Intelligent software like Swisslog's SynQ can control entire processes and maximise warehouse automation efficiency. Image credit: Swisslog
Australian businesses investing in warehouse automation should prioritise solution design and operational data before selecting equipment, according to Swisslog.
In a news release, Swisslog Australia and New Zealand Director of Sales Steve Dimitrovski said businesses should assess how automation would operate within their broader warehouse environment, rather than focusing solely on individual technologies.
“Automation is a significant investment, so businesses need to look beyond what the system can deliver on day one,” Dimitrovski said.
He said effective design should take into account historical order profiles, SKU velocities, inventory levels, peak demand and potential changes in fulfilment requirements.
“Designing around an average day can give you a very different result from designing around the reality of your operation,” Dimitrovski said. “You need to understand what happens when volumes increase, when the order profile changes and when multiple channels are being fulfilled at the same time.”
Swisslog said simulation modelling can also help identify potential bottlenecks before equipment is ordered. For automated systems such as AutoStore, modelling can be used to assess different combinations of robots, bins and ports against changing throughput requirements.
“Simulation gives you an opportunity to identify problems before they become expensive problems on the warehouse floor,” Dimitrovski said.
The company also said future expansion should be considered during the initial design process. While modular automation can allow capacity to be increased over time, Swisslog said scalability depends on decisions made at the outset.
“Growth needs to be designed in, rather than treated as an afterthought,” Dimitrovski said.
Software integration is another consideration as warehouses combine robotics, automated storage, conveyors and sortation systems. Swisslog said its SynQ software platform is designed to provide an integration and orchestration layer across automated material flows.
Dimitrovski said businesses should look beyond equipment specifications and headline pricing when comparing automation proposals.
“Two proposals can look very similar on paper, but the depth of the design work behind them can be very different,” he said.
“The right questions are: what data has been used, what scenarios have been tested, where are the constraints, and how can the system expand in the future?”
Manufacturing packaging redesigns have been associated with an average 4% increase in product volume across nearly a decade of NielsenIQ (NIQ) Design Impact Award winners, according to the consumer intelligence company.
NIQ announcedeight winners of its 2026 Design Impact Awards, recognising packaging redesigns that it said combined improved shopper experiences with measurable commercial performance.
“The Design Impact Awards reinforce the critical role packaging plays in influencing shopper decisions and driving business growth,” said Andrea Fraboni, vice president and global leader of Pack & Design Solutions at NIQ.
“Packaging has seconds to do its job. The strongest redesigns make products easier to notice, understand and choose while preserving the distinctive brand cues consumers recognize,” Fraboni said.
The 2026 winners included Aval French Cider, Liquid I.V., Root & Splendor, Hostess Cakes, Back to Nature, John West Tuna Chunks, Country Fresh Ice Cream and Rustica Pizza, spanning markets including the US, UK and South Africa.
NIQ said its Pack & Design experts assessed submissions against packaging principles and the role packaging plays throughout the shopper journey. The company also used its Retail Measurement Services data to identify brands that recorded measurable sales growth after introducing updated packaging.
According to NIQ, the winning redesigns shared a focus on making products easier for shoppers to notice, understand and select. The company pointed to factors including shelf visibility, clearer information hierarchy, simplified communication and distinctive branding.
“Packaging design isn’t simply aesthetic,” Fraboni said. “When done well, it can be a meaningful driver of shopper engagement and business growth.”
NIQ said its Pack & Design Solutions practice works with manufacturers to evaluate and optimise packaging, combining neuroscience-based research with behavioural and survey-based approaches to support packaging design decisions.
The company did not state that packaging redesign alone caused the reported volume increases, but said the award-winning redesigns were associated with measurable commercial performance following their launch.
As manufacturers look to extend artificial intelligence beyond data analysis and into physical operations, the focus is increasingly shifting towards systems that can sense, interpret and respond to conditions on the factory floor.
Cognizant has launched a sovereign Physical AI platform aimed at helping manufacturers move beyond isolated artificial intelligence (AI) pilots and connect sensors, robotics, factory automation and other operational systems through a unified intelligence layer.
The technology services company said its Physical AI Platform-as-a-Service, built on the Cognizant Intelligence Spine, is designed to support AI deployment across physical operations while keeping data, rules and institutional knowledge under enterprise control.
