Beyond the Hype: Navigating AI in Finance and Supply Chain
It’s official: we’re riding the peak of the artificial intelligence (AI) hype wave – and many of us are starting to feel exhausted by it.
While the AI discussion has been happening in the more technical parts of most businesses for some years (What is it? How do we use it? And most importantly, how do we attach value to it?), 2023 was the year when AI really broke through into the public consciousness – and how?
If you subscribe to Gartner’s Hype Cycle for Artificial Intelligence, for example, you could say generative AI (which includes ChatGPT and similar platforms) is rounding the peak of inflated expectations, and about to enter the descent into the (beautifully named) trough of disillusionment, before we enter the plateau of productivity in the next five years or so.
Following the launch of ChatGPT in late 2022, 2023 showed that a lot of people were keen to get involved in the AI conversation – and unfortunately, that conversation has often been incredibly vague. Most business leaders know they should be doing something about AI – but what that something is remains unclear for many.
Compounding the issue is the growing concern in many workforces that the potential of AI to increase efficiency and reduce redundant or manual processes may lead to certain skills (and even entire roles) becoming redundant.
AI will solve some challenges - but not all
From what we’ve observed, the question of AI is proving to be particularly vexing for financial and supply chain teams. There are no doubt a few reasons for this, but perhaps one of the most significant is that a company’s financial and operational data is, by definition, specific and commercially sensitive to that company – and must remain very specific in order to be useful. Given that AI is underpinned by large volumes of data from external sources, its output is often too general in nature to be really useful in functions where precision and accuracy matter.
AI undoubtedly has some role to play in the Finance and Supply Chain disciplines. What that looks like in a practical and operational sense is yet to be fully revealed. There are some clear applications in terms of streamlining a number of key governance and admin tasks, particularly when it comes to preparing policies and procedures. This is primarily because large language models (such as ChatGPT) are so far ahead of other iterations of the technology.
We can also foresee a handful of use cases for AI in most Supply Chain functions. One example of this could be using the technology to regularly review sales data, forecasts and inventory projections to return actionable insights in terms of outliers.
Of course, any progress on the AI front in most departments assumes a reasonable level of operational maturity.
Tangible actions for leaders
Hype and hyperbole aside, there are three tangible actions finance and supply chain leaders can take in relation to AI in 2024:
- Separate the hype from the reality. AI is an incredibly interesting technology, with much potential. However, it has also entered the realm of “buzz word” – just like its predecessors, such as big data and business intelligence.
We’ve heard and read about so-called AI applications that are really just evolutions of better understood technologies, such as statistical forecasting and robotic automation (both of which have much to offer in terms of unlocking actionable insights and efficiencies, without delving into new AI investments).
Get your fundamentals right. We can’t stress this enough – you’re not going anywhere with AI, in 2024 or any other time, unless you’ve cleaned your house. Part of this is making sure your data is clean and easy to access, so you’ll be able to enable better decision making across the business. Another ongoing focus for most teams should be eliminating or minimising manual tasks that are time consuming and not adding value.
For more on getting your fundamentals right to get ready for AI, visit here.
Watch, learn and develop use cases. Last month we told you that there’s no shame in being a “fast follower” when it comes to AI. While being an early mover makes sense for a small handful of organisations with high maturity or a brand identity that is tethered to being an innovation leader, it’s a position that also carries a lot of risk, and often limited reward.
Importantly, watching and learning is not the same as doing nothing. For many of us, 2024 may be the year to watch and learn from what others are doing, as we start to build out AI use cases for specific operational contexts.
Cutting through the AI hype
The AI conversation is already busy with hype and rhetoric, which makes cutting through the noise pretty challenging, as is having a balanced, value-based discussion that gets beyond that hype. There will always be early adopters that lead the charge around the promise of new technologies.
For many Finance and Supply Chain leaders, there are plenty of exciting ideas about what AI can solve. Setting up the right foundations and establishing your own use cases will enable your organisation to deliver successful AI projects (and any other technologies that happen to become worthy of their own hype cycles) and avoid the trough of disillusionment.
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