Food is not just a commodity; it is a system that underpins health, and environmental stability. When that system falters, the consequences ripple across economies and societies. According to our latest research surveying food and drink leaders, inflation and rising costs are now one of the biggest challenges for 57% of food and beverage leaders. This pressure eclipses even sustainability concerns, which 60% say have eased in the past year. This shift signals a hard truth: short-term survival is competing with long-term transformation.

Health: Moving beyond quick fixes

Obesity rates are climbing, and inequalities are widening. Appetite-suppressant drugs like GLP-1s dominate headlines, but they are not addressing key issues in the food system. At the IGD Future of Food Conference, Professor Chris Whitty, Chief Medical Officer for England, shared a stark reality: those in the most deprived communities can expect just 51.9 years of good health, compared to 70.7 years for the least deprived. The industry is reacting, with M&S launching a ‘nutrient dense’ range to meet changing appetites, and HFSS (high fat, sugar, salt) regulations aiming to support healthier choices . Whitty has urged the industry to look to the automotive industry for inspiration, where coordinated action and innovation dramatically reduced air pollution. Food needs a similar collective effort.

Resilience: From efficiency to agility

For years, supply chains were optimised for cost. That model is breaking down. Inflation, climate volatility, and geopolitical shocks are rewriting the rules. From our Operations Outlook 2026 research report, in the past 12 months, 57% of leaders in the food and beverage industry cite rising costs as one of their biggest operational challenges, leaving less room for error. To mitigate risk, 42% of businesses are improving supply chain transparency, 40% are strengthening collaboration with suppliers, and 40% are diversifying sourcing across geographies. These priorities show that suppliers are not only vital but the foundation of a more agile supply chain.

Sustainability: The strategic advantage

Economic pressures have softened the urgency around sustainability, but the long-term trajectory is clear. Growth models built on volume are becoming obsolete. As Henry Dimbleby MBE (long-time food campaigner and advocate for systems change) noted at the IGD Future of Food conference, growth cannot rely on volume alone. The future belongs to businesses that create value, elevating quality, provenance, and experience, and helping consumers appreciate food as something worth paying for. According to IGD and EY, sourcing costs for key commodities are expected to rise by 14.7% by 2050 if no change is made, as a direct result of climate impacts. Horticulture is projected to be hit hardest, posing risks to the fruit and vegetable supply that underpins healthier diets. Therefore, if sustainability falls down the agenda, these points show that this will not allow other priorities to succeed, they go hand in hand.

The path forward:

The future of food will not be defined by those who react to disruption, but by those who anticipate it. Leaders must embrace nutritional innovation and invest in resilient supply chains. Collaboration across the industry is non-negotiable. While many solutions already exist, the industry needs greater clarity, and alignment to implement them effectively. Regulation can help create a level playing field, but businesses must lead with ambition, not compliance.

The next decade will test the industry’s ability to balance competing priorities: profitability, health, resilience, and sustainability. Those who succeed will not only secure their future, but they will also redefine the role of food in society.

The post The future of food appeared first on Argon & Co.

The question for leaders is no longer whether AI can create value, but how to deploy it in ways that reliably lift operational performance at scale.

How did we get here?

AI is impacting nearly every part of the global economy, giving every team the chance to innovate and redesign processes. Over the past decade, organisations have explored different ways to harness AI. Today, three main approaches coexist. Each has their own distinct benefits and limits.

1) Narrow AI: specialised models for specific problems

This path involves training a model to solve a clearly defined problem better than conventional methods. Typical examples include demand forecasting models that outperform spreadsheet-based planning, or machine-learning models that optimise parameters on industrial equipment.

Narrow AI works best where the problem is stable, the data is structured, and the success metric is clear. Its limitation, though, is that it is only effective in a relatively small number of use cases.

2) Personal AI: generalist copilots for everyday tasks

The rise of generative AI made generalist assistants, internal chatbots or tools like Microsoft Copilot available to knowledge workers. They work well for cross-cutting tasks such as drafting meeting notes, summarising emails or performing generic research. But they do have two major limitations. Firstly, they lack the deep business context required for many decisions and secondly, they are usually not connected to enterprise systems. This can make them slow or impractical for operational workflows.

3) Agentic AI: job-focused agents that compound value across dozens of tasks

Agentic AI shifts the unit of design from single use cases to jobs. An agent is built around a specific function such as procurement, customer service or transport planning. It is then connected to both structured enterprise data (e.g. ERPs, WMS, TMS, CRMs and KPIs) and unstructured sources (emails, chats and meeting notes).

Most importantly, the agent is able to act within systems and communication channels. Rather than solving one big problem, it assembles 30–60 micro use cases per job. While each may seem minor on their own, they produce a step-change impact when combined.

Why agentic AI can sharpen your competitive edge

Nearly every job involves repeatable and relatively simple tasks that produce a predictable output. For example, people in support jobs (like HR or administration) spend about one-third of their working time on focused, routine tasks like data entry, scheduling or answering employee questions about policies. Agentic AI is now capable of performing these kinds of tasks quickly, accurately and at scale. For those working in functions like customer service, procurement and transport, the share of tasks that AI could perform could rise to 40% or more. Even in roles with higher variety and complexity, such as frontline management or project management, the share is still typically 20–25%. Automating these tasks doesn’t just represent a marginal potential gain – it is a systemic shift in how work gets done.

This increased efficiency is a powerful source of value that can be monetised in several ways depending on your business context. The time saved can be reinvested into higher-value activities. It can also be used to accelerate continuous improvement and transformation as well as resizing support functions to sharpen cost competitiveness without impacting service quality. When one-third of the workload of indirect operational teams can be reliably automated or augmented, the resulting capacity and performance lift becomes a durable competitive advantage rather than a one-off saving.

Agentic AI offers a strategic dual advantage for businesses. It can significantly reduce human workload, freeing up time to be reinvested in cost competitiveness or performance. By autonomously running end-to-end workflows, monitoring issues in real time and applying best practices consistently, agentic AI can potentially enhance decision quality and reduce errors. This strengthens core performance metrics such as service levels, inventory availability or customer response times. At the same time, AI extends the capabilities of teams beyond what was previously achievable, allowing organisations to improve process effectiveness and realise new levels of performance gains. This could be a powerful engine for new forms of organisational and competitive advantage.

It’s time to move beyond use cases to job redesign

Despite the obvious potential of AI, many businesses are struggling to get out of the sandbox and into deployment at scale. Many decision-makers are still thinking about AI in terms of  standalone ‘AI use cases’, rather than trying to identify workflows or jobs that can be redesigned. This tends to surface a handful of obvious, large items and leaves most of the potential value untouched.

The real potential of agentic AI comes from automating the huge array of small, repetitive tasks people perform regularly as part of their jobs. As well as saving people time, this creates the potential to redesign end-to-end processes, uncover new performance opportunities and refocus people’s daily time around value-producing tasks.

