Technology is a sector that has often been prone of over-hyping the latest trends that emerge within it. Over the past few years, virtual reality and driveless cars are just two examples of technologies that while rich in potential, have been over-hyped by both tech and business press, when they are in fact some way still from emerging fully.
The fact that industry analyst group Gartner calls one of its market analyses the ‘Hype Cycle’ further highlights the tech industry’s propensity for hype, and perhaps one of the most over-hyped technologies of recent times is Artificial Intelligence (AI). Despite the media attention and the number of vendors claiming to have world-leading solutions, the number of actual end-users deploying AI to good effect is fairly limited.
But that’s not to suggest that AI is without enormous potential. A recent (July 2017) report from the McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier, revealed that tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment. So AI is on the agenda of the biggest and smartest firms in the world. But how can organisations make the best use of AI and ensure it truly delivers on its potential?
Data and AI – the perfect match
The one thing that can help AI emerge from the hype is simple – data. Organisations hold more data than ever before, and thanks to the Internet of Things and the connected world we live and work, this data is growing all the time.
Used effectively, data can deliver unparalleled insight into customers and more, but it has been a challenge until now for organisations to manage this data in a way that enables easy extraction of actionable insight. The answer lies in AI and machine learning (ML).
But before AI and ML can get to work, a big issue for many organisations is the manner in which data is stored. It isn’t uncommon for a business to store and manage data in multiple CRM platforms, as well as a number of other repositories – ERP, other databases, on the server and desktops – across the enterprise.
This can arise through M&A activity, expansion or simply inefficient managing of IT resources, but the result is the same – data held in many siloes. This makes the management and analysis of this data a much harder job than it need be. If you cannot access data than how can you be expected to draw insight from it?
Addressing unstructured data
Furthermore, the sheer volume of big data in modern organisations can be bewildering, and it comes in files and formats that most CRM systems are unable to manage effectively. Unfortunately, this data is often the most valuable, containing rich insight into a particular customer and their specific needs and requirements.
This unstructured data includes: any social content – Twitter, Facebook, LinkedIn, Instagram – by, and relating to that customer; email conversations between the customer and brand; service call scripts that detail any recent or historical issues, and much more besides that doesn’t into the formats used by most CRM systems.
By not deploying unstructured data within a CRM, it can potentially be a major problem. It means that huge swathes of potential customer insight are missing. Utilising technology that captures both structured data in siloes and the masses of unstructured data, means businesses can begin to benefit from AI.
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The potential of AI
Used properly, AI it can be hugely transformative and have a genuine and tangible impact on businesses and their customers. For instance, enterprise search is an area that is crying out for the use of AI and ML. Knowledge workers are spending too long searching for information that might not even be filed where they are searching for it.
Using an AI-based cognitive system allows much more complex search queries and can even make relevant and contextual suggestions back to the user. This is AI effectively understanding better than the user what that user is looking for, saving time on searches and delivering better results.
AI can also deepen customer understanding. An issue for larger firms is knowing who holds which relationships at a particular customer. AI technology can look at mases of unstructured data – all emails sent to an organisation, from and to different people, in different departments and multiple locations – and provide a way for a user to take meaningful action with a customer. This outlines clearly and in real-time who knows who within an organisation, invaluable when enterprise relationships can be so complex.
These are just two examples – the potential of AI can go much further than that. But the key to taking AI beyond the hype lies with data, that’s where the potential is at its richest. By deploying AI and ML, organisations can collect data from multiple sources and in multiple formats, extracting fresh and insightful meaning from it and helping to deliver a complete view of that customer.