AI

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Could a ‘grey swan’ event bring down the AI revolution? Risks we should be preparing for

Cameron Shackell, 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 […]

AI for Manufacturing Operations: It’s the bus you can’t afford to miss

Gareth Mitchell, Associate Partner, IRIS by Argon & Co Manufacturing globally faces relentless pressure for operational excellence, efficiency, and resilience. Over the past five years, Artificial Intelligence (AI) has emerged as a revolutionary innovation and a true economic engine reshaping modern manufacturing. As companies encounter supply chain disruptions, predictive maintenance needs, and real-time quality demands, AI adoption is becoming essential. However, much confusion remains about the economics of AI. Below, we demystify this confusion and explain why integrating AI into your strategy is both the smartest and most cost-effective path for leaders focused on competitiveness and capturing new value. AI is expected to add approximately $7 trillion USD to global GDP over the next decade, mainly by automating tasks, increasing productivity, and driving new value creation. IRIS by Argon & Co is already sees significant ROI across planning, supply chain, document and spend intelligence, and manufacturing efficiency. However, AI adoption represents a fundamental shift in how businesses create value and how labour may be structured in the future. AI’s cost-saving potential is profound, and exceedingly more high-ROI use cases appear across industries. AI has in some cases increased productivity by 40% and delivered cost reductions through AI automation and optimization. Businesses see stark improvements when AI automates repetitive actions, allowing workers to focus on higher-value tasks. The types of real-world use cases IRIS and its clients are developing show real AI-enabled efficiencies, such as reduced error rates, predictive AI reasoned planning, and automation of routine service tasks, all which lead to substantial improvements at all levels of the P&L. Besides economic advantages, early AI adopters gain multiple competitive edges. Early adopters benefit from foundational AI infrastructure that supports future innovation and ongoing productivity. AI-driven companies are focused on productivity gains and disciplined ROI, enabling faster data-driven decisions, better productivity, […]

AI is peeling back the layers of ‘low-value’ work

Kenny Ching, Senior Lecturer, Business School, University of Auckland, Waipapa Taumata Rau As generative artificial intelligence (AI) advances at breakneck speed, it is upending assumptions about which jobs are “safe” from automation. Disruption now extends well beyond manual or routine work into white-collar roles once considered untouchable. Tools such as ChatGPT, Claude and Midjourney can produce policy briefs, analytical reports, software code, design assets and marketing copy in seconds. Even in specialised domains, systems such as PolicyPulse can generate structured briefs and thematic syntheses – tasks that once required teams of experts. If AI can so easily replicate large swaths of professional output, how much of the economy rests on work that creates the appearance of value rather than tangible impact? And could New Zealand – anchored in sectors rooted in physical work, human judgement and essential services – be structurally better placed to thrive? AI’s exposure effect A 2023 Goldman Sachs report estimated generative AI could automate work equivalent to 300 million full-time jobs globally. The highest exposure is in administrative, legal and other information-heavy sectors. In 2024, the International Monetary Fund warned that economies reliant on high-skilled services – such as education, law and finance – face both job losses and rising inequality. This echoes author David Graeber’s concept of “bullshit jobs” – roles that add little genuine value. Between 2000 and 2018, most net job growth came from low productivity service sectors such as marketing, consulting and corporate administration. These are precisely the kinds of tasks AI can now perform in seconds. The OECD has noted routine information processing jobs face the greatest risk. AI is not only replacing roles – it is revealing how insubstantial many of them were. Some argue finance illustrates this reality starkly: intended to allocate capital efficiently, the sector has expanded beyond its productive purpose. Businessman Adair Turner famously called much of […]

Building the Foundation: Preparing Your Manufacturing Business for AI Success

-Adam Sharman, CEO LMAC Group APAC The manufacturing sector stands at the precipice of an AI revolution that promises to transform everything from production efficiency to supply chain management. Yet for many business leaders, the question isn’t whether to adopt AI, but how to prepare their organisations to extract maximum value from these powerful technologies. The answer lies not in the sophistication of the AI itself, but in the quality of the foundation upon which it’s built. The Data Foundation Imperative Before any AI system can deliver meaningful insights, your manufacturing operation must first establish a robust data infrastructure. This begins with conducting a comprehensive audit of your existing data landscape. Map out where data is generated across your operation—from sensor readings on the factory floor to inventory management systems, quality control metrics, and customer order patterns. Many manufacturers discover they’re sitting on vast amounts of valuable data that remains siloed across different departments and systems. The key is achieving data integration and standardisation. Legacy systems often speak different languages, storing similar information in incompatible formats. A machine’s operational data might be recorded in one system while maintenance records exist in another, making it impossible for AI to identify patterns between equipment performance and maintenance needs. Investing in data integration platforms that can unify these disparate sources creates the coherent dataset that AI algorithms require to function effectively. Data quality represents another critical pillar. AI systems amplify the characteristics of the data they’re trained on—if your data contains errors, inconsistencies, or gaps, your AI will perpetuate and magnify these problems. Implement rigorous data validation processes, establish clear data governance protocols, and create systems for continuous data quality monitoring. This might involve automated checks for outliers, regular audits of data entry processes, and establishing clear accountability for data accuracy across teams. […]

