PBT Group thought leadership
Worldwide revenue in the Artificial Intelligence (AI) market is expected to increase tenfold between 2017 and 2022. With machine-learning (ML) being one of its focal points, it is no surprise that understanding data has become more critical than ever. Simply put, business needs AI (and data) to stay relevant as pressure grows in the competitive landscape.
In recent months, AI has received a lot of attention from mainstream media. And, while the consumer-centric components of the technology (think along the lines of chat bots, autonomous vehicles, and improved deliveries from e-tailers) are important, they are by no means the only aspects worth investigating.
In a McKinsey podcast discussing the potential for AI in business, ML was bought up as having tremendous applicability – even more so than any other field of artificial intelligence. For example, the proliferation of mobile devices is resulting in more data being created as users become comfortable in sharing insights – whether through direct or indirect feedback by monitoring behaviour – to businesses.
Considering how much structured and unstructured data is being pushed through more channels into the organisational back-end systems, it is perhaps unsurprising that decision-makers are feeling intimidated by the rush towards Big Data. As a result, data science, in specific its real-time analysis, is turning into a business priority if sense is to be made of all this information.
The data strategies might differ from company to company and industry sectors, where it comes down to examining how best to interrogate [data] and manage it to develop bespoke solutions tailored to the stakeholders of the business.
So, does this necessarily mean that all companies will be equally adept at implementing data-rich (and AI-driven) strategies?
According to Harvard Business Review, companies with strong basic analytics – such as sales data and market trends, for example – are able to make breakthroughs in complex and critical areas after layering in AI.
On the other hand, those businesses that are still struggling to come to terms with data and its analysis are not able to leverage ML yet. They are also likely to still (though, generally speaking) appoint specialised data science personnel capable of auditing, analysing, and building on the information inside organisational data warehouses.
This is where a Chief Data Officer (CDO) is a critical step towards establishing an AI-friendly business environment. A CDO understands that while the business might have the data required to be competitive and grow, it is not (yet) using its data assets to the businesses full potential. The reality is that business processes helped by AI – and specifically ML – are only as good as the data used.
Companies cannot therefore blindly embrace AI as ‘the next big thing’ and hope to smoothly integrate it into existing systems. A business needs an explicit understanding of what data is currently available, the resources using the data assets and, the processes required to exploit these assets fully. A CDO can provide a guiding light in this regard if the rest of the C-suite buys into its competitive advantage.
AI is here to stay. How effectively a business exploits AI for growth will determine its success in the marketplace.