The UAE inflation rate is forecast to average 3.2% this year, a considerable dip from its 2022 peak of around 6.8% in the second quarter. While these figures are orders of magnitude less unsettling than those in the US and Europe, the economy is now undeniably global, so UAE business leaders are not immune to western instability.
Recessions have been a recurring global phenomenon since 2008. In fact, many argue that the trend in digital transformation, both regionally and around the world, was mainly spurred by a “do more with less” battle cry that echoes to this day. The cloud was seen as a way to shave resource and financial costs by outsourcing many IT functions and shifting to predictable subscription-style outgoings. Artificial intelligence (AI), meanwhile, was seen as a means to tighten up production workflows, and as a tool for upending business models to stand out in a crowded market.
The looming shadows of recessions (plural) and a worldwide health crisis are scraping away at the business-intelligence function, eroding resources and manpower and threatening to demote AI to luxury status once again. But not for the innovator. Forward-looking businesses see AI as a necessary source of value — the route to market leadership and longevity. This is because they have paid attention to history. AI took time to prove itself; and prove itself it did. It has a solid track record of improving efficiency within core operations, and solving real-world problems elegantly. The innovators have already integrated AI into their culture, fusing it to their DNA and reaping the rewards of Everyday AI. As far as they are concerned, AI is no more superfluous than email.
A hot summer
Everyday AI enterprises think along similar lines. They are the enterprises who grasped the essentials of AI — that it was toolbox and not a silver-bullet. By that I mean that Everyday AI organizations understood that they needed to think about the changes they wanted to see in the business before applying AI to those changes. They knew that AI was not a business consultant, so they set themselves the task of identifying the right people and use cases to make AI work for their unique operating model.
By collaborating with one another — HR, finance, sales, and the rest — business units ensured that areas that were already highly optimized or that represent too little prospect of value returns were left to tick on by themselves while other processes got more attention. This is especially important during times of economic instability, when Everyday AI organizations would never dream of giving up their data insights, which they would see as just as effective a remedy to recession as AC is to a hot summer.
Another lean-times go-to for the Everyday AI business is training. If data teams are being trimmed or the plans to onboard that MIT-educated data scientist have been shelved, it is fruitful to look inwards. If a business can find within its ranks those with aptitude and interest in AI, they can upskill them. The lack of domain knowledge in AI experts is a common project bottleneck. By training internal domain experts in AI, costs are saved in both recruitment and operations. This scenario would have seemed untenable just a decade ago, but today, no-code and low-code AI platforms abstract the technical details of solutions development and put the intuitive design of smart systems into the hands of business specialists.
Everyday AI: some use cases
Everyday AI companies can navigate rough economic waters more easily. Once business-unit leaders get to grips with the nature of the data that is most relevant to them, they can extract meaningful insights from it to manage supply chains more effectively, optimize product lines, or spot demand spikes and dips ahead of time. Factory floors can fine-tune quality assurance and marketing teams can target their messaging in a more granular fashion.
AI not only brings intelligence; it brings automation. Just as low-code automates development, some AI platforms can do the work of a junior data scientist, automatically discovering insights and submitting them for review. When recruiting becomes prohibitively expensive, AI can solve problems on its own. This means that each insight is cheaper and comes more quickly. And everything can be led by domain experts who steer the AI and review its work to optimize the value it delivers.
The takeaway here is that data teams do not need to be large to add value — AI itself can recession-proof the talent pool. Data-science talent is rare and represents a significant portion (if not the lion’s share) of the cost of each model and insight. Recessions are great times to revisit wish lists and weed out the unnecessary in favor of the quick win. One example of an expensive to-do item is data migration, which drains monetary and human resources. The business can continue to generate insights without it, so placing it on hold will not put an end to Everyday AI.
Advantage: you
Economic jitters are no reason to drop AI completely from a business strategy, especially since it can help do many of the cost-cutting, efficiency-enhancing things enterprises look to do in a recession. While it will not be easy to harvest tangible value at scale during times of talent shortages and slimmed budgets, it is by no means impossible, and if successful, can turn a lean period into a historic turning point for an enterprise. So, while the natural instinct is to cut, cut and cut during downturns, we should remember that they stop because a few bold innovators start spending again. And while hard times last, there is always a window for gaining an advantage. That is the spirit of Everyday AI.
