AI is rarely out of industry headlines right now, and it’s clear there’s no shortage of enthusiasm around its potential applications within food and drink, writes Dr. Stephanie Brooks, Head of Research and Innovation at Foods Connected.
In fact, when we surveyed 500+ senior professionals working in grocery supply chains, 70% of businesses told us they’d already implemented AI (or are planning to do so) and 96% are planning further investment within the next five years, the vast majority of which will be allocated in the next three.
On the one hand, it’s fantastic to see AI being embraced so readily.
At Foods Connected, technology is our bread-and-butter and we are excited about AI and its transformative potential within our platform to enhance the experience for our customers.
From mitigating risk at the production level, to optimising food safety in factories and curating content for individual customers for brands and retailers, there’s no doubt that AI has a big role to play in unlocking value for the sector.
But at the same time, as with any new type of technology, there are also some pretty big potential pitfalls that could catch businesses by surprise if they haven’t taken the time to lay the groundwork for AI first. Even more so given the tight timeline on which many are planning to embed it further into operations.
That’s why, for our research, we wanted to go beyond the hype and dig into the biggest challenges that business leaders face when it comes to implementing AI. We wanted to get to grips with the concerns they have, where they lack support and what are the gaps in knowledge in their own organisations.
In doing so we hope to avoid heading down the same road we’ve been before with some previous (much-hyped) technologies.
Take blockchain. The concept of a public distributed ledger was first spearheaded within fintech as a way to trade cryptocurrency without the need for a trusted authority such as a bank. Later on, it gained traction within agri-food as a way to authenticate each stage of the supply chain and thereby disincentivise fraud. But it soon became apparent that, at least in its legacy form, it was both cost prohibitive and ill-suited to this purpose in a high volume / low margin industry like food where the sheer volume of transactions was dizzying.
Failure to adapt both the tech and supply operations at the outset saw blockchain become synonymous with cost, complexity and even (ironically) distrust in some cases, stemming from industry confusion as to its potential value if appropriately curated. Though still a highly valuable tool within manufacturing, the technology in its legacy form has arguably fallen short of its forecast potential 10 years ago.
If we want AI to avoid a similar fate then we need to lay the right foundations.
So, what early steps could turn AI from an overhyped waste of time to a valuable tool for change?
The first is data or digital readiness. More than a quarter (28%) of businesses told us that they’re either only partially digitised or not digitised at all currently.
That matters hugely because AI is only as good as the data it has to work on.
Subpar data, and by that I mean data that isn’t digitised, standardised and accessible, will generate subpar results, no matter how sophisticated the AI.
For all those food and drink businesses that told us they’re planning investments in the next 1-3 years, a lack of data readiness could either significantly diminish the benefits they see from their investment or the realisation that their data isn’t sufficient will see those three-year plans morph into five or even 10-year plans putting them well and truly behind their competition.
Which is why taking the time to evaluate your data readiness now and make any necessary changes needs to be top of the to-do list. This looks different for every business, of course. But on a simple level it’s about conversion of analogue data – think paper invoices or physical weighing scales – to a digitised format, a standardisation of this digitised data to ensure it is both accessible and shareable, and a cultural or educational intervention that ensures every single person in your business understands the importance of data quality.
The second preparatory step to ensure any AI-enabled tech is optimised in your business is closing any skills gap.
Our research found that a fifth of businesses currently lack a team dedicated to digital transformation, for example. Unlike established functions like sales, marketing or logistics, it might seem that digital transformation is a ‘nice to have’ rather than a necessity but – for those businesses hoping to avail themselves of AI – that isn’t the case.
Having the right internal skills is fundamental to making AI efficient, affordable and impactful within organisations. Rather than rely solely on off-the-shelf solutions, it provides the option to have bespoke builds, for example. It ensures that AI can be used efficiently rather than act as a hindrance to productivity. And it even allows teams to challenge AI where appropriate and understand where data may have gone awry.
This isn’t only about technical roles either. There’s now a growing understanding that every single person on your team will likely need at least a basic level of AI literacy in the coming years. In large organisations in particular, of which there are many in food and drink, delivering this will require a strategic and proactive commitment to training both new recruits and existing members of staff.
As with digital readiness this can’t be an afterthought once AI has already been invested in either. Evaluating and closing any skills gap needs to come early on in that adoption journey, to ensure any new tech can hit the ground running.
The bottom line is that, for all the buzz around it right now, AI isn’t a magic bullet.
And while we couldn’t be happier to see such a high proportion of food and drink businesses exploring its potential right now, it’s important to take a beat and consider first if your organisation is ready before committing thousands, even millions to another project.
When it comes to AI, preparation will be what separates hype from genuine business transformation.
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