Taking a job-first approach to agentic AI is a useful way of maintaining a strategic approach to agent deployment. The process involves:

• Mapping the job end-to-end, including decision rules and exception paths.
• Decomposing activities into tasks and sub-tasks
• Identifying where AI can perceive, decide and act reliably.
• Redesigning processes to be more efficient and higher performing (better service levels, fewer errors, faster cycle times).
• Quantifying impact upfront (time, quality, service, working capital), prioritise by value and complexity, and set clear implementation targets.

If we use procurement as an example, we can see how the agentic opportunity spans entire workflows:

• Contracting lifecycle checks and compliance.
• Supplier performance monitoring.
• Issue management and expediting: handling supplier production problems, delivery delays and demand changes; proposing and tracking action plans with full traceability and managerial visibility.
• Purchase order updates and confirmations, lead-time exception handling, shortage risk detection and mitigation.
• Advance shipping notice/invoice reconciliation and root-cause attribution to enable systematic cost recovery.

Individually, each micro use case looks small. Together, they transform the function’s service, cost and speed.

Designing impactful AI agents

While identifying the right opportunities is an important first step, agent design is what really determines whether you reach production-grade impact.

Effective agentic AI design combines capabilities across three layers. Your agents need to be able to:

• Read and reason over unstructured inputs like emails, chats, meetings and documents in addition to structured data/KPIs from enterprise systems.
• Apply policies, analytics and optimisation with guardrails and auditability in order to make reliable decisions.
• Operate inside systems and communicate back through the same channels teams use.

Achieving this requires the right infrastructure and a coherent technical stack. The minimum requirements for businesses are:

• A data and analytics platform to store, transform, analyse and visualise data.
• An agentic platform to design, configure and orchestrate job-focused agents with usage and accuracy monitoring.
• Enterprise integration via API connectors and batch feeds. Where APIs are missing, robotic process automations (RPAs) can be used selectively. An emerging approach is AI computer use where agents safely operate graphic user interfaces (GUIs) under strict policies, observability and logging.

Managing the risks of agentic AI

Like any transformative technology, agentic AI also introduces risks that businesses must actively manage. A key consideration is accuracy. AI can sometimes provide incorrect outputs, which can create operational issues if left unchecked. To mitigate this, organisations need rigorous pre-launch testing, continuous accuracy monitoring and systematic root-cause analysis when errors occur. Techniques such as refining prompts, breaking complex tasks into simpler components and using oversight agents to verify outputs in real time all help raise performance. The most important technique, however, remains maintaining human control and oversight at every stage of the workflow.

At the same time, companies must ensure sensitive information is not stored by external providers, personal data is handled in line with regulations and models operate within secure technical environments. There are also social and organisational risks to manage. The introduction of AI can generate anxiety among employees, particularly those concerned about job security. Clear communication, transparent change management and meaningful involvement of teams throughout the transformation are essential to maintain trust and support successful adoption.

Prioritising the right skills

This is not a tool rollout – it is an enterprise transformation that changes how work is organised, measured and improved. A successful implementation of agentic AI requires buy-in and collaboration between a number of key stakeholders:

• A business process owner (BPO) to lead end-to-end redesign, define guardrails and service levels, and prototypes quickly with users.
• A data-trained engineer who will act as an AI agent builder. They will design and productionise reliable, enterprise-connected agents with monitoring, fallback strategies and quality controls.
• Data analysts and data engineers to build analytical components and data pipelines, ensure data quality, timeliness, lineage and visualisation.
• Enablers of change and internal champions who can drive prioritisation, training, communications, adoption and performance management.

Developing the right skills is essential for scaling agentic AI, but the approach varies by company size. Large enterprises may be able to focus primarily on building strong internal capabilities while partnering with external experts to accelerate early projects, train teams and design the operating models needed for long-term scale. Smaller and mid-sized organisations often prefer more flexible arrangements, relying initially on specialist partners with the option to upskill internal teams over time. Across all contexts, the most successful companies strike a balance between strengthening internal talent without slowing the pace of transformation.

Engaging with agentic AI is no longer optional

Agentic AI is powerful because it industrialises dozens of small improvements across a job until they add up to a structural advantage. The winners aren’t chasing isolated use cases anymore. Now the race is on to redesign processes around AI capabilities, quantify impact up front, build the right stack and team and execute quickly.

With some companies moving faster than others, those that delay adoption risk higher costs, slower decision-making, weaker service levels and a widening productivity gap. Those that embrace agentic AI have the potential to operate faster, smarter and at significantly lower cost. By reimagining entire jobs and scaling performance gains far beyond what traditional automation could achieve, early adopters could set the competitive pace for their industries.

Getting started now positions you to capture today’s value and to compound tomorrow’s. In doing so, you turn AI from a promise into a durable, accelerating competitive edge.

The post From use cases to jobs: creating real results and competitive advantage with agentic AI appeared first on Argon & Co.

With the ways that dairy is consumed, produced and traded changing, companies must look to the future to adapt, pivot and transform their operations.

The emerging poles of dairy trade

Historically, the dairy trade revolved around a few key exporters supplying a select group of importing nations. However, rapid economic growth in Asia, the Middle East and Africa has fuelled new centres of demand. China has emerged as the world’s largest importer of dairy, with significant demand for milk powder, infant formula and cheese driving global trade. Meanwhile, Southeast Asia, with its rapidly urbanising populations, is increasingly reliant on imports to meet nutritional needs.

Reflecting this changing dynamic, major European companies, like Friesland Campina and Arla Foods, are expanding their Asian footprints through strategic investments and growing exports of key products.

At the same time, new and fast-scaling businesses from emerging economies, like India’s largest dairy cooperative Amul and Brazilian company Vigor Alimentos, are also increasing their share of global exports and competing with established firms from the US and Europe.

The role of geopolitics in altering trade flows

In addition to the emergence of new global trading hubs, rising geopolitical tension is further driving changes to dairy trade. The US-China trade war has already significantly impacted American dairy exports, leading companies such as Dairy Farmers of America (DFA) to shift focus towards Mexico and Southeast Asia to compensate for losses in China.

In the UK, European companies, like Müller and Ornua, have faced regulatory hurdles following Brexit, leading Ornua to expand exports to the US and Asia to reduce reliance on the UK.

Additionally, food security concerns have prompted some nations to increase domestic dairy production. Yili Group, one of China’s largest dairy companies, has heavily invested in local farms to reduce dependency on imports. With economic headwinds persistent, businesses throughout the dairy supply chain are being forced to adjust and adapt, further driving changes to global trade flows.

