Anthony Michael, Senior Practice Director at Searce, looks at the role of AI in helping supply chain organisations elevate their location intelligence.

The global supply chain industry is at an inflection point. Rising demands, sustainability pressures, and evolving consumer expectations are pushing companies to rethink operations. Modern technologies like AI and location intelligence are no longer optional – they’re essential. To navigate this complexity, companies are actively exploring modern technologies like AI and location intelligence – not as optional tools, but as essential drivers of efficiency, resilience, and cost savings. And we’re already seeing the real impact. Benefits range from enhancing demand forecasting and optimising delivery routes to enabling real-time decision-making. With AI, supply chains are beginning to unlock their full technological potential.

Forward-thinking enterprises are leading the charge, proving that the right innovations can drive measurable outcomes. Admittedly, some use cases may initially seem generic. However, their real value becomes clear when they deliver tangible improvements in key KPIs. These improvements include, for example, profitability and improved customer lifetime value (LTV).

Location Intelligence as a catalyst for efficiency

Simply put, location intelligence uses geospatial data – such as routes, distances, and landmarks – to generate actionable insights for transportation, logistics, and inventory management. For supply chains, location intelligence plays a pivotal role in improving operational efficiency, enabling smarter decision-making across site selection, delivery optimisation, and fleet tracking.

Consider the case of one of Southeast Asia’s largest utility companies. In conversation with its CTO, they shared a unique challenge. The organisation struggled with ensuring that all utility drivers operating across multiple routes finished their shifts at the same time. This wasn’t just a matter of convenience – it was critical for avoiding union disputes and maintaining operational harmony. Location intelligence proved to be key. By analysing routes, stop times, and driver schedules, the company synchronised shift end times across its workforce. This not only prevented potential conflicts but boosted overall efficiency, showcasing how geospatial insights can solve complex challenges.

A similar transformation took place with a logistics provider in EMEA. Faced with rising toll costs and inefficiencies in cargo delivery, loading, and unloading times, they turned to location intelligence to optimise operations. By analysing optimal routes and streamlining workflows, they successfully reduced toll expenses from 4% to 1%. Also, they managed to cut overall unloading times by 40%.

Elevating customer experience through real-time insights

For supply chain businesses, transparency and communication are critical. Customers expect real-time updates on their orders, and companies that provide accurate tracking can improve satisfaction while reducing service costs by minimising inquiries and complaints.

Beyond basic tracking, real-time insights powered by AI and location intelligence are transforming supply chain management. Predictive analytics enable businesses to anticipate order surges, optimise inventory, and provide more accurate delivery timelines. Advanced delivery prediction takes this further, allowing companies to forecast delays and proactively re-route shipments to minimise disruptions.

AI’s pervasive influence on supply chains

We’re living in a transformative era where no article on any industry is complete without the mention of AI and the impact it’s started to have on key industry KPIs. AI is revolutionising how the supply chain industry operates, transforming every aspect from route optimisation and demand forecasting to risk management and predictive maintenance. By harnessing advanced algorithms and real-time data analysis, AI empowers businesses to anticipate disruptions, enhance safety, and improve efficiency across the supply chain.

One of the most impactful applications is predictive analytics. By analysing historical data and external factors like weather patterns, traffic conditions, and geopolitical risks, AI can forecast supply chain bottlenecks before they occur. This allows businesses to proactively adjust routes, optimise inventory levels, and avoid costly delays.

The GenAI factor

Generative AI, a subset of the larger AI universe, is also helping businesses by streamlining customer service through AI-powered chatbots, significantly reducing complaint resolution times. Additionally, Gen AI models are being trained on company-specific documents – legal, operational, and financial – to make sure disputes are managed efficiently.

But AI’s influence extends beyond text-based solutions. Video and audio analytics are being applied across a wide range of use cases. For example, AI-powered cameras installed on trucks can capture real-time road conditions, enabling drivers to adjust routes and rest periods for safer, more efficient journeys. Meanwhile, Optical Character Recognition (OCR) technology, once limited in its scope, has advanced significantly, now achieving greater accuracy in address recognition and faster delivery times. This not only speeds up order fulfilment but also cuts operational costs. In fact, we’ve seen up to 100x cost savings in courier companies by reimagining workflows and optimising document extraction models.

That said, AI’s influence extends beyond operational improvements. As businesses face mounting pressure to adopt environmentally responsible practices, AI is emerging as a critical enabler of sustainability, helping companies reduce emissions, optimise energy use, and build more resilient supply chains.

Building a sustainable future

As the earth experiences record-high temperatures, with global warming predicted to pass 2.9°C this century, the need for sustainable supply chain practices has never been more urgent. Supply chains account for a significant portion of global greenhouse gas emissions, particularly Scope 3 emissions – those generated across the value chain, from production to transportation. These emissions are both dangerous and complex to measure, as more than 70% of them stem from supply chain activities and often extend beyond a company’s direct operations.

AI plays a crucial role in addressing these challenges. By optimising transportation routes, improving energy efficiency, and enhancing supplier audits, AI helps companies reduce their carbon footprint without compromising operational performance. Real-time data analytics enable smarter decisions, such as consolidating shipments and choosing lower-emission routes.

AI-powered carbon tracking provides clear insights into supply chain emissions. This helps businesses set realistic sustainability targets and meet evolving ESG requirements. With frameworks like the EU’s Corporate Sustainability Reporting Directive (CSRD) driving accountability, adopting AI-driven solutions is key to staying compliant and competitive.

Navigating ethical and practical challenges

AI holds significant promise for improving supply chains’ operational efficiency. However, the technology also poses ethical challenges.

Concerns around data privacy, algorithmic bias, and job displacement are among the most pressing. Without clear frameworks, AI-driven decision-making can undermine trust and expose companies to reputational and regulatory risks.

Addressing these challenges starts with establishing a strong governance. Businesses need clear AI frameworks that ensure ethical standards, data security, and regulatory compliance. This involves an inclusive approach through cross-functional collaboration across departments and stakeholders. Bringing together employees, partners and customers, ensuring AI systems are transparent, fair and accountable needs to be a holistic effort.

qually important is ensuring workforce readiness. As AI reshapes supply chain operations, businesses must invest in upskilling employees to work alongside intelligent systems. Ideally, this will turn potential disruption into an opportunity for innovation.

By embedding ethical practices into AI adoption, companies can not only unlock supply chain efficiencies but also build resilience and trust across their ecosystems.

Originally published on SupplyChain Strategy