Supply chain and logistics organisations have spent several years responding to one disruption after another. What was once treated as exceptional has become part of the everyday operating environment: geopolitical tension, climate-related disruption, changing trade requirements, rising customer expectations and persistent pressure on costs and margins.
CIOs and CTOs are being asked to create connected operations that can identify risk earlier, respond faster and continue delivering, even when conditions change, or seem impossible.
Technology investment is consequently becoming harder to defer. Recent analysis from Logistics UK and HSBC UK describes investment in AI, cyber security and software modernisation as increasingly “non-discretionary” for logistics businesses. NashTech’s own research also found that more than 70% of supply chain and logistics leaders expect AI to significantly affect custom software development.
To explore what these changes mean in practice, we spoke to NashTech’s supply chain and logistics technology expert, Stuart Simpson. We asked where technology is creating genuine value, what is holding organisations back and how technology leaders can build supply chains that are ready for whatever comes next.
Stuart Simpson:
There is probably no single right answer to that but as I see it, there are three big changes that are shaping priorities.
The first is legacy technology and technical debt. Many logistics organisations are long-established businesses, and very few are digitally native. Years of consolidation, mergers and acquisitions have left a lot of businesses with disparate systems that were never designed to work together, so CIOs and CTOs are under pressure to modernise, integrate and reduce complexity without disrupting day-to-day operations.
The second is the need to find marginal gains in a market where margins are already very tight. In areas such as last-mile delivery, small improvements across multiple parts of the operation can have a significant impact on the bottom line. Reducing failed deliveries, returns and inefficiencies in the final stage of the journey is therefore becoming a major technology priority.
The third is data security. CIOs and CTOs are having to broaden their focus beyond core systems and pay much closer attention to peripheral platforms, partner systems and the wider data supply chain. In logistics, security isn’t just about protecting the physical supply chain; it is about protecting the data supply chain, and data flows that make that supply chain work.
Supply chain organisations are not short of data. Information is generated by warehouse management systems, transport platforms, enterprise resource planning systems, telematics, customer applications, connected equipment and external partners.
The difficulty is turning that data into a coherent and timely view of what is actually happening.
A dashboard may show that a shipment is delayed. But a genuinely connected operation should help the business understand which customers, orders, routes, and service commitments are affected, what the likely financial impact will be, and what action to take next.
Stuart Simpson:
Technology leaders should start by understanding the organisation’s data landscape. That means identifying where data sits, who it is shared with, whose data the organisation depends on, and how information moves between internal systems, partners and customers. The data supply chain needs to be managed with the same care as the physical supply chain.
A practical first step is to create a focused task force to map the data estate. If you cannot see where the data is, you cannot measure it or manage it. That exercise should include legacy applications, spreadsheets, operational systems, partner feeds and any external data sources the business relies on.
Once that landscape is understood, the next priority is data quality. Leaders need to assess how up to date, accurate and useful the data is before deciding whether to centralise it, integrate it, or move parts of it into the cloud. There is no one-size-fits-all answer, but the goal should be to make critical data as accessible, clean and reliable as possible.
If a business doesn’t have the capacity to do this in-house, leaning on specialist suppliers, like NashTech, would be a good starting point.
AI is already being applied across forecasting, inventory management, route planning, predictive maintenance, warehouse operations, proof of delivery and customer service. Of course, NashTech is helping many logistics businesses across many of these use cases.
But as AI begins to influence live decisions, organisations must consider data quality, integration, governance, security, human oversight, and how value will be measured.
Stuart Simpson:
One clear area is legacy modernisation. AI can help organisations assess technical debt, review ageing codebases and accelerate parts of the modernisation process that were previously highly manual and time-consuming. But it should be used as a tool, not a replacement for expertise. The human has to stay in the loop, particularly when outputs need to be interpreted, tested and trusted.
Another strong use case is route optimisation and simulation. AI can help create digital twin environments where logistics processes can be tested without affecting live operations. For example, one of NashTech’s clients can now model new route options, play them through over several days or weeks, and assess whether they are likely to work before making changes in the real world.
Warehouse automation is also moving quickly. Established organisations are already using AI to support automated warehouse operations, whether that involves new AI-enabled hardware and software or retrofitting intelligence into existing systems. The measurable value comes from AI helping people make operational decisions faster, more accurately and with greater confidence.