In an exclusive interview with Australian Manufacturing, Cognizant Managing Director for ANZ Carla Ramchand said Physical AI differs from conventional industrial AI by enabling systems to interpret their surroundings, respond to changing conditions and take action in real time.
“Many AI tools today analyse data, predict faults, or recommend what someone should do next,” Ramchand said. “Physical AI takes that step further by enabling machines to understand their environment and context, provide reason for changing conditions, and act in real time.”
She said the shift is being enabled by the development of advanced sensors, robotics, multimodal AI and low-latency connectivity.
For manufacturers, Cognizant said this could mean moving from systems that identify problems and alert operators towards systems that can also determine causes and undertake authorised corrective actions within defined safety boundaries.
Moving beyond AI pilots
Cognizant said scaling AI from controlled pilots across multiple machines, production lines and sites can introduce technical and organisational challenges.
“Factories are complex environments,” Ramchand said. “Legacy equipment operates alongside newer sensors, robots and digital platforms, while data ownership and connectivity can vary across.”
She identified latency, integration, cybersecurity and safety as potential challenges when AI systems move into live operations. Organisational issues, including unclear ownership, inconsistent workflows and workforce readiness, can also emerge during implementation.
Cognizant recommends that manufacturers begin with a clearly defined operational problem and measurable target, while designing for production from the outset. Ramchand said security, governance, workforce training and lifecycle support should be incorporated, with digital twins used to test decisions before they reach live operations.
The Cognizant Intelligence Spine is intended to provide a framework between physical systems and the AI layer that reasons and acts, allowing individual AI applications to be connected rather than operating as standalone deployments.
Connecting existing factory infrastructure
Cognizant said its platform is designed to work across legacy and newer technologies, allowing manufacturers to modernise without necessarily replacing existing equipment.
“Interoperability is essential because most factories run on a mix of old and new technology,” Ramchand said.
She said sensors and integration tools can connect older equipment to newer factory systems, allowing existing assets to generate and share data that can be used by AI.
Rather than replacing machinery across an entire facility, Ramchand advised manufacturers to begin with a high-value asset or workflow, secure and connect its data, measure the return and then apply the approach elsewhere.
“Making existing equipment more connected and intelligent should be the focus of factories and not replacing every machine,” she said.
Cognizant said its Physical AI service supports manufacturing applications including predictive maintenance, autonomous quality control, robotic process integration and yield optimisation. Connecting these applications could allow information from cameras, equipment, maintenance systems and production processes to inform decisions across multiple operational areas.
The company also sees potential for Physical AI to address productivity and workforce pressures by continuously monitoring machinery, automating repetitive inspections and helping employees assess faults using live data.
However, Ramchand said the objective should not be complete automation.
“The goal should not be complete automation,” she said. “Manufacturers need to focus on targeting one clear result, prove it works safely, then roll the same system out to other lines and sites.”
Cognizant expects Physical AI to contribute to a shift from isolated automation towards more adaptive manufacturing operations over the next five years. Ramchand said this would not necessarily mean fully autonomous factories, but greater integration between AI-enabled systems and human workers.
As AI begins influencing physical operations, she said manufacturers should establish clear limits on autonomous actions, retain human oversight for safety-critical or irreversible decisions, and use manual overrides and fail-safe modes.
“Manufacturers must have control over the data, rules, and institutional knowledge shaping their systems,” Ramchand said. “Making every action visible, testable, and accountable is where we see this hybrid industrial workforce become a place of trust.”
This article contains information provided by Cognizant and is intended for general use only. It does not take into account your personal, professional, or business circumstances.
Seeing Machines has launched a Physical AI Platform designed to give humanoid robots and industrial automation systems greater awareness of people and their surroundings, with manufacturing among the industries identified as a potential application.
In a media release, the Canberra-based computer vision company said the platform extends its Human-Centred AI approach, developed through more than 25 years of research into human factors and artificial intelligence.
Seeing Machines said its driver and occupant monitoring technology is already deployed in more than eight million vehicles globally.
According to Seeing Machines, the new platform creates a dynamic three-dimensional perception map of people, objects and their surroundings. It is designed to help robots understand spatial relationships, interpret human behaviour, anticipate risks and make decisions in real time.
Seeing Machines CEO Paul McGlone said the technology represented an extension of the company’s work in human-machine interaction.
“For more than two decades, we’ve been teaching machines to understand people,” McGlone said. “With the launch of our Physical AI Platform we are extending that capability into robotics, enabling machines to interact with people safely, naturally and intuitively.”
