Peak: The AI skills gap in the logistics sector
What makes a great customer experience in retail? Choice of product is up there. So too is availability. But what about delivery and logistics? Making sure that the supply chain is working and that purchases get to buyers in good time and good condition.
Increasingly, that last piece of the process is growing in importance. And while this should be unsurprising, given that more and more goods and services are bought online – either through retailers, ‘box businesses’ like HelloFresh and Harry’s Razors, or to-your-door services like Deliveroo – it does mean that logistics companies are transitioning from being a functional part of the customer experience to the star that will make or break it.
With this comes huge potential. And the firms that deliver excellence will gain custom and reputation. But doing that is not necessarily an easy ask, and it will require a level of digital transformation within logistics, including greater use of the Artificial Intelligence solutions that will make things faster, smarter and more effective.
This is a natural step to take – logistics companies working in retail have a huge amount of data that AI and machine learning can draw on to solve problems and find opportunities. But it’s also where the challenge begins.
Harnessing AI traditionally relies on having data scientists who understand it, work with it and who can get creative with it. Yet competition to find and hire these people is fierce. And with logistics firms unlikely to have university or industry ties that open up talent pools, and even less likely to be located in the trendier areas where tech innovation tends to focus, hiring talent is much harder than knowing it’s needed.
The upshot is that instead of being able to take advantage of the opportunity AI is creating, logistics firms are looking at a skills gap where an expert should be. The question for the ambitious innovators in the industry is how they go about closing it.
Why is there an AI skills gap in logistics?
AI is being rolled out across every area of every business in every industry, which means that nearly every CIO needs a capable hand to oversee their operations. Unfortunately, it also means that more companies would like to hire a data scientist than there are data scientists.
It’s a competitive talent market. And the reality is that there simply aren't enough trained people to meet the need for data analysis, machine learning and AI development. So the logical solution of hiring to fill new roles is extremely difficult. Especially in the logistics industry, which is not always the most attractive proposition, and has been known to struggle to hire young talent (partially why up to 33% of some businesses are approaching retirement age).
Enter the skills gap. One that should force logistics firms to ask themselves whether or not fighting for a data scientist the best option. Particularly with technology developing at a rapid rate and touching upon many different facets of the business.
Remember that data scientists don’t just need official training. They also need on-the-job experience. There’s a need for them to have more commercial focus so they can deliver on the objectives set for AI within a commercial setting. With that being the case, and the skills gap making hiring dedicated data scientists tough, training non-specialist members of the company in AI is a sensible route for many. Firstly, you get AI skills into the business; secondly, you negate the need to hire from a small and expensive talent pool.
Using AI effectively
The nature of logistics today means that AI can’t solely be the domain of scientists and engineers. Its use and application will expand into all job roles. And while this is no doubt a positive thing, the increase in AI-enabled solutions brings into question how well educated the wider workforce is in understanding and utilising it.
Professionals from entry to board-level will need to know how AI works and the part it plays in their organisation. But they won’t necessarily need to know it at the level of a scientist. Instead, they need to work with a system that develops with data, and puts the power of AI in their hands.
Today’s leading AI technologies in retail and logistics enable businesses to apply solutions across all departments – starting in one place, then growing out from there. And the technology learns as it goes – when given more data to work with it gains context and detail, which it uses to enable smarter decision making and greater efficiency. Over time, this kind of system will have less of a need for data scientists to manage it. Rather, it can be put in the hands of existing employees.
There is a huge opportunity to get this started right now and address the problems brought about by the hassle of hiring.
The business benefit of AI
AI has the potential to be invaluable for the logistics sector, addressing some of the most prominent challenges it faces over the coming years. Take optimisation for example. AI will enable smarter route planning and optimal filling of trucks. This in turn will mean more efficient journeys, fewer vehicles on the road and a reduction in tricky journeys to city centres.
It will also mean that logistics controllers can respond to issues that occur during journeys faster – the technology will understand what’s happened and will automatically find the best way to respond to it.
All this is possible because AI solutions see different data sets in context with each other in a way that people can’t. UPS for example has used AI to reduce its delivery miles in the USA by up to 100 million, resulting in estimated cost savings of $50 million.
All told, the industry can be completely transformed with the right technology. But only if people with the right training power it.
Bridging the gap
AI is in all of our lives, and its place in business is going to expand in the coming years. This means that the skills gap in logistics isn’t going away. Instead, it’ll pick up speed, with a battle over the top talent emerging.
Huge businesses have spent millions on building data scientist capabilities in-house. And while that approach works for some, it’s not sustainable for others – both in terms of cost and retaining talent.
This is why smart businesses will tackle the AI skills gap with a mix of upskilling and retraining in the AI technologies that are essential to people’s jobs. Or they might gain access to the best talent by bringing onboard commercially-minded AI companies who have the infrastructure, solutions and people to implement AI quickly and effectively. This way, they'll be harnessing the predictive power of AI for potentially millions of tasks needed every day, allowing staff to focus on higher-level decisions.
We all know that AI can be revolutionary and hugely beneficial. But without the right training to use it, the potential won't be realised.