Many supply chain technology estates have evolved through acquisitions, local buying decisions and years of adding new platforms around established operational systems. Replacing everything at once is usually too expensive, too disruptive and, frankly, unnecessary.
However, fragmented data and tightly coupled legacy applications can make it harder to introduce AI, automation and real-time services. Organisations can find themselves with powerful individual platforms that do not share information quickly enough to support an end-to-end process.
This is renewing the debate around build versus buy. Commercial platforms remain valuable for standard capabilities, but custom software can provide the integration, workflow and experience layers that differentiate the business.
Stuart Simpson:
Commercial software still has an important role, particularly when a business needs standard capability quickly. But the gap between packaged software and custom-built software has closed considerably. AI-enabled development practices can reduce the time and effort involved in producing new functionality, making custom software a more accessible option than perhaps it once was.
The important caveat is assurance. If AI is used to support development, the output still needs to be checked thoroughly by experienced teams who understand the system, the business process and the operational risk. Speed is valuable, but not at the expense of resilience or trust.
To modernise without disrupting always-on operations, organisations need proven DevOps and, where appropriate, DevSecOps disciplines. One effective approach is to run development and production environments in parallel, allowing new code to be built, tested, signed off and switched into production with minimal impact on users and with a clear route back if something does not work as expected.
Custom software becomes a great option when an organisation needs something that fits the way it actually operates, rather than having to over-configure a platform that is only nearly right. In one modernisation programme, NashTech helped one supply chain business rewrite a 20-year-old ERP system, delivering new code alongside old code so users could continue working while the underlying estate was progressively transformed.
The transition to lower-emission logistics is often framed as a fleet procurement decision. In practice, it touches far more of the technology estate.
Electric fleets introduce new decisions around depot energy capacity, charging schedules, route suitability, vehicle availability and total cost of ownership. These decisions require data from vehicles, transport planning systems, energy platforms and operational schedules to work together.
In March 2026, the UK Government announced £1 billion of support for zero-emission trucks, vans and depot charging. The funding may help reduce some of the financial and infrastructure barriers, but operators will still need the right digital capabilities to plan and manage the transition effectively.
Stuart Simpson:
Just like technical debt, there is fleet debt. Many operators still have valuable diesel assets on their books, so the transition will be gradual. As electric vehicles are introduced, the operational challenges do not disappear; they change. Range, charging capacity, route suitability and vehicle availability all become data and planning questions.
Data and connected platforms will be essential because decarbonisation will require more collaboration across organisations, not just within organisations. For example, I’ve been involved in conversations with bus operators who are considering making their charging stations available to large haulage operators. So, if the charging infrastructure is shared between fleet operators, it will require things like platforms supporting availability, access and billing. Intelligent planning will also become more important as vehicles report performance data in real time, allowing teams back at base to adapt routes, charging schedules and operating models around actual conditions.
The opportunity landscape is broad: AI, automation, real-time visibility, platform modernisation, and fleet transformation are all competing for investment.
Few organisations can address everything simultaneously. Leaders need to ask themselves where technology can remove a genuine operational constraint, reduce material risk or create measurable value. But this does require technology and operational leaders to agree on the problem before selecting the solution.
It also requires a realistic view of readiness. An organisation may have an exciting AI use case, but if the necessary data is inaccessible, the process varies between sites or nobody owns the outcome, the project is unlikely to move successfully into production.
Stuart Simpson:
I would not start by telling them exactly where to focus. I would start by understanding the organisation’s aspirations for the next three to five years: what matters most, where the operational constraints are, and what the business needs technology to achieve for them. The right priorities should come from that discussion, not from chasing the most fashionable use case.
That is where a technology advisory approach can be valuable. The best advice often comes from people who combine industry knowledge, technology experience and the scar tissue of having delivered similar programmes before. An honest advisory conversation can help CIOs and CTOs build a practical roadmap, secure the right investment case and understand the risks of doing nothing, and we have those people in our NashTech Advisory practice. Because as all logisticians know, in supply chain and logistics, standing still for too long can mean getting left behind.
To explore how NashTech helps supply chain and logistics organisations modernise platforms, connect data and turn AI into practical operational value, discover our logistics technology capabilities.