Seeing Machines said the platform differs from AI systems focused primarily on identifying individual objects or performing predefined tasks by interpreting people, objects and the surrounding environment as a unified scene.
McGlone said this capability would become increasingly relevant as robots move into environments where they work alongside people.
“As robots begin moving beyond research labs and into factories, workplaces, hospitals, homes and public spaces, understanding people and the environment they’re in becomes just as important as understanding the task they’re performing,” he said.
Seeing Machines identified manufacturing, logistics, healthcare, aged care, warehousing, mining and industrial automation as potential application areas for the platform.
Article by Michael Murphy, director, operational technology (OT), Asia Pacific, Fortinet
Australia’s manufacturing sector is entering a new phase of digital transformation. Artificial intelligence (AI), automation, and connected operational technologies are reshaping production environments, helping manufacturers improve productivity, strengthen supply chain resilience, and remain globally competitive.
The recent “AI in Australia’s interests” announcement by the Australian Government reinforces the growing importance of AI as a driver of Australia’s future economic growth, with advanced manufacturing expected to play a significant role in that transformation.
As manufacturers embrace these technologies, cybersecurity has become an equally important consideration. In June, the Five Eyes cybersecurity agencies, including the Australian Signals Directorate (ASD), warned that AI is increasing both the speed and sophistication of cyber threats, urging organisations to treat cyber resilience as a strategic business priority rather than simply an IT responsibility.
For manufacturers, that advice couldn’t be more relevant.
Today’s production environments are more connected than ever before. Industrial control systems, sensors, robotics, cloud platforms, and remote maintenance technologies are creating unprecedented visibility and operational efficiency. However, every new connection also creates another potential entry point for cyber attackers.
Unlike many cyber incidents affecting traditional IT systems, attacks on operational technology (OT) can have immediate physical and operational consequences. Production lines can be interrupted, supply chains disrupted, and worker safety placed at risk. In an industry where uptime directly affects profitability and customer commitments, cybersecurity has become a fundamental component of operational resilience.
This growing convergence between IT and OT is changing how manufacturers approach cyber risk. Historically, factory systems operated separately from enterprise networks. Today, manufacturing organisations increasingly rely on integrated environments that let production data flow seamlessly across engineering, operations, and business systems. While this connectivity creates significant business value, it also requires organisations to rethink how they secure operational environments.
Encouragingly, manufacturers are making progress.
Fortinet’s latest State of Operational Technology and Cybersecurity Report found organisations continue to improve their OT cybersecurity maturity, with stronger executive engagement, greater collaboration between IT and OT teams, and improved visibility across industrial environments. These findings suggest manufacturers increasingly recognise that cybersecurity is a business resilience issue that supports safe and reliable operations, rather than just a technical issue.
However, the research also shows cyber incidents affecting OT environments remain common. As industrial environments become more connected and threat actors continue adapting their techniques, resilience depends on continually strengthening cybersecurity instead of viewing it as a one-time investment.
One of the most significant opportunities for manufacturers is improving visibility across operational environments.
Many Australian manufacturers operate facilities that have evolved over decades, combining modern digital technologies with legacy industrial equipment that continues to perform critical production functions. Without a comprehensive understanding of every connected asset, organisations cannot effectively assess vulnerabilities, detect abnormal behaviour, or respond quickly when incidents occur.
Visibility forms the foundation of effective cyber resilience. Knowing exactly what assets exist across operational environments means organisations can better understand operational dependencies, prioritise remediation efforts, and make informed decisions about where security investments will have the greatest impact.
At the same time, manufacturers must recognise that legacy equipment is often an operational necessity rather than a cybersecurity weakness that can simply be replaced. Industrial assets are designed for long service lives, and wholesale replacement is rarely practical. Instead, manufacturers should focus on reducing risk through network segmentation, continuous monitoring, secure remote access, and layered security controls that protect critical systems while letting production continue uninterrupted.
AI also presents opportunities beyond manufacturing productivity. It can strengthen cyber defence by helping organisations detect anomalies more quickly, analyse large volumes of operational data, and improve incident response. Used responsibly, AI has the potential to become an important tool for protecting increasingly complex industrial environments.
Ultimately, cybersecurity is becoming inseparable from manufacturing performance. Decisions about digital transformation, operational efficiency, and AI adoption increasingly influence cyber risk, making collaboration between engineering, operations, and cybersecurity teams essential.
