“AI is in a golden age and solving problems that were once in the realm of fantasy.” – Jeff Bezos.
Indeed, only a couple of decades ago, talking to robots, measuring emotions, and predicting customer behaviours may have seemed mere fantasy.
Now, companies are seizing these AI use cases to reimagine customer experiences, kickstarting a path to CX innovation that is set to transform the field.
The Development of AI in CX
In 2018, the Harvard Business Review conducted a study of 152 AI transformation projects. Its findings showed: “Companies do better by taking an incremental rather than a transformative approach to developing and implementing AI, and by focusing on augmenting rather than replacing human capabilities.”
At this time, many companies started to build large-scale AI transformation projects. Yet, the study suggests that these projects often failed to live up to their hype. Coming in over budget, they were whittled down to a shadow of the original intent.
Luckily, Brian Atkinson, Vice President EMEA at Five9, has noticed a significant shift in the recent development of AI transformation projects. He says:
“Multiple AI use cases are beginning to showcase their value, delivering on critical customer, employee, and business outcomes. Understanding these, CX teams have a better grasp of how to apply AI throughout the enterprise – which is now much easier thanks to cloud-based platforms.”
Yet, due to early horror stories, many CX leaders face an uphill task convincing business leaders to invest in AI. Indeed, fewer than half (48%) of CIOs have already deployed or plan to deploy AI and machine learning technologies within the next 12 months – as per a November 2021 Gartner study.
As such, building confidence in AI is critical to changing the CX status quo within many companies. Perhaps this starts with the incremental approach recommended by the Harvard Business Review in 2018, seizing low hanging-fruit use cases already proven to drive CX value.
Where is AI Currently Driving Value in CX?
Enterprise-wide automation opportunities are an excellent example of low-hanging fruit AI use cases that CX teams can exploit. Another is the capacity of AI to draw value from data and better inform business strategies.
Making this point, Atkinson states:
“Many CX leaders are having success by engaging with data and analytics teams to pinpoint AI use cases that fall in line with and build upon their current CX initiatives.”
“Take an initiative to identify and remove customer journey pain points. AI-generated insights – through tools such as speech analytics - pave the way for smoother customer experiences.”
One such pain point is the IVR, which more contact centres are also addressing through AI – to be more specific, bot technology. Perhaps this is the best example of AI progression in CX.
Just a few years ago, numerous bots – deployed by global enterprises – failed to deliver positive customer experiences. Indeed, 2019 Forrester Research suggested that 54% of US online consumers believed interacting with a chatbot would have “a negative impact on their quality of life.”
However, many were fuelled by little more than a script and natural language understanding (NLU). Essentially, several models equated to little more than a search engine 2.0.
Now – harnessing machine learning (ML), natural language processing (NLP), and robotic process automation (RPA) - bots are much more advanced. As such, many brands are starting to deploy popular chatbots across different channels. Indeed, conversational AI is no longer tucked away in individual silos, as the same engine may even power a voice bot.