Complex Made Simple

Cutting Through The Noise on AI

AI for the sake of having AI is counterintuitive. Does it solve your business needs?

The fact of the matter is that organizations do not need "AI" — they need ‘real’ intelligence Today, there are more than 300,000 Facebook chatbots in action, and a little less on Twitter The addition of machine-learning (ML) into the mix makes bots optimized for learning about the visitor or user

AI is already driving value across a range of business functions, writes Ahmed Helmy, CTO, Avaya International

‘Artificial intelligence’ is fast-becoming a catch-all phrase to make whatever you’re doing sound smarter. These days, it’s used to describe all manner of technologies – from basic automation all the way to the robots from The Terminator. 

This is understandable. Make no mistake – the widespread adoption of AI is going to profoundly impact the way that people, organizations and governments operate. But with so much noise about what the future holds, it’s difficult to determine the real value that AI-based solutions can drive for your business today. 

The fact of the matter is that organizations do not need "AI" — they need ‘real’ intelligence. It just so happens that this real intelligence is being powered by AI-based solutions. 

The practical uses of AI may not yet deliver all-knowing humanoid robotic assistants, but they’re certainly driving value across a range of business functions. 

Take the simple chatbot as an example. Innovations around AI have turned the chatbot from an annoyance to an integral part of the customer experience. Even the simplest bots, so-called ‘rule-based-chatbots’, have come on leaps and bounds. Rule-based chatbots are now able to hold basic conversations using “if/then” logic. A human operator – typically a digital marketer – will map out the bot’s conversation using logical next steps and clear call-to-action buttons.

If you’ve run into a chatbot on social media, there’s a good chance that it’s rule-based. Today, there are more than 300,000 Facebook chatbots in action, and a little less on Twitter. They’re mostly used to automate customer service, online sales and marketing. They communicate with users using a “call to action button”, and they’re proving extremely effective at resolving simple queries – you could be an insurer giving out quotes, or a telecoms firm explaining the latest offers – and all those routine requests could easily be handled by a rule-based chatbot.

Things are moving beyond rule-based bots very quickly with the adoption of ‘real’ artificial intelligence. This is what you see with what we call natural language processing (NLP) bots. NLP assists machines with understanding human language, so instead of the visitor having to navigate through buttons and menus (as they have to with rule-based bots), they can simply have a conversation with the bot in the way they would message a friend.

Apple’s Siri and Amazon’s Alexa are NLP bots. Their AI capabilities include the ability to understand human language in the form of voice and text and give intelligent replies or carry out certain tasks.

What’s making them even better, though, is the addition of machine-learning (ML) into the mix. This makes bots optimized for learning about the visitor or user, retaining information on them, and predicting a conversation’s next steps. 

The end result? A bot that you can converse with naturally, and use to perform relatively advanced functions. Perhaps you want to pay your utility bill using money from your current account – all you’ll have to do is ask. And the bot will have the intelligence to not only register the request from your natural language, but will also know to check the balance of your outstanding bill, check the balance of your current account, and facilitate a payment between the two. 

The world is full of these simple interactions that are actually quite difficult to automate. The good news is that, as AI becomes more powerful and pervasive, we’re seeing time-intensive, routine operations being put into the hands of machines – freeing up time for human operators. And these solutions are available today. 

To cut through the noise around AI, you can ask yourself a simple question: Do I follow a vendor selling science fiction, or do I follow a vendor that delivers comprehensive use cases delivering clear outputs?