“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.
Through these developments, bots not only replace the routing functionality of an IVR but offer self-service capabilities, enabling contact automation. Customers may then solve queries through a smart speaker alone, without lifting a finger.
Other excellent examples of potential “low-hanging fruit” use cases, which many contact centres are currently taking advantage of, include:
- Desktop automation to mechanize repetitive agent actions
- Speech analytics identify customer journey improvement opportunities
- Biometrics technologies streamlining to streamline security processes
Finally, stalwart enterprise software is also being augmented with AI. Again, take the contact centre as an example. AI-driven algorithms embedded into WFM tools now automate forecasting and scheduling processes. Analytics tools automate quality scoring within performance management systems. Meanwhile, such analytics can enhance reporting software, enabling the accurate tracking of metrics like first contact resolution. These are just a handful of examples.
An Exciting Path Ahead
The increasing presence of AI within enterprise systems will increase, with many leveraging real-time data to enable advanced CX strategies, such as hyper-personalization.
Yet, the ability of these tools to work in unison is perhaps most exciting. Consider the contact centre experience. Leveraging conversational data, automation tools can auto-fill forms. Meanwhile, AI-infused performance management tools offer coaching advice and analytics tools share customer sentiment insights. Such support allows agents to focus on creating better customer experiences.
As confidence continues to build, many more complex AI trends come to life. These may include:
- Composable applications enabling developers to use and reuse code, accelerating the pace of digital innovation
- Decision intelligence allowing better decision making by combining the power of analytics with simulations
- Autonomic systems self-managing software that learns from its environment and modifies its algorithms in real-time
Also – perhaps most pressingly – the rebirth of more comprehensive AI transformation programmes will likely arise, disrupting the status quo further. Yet, this time with in-built agility, which enables teams to fail fast, learn quick, and adapt quickly.
Creating “test-learn-optimize-embed” workflows may be central to such a strategy while harnessing AI-driven data to direct innovation. Atkinson recommends such an approach, stating:
“Challenge people to not think like for like. Focus on making improvements at the moments that matter most to customers.”
However, with AI tools coming increasingly out-of-the-box – lowering the expenses related to managing and maintaining AI transformation initiatives – such creativity may seem challenging to achieve. Yet, thanks to the current low-code revolution, enabling developers to add brand-defining finesse to CX solutions, vendors are paving the way for creative thinking.
Consider the approach of Five9. The CCaaS provider has developed a simple interface that allows innovators to access some of the best NLP engines in the world. Partnering with other leading vendors, Five9 provides clients with a pre-packaged offering – providing drag-and-drop functionality.
Indeed, through this open cloud foundation, contact centres can develop no-code/low-code environments that enable faster, streamlined AI transformation.
Disrupt the Status Quo With Five9
Five9 AI and Automation solution combines the power of practical AI solutions to automate everyday contact centre tasks. Doing so often sets the basis for a more comprehensive AI strategy, enabling operations to harness proven use cases and inspire confidence in the technology.
So, for brands on the precipice of AI adoption, engaging with a Five9 expert may prove the ideal starting place to drive positive change across the entire enterprise.
