Scott Horn, CMO at
, discusses how artificial intelligence (AI) works and what businesses should look for when adopting it
So, Salesforce’s ‘Einstein’ was finally launched at Dreamforce in early October. Billed as ‘AI for everyone’, Einstein is set to revolutionise how artificial intelligence changes how businesses do business.
A series of speakers extolled the benefits of Einstein – from being able to predict the sports gear that a consumer might like, to medical uses such as being able quickly diagnose bleeding in the brain.
The real impact of Einstein, however, is that it validates the larger trend of artificial intelligence or “AI” being applied across a wide variety of both enterprise and consumer tasks. One of my favourite comments from the keynote speech at Dreamforce is that AI is like electricity, and that when it was first incorporated into appliances they were referred to by names such as “the electric toaster.” Now it’s just a “toaster”. Soon we’ll have the same sense of expectation that AI is just a core part our computing experience.
This year we’ve seen a spate of announcements from Facebook, Google, IBM, Microsoft and Oracle, along with scores of smaller companies. Fuelled by technology advancements (e.g. big data processing power, advanced machine learning, predictive analytics and natural language processing) and by the marketing engines of tech heavyweights, media are latching onto AI as the next big technology trend. It seems like 2016 is the year that that AI technology finally arrived.
But has it? Today’s AI is a bit like early hybrid car technology. All the major automakers (Ford, GM, Toyota, Honda, etc.) rushed to introduce the technology into their vehicles, with varying levels of success. For some, like Toyota, it was smooth extension of their previous offerings. For others, it was an awkward, clunky experience.
How AI works
AI enables computers to mimic human learning, understand the user and perform tasks that normally require human intelligence (e.g. visual perception, speech recognition, decision-making, and translation between languages). AI is great for things such as deciding whether to increase the credit limit on a card, where the steps are clear and the text of the interaction can be culled from actual human conversations that took place previously with call centre operators.
Companies have histories of these interactions and can cherry pick the ones that had very high customer satisfaction scores. That way, the system keeps getting better based on interactions. Every time something in an automated conversation goes wrong, it gets routed to a human who teaches the system a little bit more, effectively making it smarter. That’s how learning happens over time.
Unlike with personal virtual assistants such as Google, Siri, Echo or Cortana, where there isn’t a big downside if the system misunderstands a consumer’s query or returns the wrong answer, it’s critical for businesses to get this right. No business can afford the customer churn that comes from providing inaccurate information and a broken experience to consumers. In the realm of customer service, every interaction matters and any investments in these types of technologies will falter if the
comes up short.
How machine learning works
Machine learning is a type of AI in which computers can learn without being explicitly programmed. It focuses on the development of programs that can teach themselves to grow and change when exposed to new data. It often comes into play when there’s a need to process huge amounts of data that are beyond the scope of humans to process on their own. The best part is that the data can then be used to learn about human patterns and behaviour.
There are two main human-assisted machine learning techniques: supervised and semi-supervised.
, the most common technique, uses collaborative tagging by humans so that models can identify consumer intent. With semi-supervised learning, some data is processed by the system and some is manually tagged. Supervised or semi-supervised techniques perform well for the majority of enterprise applications with complex business requirements.
, by contrast, is much harder. It is best thought of as a continuum between (a) the entire system being one gigantic, autonomous, self-learning machine and (b) solving certain problems within a much larger system that also involves humans and supervised learning techniques. For many enterprise solutions we are very close to (b). For personal assistants like Siri, we are a little closer to (a), but even in such applications,
autonomous AI is still quite far away.
What businesses should look for when adopting AI
In most near-to-mid-term scenarios, businesses will be best served by supervised or semi-supervised machine learning. The true value in AI will be in accomplishing the tasks that your business and its customers (whether consumers or other businesses) set out to do. As such, I suggest businesses look for three things:
Accomplish tasks quickly –
For informational tasks (e.g. answering the question, “What’s the highest rated smartphone?”), a machine could automatically assemble answers from agent responses, social media sentiment, online reviews, and other knowledge sources. For transactional tasks (e.g. a consumer making a purchase), it is difficult for a machine to generate logic through autonomous learning, and therefore machines are much more reliant on humans.
Do things that humans can’t do
– Companies possess great amounts of data that they haven’t tapped into because it would be far too labor intensive for a human to do it. By quickly mining and processing this data, a machine can not only personalize user experiences, but also predict what a person wants to do and proactively provide information. From a consumer standpoint, that’s an extremely powerful moment.
Bring out the best in your people
– We all dream of is one in which we’re freed up from menial tasks and can focus on creativity. In the next several years, we’ll see a shift that will lead companies to greater profit, and employees to greater productivity. In the contact center industry for example, we’ll see a shift from having an agent actively chatting with consumers, to having someone who designs and oversees the customer experience. Individual conversations will be handled almost exclusively by chat bots and AI.
Despite the current limitations of AI, the investments we’re seeing from major technology companies, and most recently with Salesforce, point to the true potential of AI as a valuable counterpart to human intelligence. These investments mean that the technology is only poised to get better, and make the end user experience that much more personalised, intelligent and efficient.