From Survive to Thrive: FMCG Strategies for Growth and Profitability in 2025

From NZ Manufacturer magazine, February 2025. Chris Foord, Partner, Argon & Co New Zealand As we look forward to 2025 the outlook for business and NZ are improving. Many businesses in 2024 were focused on the “survive till 2025” mindset, controlling costs and avoiding discretionary spend. Very few though forgot about the consumer/customer and the need to continue to serve, surprise and delight with excellent product, service and pricing. Most FMCG businesses will be looking to return to better margin positions in 2025 and indications are favourable that this is achievable. The inflation beast has been tamed, so interest rates have fallen and are predicted to drop further (albeit not by a huge amount) through 2025. Business sentiment is on the rebound. Consumer confidence is at its highest level for two years, which in turn improves business confidence. Whilst the signs are positive, navigating from the survival mindset to a thrive mindset doesn’t happen without intent. So, what are some helpful tips to consider as FMCG businesses transition into what should be a period of growth, and importantly how to create value and embed sustainable profitability. Here are some areas to consider and explore: Align operations to execute your business strategy Plan to drive profitable growth Unlock, deliver and celebrate quick wins Create and embed a continuous improvement culture AI: Proof of Value Align operations to execute your business strategy Operations is where the rubber hits the road for your business strategy. Are your operational teams involved in the key decisions as you launch new products, drive innovation, review range, pricing and promotional plans? If not, they should be. They operationalise strategy, plans and tactics into reality. Get them onboard early and you will realise value faster and more effectively. Plan to drive profitable growth Continue your focus on productivity. […]

Feeding the Machine

From: September issue of NZ Manufacturer www.nzmanufacturer.co.nz The Hidden Human Labour Powering A.I. Callum Cant, James Muldoon, Mark Graham For readers of Naomi Klein and Nicole Perlroth, a myth-dissolving exposé of how artificial intelligence exploits human labour, and a resounding argument for a more equitable digital future. Silicon Valley has sold us the illusion that artificial intelligence is a frictionless technology that will bring wealth and prosperity to humanity. But hidden beneath this smooth surface lies the grim reality of a precarious global workforce of millions labouring under often appalling conditions to make A.I. possible. This book presents an urgent, riveting investigation of the intricate network that maintains this exploitative system, revealing the untold truth of A.I. Based on hundreds of interviews and thousands of hours of fieldwork over more than a decade, Feeding the Machine describes the lives of the workers deliberately concealed from view, and the power structures that determine their future. It gives voice to the people whom A.I. exploits, from accomplished writers and artists to the armies of data annotators, content moderators and warehouse workers, revealing how their dangerous, low-paid labour is connected to longer histories of gendered, racialised, and colonial exploitation. A.I. is an extraction machine that feeds off humanity’s collective effort and intelligence, churning through ever-larger datasets to power its algorithms. This book is a call to arms that details what we need to do to fight for a more just digital future.