Australia’s manufacturers have always embraced innovation to remain competitive. Today’s transformation is no different. The organisations that will be best positioned to succeed are those that build cybersecurity into every stage of their digital transformation as a core enabler of resilient, efficient, and sustainable operations.
As Australia’s manufacturing sector continues to modernise, resilience will depend not only on adopting new technologies and securing them well. Manufacturers that also strengthen visibility across operational environments, protect critical systems, and integrate cybersecurity into business decision-making will be better placed to maintain production, support their workforce, and capitalise on the opportunities created by the next generation of industrial innovation.
Researchers from IMDEA Materials Institute have developed an artificial intelligence algorithm designed to improve automated manufacturing by identifying the unique operational characteristics, or “personalities”, of individual machines, even when they are the same make and model.
The researchers said one of the key challenges in large-scale automated manufacturing is that identical machines do not always perform in exactly the same way.
These variations can accumulate over time, potentially resulting in manufacturing defects and reduced reproducibility, particularly in high-precision industries such as additive manufacturing for architecture and aerospace.
To address this, the AI algorithm first assesses each machine individually to create a performance profile using statistical analysis. It then determines whether the machines are similar enough to be optimised collectively for efficiency or whether each requires an individual optimisation strategy to improve accuracy.
To validate the approach, the research team tested the algorithm using three theoretically identical 3D printers. While the machines were expected to perform similarly, the system detected measurable differences between them and concluded that each printer required its own optimisation strategy.
According to the study, analyses of the printed components showed clear differences in performance between the machines, with the researchers finding that individual optimisation provided the most suitable approach for the case study.
The researchers said the findings demonstrated “significantly faster convergence and a substantial reduction in errors in the weight of printed parts compared with treating all machines equally and thus failing to correct appropriately for individual biases.”
They added: “Even mass-produced machines may have their own operational ‘personality’. Our system learns these differences and uses them to our advantage, determining whether it is more efficient to treat them as a team or as individuals.”
According to the researchers, the approach could improve manufacturing accuracy while reducing wasted resources.
“This not only improves accuracy, but also saves resources by avoiding failed experiments, a key step towards the fully automated laboratories and factories of the future,” they said.
Although the study focused on 3D printing, the researchers said the methodology could also be applied to other high-throughput experimental fields, including new materials discovery, chemical synthesis and sensor calibration.
The study was conducted by Dr. Christina Schenk, Miguel Hernández del Valle, Luis Calero and Dr. Maciej Haranczyk from IMDEA Materials Institute, together with Dr. Marcus Noack from Lawrence Berkeley National Laboratory.
The work received funding from the MAD2D-CM project, the Community of Madrid, Spain’s Recovery, Transformation and Resilience Plan, NextGenerationEU, the U.S. Department of Energy’s Office of Science through the CAMERA program, and the Spanish Ministry of Science and Innovation.
Manufacturers that modernise frontline workflows using technologies such as artificial intelligence (AI), automation, and data-driven tools are reporting productivity improvements and potential profitability gains, according to new research released by Zebra Technologies in collaboration with Oxford Economics.
The updated global study, which surveyed leaders across the manufacturing, retail, and transportation and logistics (T&L) sectors, found that 56 per cent of manufacturers reported productivity gains after modernising production lines.
The research also identified potential financial benefits, with manufacturers improving supply chain and inventory management reporting a potential profit increase of $6.6 million for a typical organisation, while modernised maintenance workflows were associated with a potential $6.2 million uplift and upgraded production lines with a potential $3.5 million increase.
According to Zebra Technologies, the findings suggest that improving frontline workflows with AI, automation, and real-time data can strengthen operational performance while supporting employee engagement.
The research also found that AI adoption is accelerating across industries. In manufacturing, half of the surveyed leaders seeking to improve production lines identified AI as their top priority for future progress.
“This updated research confirms that intelligent operations deliver a quantifiable human and financial impact, simplifying complexity, elevating frontline productivity, and improving engagement,” said Tom Bianculli, chief technology officer at Zebra Technologies.
“As a leader in AI for the frontline, we see how embedding intelligence directly into daily workflows turns insights into measurable action, unlocking the next wave of productivity and value for our customers.”
Beyond manufacturing, the study reported that retail organisations modernising point-of-sale operations could achieve a potential $4 million increase in profits, while transportation and logistics organisations improving shipping and loading workflows could see potential profit gains of $2.8 million.
