When American futurist Roy Amara said, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run,¹” he predicted the way artificial intelligence (AI) would become subject to hype.
Over the last few years, the buzz around the rise of humanoid robots, machine learning and big data have seen many businesses rethink their business models or scramble to implement new technologies. This is understandable. PwC’s 2017 global artificial intelligence study, Sizing the prize revealed that the adoption of AI will create an additional US$15.7 trillion in global GDP by 2030.
For many companies, the potential of AI to increase productivity and drive business growth has meant that being on the bleeding edge of the technology has become high priority. It’s a focus that’s exacerbated by the fact that being seen as innovative is fast becoming marketing gold — a way to position yourself as more forward-thinking than your competitors.
But since the first machine learning algorithms and artificial intelligence neural networks were developed in the 1950s, AI has been the subject of major hype cycles. Too often, this sees businesses focus on the flashiest, newest technology without taking the time to clarify the underlying challenges of managing data and governance or clearly identifying what AI means to them.
The fact that AI is often conflated with science fiction — think robot icons like Optimus Prime and R2-D2 — has meant that organisations are increasingly forgetting that AI isn’t synonymous with androids alone.
AI can span anything from non-linear regression (familiar to anyone who’s taken year 10 maths class!) to machine learning, to a virtual agent that might connect with your customers using human mannerisms. When it comes to AI, embracing the newest technology isn’t half as important as using the right technique for the right business problem — despite what the tech pundits might have you believe.
Rather than squandering money and resources on technology that might be irrelevant for your business, it’s worth considering the less-hyped aspects of AI.
Capitalising on the latent data assets inside your organisation may yield more value, and faster, than rolling out shiny new platforms and systems. Often, vendors convince you to buy expensive hardware and software without reminding you to make better use of the data that you can already access. Big Decisions, a 2016 data analytics survey by PwC, succinctly highlights this wasted opportunity: it reports that 33% of executives still use intuition and experience to make business decisions rather than letting data-driven insights do the talking.
There’s also not nearly enough emphasis on enhancing the quality of your data and properly deploying it, a barrier that exists regardless of how advanced your technology is. Consider an AI that doesn’t grant credit to a customer who is highly desirable because it’s privy to incorrect data or hasn’t been trained correctly. Before getting swept up in the hype cycle, get your data up to scratch first.
Sure, you want to improve your customer relationships by introducing a virtual agent or invest in a machine learning platform to identify emerging patterns. But have you actually identified the business problem that artificial intelligence will help you solve?
When you’re caught up in technology trends, it’s easy to overlook that every new strategy you implement should either save you money or make you money. That’s why paying attention to processes, governance and data transformation should trump using AI to overhaul your business — even if you want to be seen as an early adopter.
As Sizing the Prize puts it, getting clear about your business problem means that you’ll be able to more accurately identify the pain points that AI and automation could help you address.
Instead of jumping headfirst into AI investments that might not be right for business goals, it might pay to start small.
For instance, trialling AI techniques via short, six-week sprints is often a better bet than investing an enormous amount of money on extended AI projects. Taking an agile approach and adopting a test-and-learn philosophy will help you glean insights from your mistakes.
When it comes to using algorithms and deep learning to guide business decisions, there’s no shortage of leading companies that are writing the rulebook on how it’s done. E-commerce giant Alibaba uses data to drive everything from its inventory to online merchandising². Elsewhere, Netflix draws on customer data to inform its programming³. And government departments could use data to fight everything from family violence to environmental pollution.
Remember that enterprise-wide AI isn’t a standalone technology. Ensure that the foundations of your business — such as your business goals, data and analytics infrastructure — will win out over getting caught up in the AI hype cycle every time.
To learn more about how AI will impact your organisation, read Sizing the prize: What’s the real value of AI for your business and how can you capitalise?
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