The sustainability imperative

Another significant source of change to established dairy trade flows is the mounting pressure for dairy businesses to become more sustainable. Climate change, water scarcity and growing scrutiny of methane emissions from cattle have made the environmental impact of the dairy sector a central issue for consumers and regulators alike.

In response, leading companies like Danone have committed to achieving carbon neutrality. To drive its 2050 carbon neutrality goal, Danone has invested in regenerative farming methods, methane reduction technologies and biodiversity initiatives. In another example, Synlait Milk has taken a leading role in sustainable dairy production by promoting low-emission farming techniques throughout its supply chain.

The rise of dairy alternatives

One of the biggest disruptions to traditional dairy is the explosion of dairy alternatives. Plant-based milk products, such as oat, almond and soy milk, have surged in popularity, driven by health-conscious consumers and sustainability concerns.

To adapt, traditional dairy companies have entered the plant-based space. Bel Group, the company behind Babybel and The Laughing Cow, has expanded into plant-based cheese alternatives with its Nurishh brand, offering vegan-friendly cheese products made from coconut oil and pea protein. Meanwhile, Danone has been growing its Alpro brand, a leader in almond, soy and oat milk, to compete with independent plant-based brands.

Beyond plant-based options, precision fermentation is emerging as a major threat to traditional dairy. Companies such as Perfect Day are developing lab-grown dairy proteins that mimic the taste and texture of milk without using cows. These proteins are already being used in products such as Brave Robot ice cream. Major food companies are investing in this space to future-proof their dairy portfolios as they continue to face headwinds driven by trade disruption.

Changing consumer preferences

Conscious consumers are increasingly looking for nutritious products which have low environmental impact, further driving changes to the global dairy sector.

1. High-protein and functional dairy

Shoppers in many key dairy markets want high-protein, functional foods, and this is driving significant growth in specialized dairy products. Companies like Glanbia Nutritionals and Arla Foods Ingredients are developing high-protein dairy powders used in sports nutrition, medical foods and protein-fortified snacks, with these ingredients now a major part of the dairy trade, opening new revenue streams for producers.

2. Cheese and premium dairy products

While liquid milk consumption has declined in markets like the US and Europe, cheese consumption is rising. Specialty and artisan cheeses, like those produced by Bel Group and Saputo, are seeing strong global demand, particularly in China and India.

3. Dairy in plant-based foods

Dairy has an important role to play in plant-based foods as well. Some hybrid products combine dairy and plant-based ingredients, such as those made by Daiya which produces plant-based cheese, and experiments with formulations that include dairy proteins to improve taste and texture. This blending of dairy and alternatives may shape the future of dairy consumption.

The future of dairy trade

The global dairy industry is in a state of flux, shaped by sustainability pressures, geopolitical shifts, emerging markets, and the rise of dairy alternatives. Companies must navigate an increasingly complex landscape, balancing the need to maintain traditional dairy production while embracing innovation in ingredients, sustainability, and new product categories.

Key trends to watch:

• Regionalisation of supply chains: With increasing geopolitical risks and food security concerns, expect dairy supply chains to become more localized.
• Growth in dairy ingredients: High-protein dairy products and functional ingredients will continue to drive demand.
• Expansion of premium dairy: Cheese, specialty dairy, and high-value products will be key growth areas.
• Rise of alternative dairy proteins: Lab-grown and precision-fermented dairy will increasingly compete with traditional dairy in processed foods.

For producers, adaptability is key. Those who can embrace sustainability, leverage dairy’s evolving role in food innovation, and respond to changing consumer demands—whether by producing traditional dairy or alternatives—will lead the next era of dairy trade.

Find out how Argon & Co can help you adapt to the changing face of the dairy sector.

This is the final article in our mini-series titled ‘The future of dairy: how technology, trade and transformation can drive performance in a changing industry’

Read more here:

Article 1 – Connected, agile, informed: The next era of dairy supply chains

Article 2 – The next frontier of dairy: the four technologies shaping the future of the dairy industry

Article 3 – Unlocking value through sustainability: the opportunities of circularity for dairy businesses

The post The Shifting Landscape of Global Dairy Trade: A Look to the Future appeared first on Argon & Co.

What environmental impact does dairy have – and how do alternatives compare?

Dairy is a nutrient-rich food group, but its environmental footprint is larger than many alternative protein sources when measured per unit of nutrition delivered. The majority of this footprint is generated at the farm level, where emissions from livestock, feed production and manure management combine to account for well over half of the total lifecycle emissions. Among the most notable drivers of dairy’s environmental footprint are:

• Methane emissions: Dairy farming accounts for approximately 4% of global greenhouse gas emissions, with methane from cows and manure the biggest contributors.
• Feed production: Feed alone can account for up to 36% of a dairy farm’s total greenhouse gas (GHG) emissions. The cultivation of crops like soy and maize involves high nitrogen fertiliser use, leading to nitrous oxide emissions and nutrient runoff that pollutes waterways.
• Water use: Producing one litre of milk can require up to 7.68 litres of water, much of it embedded in feed production, placing significant pressure on water resources, particularly in water-stressed regions.
• Land use: So that one person can drink a glass of dairy milk every day for a year, businesses will need to use around 650 square metres of land – 10 times more than oat milk. While pasture-based systems may support biodiversity and soil health, intensive systems often rely on imported feed, shifting land impacts abroad.

Compared to plant-based alternatives, research shows that dairy milk generates roughly three times more GHGs per litre and has the highest water footprint of any commonly consumed milk. Almond milk, while water-intensive at around 74 litres per glass, still uses substantially less water than dairy. These environmental differences are increasingly shaping conscious consumer preferences which are driving demand for lower-impact dairy options and alternatives.

The shifting regulatory picture

Businesses face the necessity of embedding more sustainable, resilient and circular practices into their operations.

For example, under the EU Deforestation Regulation (EUDR), companies sourcing cattle, soy and palm oil must ensure their supply chains are deforestation-free by December 2025 for large enterprises, and by July 2026 for medium and smaller businesses. Scope 3 emissions reporting is also becoming standard under frameworks like the EU’s CSRD, while voluntary initiatives such as the TNFD are encouraging companies to assess and disclose nature-related risks.

Investor and retailer expectations are also evolving. Science-based targets, particularly those aligned with the SBTi FLAG guidance, are becoming standard. Fonterra, for instance, aims for over 78% of its suppliers and customers to have validated science-based targets by 2028. Retailers are also increasingly looking for traceability, emissions data and regenerative sourcing commitments from their suppliers.

Changing customer preferences are another dynamic influencing dairy business decision-making. Demand for more sustainable dairy options is growing, and sustainability credentials are playing a larger role in purchasing decisions. Companies that fail to adapt risk losing relevance in an increasingly values-driven marketplace.

How is the dairy industry responding to environmental challenges?

To respond to changing customer preferences and regulatory demands, dairy businesses have already been taking action to improve their sustainability.

