In a landscape dominated by AI hype and shiny tech demos, some businesses are of the mindset that the technology meant to simplify operations is often doing the opposite.聽
According to David Tyler, founder and CEO of London-based Outlier Technology, it鈥檚 time to stop asking how AI can be used 鈥� and start asking what problems actually need solving.
鈥淭he worst thing you can do is ask the question, 鈥楬ow can I use AI in my business?鈥�,鈥� he told 老九品茶Cloud.
鈥淚t鈥檚 a non-question 鈥� it鈥檚 meaningless and it鈥檚 pretty much guaranteed to help you waste a lot of time and money on white elephants.鈥�
Outlier Technology, a consultancy that helps organisations untangle and streamline complex systems, is pushing back against the tech-first mentality that seems to be the norm. He believes the answer isn鈥檛 more tools, but better thinking.
Tech as a tool, not a goal
With decades of experience in technical and systems roles, Tyler has seen a recurring pattern of businesses investing heavily in platforms like ERPs, CRMs and data warehouses, only to be left disappointed.
He said: 鈥淚 had a long and successful career in technical roles and kept seeing time again technology taking centre stage in so many businesses, but it quickly failing to deliver on any of the promises made during the sales cycle.聽
鈥淔rom my perspective, it was clear that so many organisations were being blinded by technology and its bells and whistles but lost focus on the value it could provide when implemented properly.
鈥淥ur objective is to make technology actually work for organisations by using less of it rather than more.聽 You can鈥檛 fall out of bed without landing on some form of technology 鈥� it鈥檚 the easy bit when you understand the core fundamentals.聽聽
鈥淲e take a human-centred approach to system design 鈥� we focus on the outcomes, the decisions and the actions people need to take and we align technology with that to solve real problems.鈥�
Cutting through the noise
From trading systems to data platforms in regulated industries, Outlier鈥檚 work spans sectors. But the throughline is clear – fewer buzzwords, more clarity.
鈥淲e鈥檝e developed a framework for this where we look at the decisions being made and the actions being taken at different levels of an organisation,鈥� continued Tyler, who was previously a lead data scientist at IHS Markit.
鈥淲e look at the roles of those involved and we look at the model for making the decision.聽 Once we understand those things we can make very targeted decisions about what to automate and why.
鈥淚t鈥檚 so easy to automate for the sake of it and it鈥檚 easy to get lost in the fun and spectacle of doing something 鈥榗ool鈥�.聽
鈥淏ut if we focus on the people, the decisions and the actions we can see where it makes sense to add 鈥� or remove technology.鈥�
The AI reality check
While most tech headlines are dominated by generative AI, the two-time graduate believes a tweak could be in the offing.
Tyler explained: 鈥淲e鈥檙e headed for a massive correction. The hype cycle in AI is massive and we鈥檙e seeing open source presenting an existential threat to the likes of OpenAI, Anthropic and so on.聽 Their business model is built around technology for which there is no moat.
鈥淚 think what鈥檚 likely is we will see the likes of Microsoft and Amazon focus on helping smaller businesses host their own private models and offer a selection of open source models.
鈥淚 think the biggest thing businesses can do is start by forgetting that AI exists.聽 Then analyse their business and look at where decisions are made, how they鈥檙e made and what can be done to improve those decisions.聽 Once you have that, then start looking at which elements of AI can help you.
鈥淯ltimately, to get anything meaningful out of AI, you need to get very good at defining the problem you want to solve 鈥� in detail.聽 That will become the new superpower.鈥�
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