Artificial intelligence has reached a tipping point. It can no longer be ignored as a phase or something only for companies that are typically early adopters of technology. It’s impact on all areas of life, from medical research to your next film recommendation from Netflix, is evidence of its wide-ranging capabilities.
It’s for this reason that most organizations are considering some form of AI strategy, even though experts temper their praise for the technology with warnings that implementing AI isn’t always as easy as it seems.
And they’re right!
There are many challenges, not least the vast amount of data, properly cleaned and categorized, required for machine learning to work effectively. So where is the most practical place to start your AI strategy?
In AI terms, conversational AI is undoubtedly one of the more “mature” sub-sets of AI technologies in the sector. It’s one of the founding building blocks of AI. From humble beginnings, it found commercial success nearly a decade ago in customer service applications. Customers frustrated with inadequate FAQs and increasingly complex navigational menus on websites, can now simply ask what they want to know using their own words, their own terminology and whatever language they choose – plus they don’t have to conform to company jargon – all thanks to conversational AI.
This in turn hasn’t just lessened the burden on the contact center and helped to reduce costs, it’s delivered other benefits, such as creating a better working environment for live agents, which translates into reduced staff turnover. Further, by analyzing the resulting conversational data, enterprises can also identify problems and opportunities faster than ever before.
Today, conversational AI has expanded beyond customer service and it is now increasingly used in other areas of the business, from sales to human resources. It’s being combined with other technology such as virtual reality and robotic process automation (RPA) to improve business efficiency and deliver dynamic, immersive experiences.
The benefits of conversational AI across any organization is undeniable. Furthermore, with the right technology, building conversational AI applications is relatively low risk. But there are several key considerations that large enterprises need to consider.
There are probably around a couple of hundred companies that on face value could potentially help you develop your own digital assistant. But when you look a little deeper, most are only able to offer point solutions that are hand-crafted for each client. Applications that work across any device or channel and speak in a multitude of languages plus cover multiple use cases, are very, very rare indeed.
But that’s exactly what enterprises need.
If you’ve already started your AI journey, you’ve probably tried out a few projects to solve specific pain points. Perhaps in customer service, or a maybe a more specific task such as resetting passwords or developing an Alexa skill. It’s also probably more than likely that within your organization there are several projects being carried out simultaneously by different departments, using different technology.
For large enterprises, this type of development is unsustainable. Efforts are being duplicated and resources dispersed, often with no way to take the application and port it easily to additional channels and languages. And almost certainly with no method to extract specific dialogue flows or other developed components to be reused in a totally separate part of the business. From this standpoint, AI is no longer delivering the expected efficiency benefits.
In the last six months many enterprises have started to form centers of excellence and incorporate them into their wider digital transformation teams. In this way, not only do they ensure that resources are not duplicated but that the experience gained through use of the technology – “what worked with one project, might solve a problem in another” – can be shared to the company’s advantage.
This style of global collaboration allows enterprises to be innovative, proactive and efficient in solving the challenges facing their business in the digital age. It has the added benefit of allowing other stakeholders such as the CIO to have control over how data is stored and protected in-line with corporate policies.
Key to this strategy is using a central development platform for conversational AI. One that makes it easy to deliver complex applications that can be ported to any language, channel or device. This combined with the ability to incorporate third-party data sources, existing AI assets, complementary technology such as augmented reality and even other chatbots, will allow enterprises to take full advantage of AI; now and in the future.
The experts are right, conversational AI is all about the data — after all one of the key benefits is the conversational data generated that can be used to form closer customer relationship, deliver business insights and improve the system.
Conversational data is key because when people speak in a conversational way with each other, they reveal an awful lot about what they’re thinking: their likes, their dislikes, what they’re looking for, their sentiment and inner views. If you can capture this conversational data – and retain the full context of the conversation, then it’s like having a focus group at your finger-tips. But not a focus group of 10 people for an hour. With conversational AI, enterprises now have access to potentially millions of conversations, 24/7/365. And this access allows them to get into the minds of their customers and really start to understand how they think enabling them to make better informed decisions on products and services and to personalize responses to help build a more engaging relationship with their customers.
Of course, this raises another important question: who owns your data? It might seem obvious, but it is crucial that enterprises own their customer interaction data and are able to use it to the business’s advantage. The reality is that few bot-frameworks give you full access to your data so take care when choosing a conversational AI platform for your organization.
And there’s another consideration. Conversational AI platforms that exclusively rely on data to build their conversational flow have a significant Achilles heel….they need data in the first place! But what if you don’t have that data? Our platform, Teneo overcomes this potential issue of a lack of training data by providing developers with a unique body of data built on billions of real conversations. Known as the Teneo Language Resources (TLRs), these natural language understanding building blocks are crucial in enabling users to quickly build their own natural language applications.
Developed using machine learning techniques by some of the finest minds in computational linguists, the TLRs allow enterprises to teach new conversational applications all the possible language permutations in a matter of moments. The user simply enters a few representative queries, and the TLRs will enable the application to learn all the different ways a user might ask the same exact question. Because Teneo is available in 35 languages, the TLRs enable the application to ‘think’ in an enterprise’s native tongue, while delivering the same linguistic sophistication across every other language required.
So where does this all leave us?
Well, if you want your AI strategy to be more than a one-trick pony, an advanced conversational AI platform is a good place to start.
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This post was written by Andy Peart, Chief Marketing and Strategy Officer at Artificial Solutions, an international AI-focused business with the vision of Making Technology Think.
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