Cutting methane emissions

Across the industry, companies are investing in a range of strategies to reduce their environmental footprint, with methane reduction a top priority. Use of feed additives, such as Bovaer, is one promising option. Trials of Bovaer at Danone farms in Belgium shows a cut to enteric methane emissions by nearly 20%, with similar trials at Arla reporting reductions of up to 30%.

Other methane-cutting approaches include improving feed composition to reduce fermentation and investing in anaerobic digesters that convert manure into biogas, simultaneously cutting emissions and generating renewable energy.

Boosting regenerative agriculture practices

By improving soil health, increasing biodiversity and enhancing water retention, regenerative practices offer a range of environmental and economic benefits. Nestlé, for example, is working with over 500,000 farmers to implement regenerative practices such as multi-species grazing, cover cropping and nutrient cycling, and has so far committed CHF 1.2bn to support this transition. Nestlé is also offering financial incentives to farmers who meet sustainability benchmarks.

We see cooperative structures throughout supply chains as an important way for businesses to spread regenerative practices, with farmer cooperation and incentivisation key for driving systemic change. For example, Arla’s cooperative structure enables close, ongoing collaboration with its farmer-owners. Through its FarmAhead tool, Arla collects detailed sustainability data from its farmers and allocates up to €500m annually to incentivise sustainable practices. This data serves as the backbone of the FarmAhead programme, which rewards farmers for actions such as enhancing feed efficiency, improving manure management and promoting biodiversity.

Sourcing energy responsibly

Businesses are also increasingly transitioning their energy consumption away from fossil fuels and towards renewables. Nestlé and Fonterra are two examples of dairy companies that have pivoted to  renewable energy, with Nestlé reporting that over 95% of electricity at its sites now comes from renewable sources. These and other initiatives have helped Nestlé reduce its GHG emissions by 13.5% since 2018, including a 15.3% reduction in methane.

Driving circular packaging and reducing food waste

As with all food and drink sectors, packaging and food waste present critical environmental challenges for dairy producers. Traditional packaging formats such as plastic bottles and multilayer cartons are often difficult to recycle and contribute to landfill and ocean pollution. Companies such as Nestlé and Arla are advancing sustainable packaging through recyclable, reusable and lightweight designs, with Nestlé already reporting that 86.6% of its packaging is now recyclable, while Arla is aiming for 100% recyclability by the end of 2025.

Capturing confidence through proactive transformation

Dairy COOs should act now to ensure they continue to earn the trust of their stakeholders. Strategic investment in traceability and data tools – such as Arla’s FarmAhead – is increasingly important for tracking emissions, water usage and animal welfare metrics across the supply chain.

Supporting regenerative agriculture pilots and scaling successful models should be another area of focus and this will involve funding, and close collaboration with farmers, researchers and NGOs.

Finally, collaboration across the value chain is critical. From feed suppliers to retailers, alignment on sustainability goals and metrics through shared incentivisation will drive progress. Companies should also prepare for climate risk through scenario planning, diversification and investment in local sourcing and infrastructure.

A final word

The environmental impact of dairy is real, but so is the potential for transformation. From methane reduction to regenerative farming, the tools and strategies are already in play. Companies that act now will not only reduce risk but unlock new value, from operational efficiency to brand trust.

Sustainability is not a cost – it’s a competitive edge. The question is no longer if the dairy industry must change, but how fast it can lead.

At Argon & Co, we help businesses transform and adapt to more sustainable ways of operating. Find out how we can help you today.

This is the third article in our mini-series titled ‘The future of dairy: how technology, trade and transformation can drive performance in a changing industry’

Read more here:

Article 1 – Connected, agile, informed: The next era of dairy supply chains 

Article 2 – The next frontier of dairy: the four technologies shaping the future of the dairy industry

Upcoming articles to look out for…

Article 4 – The Shifting Landscape of Global Dairy Trade: A Look to the Future

The post Unlocking value through sustainability: the opportunities of circularity for dairy businesses appeared first on Argon & Co.

With AI and machine learning marking a new era of evolution for dairy businesses, let’s take a closer look at the new technology helping companies work smarter and more efficiently than ever.

Operations efficiency is the key to business success

Leveraging successive evolutions in technology over hundreds of years, the dairy sector has long placed a keen emphasis on innovation, from electrification to automation. Today, with efficiency and value chain optimisation a never-ending focus, business success depends on having well-oiled and deeply integrated operations. All so that businesses can that deliver the highest-value protein to the most profitable markets while meeting growing consumer expectations for traceability, ethical sourcing and sustainability.

Yet dairy businesses continue to face uncertainty even with well-established operations. Dairy is a disassembly process. Manufacturers must tailor their product mix based on market demand and value, which is especially challenging due to the seasonal nature of farming. This requires extensive real-time data monitoring on everything from cow health and pasture conditions to milk composition and logistics. And all long before milk reaches the factory.

Once in the factory, high-capital processing plants demand reliability and precision. Technologies such as thermography, infrared scanning, vibration analysis and autonomous maintenance are used to ensure uptime and product quality. Beyond the factory, the value chain extends to containerisation, temperature-controlled logistics, export documentation and distribution network optimisation.

What are the emerging technologies driving innovation in the dairy sector?

Under pressure to continue boosting operations efficiency, emerging technologies can play a key role in helping businesses boost positive outcomes.

1. Robotics

Automated milking systems now allow cows to self-milk, increasing yield and enabling real-time compositional analysis. These systems reduce labour costs and enhance animal welfare by allowing cows to follow natural milking rhythms.

2. Artificial Intelligence

AI can help optimise herd and pasture management and forecast demand based on social and environmental data, as well as support early disease detection and reproductive planning, improving herd health and productivity.

Other tools, such as real-time herd management and e-shepherd systems, allow farmers to monitor and guide livestock remotely, improving pasture utilisation and animal health while reducing farm management and improving data capture.

In demand forecasting, AI models analyse consumer behaviour, weather patterns and market trends to help producers align supply with high-value opportunities.

3. Internet of Things (IoT)

Underlying the rapid evolution of AI is the increased utilisation of data as a way of informing strategic and operational decisions. Through data, activities like milk collection have evolved away from mere route optimisation and towards more value-based optimisation. That is, prioritising the best quality milk for the best processing outcome. GPS and milk composition data feed into AI models that optimise logistics and network performance. This integration reduces waste and enhances responsiveness to market fluctuations. IoT sensors monitor milk temperature, age and composition throughout the journey, ensuring quality and compliance with safety standards.

4. Blockchain

Blockchain ensures end-to-end traceability and supply chain integrity. Consumers increasingly demand transparency and ethical sourcing, which blockchain and IoT technologies can deliver in real time.
Blockchain can also help simplify export compliance and documentation, reducing administrative overhead and improving transaction speed.

