For years, we saw conversational artificial intelligence discussed as a novelty. It was a nascent, untested technology that, at best, could be used for narrow use cases—and only in certain industries.
As we all know, that’s changed.
Conversational AI is hardly a luxury these days, nor does it only power simple, limited chatbot solutions. It’s no longer a technology meant for limited aspects of their organisation.
Rather, they’ve discovered the importance of operationalising AI across their business.
When you operationalise conversational AI, you’re putting automation to work in meaningful, discreet ways that span across your business operations and deliver long-lasting ROI. This means that your company is moving beyond AI as an experiment or narrowly focused tool and, instead, integrating it in a way that aligns with your business goals.
At Verint, the companies we’re seeing adopting widespread conversational AI initiatives with a vision beyond go-live are the ones that realise that this technology can grow to support self-service initiatives across the enterprise, while driving value and ROI at each phase.
Path to Operationalisation
When we look at the current AI landscape, many are intimidated by one well-circulated statistic: more than 80 percent of AI projects never get out of development or ultimately failed during the deployment process.
When we investigate those alarming numbers a bit further, we see why the failure rate for AI projects is so high. Far too often, extending conversational AI is a challenge for organisations. That’s because they leave it for basic customer service tasks without realising the other, more dynamic instances in which it could deliver ROI and real results for their customers, employees and partners.
But the tide is changing. Gartner® also says “75 percent of enterprises will shift from piloting to operationalising AI, driving a 5X increase in streaming data and analytics infrastructures.”* This means we’ll see companies taking a bolder stance, especially when it comes to implementing conversational AI for more use cases. For example, this will include assisting employees working in remote or hybrid environments, streamlining HR and IT processes, and investing in a more holistic AI philosophy.
In many ways, the operationalisation of conversational AI is no different than the evolution of any other software that businesses have relied on for decades. To provide the maximum value, any software needs to be both accessible and extensible. Truly great software solutions not only democratise access, they also prove to be robust and flexible enough to support the most ambitious and far-reaching business goals.
The problem we’re seeing, however, is that many companies are leaving their conversational AI siloed—therefore, they’ll rapidly fall behind those that are realising the extensibility of the technology and making use of the data they are able to access through this operationalised approach.
It’s Time for Your AI to Mature
Another way to think about operationalising your conversational AI is to see the process as the technology “maturing,” especially when it comes to how you use it for self-service—whether that’s for customers or employees.
At the far left of the model (see below), you’ll find the basic and often simple conversational AI technologies (Standalone or Ad-Hoc), such as question-and-answer chatbots that are used in a very limited capacity. These may provide some limited relief to your contact centre but don’t deliver extensive value or learn from your customer data.
More toward the centre of the maturity model, you’ll find your Bolt-On Service. Some of these may deliver more value to the customer, but they are often created for specific use cases, meaning your data is likely siloed within that department. Again, most of these technologies are a static solution that may work for a while but can’t keep up with your changing company.
At the Department level, we begin to see conversational AI that is delivering a higher rate of value by functioning across channels and your company’s departments. It goes beyond mere contact centre use cases and provides a seamless experience for customers and employees.
Finally, there’s the Enterprise level, which is fully operationalised conversational AI. This is the technology found on the Verint Cloud Platform, and perhaps most notably in our award-winning Intelligent Virtual Assistant (IVA). When an organisation is at this level, they are using conversational AI to handle complex interactions with a high level of personalisation and sophistication.
They are using IVAs that continuously improve through machine learning and align with their business goals and evolve with their growth. At this level, conversational AI isn’t merely a business tool, but rather an integral aspect of their entire operation.
Operationalised AI Makes Data Valuable
We saw an acceleration of self-service use during the COVID-19 pandemic. Healthcare organisations, insurance companies, banks, retailers and others turned to IVAs and chatbots to meet their customers when and where they needed help—and also assisted companies that had shifted to a remote work environment. This growth in self-service is expected to continue in the years to come.
More self-service means more data. So, as companies grow their self-service abilities and operationalise conversational AI—bringing them to the enterprise level of the maturity model—they need to realise the power of the data available to them.
True operationalisation means that the company is breaking down silos across departments and capturing data so that information can be processed with machine learning and used to drive their visions forward.
So, all those interactions your self-service has with your customers or employees—whether through voice, digital, mobile or community forums—turn into meaningful business that’s analysed and turned into actionable information for your future growth.
*Smarter with Gartner, Gartner Top 10 Trends in Data and Analytics for 2020, Laurence Goasduff, October 19, 2020
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