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AI in the contact centre: Should we believe the hype?

By Karl Reed, Chief Solutions Officer at Pivotal Data

Given the topic's relative share of voice in industry media, it is easy to overstate the role artificial intelligence (AI) currently plays in contact centre digital modernisation strategies.

While nascent AI applications first gained traction in the contact centre environment as far back as the early 2000s, all the hype around the adoption and use of this advanced intelligent technology is unfounded.

While AI is currently on everyone's radar, and applications are available and ready for use, the contact centre industry's ability to implement and adopt true AI remains some way off.

As it stands, AI permeates the contact centre in pockets, most commonly in intelligent chat bot solutions that automate responses, or allocate calls to the appropriate agent.

This form of narrow AI is adept at determining intent, and finding and accessing relevant information from back-end systems to transform front-end engagement and streamline CX processes.

However, this is not true AI. For AI to deliver on its full promise, contact centre operators need to embed it into every channel and platform to give the AI engine sight of every interaction and data point.

Before that level of integration can happen, contact centres need to get the basics right by first initiating a process that prepares back-end systems for AI integration.

This process begins with a cloud migration strategy that ultimately integrates front-end contact centre solutions with back-end systems as these engines require high compute power which Cloud environments can provide. Additional yet mission-critical steps in this process include data sanitisation and data consolidation across the enterprise.

Ultimately, AI engines require access to all organisational data to consume and process this information and continually learn. And the quality of those inputs matter to the eventual output. Poor quality data or information will erode the quality of AI-driven customer interactions and negatively impact the customer experience.

Furthermore, sub-standard inputs impact an AI engine's ability to accurately predict customer requirements and engagement preferences, make relevant or personalised recommendations, or anticipate spikes in usage or peak volumes – a key tenet of the AI value proposition.

And getting personalisation right pays. According to the 2021 State of Customer Experience report, using data and AI for customer insights and personalisation emerged as the leading strategic priority for CX leaders in 2021. Survey feedback revealed that consistently personalised experiences are powerful drivers of wallet share and peer advocacy.

But no one is yet able to implement AI on this scale due to numerous factors. First and foremost, the processing power needed to run true AI engines is enormous.

As such, the costs are currently prohibitive – only the world's biggest tech companies like Alphabet, Microsoft, IBM and Facebook, to name a few, have the technological capabilities and financial resources to run true AI engines.

In addition, many contact centre operators don't have the big data sets or access to third-party databases that AI engines require to function optimally.

And creating the modelling needed to derive insights from AI engines is intensive, requiring scarce and expensive resources like data scientists and other technical experts to pull it all together.

Given these constraints to adoption, AI is currently just the next industry buzzword. However, contact centre operators ignore this rising trend at their peril.

Based on the exponential rate of technological advancement, AI will soon move beyond its current hype cycle and officially claim its status as the next big thing in contact centre digital transformation.

For instance, technology adoption has advanced more in the last two years than it has in the previous two decades. That means contact centre operators need to cut through the noise and marketing hype to begin their journey to an AI-enabled future.

The first step on this journey is defining the need and role that AI will play in the organisation. For many, working through this needs analysis will reveal that AI is not actually the most suitable solution to the business problem or challenge.

Those operators that require true AI capabilities will need to create a cloud transformation roadmap that plots the steps and requisite technologies needed to realise their AI vision.

Cloud is the gateway for most businesses to true AI capabilities as the technology is too costly to build and maintain in-house. And end-to-end system integration can only happen on the back-end when a company hosts all systems in the cloud.

While this is by no means an easy integration, contact centre technology vendors are playing their part by trying to simplify the path to the cloud and thus to AI for their customers.

Eventually, in the not too distant future, AI will augment human capabilities in the contact centre. By orchestrating the customer journey in the back-end, AI will support agents to deliver exceptional customer interactions and elevate service delivery amid a boom in digital engagement and customer data points.

AI will play a vital role in analysing customer data across voice and digital channels, including metrics and surveys, sentiment analysis, social listening, and focus groups.

As the State of Customer Experience report states: “Cloud and AI-based technology can empower teams with rich data and dashboards, support them in the moment, offload administrative tasks, and drive quality and consistency.”

Ultimately, people still want to talk to a human but empowering machines to do the heavy lifting through AI's ability to analyse massive data sets and make recommendations or suggestions in real time will become the minimum standard to meet evolving CX expectations.

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