The future of the dairy sector

The rate of change is only accelerating and emerging technologies are increasingly creating transformative opportunities for dairy companies. These are some trends we are seeing:

• The ability to scale investment and aggregate data is accelerating. Smaller non vertically integrated producers will struggle to compete with the value chains of larger more integrated producers
• Larger producers will extend their reach through acquisition or collaboration into new markets and geographies to leverage scale and data
• New technologies such as 3D printing of protein utilising dairy proteins as a base are being researched, and small scale on farm drying capability to reduce asset investment and create flexibility
• Intra company collaboration to balance supply and demand for optimal product mix

To remain competitive, organisations must assess their ability to adapt to new technologies so as to begin building a roadmap for human-centric machine integration.

Argon & Co helps dairy companies around the world to optimise their dairy supply chains and lessen their environmental footprints. Find out how we can help you.

This is the second article in our mini-series titled ‘The future of dairy: how technology, trade and transformation can drive performance in a changing industry’

Read more here:

Article 1 – Connected, agile, informed: The next era of dairy supply chains 

Upcoming articles to look out for…

Article 3 – Unlocking value through sustainability: the opportunities of circularity for dairy businesses

Article 4 – The Shifting Landscape of Global Dairy Trade: A Look to the Future

The post The next frontier of dairy: the four technologies shaping the future of the dairy industry appeared first on Argon & Co.

Today, the industry stands at a tipping point. Evolving technology, climate volatility and shifting consumer expectations are reshaping the sector’s established ways of working. Once aspirational goals, like real-time scenario planning, AI-driven forecasting and end-to-end visibility, are now within reach, meaning the challenge is no longer whether these capabilities are possible, but how quickly and effectively they can be adopted.

A sector defined by constraints

Dairy is a notoriously low-margin business. Every small saving counts, and capital investment is often difficult to justify. As a result, many companies operate with ageing infrastructure and limited automation. This makes agility harder to achieve and amplifies the impact of disruptions.

Freshness is another defining counter-constraint. With short shelf lives and strict safety standards, dairy products cannot be stockpiled to buffer against uncertainty. Strategic safety stocks are used selectively – often to support campaign-based production runs – but they are no substitute for real-time responsiveness.

Compounding these issues is the nature of dairy production itself. Milk can be transformed into more than 20 different products, each with its own by-products. For example, producing milk powder yields cream, which must pass through its own value chain to be sold profitably. Cheese, which has ageing requirements that span months or even years, introduces further complexity for supply chains by making planning for short-notice promotions with retailers a delicate balancing act.

Towards connected supply chains

Like other agricultural sectors, dairy operates in a push-pull dynamic. On the supply side, production is biologically and seasonally driven, affecting what can be produced and when. On the demand side, consumer preferences, retail promotions and export markets exert constant pressure for responsiveness. Under pressure from the supply and demand side, dairy’s often-siloed and static planning systems struggle to keep up with the pace and complexity of modern operations.

To better manage the growing stresses facing dairy companies, truly connected supply chains that directly link farmers to consumers through seamless data flows offer one solution. With data from upstream suppliers filtering through to the rest of the value chain, dairy businesses can better communicate with retailers and suppliers, and build further resilience and agility into their business model.

When data flows efficiently across the value chain, decisions can be made faster and with greater confidence. Processors can optimise production runs based on real-time inventory and sales data, and retailers can plan promotions with full visibility into supply constraints and lead times.

Though transforming supply chains in this way poses challenges, its upsides can lead to reduced waste, improved service levels and a more resilient supply chain.

AI and the next frontier of forecasting

AI can play an important role in how businesses build more resilient supply chains, and is already unlocking new possibilities. For example, AI is already helping to forecast milk protein levels ahead of time by analysing weather patterns, feed quality and historical yield data. With this data being passed along the supply chain, processors can plan product mix and capacity more accurately, in turn reducing inefficiencies and boosting profitability

AI is also being used to enhance demand forecasting, optimise transportation routes and dynamically adjust plans in response to disruptions, with many modern planning systems featuring AI predictability as standard. These capabilities are especially valuable in dairy, where shelf life is short and the cost of getting it wrong is high.

As AI matures, it will increasingly serve as a decision support system by augmenting human judgment with data-driven insights and enabling planners to focus on strategic exceptions rather than routine tasks.

Scenario planning in an age of disruption

Climate-related events such as floods and bushfires are becoming more frequent and severe, impacting the availability of milk supply with little warning. Scenario planning has always been important in countering emerging threats, but AI is now helping planners manage and respond to scenarios in real time by testing alternatives and evaluating trade-offs. Through intuitive APS platforms, for example, businesses can benefit from real-time scenario modelling that supports fast decision-making on both long-term options and short-term disruption response. These capabilities are transforming the ability of companies to make the right choice and not just the fast choice.

An important tool in the arsenal of the sector will be digital twins – virtual replicas of supply chain networks. These will help companies rigorously simulate different scenarios, test responses to disruptions and evaluate the impact of strategic decisions before implementing them in the real world.

Asset optimisation and maximising milk solids

In addition to bolstering resilience and risk planning, dairy businesses embarking on a transformation of their supply chains can also benefit from improved asset optimisation.

A defining feature of dairy processing is the need to optimise the return on milk solids. After satisfying consumer demand – particularly for fresh milk – most dairy companies focus on maximising the value extracted from the remaining solids. This includes converting milk into products like cheese, butter, powders and other derivatives, each with its own yield, by-products and market dynamics.

Optimising this mix is a complex, high-stakes exercise. It requires balancing profitability, shelf life, production capacity and market demand all while ensuring that by-products are monetised effectively. This is one of the key differentiators of the dairy sector and a major driver of planning sophistication.

A note on emissions transparency

While sustainability may not be the primary driver of planning transformation, it is an important co-benefit. A more connected and data-rich supply chain provides greater clarity on Scope 3 emissions—those generated across the value chain, including feed inputs, on-farm production and logistics. As regulatory and investor scrutiny increases, this transparency will become increasingly critical.

By embedding tracking of emissions and the many other ESG impacts into planning systems, companies can not only meet compliance requirements but also identify opportunities to reduce environmental impact in ways that align with operational efficiency.

Planning for the human factor

Technology is a powerful enabler of capable and agile supply chains, but people remain at the heart of supply chain success. So, as dairy businesses shift to intelligent, agile supply chains, they should also look at the skills, mindset and ways of working that will be crucial to any business transformation.

Organisations should invest in upskilling their workforce by training planners to use advanced tools, interpret data and make strategic decisions. They should also foster a culture of collaboration, experimentation and continuous improvement that ensures nobody feels left behind.

Creating smarter and stronger dairy supply chains

The future of supply chain planning in the dairy industry is not just about managing milk – it’s about managing complexity, uncertainty and change. By embracing digital innovation, building connected ecosystems and empowering people, dairy companies can build supply chains that are not only more efficient, but also more resilient, responsive and ready for what’s next.