It also found that 34 per cent of T&L firms that improved picking and packing operations reported better staff retention and satisfaction, while 54 per cent recorded faster operational speeds.
Zebra said the findings indicate that investments in technologies supporting frontline workflows can contribute to improved financial performance while enhancing employee satisfaction and customer experience.
The company added that its portfolio of products and solutions is designed to help organisations make real-time operational decisions, reduce manual errors, and streamline repetitive tasks.
According to Zebra, these capabilities enable frontline workers to improve productivity while supporting the broader adoption of AI-enabled workflows across manufacturing and other industries.
Australian manufacturers have an opportunity to strengthen both safety and performance by making psychological safety a practical part of day-to-day operations, according to Dr Rod Gutierrez, Senior Director at global operations and safety consultancy dss+.
In an exclusive interview with Australian Manufacturing, Dr Gutierrez said psychological safety is becoming increasingly important as manufacturers face complex operational environments while adopting new technologies such as artificial intelligence (AI).
He said organisations that encourage workers to speak openly about concerns, mistakes and ideas are better positioned to identify risks before they escalate.
“Psychological safety in a plant looks like information moving,” Dr Gutierrez said.
“The operator who stops the line because something feels wrong. The maintainer who reports the near miss nobody else saw. The apprentice who says out loud that the instruction did not make sense.”
“When people will tell you the truth about the work while there is still time to act on it, you have psychological safety. When they will not, you are running the operation on filtered information, and every other system you own is degraded by it.”
Beyond physical safety
While manufacturers have made significant progress in managing physical hazards, Dr Gutierrez said psychosocial risks require a different approach because they are interconnected and cumulative rather than isolated.
He said organisations often assume traditional physical risk management methods can be applied directly to psychosocial hazards, despite issues such as workload, rostering and leadership combining over time to affect workers.
Another common challenge, he said, is focusing on measuring psychosocial risks without implementing meaningful controls.
“I see more effort go into measuring psychosocial risk than into controlling it,” Dr Gutierrez said.
“Measurement without effective controls becomes ornamental.”
Dr Gutierrez also pointed to trust as a critical factor, arguing that workplaces most in need of honest feedback are often those least likely to receive it because employees are reluctant to speak up.
Building a speak-up culture
According to Dr Gutierrez, manufacturing leaders can foster psychological safety by maintaining a visible presence on the shop floor, responding constructively when concerns are raised and involving workers in designing solutions.
He said employees pay close attention to how organisations respond to the first person who reports an issue.
“If the response is defensiveness, interrogation or quiet consequence, the channel closes for years,” he said.
“If the response is thanks, transparency and visible action, the channel opens.”
Dr Gutierrez added that organisations should also communicate what changes result from employee feedback, as demonstrating action encourages continued reporting.
AI and workplace trust
As AI becomes more widely used in production planning, workforce management and operational decision-making, Dr Gutierrez said manufacturers should carefully consider its impact on trust between employers and employees.
He said trust depends on workers believing that decisions affecting them can be explained by someone who is accountable for them. AI systems, however, can create situations where decisions appear to have no clear owner.
“A rostering decision, a performance flag or a shift allocation now arrives with no author,” Dr Gutierrez said.
“The worker asks why, and the supervisor consults the system and confirms the decision was correctly applied.”
He added that AI-powered monitoring can also increase perceptions of workplace surveillance if employees cannot understand or question how decisions are made.
To strengthen trust, Dr Gutierrez said organisations should ensure a person remains accountable for every significant decision affecting employees, involve frontline workers before deploying AI systems and regularly review AI performance after implementation.
He also argued manufacturers should prioritise using AI to improve working conditions rather than evaluating individual workers.
“AI that senses overload building in a schedule, smooths demand or predicts fatigue strengthens both performance and safety,” he said. “AI aimed at scoring individuals mostly builds a more sophisticated substitute for listening to them.”
Leadership remains central
Dr Gutierrez said frontline supervisors play a critical role during periods of technological change because they represent the organisation in workers’ day-to-day experience.
He said supervisors should communicate openly about what is changing, acknowledge uncertainty where necessary and avoid making promises they cannot keep.
Looking ahead, Dr Gutierrez believes Australian manufacturers have an opportunity to view safety and performance as complementary rather than competing priorities.
