Some companies see AI as a tool to help execute cheaper or faster, while others see AI as a strategic asset that is going to help them do something no one else in the industry is doing.
Yet one thing is clearer than ever today: any economic downturn is threatening to trim data teams, and, as with any technology, AI is under pressure to become a productivity multiplier quickly to prove its value.
However, if AI can continue to help businesses improve the efficiency of their core operations, companies will continue to look to it to generate value. Where companies need to focus now is on the engagements that solve real, pressing everyday AI issues, and help companies optimise costs and increase efficiency.
Select the Right Use Cases – and the Right People
As economic volatility makes companies reassess the value of their AI initiatives, and the enterprise platforms supporting them, we’ll see companies look to experts to focus on building realistic, lasting, and scalable impact. The best way to do that is to choose not to do certain things. AI-driven survival and success may come down to ensuring you don’t select the wrong use cases, as doing so might break stakeholder support for AI in your organisation.
When organisations are looking to where they should first apply AI, sometimes the parts of the business that seem to present a massive opportunity are actually already highly optimised, and there’s no further gain to be had from introducing AI. Other times, companies focus on use cases that are more moonshot or experimental, and unlikely to produce insight at scale. In both these instances, AI becomes an overly expensive technology for the value it generates.
In a recession, companies should be looking at practical use cases or everyday AI. This doesn’t just mean selecting the right use cases – it means empowering more of the right people within a company to extract value from AI.
If you leave AI to data scientists, it’s unlikely to reap rewards quickly enough to keep investors and key stakeholders happy. Businesses have to empower their domain experts to get insights from data. More often than not, companies are looking to collaborative, no-code or low-code AI platforms to help them achieve this.
Opening up platforms to business subject matter experts can help move these key people from observers of AI to AI creators, transforming AI from an experimental (and expensive) technology to a hands-on, practical business enabler. Practically, this might look like:
• Supply chain managers or merchandisers who are empowered to leverage massive amounts of data to extract key business patterns from sales and optimise product assortment.
• Factory operators armed with AI-powered dashboards that help monitor production quality in real-time, reacting to alerts before major issues emerge.
• Marketing teams that can incorporate machine learning to understand the customer mix better
The reality is that practical, everyday AI can recession-proof your labour force. Optimised AI can help you increase your headcount without hiring more people. With the correct management and tools, AI should help your data scientists start solving the hard problems 60% faster and at 50% of the cost – while helping your non-data scientists take on the less difficult or leading-edge tasks.
Get Real When it Comes to Calculating the Value and Business Justification of AI
If you’ve got 40 data scientists and each AI use case is only delivering half a million dollars in benefits, the numbers simply won’t add up. There are however many examples of small teams that have become powerful forces – even with only a handful of data scientists. It’s a great example to larger companies – if a small team could do it during good times, you can do it in a recession.
Control Spending on Big Projects
Right now it is more critical than ever to control the cost of delivering insights and models into production and day-to-day usage. So, consider your essential tasks: if you want to continue to accelerate insight with AI during this volatile time, you may need to pause on some tasks.
For example, moving data around is expensive. It may be worthwhile putting data migrations on hold while continuing day-to-day operations, especially while not being able to replace lost or reduced headcount. This means continuing to do analytics, building new models, and moving data to the most cost-effective platform for productionalisation.
Accelerating AI During Difficult Conditions
The value of AI is clear, but delivering tangible value from AI at scale is hard, especially in a recession. This is why the ability to do it — or not — is often a differentiating factor in which companies will succeed and which will not across industries. In 2023, investment in AI initiatives is likely to continue as enterprises build AI capabilities for themselves, and if managed carefully, AI initiatives can be accelerated rather than cut back – even during times of economic volatility.
About the Author
Shaun McGirr is RVP of AI Strategy at Everyday AI provider Dataiku. Shaun McGirr joined Dataiku in 2021 as AI Evangelist, and was promoted to EMEA RVP of AI Strategy in 2022. McGirr has nearly 20 years of experience working with data as a practitioner across multiple industries, including doctorate-level training in applied statistics, consulting, and automotive.
His focus is on helping customers maximise value on their paths to Enterprise AI, working with data science as both a creative and technical discipline across a diverse range of stakeholders as they collaborate from the beginning of the data journey.Prior to joining Dataiku, McGirr served as Head of Data Science & Business Intelligence at Cox Automotive UK, where he led a team helping customers access, understand, and engage with data to improve decision-making. He is also co-host of the Half Stack Data Science podcast.