As the world’s appetite for dairy continues to evolve, those who plan smarter and faster will lead the way. Find out how we can help you transform your supply chains and prepare your businesses for the future.

This is the first article in our mini-series titled ‘The future of dairy: how technology, trade and transformation can drive performance in a changing industry’

Upcoming articles to look out for…

Article 2 – The next frontier of dairy: the four technologies shaping the future of the dairy industry

Article 3 – Unlocking value through sustainability: the opportunities of circularity for dairy businesses

Article 4 – The Shifting Landscape of Global Dairy Trade: A Look to the Future

The post Connected, agile, informed: The next era of dairy supply chains appeared first on Argon & Co.

To help leaders navigate this shift, Argon & Co and Supply Chain Movement have mapped the entire agri-food ecosystem (from farmers and manufacturers to data providers and governments) highlighting the critical role of collaboration and digitalisation in future-proofing supply chains.

This article explores the ecosystem’s dynamics and showcases four powerful case studies that reveal how top companies are leveraging technology to stay ahead:

• Future-proofing dairy: building a network digital twin to boost resilience
• Smarter forecasting: applying machine learning for demand accuracy
• Margin mastery: using data to optimise pricing and profitability
• Strategic integration: evaluating network modelling for smarter decision

Download the article here:

Reinventing agri-food: how industry leaders are driving sustainable innovation

The post Reinventing agri-food: how industry leaders are driving sustainable innovation appeared first on Argon & Co.

However, Trump 2.0 is just the latest in a sequence of disruptive events businesses have had to navigate – including the financial crisis, Trump 1.0 and Covid-19. And the constant to and from of turbulence will continue into the foreseeable future. Therefore, by redefining supply chains as part of a level-headed, long-term strategy, it’s possible to thrive instead of just survive in the next four years and beyond.

Supply chain disruption in 2025

Trump’s trade wars are forcing businesses to seriously rethink their sourcing strategies. This process will lead to short-term cost increases by driving the underlying need to control your own destiny, and long-term impacts which will undoubtedly reshape global trade and logistics.

How tariffs affect manufacturing costs

The main target of Trump’s tariffs is China (to the tune of $360 billion). And this has rocked supply chains – especially for verticals heavily reliant on Chinese manufacturing, such as electronics, automotive and consumer goods.

Suddenly, companies depending on just-in-time inventory models and highly integrated supply chains have faced price increases and delays in a deeply uncertain environment.

And organisations that previously relied on seamless cross-border trade with Canada and Mexico now face increased costs and logistical complexities. For example, the US automotive sector, which relies on components often crossing borders multiple times during production.

Supply chain resilience strategies

Untangling cross-border trade during Trump 2.0 can certainly feel complex. However, finding a lasting solution to supply chain resilience now will arm forward-thinking companies with a powerful competitive advantage in future.

China + 1 strategy

Trump 1.0 already caused a sea change in global supply chains, exposing their fragility and catalysing a shift to diversification. One positive adaptation many companies have since maintained is China + 1 – put simply, reducing their dependence on China. And this solid long-term resilience strategy can continue to reduce supply chain risks and vulnerability as Trump 2.0’s trade wars unfold.

Companies yet to adopt China +1 may be nervous doing so, given the trade behemoth’s sophisticated logistics networks, low costs and entire value chains. But nevertheless, a steady and sensible retreat should prove sagacious in the long term.

Supply chain contingency strategies: redefine, don’t redesign

The conversation around supply chains has evolved from efficiency-driven models to resilience-focused strategies. Consequently, the prevailing notion of transitioning from ‘Just-in-Time’ to ‘Just-in-Case’, the fragmenting of supply sources, onshoring manufacturing back to the US and nearshoring to neighbouring countries are all strategies businesses are using to safeguard against disruptions, while accepting the attendant increase in costs.

However, this shift has not been without its pitfalls. Many companies have overemphasised buffering against potential crises, meaning inflated costs and inefficiencies which were initially justified must now be reassessed.

Redefining supply chains (and reducing risk through diversification)

Despite the challenges, with judicious planning, onshoring and nearshoring can bring stability and sustainability benefits. Investing in state-of-the-art equipment with high levels of automation – and reduced labour requirements – may look like a high capital cost. Nevertheless, compared to the risk of wage inflation and labour availability, it could still be beneficial for some of your supply chains. And compared to offshoring, the only differentials are now land and energy, which are often a much lower percentage of the profit and loss.

However, to sustain success it’s essential to take a holistic view – integrating logistics, manufacturing, procurement and other components. Rather than viewing these elements in isolation, they must work together harmoniously for optimal results, carefully counterbalancing necessary trade-offs.

Diversifying your supply or manufacturing base should also be based on product segmentation. This means you won’t necessarily shift all of your categories to near or onshore, because you have to carefully calculate which products or categories the strategy best fits. For instance, it’s easy to move sewing machines elsewhere for apparel manufacturing, but not so simple moving to another country when you have mold injected manufacturing machinery and sophisticated techniques.

And taking a step back, it’s also wise to carefully review your product development process and bill of materials, to work out where you could source alternative raw materials or inputs at each stage. Provided alternatives don’t compromise quality, this allows you to maintain your product integrity and value proposition while reducing cost of goods sold.

Last but by no means least, tax has long played a significant role in logistics network design and analyses of the best routes to market. But now it is a central element, requiring studies to be conducted jointly between businesses’ supply chain teams and their tax and legal departments. There are certainly optimization opportunities, but they require agility in execution. This is illustrated by the fact we are currently working with clients considering switching back to direct supplier flows, using warehouses located in other countries, or switching their warehouses to Free Trade Zones.

Self-healing supply chains: move with the times

There’s no easy answer to future-proofing your supply chain. But perhaps one concept will stand the test of time: the idea of a self-healing system.

As supply chains evolve, leveraging new technologies like AI and advanced analytics can enhance visibility and responsiveness, thus turning adaptable, self-healing supply chains into a competitive advantage for early mover businesses.

The self-healing supply chain adjusts and optimises by rebalancing, prioritising or resequencing events for better outcomes. And while human oversight may be necessary for approvals, it predominantly operates on data driven automation.

For maximum effectiveness, the supply chain must be well-integrated across suppliers and customers, functioning cohesively like a single organism. A self-healing system requires a nuanced approach to supply chain management, where physical elements such as the length of chains, flow paths and critical stock points are constantly evaluated and balanced.

In this way, the muscle memory of future-proof supply chain management is built up – augmenting specialist team capabilities with powerful systems, generative AI and machine learning. Adapting dynamically, it prioritises needs and optimises responses to satisfy customers and enhance operational efficiency.

