20 years ago, a Sat-Nav and Bluetooth connectivity might have made the most stalwart of autophiles raise an eyebrow – but two decades on these things are now below the out-of-the-box standard. Back then, while most vehicles were mechanically sophisticated, they were limited in terms of digital integration. Today, the modern van or truck is a super- computer on wheels: defined by software, cloud-connected, increasingly autonomous and constantly evolving through over-the-air (OTA) updates. But what’s changed since 2005?
Inexpensive sensors, high-speed wireless networks and the cloud have enabled a complete transformation – with data, analytics and AI now central to the entire lifecycle of the automobile. And with these updates, fleet owners are constantly looking for more – more features, more add-ons, more ways to get ahead of the competition. However, with every sudden advancement, which only continues to gain momentum, there comes the challenge for major players to keep pace – and in such a highly competitive industry, unfortunately it’s first past the post.
But what can OEMs who serve fleets do to remain competitive and ensure they keep their foot firmly planted in their customer’s doorways? That’s straightforward – competitions are won by those who are willing to innovate, and in this business those who innovate always start in the right place, with good data.
Innovation without good data is what a van is without fuel
Solid, high-quality data is the foundation of digital success, and in the modern AI era, having that critical foundation in place first is no longer a “nice to have”, but more a “can’t start without.” The challenge here is that traditionally, gathering the required level of consistent data streams is difficult, with many companies adopting AI at scale finding themselves in the same pitfalls.
These include:
- Data Silos: If different departments are all working in silos, whether that be from design to production to aftermarket products and systems, then it’s virtually impossible to measure or analyse the end-to-end process across a vehicle’s lifecycle.
- Data volume: Even if teams improve their data sharing processes, you also have to consider the sheer volume of data that is being generated these days. In an age of connected vehicles, a singular car is capable of generating approximately 20GB of data every hour. That’s an astronomical amount of information to sort and analyse.
- Legacy infrastructure: Then you have to consider what technological infrastructure an OEM is using. If systems are older, their scalability is then limited, impacting the ability to innovate.
- Difficult cross-enterprise collaboration: On top of this, automakers are dealing with hundreds, if not thousands of suppliers and partners. This creates a complicated loop of communication and data management that is both complicated and unwieldy.
It’s fair to say that this cloudification journey presents new challenges, while attempting to solve older ones. What’s more, the substantiated worry of vendor lock-in has pushed many brands to adopt multi-cloud strategies, which unfortunately leads to persisting data silos – evolving beyond on-premises systems and leaking into cloud infrastructure as well.
As major OEMs embark on an AI-driven digital transformation, the key to succeeding on this journey is having a robust data strategy and a data management platform agnostic of underlying cloud infrastructure.
End-to-end visibility leads to end-to-end perfection
The right all-in-one platform which allows for a unified data and AI strategy, helps manufacturers ingest, transform and distribute data at scale while also having the ability to compute massive amounts of said data and analytics. But how exactly does this help fleet manufacturers innovate?
It creates an opportunity to unify enterprise shop floor (IT/OT) and vehicle data, whether it’s structured or unstructured, all in one place, allowing teams to analyse it all without moving the data around. This enables seamless end-to-end visibility throughout the value chain, from R&D to the after-sales domain.
With this data then unified in a well-managed infrastructure, manufacturers can scale more efficiently and with stronger governance while taking advantage of integrated AI services and cross-collaboration capabilities.
Over the last few decades, the auto industry’s focus for efficiency has usually been in a particular process area or a siloed system, such as product lifecycle management, enterprise relationship planning (ERP) and manufacturing execution systems. But now, with unlimited computing capabilities available in the cloud, along with big data analytics tools maturing with AI, companies can plot out, test and achieve efficiencies that go far beyond one department or workstream.
Continuous development of AI models has created unlimited possibilities for innovation, and more importantly, business efficiency. Tasks and processes which would have taken hours if not days to finalise are being expedited to mere minutes as models continue to improve and be trained to run complex data analytics. And this is something we’re going to hear more about in the coming year as the prominence of powerful agentic AI agents continues to rise.
In 2026, more and more manufacturers, particularly those serving the ever-expanding needs of fleet businesses, will deploy AI agents to make autonomous operational decisions that directly impact efficiency and cost reduction. This could take many forms, for example, by expediting product lots to meet delivery deadlines or automatically routing products for quality inspection or determining optimal manufacturing sequences.
However, these tangible efficiencies can only truly be achieved through an end-to-end scope, looking at the entire process of development without separating workstreams.
If high-quality data can be fully integrated, end-to-end, without fail – then firms looking to create something new for customers can soon speed toward delivering AI-powered digital twins, fully autonomous fleets and personalised mobility services.
Good data, managed well, leads to competitive innovations to fuel growth across the board, the challenge set to all is to grab the wheel and steer towards success.
Author: Tim Long, Global Head of Manufacturing, GTM, Snowflake
