or less rework? Clear objectives make it much easier to spot a valuable tool and avoid an expensive distraction. Mapping the current workflow also helps. Where are decisions slow, manual, repetitive, or error-prone? Where does information stall, and what information is needed to move things forward? A simple process map can quickly reveal whether the problem is poor process design, […]

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Resilient firms and economies: Unlocking growth in emerging markets

Resilience is like a bridge spanning a turbulent river. The waters below may surge unpredictably, but the bridge’s strength lies in its design, maintenance, and ability to withstand. The Resilience Consortium brings together governments, businesses, and multilateral development banks (MDBs) to strengthen resilience in emerging markets. Drawing on insights from the 2025 Resilience Consortium Pulse Check Survey, completed by more than 270 executives, along with a quantitative follow-up analysis, structured interviews with community members and the Consortium’s leadership group, and global, high-level convenings held throughout the year. The importance of collective action: In an era of disruption, resilience has become a key driver of competitiveness and innovation. No single actor can mitigate systemic risks alone: Businesses contribute agility, innovation, and investment; governments enable progress through effective policy and infrastructure; and MDBs mobilise capital, derisk investment, and coordinate cross-sector efforts to address systematic risks. Together, these actors can build systems capable of withstanding and adapting to shocks. Focus on emerging markets: Emerging markets, which represent nearly 60 percent of global GDP, are vital to the resilience agenda. They face exposure to trade disruptions, commodity volatility, and climate risks, yet they hold the potential to drive global growth in the decade ahead. Strengthening resilience in these economies is critical not only for their own prosperity but also for safeguarding global stability. Resilience preparedness: Organisations are becoming more resilient, but progress remains uneven. Preparedness has improved by 13 percentage points since 2024, yet only one in four companies considers itself ready to withstand major disruptions across all resilience dimensions (e.g., financial, organizational, digital and technology resilience) and this share falls to one in five when assessing resilience capabilities (i.e., crisis response, foresight, disruption preparation, and strategic re-orientation). Financial resilience remains the strongest dimension, with 40 percent of firms reporting readiness, while digital and technological […]

Could a ‘grey swan’ event bring down the AI revolution?

Cameron Shackwell, Sessional Academic, School of Information Systems, Queensland University of Technology The term “black swan” refers to a shocking event on nobody’s radar until it actually happens. This has become a byword in risk analysis since a book called The Black Swan by Nassim Nicholas Taleb was published in 2007. A frequently cited example is the 9/11 attacks. Fewer people have heard of “grey swans”. Derived from Taleb’s work, grey swans are rare but more foreseeable events. That is, things we know could have a massive impact, but we don’t (or won’t) adequately prepare for. COVID was a good example: precedents for a global pandemic existed, but the world was caught off guard anyway. Although he sometimes uses the term, Taleb doesn’t appear to be a big fan of grey swans. He’s previously expressed frustration that his concepts are often misused, which can lead to sloppy thinking about the deeper issues of truly unforeseeable risks. But it’s hard to deny there is a spectrum of predictability, and it’s easier to see some major shocks coming. Perhaps nowhere is this more obvious than in the world of artificial intelligence (AI). Putting our eggs in one basket Increasingly, the future of the global economy and human thriving has become tied to a single technological story: the AI revolution. It has turned philosophical questions about risk into a multitrillion-dollar dilemma about how we align ourselves with possible futures. US tech company Nvidia, which dominates the market for AI chips, recently surpassed US$5 trillion (about A$7.7 trillion) in market value. The “Magnificent Seven” US tech stocks – Amazon, Alphabet (Google), Apple, Meta, Microsoft, Nvidia and Tesla – now make up about 40% of the S&P 500 stock index. The impact of a collapse for these companies – and a stock market bust – would be devastating at a global level, not just financially but also in terms of dashed hopes for […]

Jobs and the future of work

How to build the skills needed for the age of AI AI is changing how we work and what skills workers need to thrive in a world of intelligent machines and cognitive computing. Only a sliver of education initiatives are devoted to AI and these are not yet integrated into most education systems. Collaborative initiatives between education and industry can accelerate the transition to a greener, more efficient and inclusive world where jobs and skills development pathways are readily available. New technologies can be exciting and disruptive at the same time. Over the past few years, no innovation has been more contentious than artificial intelligence (AI). It is changing the way we think about when, where and how we work and what skills workers need to thrive in a world of intelligent machines and cognitive computing. But it comes with a colossal caveat How will it affect jobs and livelihoods? And, what steps can we take now to futureproof our workforces and equip them with the skills and know-how they’ll need to succeed in a more digital, automated and fast-paced jobs market? Job displacement – or job reconfiguration – is a primary concern. Labour-intensive work, such as assembling electronics or automobiles, handling materials and overseeing supply chains – careers that employ tens of millions of workers, which are critical to the economies of many developing countries – will certainly be impacted as AI-powered activities, such as quality control and robotics, replace some jobs. Knowledge-based workers in industries such as banking, marketing, and human resources may already need to skill-up to coexist with sophisticated technologies for financial analysis, algorithmic trading, predictive analytics and software development. AI will massively impact the workforce The IMF recently estimated that almost 40% of jobs will be impacted by AI. This effect varies by a country’s income level, […]