Next steps

Businesses need to take a holistic view to solve the complex challenge of navigating global supply chain pressures. Therefore:

• Consider the unique needs of your operations, suppliers and customers
• Ensure data connectivity and understanding across all points
• Take informed decisions, adjust needs and rebalance resources, enabling the entire supply chain to adapt and self-heal

This approach requires collaboration – with the entire value chain, from raw materials to end customers, interacting and responding effectively. It’s no easy task. But remember: the post-pandemic era already demands a radical rethinking of supply chain strategies, so the speed of change in Trump 2.0 has merely accelerated the need for more responsive and cost-effective models. Indeed, the self-healing supply chains you engineer today will soon be battle-tested by geopolitical events that transcend Trump. Drawing on human expertise combined with intelligent automation, they’ll cope with:

• Ongoing tensions in the Middle East and Red Sea
• The Ukraine War (and the trade implications of its settlement)
• The looming threat over Taiwan

Not to mention recurring ocean freight bottlenecks, global port congestion problems, strategic sourcing conundrums, and environmental and sustainability pressures.

There are always challenges on the horizon. But resilient, self-healing supply chains won’t collapse under pressure. Instead, they’ll roll with the punches, intelligently anticipating each blow and counterattacking with confidence.

This is how future-focused businesses will flourish – through Trump 2.0 and beyond.

For expert guidance on navigating tomorrow’s world, contact Argon & Co today.

The post Can self-healing supply chains trump trade turbulence? appeared first on Argon & Co.

Argon & Co’s recent event “Navigating 2025: Innovation, Sustainability and Economic Growth” aimed to address some of those questions. As with any technological leap, AI’s future is hard to predict. At a global level, the technology could be transformational and become embedded in every aspect of our lives. Or it could plateau and adoption could slow. For every game changing advance, such as cloud computing, there is a disappointment like the “Internet of Things”. Instead of predictions, the discussion offered “intuitions” that consider where AI is capable of going.

Exponential growth that’s redefining possibilities:

AI is not just growing—it’s accelerating. What began as simple chatbots is now capable of generating human-like conversations, solving complex problems, and making autonomous decisions. ChatGPT-4 represents a 10,000-fold increase in computing power over ChatGPT-2, released in 2019, and we can expect a similar leap in the computing power of AI models before the end of the decade.

For businesses, this represents more than a tool; it’s the emergence of AI agents that will one day autonomously manage tasks, make strategic decisions, and solve problems like a human team member. The question is not whether this will happen—it’s when. If that sounds far-fetched, then consider if you believed that the first news reports in January 2020 about the emergence of a flu-like virus in China would result in global lockdowns and economic shutdowns.

A learning system, not just a knowledge repository:

The power of AI lies in its ability to learn—not just store information. AI models have access to vast amounts of data, enabling them to learn patterns, adapt, and predict outcomes. For example, a model might start by guessing that “the cat sat on the… mat,” but soon, it’s making highly accurate predictions across various domains.

What’s more intriguing for businesses is that this learning ability means AI is continually evolving. By 2050, some experts predict AI could rival or even surpass human cognitive abilities. The key takeaway for C-suite executives? The AI you invest in today is a small fraction of what it will become. Those that take early action in incorporating AI will gain a competitive edge.

AI’s capabilities: discovered, not designed:

Unlike traditional software that’s built for a specific purpose, AI’s power lies in its ability to discover solutions to problems it wasn’t explicitly programmed to solve. For example, when ChatGPT-4 was released, no one told it how to play chess, yet it can now hold its own against experienced players—and it’s improving.

This self-discovery process, while exciting, also introduces risks. AI can find vulnerabilities in systems and uncover information that wasn’t initially accessible. Therefore, while AI is a game-changer, it’s critical to implement safeguards and continually monitor its use.

AI will get political:

Nations are recognising the strategic importance of AI, governments around the world are restricting access to critical technologies. For instance, the U.S. CHIPS Act of 2022 sought to limit China’s access to advanced AI hardware to maintain its technological edge.

For businesses, this has direct implications. AI’s future will be shaped not only by innovation but also by political decisions. Navigating AI’s potential requires awareness of the geopolitical landscape, as access to AI technologies may become restricted or subject to political shifts.

What does this mean for businesses?

The rapid pace of AI development can feel overwhelming, but there’s no denying its business potential. The most successful companies will be those that embrace AI today, focusing on small, achievable projects that deliver tangible results. Hence, we believe that businesses must:

• Fix their data: AI thrives on high-quality data, so organisations must first clean, structure, and govern their data effectively.
• Develop internal AI capabilities: building an in-house team skilled in AI will ensure that companies remain agile and capable of harnessing AI’s evolving potential.
• Start small, scale fast: start by deploying AI in focused areas that can deliver quick wins, such as inventory optimisation or predictive maintenance. Use these initial successes as stepping stones to more complex applications.

By taking these steps, businesses will not only leverage AI’s current capabilities but will also position themselves to unlock its transformative potential in the future.

Author: Mohib Rahmani

The post AI for the C-suite: cutting through the hype to real business impact appeared first on Argon & Co.

Imagine a world where AI handles the automation of complex processes without requiring stakeholders to follow rigid structures and templates: this is the vision of Soft Process Automation (SPA).

SPA has been developed by Argon & Co to enable process improvement teams to move away from traditional workflows, allowing for efficient conversion of raw free-form inputs into meaningful outputs tailored to their needs.

SPA deploys AI to automate tasks that are not well-defined, unstructured, non-linear and repetitive, but do require a person to apply human judgement, creativity and problem-solving skills.

Features of Soft Process Automation

Examples of such tasks can be seen across the entire value chain, so automating the non-valuing parts of these processes can bring significant benefits in terms of efficiency and job satisfaction:

• Reducing operational errors by automating manual tasks, such as data entry, validation, and verification
• Providing faster, more accurate and consistent outputs
• Freeing-up time for more value-adding and creative activities, such as upselling, cross-selling and problem-solving
• Enabling the same team to process higher volumes, accommodating growth
• Breaking data silos by creating a single source of truth and a common language
• Ensuring one-time error-free uploads to ERP systems

SPA represents a significant leap forward in process automation. Unlike RPA (Robotic Process Automation which excels at rule-based, repetitive tasks), SPA can handle complex, dynamic scenarios that require human-like interpretation and decision-making. Traditional workflow automation tools often struggle with unstructured data or processes that deviate from pre-defined paths. SPA on the other hand, leverages the power of Large Language Models to adapt to variations and nuances in inputs, making it ideal for tasks like customer service interactions or complex order processing.

We recently completed the deployment of an SPA application focused on improving the processing of all incoming orders from those customers that were not on Electronic Data Interchange (EDI) for the Customer Service function of a major drinks manufacturer.

