Suddenly, everyone is talking about artificial intelligence (AI). But the difference between this ‘next big thing’ and, say, the cloud, big data or any other widely-discussed technology, is that the AI talk goes way beyond the IT department. This time it’s being discussed by sociologists, economists and politicians too.
Quite rightly so; ultimately, AI has the potential to transform the world of work as we know it, propelled by the promise of high productivity growth – think of those science fiction films of robots replacing workers. According to the script, world domination usually follows. For example, in the Master Algorithm, Washington-based computer science professor, Pedro Domingos, predicts ‘the last invention man will make’; a formula to create a computer superintelligence that will discover everything that’s left to discover.
But let’s not get too carried away. Although Gartner predicts that machine (or artificial) intelligence will be “the most disruptive class of technologies over the next 10 years”, the latest Gartner Hype Cycle has several AI-related technologies such as ‘machine learning’ and ‘natural language question answering’ heading for the cycle’s ‘trough of disillusionment’.
Plus, surely we have more intelligence than turkeys looking forward to Christmas? It’s still up to us how we use AI to our advantage.
In fact, there’s a huge gap between all the highly advanced AI work and the typical organisation, most of which will have heard of AI, but don’t really have any idea how they would use it. Most wouldn’t be able to afford AI at the moment anyway, because the knowledge and infrastructure demands are just too huge.
Practical and within reach
I concur with the online technology publication InfoWorld that: “Sensible discussion about machine (or artificial) intelligence hold it as an augmenter, not replacement for human insight and understanding…. If any variety of machine intelligence makes its way to Gartner’s ‘plateau of productivity’ it’ll be the most dialled-down and immediately practical variety.”
So is there a way to make AI practical and within reach of most companies? The tech giants – Amazon, Google and Salesforce, for example – are certainly working on it while at the same time building their knowledge of the commerciality of the technology and how it can support their respective platforms. They have their research and development teams and have targeted appropriate acquisitions.
They see the technology working as decision support, adding value to the workforce and helping to meet strategic goals and drive business advantage – not to completely take over the world.
But, however advanced and complex they are, technologies only really reach the mass market when they become streamlined and the knotty challenges ironed out, so there’s no need for highly-skilled but also highly-paid experts such as data scientists to develop and customise.
Albert Einstein’s quote: ‘the definition of genius is taking the complex and making it simple,” is highly apposite here. This is why Salesforce, for example, has named its AI technology after the great man and has decided to include it as an integral part of its CRM and other software.
This now puts AI capabilities in the hands of all customers. Powered by advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery, Einstein’s models will be automatically customised for every business. It will learn, self-tune and become smarter with every interaction and additional piece of data. Its intelligence will be embedded within the context of the business, automatically discovering relevant insights, predicting future behaviour and proactively recommending next best actions.
For example, Einstein might be monitoring the LinkedIn pages or other social media of a prospect. It alerts its company that this prospect has hired a new sales director or some other news, giving the company the opportunity to contact its target. Or suppose you are about to close a sale, but have an inkling that things may not be smooth-sailing. Einstein might inform you that there’s no email traffic or phone calls and your contact has just requested to become a LinkedIn contact with a competitor.
On the marketing cloud side, Einstein will again analyse social media to measure customer sentiment and suggest the best targets. Manufacturers will be able to use it in conjunction with the Internet of Things, quickly pinpointing, for example, not just that devices are failing, but in what area this is happening and what might be the cause.
Even for Einstein, it’s still early days. Salesforce may be the first of the big names to consolidate all their acquisitions and bring the results to market but the software is still evolving and will, no doubt continue to do so. Salesforce partners have a huge role to play here in working with customers to first show them that AI will complement their skills to help them work and act smarter and then help them implement the technology in the best way for their organisation.
The best way to do this is to give real-life scenarios where Einstein could help them become more productive. For example, what if their whole day was scheduled and rearranged depending on traffic? Einstein could even send out emails to make new appointments – so not replacing people, only complementing their lives.
It’s not surprising that some organisations are wary. The business world has been battered by successive waves of new technologies over the past few years and AI could ultimately prove to be the tsunami of them all. But, in my view, this is very unlikely – and failing to embrace it could mean missing out on an opportunity for early transformation.
Sean Harrison-Smith, managing director, Ceterna
Image Credit: PHOTOCREO Michal Bednarek / Shutterstock