Q & A:Sean Doherty Advanced Manufacturing,Callaghan Innovation

How can Advanced Manufacturing take NZ forward? Advanced manufacturing involves the use of technology to supercharge the manufacturing sector. Already an incredibly pivotal part of our nation’s economy and operations, there is real value to be found in unlocking the full potential of our manufacturers. The implementation of smart products and processes such as artificial intelligence, robotics and 3D printing are just some of the key ways we can look to automate, digitise and further develop operations. New Zealand hopes to reach the level of manufacturing represented by Industry 4.0 – a blueprint made up of a series of government initiatives from around the world. As a nation, reaching this goal would propel the nation forward in several critical ways. Manufacturers can harness new technologies to revolutionise their products and processes, effectively increasing their quality of output, and driving productivity forward. Economically, this creates an increase in profitability, enhances market competitiveness, and helps to generate higher-value jobs by automating lower-level tasks. Fostering innovation, this strengthens our supply chain resilience, and reduces our dependence on foreign sources. We can also work towards realising our full potential as a ‘clean, green New Zealand’, by encouraging resource-efficient, environmentally friendly practices. We are not a country rich with natural resources, except for dairy, horticulture and agriculture. We once made cars and TV sets. When that stopped it couldn’t be started again. So where can Advanced Manufacturing help to turn things around? Market demand, competition, global economic conditions and access to raw materials play crucial roles in the ability for a country to operate in a highly competitive industry – which is largely why dairy, horticulture and agriculture are particular strengths for New Zealand. However, advanced manufacturing holds endless possibilities for improvement. As we increase our capabilities, this certainly creates new opportunities for growth […]

What is prompt engineering?

Let’s say you’re making spaghetti marinara for dinner. Sauce from a jar is perfectly fine. But what if you buy your tomatoes and basil from the farmers market to make your own sauce? Chances are it will taste a lot better. And what if you grow your own ingredients from your garden and make your own fresh pasta? A whole new level of deliciousness. Just as better ingredients into a generative AI model can make for better results. These inputs are called prompts, and the practice of writing them is called prompt engineering. Skilled prompt engineers design inputs to interact optimally with other inputs in a generative AI tool. These inputs help elicit better answers from the AI model, meaning the model can perform its tasks better, such as writing marketing emails, generating code, analysing and synthesising text, engaging with customers via chatbots, creating digital art, composing music, or any of the other hundreds, if not thousands, of current applications. Let’s look at an example. Say you want a generative AI model to help you promote an upcoming financial-planning workshop with the CFO of your organisation, Acme Inc. You provide a generative AI chatbot with a simple prompt: “Write a social post advertising a class.” Here’s the output: **Elevate Your Skills: Join Our Class!** Unlock new knowledge, connect with experts, and embrace growth. Limited spots available—reserve yours now! #LearnWithUs #SkillBuilding #ClassTime It’s pretty generic and unlikely to generate much interest. Let’s try again. This time, you make your prompt a little more specific: “Write a short social post advertising a financial-planning workshop with the CFO of Acme, Inc.” Here’s what the model gives you this time: **Master Your Finances with the CFO of Acme, Inc.!** Get ready to take charge of your financial future! Join our upcoming Financial Planning Workshop featuring the CFO of Acme, […]

Dsifer releasing the New Zealand Manufacturing Industry Data and AI Survey

This survey aims to build industry understanding of the way in which data analytics and AI is being considered and implemented across the manufacturing sector in New Zealand for the purposes of informing organisations on how their peers are approaching these tools, informing support organisations on where opportunities exist and understanding benchmarking New Zealand against the global trends. The survey takes less than 10mins to complete and all information provided will be aggregated and anonymised for analysis and publication. The survey can be accessed using his link: https://www.surveymonkey.com/r/Data_AI_2023 and is open until 8th December 2023. We encourage as many people as possible to contribute their feedback so that we are able to generate an accurate picture of the New Zealand manufacturing industry and provide crucial insight in to shaping the future of data and AI adoption in the manufacturing industry.

Will digital enablement make our company less human?

-Scott Adams, Argon & Co The answer is a clear “no,” quite the opposite, as it will require clever design, selection, implementation, and maintenance. And if you have done it right it will provide you with more time to do human things like strategy, talking with clients and colleagues, and with increased overall productivity rather than less time working, and more time enjoying life. The automation of routine, complex or dangerous activity is an inevitable and logical path forward for New Zealand businesses. We should be increasingly looking first to a digital solution for any problem or opportunity and always one that is easy to use and maintain. For instance, there is a vast amount of software solutions that are easy to buy, and use, and if we have done well the software will even integrate with other software to digitally enable an end-to-end process. Therefore, we can make our working life much easier if we understand our business processes and therefore our requirements. Many of us in business are getting quite practiced at this new and necessary skill set. Right now, depending on your role you are doing a good proportion of your work without having to think too hard. You, and NZ, would be better off if a larger proportion of our working time was spent thinking hard. Solving big problems, dreaming up new designs, creating rich content, crafting a chair, and writing songs. For most of us we would need that harder work compensated for with more rest and leisure time. So, this might seem like a logical and forgone conclusion of increasing digitisation, but the journey and destination are not certain at a company level. It would be best to prepare well to approach this increasingly digital future with more confidence. Firstly, start by educating the […]

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