This resulted in:

• 73% reduction in order processing time
• 50% reduction in switches in applications whilst processing orders
• Significant improvements in job satisfaction

Design phase: the approach to the application development

During the design phase, we identified and prioritised the specific tasks within the customer service area that could be automated or enhanced with SPA assessing:

• The complexity and frequency of these tasks
• The incremental value unlocked through Soft Process Automation

Based on this evaluation, we selected the customer order entry process to be the pilot.

Developing an SPA solution involves a systematic and iterative approach

The tool developed enabled the high-speed completion of an array of tasks:

• Highlighting PO discrepancies or misalignments to master data
• Assisting users with customer email content generation in case of such discrepancies or misalignments
• Identifying and extracting the relevant information from POs using LLMs, and translating to customer master data (e.g. unit of measurement, product descriptions, customer code, customer address, delivery date etc.)
• Performing allocation and availability checks through a live interface with the ERP system
• Breaking down the orders into correct container sizes based on optimisation (highlighting where there is an opportunity to upsell based on current container space utilisation)

The solution was implemented in two distinct phases; an 8-week development phase followed by a 4-week deployment phase. As with any new solution, user adoption is crucial and cannot be overlooked. To ensure user adoption, a smooth transition and promptly address any issues, a hyper-care period of 3 months was included.

Key takeaways: best practices and challenges for SPA adoption

It is easy to think of the deployment of such applications as one-off projects, but in reality, SPA is a continuous journey. It involves committing to user adoption, ongoing improvement and adaptation to evolving needs and opportunities:

• Align SPA with the business strategy and needs: SPA should not be implemented for the sake of technology, but rather to solve real business problems and enhance your experience. SPA should be aligned with the overall business strategy and your needs and deliver tangible and measurable value and outcomes.
• Involve the stakeholders and users throughout the process: Collaboration with stakeholders affected by the Customer Service process is key. The user must always be at the centre of the design, and any application development should incorporate their feedback and input throughout the process. Getting early IT buy-in is fundamental to ensure the solution design for the application aligns with your company’s data security and privacy criteria.
• Ensure data quality and security: Data is the fuel for SPA, and its quality and security are critical for the success and trustworthiness of the application. Data should be accurate, complete, and relevant, otherwise the benefits achieved from the application can risk being diminished.
• Manage change and expectations: SPA can bring about significant changes and challenges, such as new roles and skills, new processes and workflows, and new cultures and mindsets. With a robust change management and communication plan in place, addressing challenges related to data availability and quality, model accuracy and reliability, and user acceptance and adoption becomes significantly easier.
• Ensure robust process ownership: It is crucial to ensure that a business process owner is identified to onboard the users, ensure their adoption and satisfaction with the SPA application and provide them with training and support. This role should also collaborate with users on collecting feedback to consider for future versions of the application as the process evolves.

This approach ensures the application remains effective, relevant, and capable of delivering long-term value to your organisation.

Discover SPA in action: watch our video

To fully explore the capabilities and benefits of SPA, we invite you to watch our detailed video. This visual guide offers an in-depth look at how SPA works and the transformative impact it can have on your customer service operations.

If you are ready to explore SPA further, please contact our IRIS expert below.

We would love to hear from you and discuss how we can help you achieve your SPA goals.

Learn more about IRIS by Argon & Co

Author: Marjan Torshizi

The post Case study: how Soft Process Automation transforms customer ordering with AI appeared first on Argon & Co.

By leveraging GenAI and Large Language Models (LLMs), but keeping the “human in the loop”, the app converts non-standard orders received in any document format into an ERP-compliant format in a few minutes. Allowing your team to get more time back to focus on value-add activities.

Watch the full demo below.

To find out more, visit IRIS by Argon & Co or reach out to our IRIS expert below.

The post Video demo: Soft process automation in customer service appeared first on Argon & Co.

What’s new in Copilot’s Wave 2?

• Accessible automation agents: Users can now create autonomous Copilot Agents to automate tasks and processes across various platforms including SharePoint and Teams. Accessible through Copilot Studio, Agents can provide immediate value in automating tasks such as updating regular reports or creating frequent documents.
• New collaboration option: “Copilot Pages” extends the reach of AI using Microsoft Loop’s functionality, allowing users to create spaces for fast, online collaboration, including outputs from Copilot chat.
• Analytics capability: Copilot now provides a low-code avenue for users to create and execute python code within Microsoft Excel, lowering the bar for business users to perform data science tasks.
• Enriched narrative creation: Copilot’s Narrative Builder in PowerPoint is a significant improvement, allowing users to generate presentations in line with your company’s branding. However, we view this is an incremental step towards where Microsoft needs to take PowerPoint to create high quality, first-time outputs.
• AI assisted prioritisation: Outlook’s new “Prioritise my inbox” feature lists emails in order of importance, including a rationale of the prioritisation and including a summary of the email.
• Enhanced contextual assistance: Microsoft has adapted Copilot’s ability to understand the context of your work, improving the quality of your outputs.

Common mistakes to avoid when rolling out Copilot:

Despite the enhanced functionality of Wave 2, many companies will struggle to realise the full value from Copilot due to these common mistakes:

1. Underestimating the transformation: automation implies tasks and processes require less effort for similar or better results. Copilot, on the other hand, is a tool that facilitates automation across numerous Microsoft applications, each with their own distinct features and interactions. To fully leverage this potential, training is required to firstly identify the automation potential of a task and then be able to take advantage of that potential.
2. Not setting clear objectives: Copilot’s range of functionality across several applications often distracts companies from setting clear objectives they want achieve from their roll-out. Whether it be to automate a specific process or improve the performance of a business function, ensure the aims of your Copilot implementation support your overall business strategy.
3. A lack of strategic targets:Not all processes or business functions are equally suited to automation. It’s important to understand where Copilot will drive the most value and prioritise the roll-out accordingly.
4. Overlooking foundational Microsoft knowledge: one common feedback from our Copilot training is that users often lack a foundational understanding of Microsoft applications. To better leverage Copilot and access the advanced and powerful features, it is essential to understand core Office apps like Word, Teams and Excel, as well as the more specialised tools like Power Automate and Power Apps.

Wave 2 is an impressive extension of Copilot’s previous capabilities, building upon an already impressive suite of functionality. To continue to be at the forefront of AI, Microsoft will need to continue to improve Copilot’s functionality and ease of use in the core applications including Excel, PowerPoint and Outlook. As AI continues to learn and adapt, at IRIS by Argon & Co we see a clear trajectory towards increased sophistication, integration and business value. IRIS by Argon & Co’s extensive Copilot Training Programme now incorporates Wave 2 functionality and the most recent Copilot features.

Learn more about IRIS by Argon & Co or get in touch below.

Author: James Byrne

The post Capturing the benefits of Copilot Wave 2’s new functionality appeared first on Argon & Co.