“The opportunity is to see safety and performance as one capability rather than two rival goals,” he said.
“Psychological safety and high performance are the same thing: truth moving through the organisation fast enough to act on.”
However, he warned that organisations must ensure governance keeps pace with technological adoption.
“The technology clock runs faster than the governance clock,” Dr Gutierrez said. “Trust erodes quietly, one unexplained decision at a time, and rebuilding it costs more than governing well from the start ever would.”
Oracle has announced new artificial intelligence-powered capabilities for its Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) platform, including four new Fusion Agentic Applications designed to help organisations improve manufacturing efficiency, strengthen supply chain performance, and enhance inventory management.
The announcement said the new applications are built into Oracle Fusion Cloud SCM and are designed to help organisations improve inventory visibility, reduce supplier and operational impacts, and support faster decision-making across planning, procurement, and manufacturing. Oracle also introduced new inventory optimisation capabilities aimed at improving supply chain resilience.
According to Oracle, the new Fusion Agentic Applications are powered by teams of specialised AI agents that are designed to proactively identify issues, recommend actions, and automate routine work within established business controls.
“Supply chain leaders are under increasing pressure to improve service levels, control costs, and respond faster to disruption amid ongoing economic and operational uncertainty,” said S.Y. Shenoy, senior vice president of Fusion SCM development at Oracle.
“With the new agentic applications and inventory optimisation capabilities in Oracle Cloud SCM, organisations can identify issues sooner, prioritise actions, and make faster, more informed decisions across planning, procurement, and manufacturing,” Shenoy said.
Oracle said the four new applications include an Inventory Planning Command Centre to improve inventory availability and reduce stockouts, a Supplier Qualification Workspace to streamline supplier onboarding and compliance, a Production Readiness Workspace to help manufacturing teams reduce setup errors and prevent production delays, and a Kanban Administrative Workspace to support production flow through proactive replenishment management.
The company said the applications operate within the existing Oracle Fusion Applications security framework and are designed to automate routine processes while escalating exceptions or decisions requiring human judgement.
In addition to the new applications, Oracle introduced inventory optimisation capabilities within Oracle Fusion Cloud Supply Chain Planning.
These include multi-echelon inventory optimisation to recommend safety stock levels across supply chain networks, interactive inventory network visualisation to provide planners with greater visibility of inventory performance, and an Inventory Optimisation Advisor Agent that analyses service-level risks and recommends inventory adjustments.
Oracle said the updates form part of its broader Oracle Fusion Cloud Applications platform, which integrates supply chain planning and execution processes using embedded AI capabilities.
The platform also includes AI Agent Studio for Fusion Applications, enabling customers to build and deploy AI-powered automation using Oracle, partner, and external AI agents.
The company said the latest enhancements are intended to help organisations improve resilience and respond more effectively to changing market conditions through AI-assisted supply chain and manufacturing operations.
The future of environmental, social and governance (ESG) performance is being increasingly influenced by how organisations use technology in their day-to-day operations, according to thought leadership from Konica Minolta Australia.
John Harding, general manager, managed services at Konica Minolta Australia, said ESG outcomes are often shaped earlier in the operational process than many organisations realise.
“Organisations often treat ESG as something they report on after the fact; however, operations determine ESG performance. The way information moves, workflows are managed, and decisions are made all influence how efficiently and responsibly a business operates,” he said.
Harding said the concept of a “future office” is less about individual technologies and more about how systems and information platforms work together.
He noted that while many organisations are investing in digital tools, including cloud platforms, document management systems and automation, the value is maximised only when these tools are aligned with how a business actually operates.
“Paperless alone is not the end goal. The real value comes from improving how information flows through the organisation. When documents, data, and workflows are connected, businesses can operate with greater confidence,” he said.
He added that inefficiencies embedded in everyday processes can have a direct impact on sustainability outcomes.
“A lot of waste in business is hidden inside process, whether it be in form of duplicated effort, unnecessary manual handling, delays, or poor visibility. Technology helps organisations identify and remove friction,” Harding said.
Looking ahead, Harding said artificial intelligence (AI) will play a growing role in ESG, but should be applied in practical, operational contexts rather than as a conceptual solution.
“AI should not be treated as a shortcut to ESG maturity. Its value comes from applying it to real operational challenges,” he said, adding that stronger ESG performance will depend on how well organisations integrate technology, visibility and decision-making into their core operations.
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