Agents for growth: Turning AI promise into impact

As CEOs and CMOs ask where AI is moving from hype to real results, frontrunners demonstrate that tighter human–AI collaboration and sharper governance is required. A global retailer sees demand for a top product surge in one region while inventory piles up in another. Within seconds, a team of AI agents reallocates ad spend, adjusts pricing, reroutes stock, and refreshes creative assets to offers that match shopper intent. In this scenario, what comes next is coordinated action triggered by customer signals, orchestrating business growth in real time. This is not a fantasy scenario; it’s the new frontier of AI in growth functions. Agentic AI embeds automated reasoning directly into marketing, sales, and customer service workflows. We estimate that agentic AI will power more than 60 percent of the increased value that AI is expected to generate from deployments in marketing and sales. It’s no exaggeration to say that marketing and sales represent the tip of the spear when it comes to translating agentic AI’s potential into meaningful value. Early leaders are already seeing measurable impact. For example, according to McKinsey analysis, some Fortune 250 companies have estimated that they are seeing campaign creation and execution speed up 15-fold, driven by faster innovation cycles and process optimisation. The value from agentic AI comes from the tasks it is able to do. Unlike gen AI and chatbots that largely assist in the completion of marketing and sales tasks, AI agents can act, decide, and collaborate. They are able, for example, to optimize prices, advance leads, tailor offers and manage customer interactions end-to-end. As organisations deepen their adoption of agentic AI, gains can scale. Analysis shows that effective and scaled agent deployments could deliver productivity improvements of three to five percent annually and potentially lift growth by 10 percent or more. Most organisations, however, […]

Using AI to enable scalable, autonomous operations

By Troy Mahr, director – Kalypso. Kalypso – acquired by Rockwell Automation in 2020 – is a diverse team of consultants, innovators, strategists, data scientists and technologists. What we consistently hear from industry leaders is the need for real-time visibility across their global operations, which is key to ensuring their operations stay agile and scalable. However, achieving this isn’t possible without removing laggy manual data collection through the deployment of connected assets and contextualised data. By eliminating data silos and unlocking industrial data and artificial intelligence (AI) capabilities, companies can enable autonomous decision-making that optimises costs, efficiency, and production resilience. This moves their organisation closer to achieving autonomous operations. Autonomous operations are the manifestation of “self-governing” systems at every step of the manufacturing process. These systems derive their autonomy from data-led decision-making models that enable them to reliably adapt their behaviour in response to dynamic environments during operation without any manual intervention. Achieving autonomy across an enterprise requires capabilities that span the full intelligence spectrum, from observation and inference to decision-making and action. These capabilities are relevant across all operational areas, including product design, manufacturing, supply chain, distribution, direct-to-customer channels, and demand forecasting. Manufacturing operations, in particular, have seen progress through Model Predictive Control (MPC), which continuously analyses real-time and forecasted data to optimise process control within defined constraints. While MPC is a strong example within manufacturing, broader autonomy demands extending similar intelligent systems across the enterprise. This journey is captured in the industrial AI maturity pyramid, which outlines a progression from basic data integration and visualisation to predictive analytics, prescriptive decision-making, and ultimately, autonomous operations. As organisations climb this pyramid, they adopt machine learning, real-time automation, and self-learning systems. Each stage requires not just technological upgrades but also cultural and structural transformation. Asset Monitoring: Find Downtime Root Causes Looking at […]

After the Idea: What It Really Takes to Create and Scale a Startup

by Julia Austin Entrepreneurship expert Julia Austin shares battle-tested strategies to help founders and startup joiners build their venture from the ground up in this masterclass in intentional entrepreneurship. So you want to start a company. Or you were crazy enough to join a startup. You had a great idea, you built a prototype, and maybe you even raised some money. Now what? Julia Austin is here to answer that big question. She has both experienced and observed that the differentiator between the startups that succeed and those that fail is operational excellence. A lot of entrepreneurs are great at the idea part but do not anticipate the details required to actually run and scale a new venture. Drawing on Austin’s extensive experience at renowned startups like Akamai, VMware, and DigitalOcean and the hundreds of founders and startups she has educated, coached, and advised, After the Idea is full of time-tested strategies to help founders, investors, and employees navigate the operational challenges of startup ventures, including customer development, scalability, process optimisation, team management, and more. This accessible set of techniques is for anyone determined to turn a great idea into a solid success.

Powering remanufacturing with AI

Companies are accelerating remanufacturing as a way to mitigate supply chain shortages, reach new customers through affordability, and implement high-margin alternatives for parts. However, those looking to build or optimise their remanufacturing operations face unique challenges, such as pricing a long tail of SKUs and undertaking accurate core forecasting—that is, predicting the volume, timing, and quality of returned products (core) that will be available for remanufacturing. Enter AI. As the costs of cloud storage and processing and prepackaged tools decrease, AI is becoming more accessible for organizations, helping them improve efficiency, yield, and margins. Unlocking AI in remanufacturing AI is an umbrella term for leading-edge techniques and methods of analysing data, predicting outcomes, and generating insights. While AI and gen AI technologies have garnered a lot of attention, they are only part of the AI landscape. Core forecasting The unpredictable nature of core availability is a challenge for remanufacturers, and conventional analytics lack the sophistication needed for predictive analysis. Introducing a specialised system such as a forecasting tool could help remanufacturers evaluate core availability at a per-SKU level. This forecasting tool could be trained on historical part performance data to assess the following: the estimated useful lifetime of a part the historical use rate of a part (such as hours or miles per day) macroeconomic conditions by region and industry that may affect trade-in timelines Adopting AI tools for core forecasting could help remanufacturers reduce core safety stock by 2 to 4 percent and save 3 to 5 percent in freight costs by reducing the cost of expedited shipping. AI tools could also help remanufacturers reduce overtime expenses, lose fewer sales due to stockouts, and ensure they have the parts that end customers need. Case study: Incentives for core availability. A top technology OEM struggled to match the regional availability of core […]

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