TL;DR Artificial intelligence promises huge opportunities for supply chain and logistics organisations, from demand forecasting and warehouse optimisation to route planning and customer service. Yet while investment continues to rise, many technology leaders are discovering that moving from AI pilot to production is far more difficult than launching a proof of concept.
Our latest research found that 87% of supply chain and logistics organisations expect to increase their use of AI within the next two years, while 71% believe AI will have a high impact on their software development strategy.
However, AI execution is becoming a problem.
This article explores just some of the reasons AI initiatives stall in supply chain organisations and explains how technology leaders can build the foundations needed to move beyond experimentation and into measurable business value.
The supply chain and logistics industry has become one of the most exciting sectors for artificial intelligence.
From predicting demand and identifying disruption before it happens to improving warehouse productivity, automating customer communications and optimising transport routes, the opportunities are enormous.
Technology leaders know this.
Our latest research found that 71% of supply chain and logistics technology leaders expect AI to have a significant impact on custom software development, while 87% expect their organisation to adopt or significantly increase the use of AI over the next two years.
Yet despite this enthusiasm, many organisations remain trapped in what can only be described as pilot purgatory.
According to McKinsey's State of AI research, while AI adoption is becoming widespread, relatively few organisations have embedded AI deeply enough into business processes to achieve enterprise-wide value.
So why are so many organisations struggling to move beyond AI experimentation?
This is perhaps the biggest lesson emerging from our latest research: that many organisations still approach AI as another technology initiative.
Instead, AI needs to be treated as a business transformation programme.
By doing this, it changes the conversation completely.
Instead of asking ‘Which AI platform should we deploy?’ Technology leaders need to understand the business and ask ‘Which operational problem are we trying to solve?’
For supply chain organisations that could mean:
Then, AI becomes the enabler. The business outcome becomes the objective.
Which is key, as boards invest in outcomes, not technology.
When technology leaders lead with measurable operational improvements rather than AI capabilities, conversations become easier.
Projects gain greater executive support, and investment becomes easier to justify.
If AI is going to make intelligent decisions, it first needs reliable information.
That sounds obvious, but it's still one of the biggest reasons AI initiatives stall.
Supply chains generate huge volumes of information every day across ERP platforms, warehouse management systems, transport management systems, autonomous vehicles, procurement platforms, customer service applications and IoT devices.
If those systems contain inconsistent, duplicated or incomplete data, AI simply learns bad habits faster.
Gartner research found that one-third of supply chain leaders identify a lack of trust in data as a major barrier to analytics and AI, while 21% say poor data quality is their biggest obstacle.
What's interesting is how this compares with our own NashTech research.
When asked which areas of the AI technology stack supply chain technology leaders plan to invest in over the next two years:
The most sophisticated AI model in the world cannot compensate for poor data quality, yet only 1/5 of leaders are prioritising investment in this area. Perhaps with good reason. But before scaling AI, technology leaders should be asking:
Investing just as heavily in data foundations as the AI itself is key to strong foundations.
Technology isn't the only challenge.
People matter just as much.
As AI becomes embedded into day-to-day operations, organisations need specialist capabilities that many simply don't have today.
Our research found:
These findings reflect what many CIOs are already experiencing.
AI initiatives are struggling because organisations don't yet have the most up-to-date skills, particularly amongst:
At the same time, technology teams are expected to maintain existing operations while delivering AI transformation programmes.
That's a difficult balance to achieve.
Rather than trying to build every capability internally, many organisations are partnering with software engineering specialists, like NashTech, who already understand how to deliver AI safely at scale.
In fact, 27% of logistics organisations say they are already exploring partnerships with custom software providers to accelerate AI adoption.
A proof of concept can work in isolation. Production AI cannot.
AI needs to connect with the technology that runs your supply chain.
That means:
For many organisations, those systems have evolved over decades and were never designed with AI in mind.
Our research found:
Without connected systems, AI cannot access the information it needs.
Instead of creating better decisions, it simply produces faster decisions based on incomplete information.
This is why integration should never be viewed as "technical housekeeping."
It is the foundation that enables every future AI capability.
Interestingly, 31% of supply chain technology leaders are already investing in modular, scalable platforms, while 21% are actively re-architecting their technology estates to support AI.
Perhaps the most telling statistic of all?
Zero organisations said they intended to maintain their current technology approach with minimal AI integration.
Everyone knows change is coming.
The challenge is building the right foundations before accelerating.
Ready to move your AI ambitions forward?
NashTech works with supply chain and logistics organisations to turn AI ambition into measurable business value. Our specialists combine AI advisory, data foundations, software engineering and delivery expertise to help you understand where AI can create the greatest impact, build the right data foundations and move from experimentation to scalable, production-ready solutions.
Whether you need support shaping your AI strategy, strengthening your data and integration foundations, or building custom AI solutions, we can help you progress with confidence through a trusted partnership focused on practical outcomes.