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Lessons Learned from Previous Technology Shifts: The Impact of AI on Contact Centers

There has been a lot of talk over the last 6 months about the effect that the sudden rise of Generative AI will have on contact centers. I think there is now a general consensus that this technology will have a huge impact on the way we work. Many can see (or maybe dream) of a world where AI will replace the need for contact center agents altogether. What is not so clear is just how fast this impact will take effect. In this article, I will look at a few other large technology shifts that offer some clues about how this will flow through the industry and things you should consider along the way.


The benefits and costs need to be clear to overcome inertia

There are a lot of parallels between how businesses are approaching Generative AI and the general move to the Cloud. Cloud Contact Centers and CCaaS started emerging in the early 2000s and the migration progress has been slow. Businesses had made huge investments over many years, so often, the benefits didn’t seem to outweigh the cost of change. It took a one-in-one-hundred-year global pandemic to finally overcome the inertia. These same businesses are now looking at the sudden emergence of Generative AI and weighing the possible benefits against the costs. Until the benefits are proven and the costs are clearer, we will see a similar reluctance for adoption.


Tech needs to be integrated into core systems

The rise of Chat as a channel in contact centers has followed a similar path. Although chat is now ubiquitous and expected by many consumers as a way to communicate with businesses, chat is often seen as an after-thought for contact centers. Technology providers share some (or most) of the blame here - the large providers of voice for contact center often treat chat as an after-thought too (if at all). This makes it much harder for businesses to seamlessly integrate chat into their operations and often forces them to use a separate dedicated chat platform. Separate systems lead to a disjointed process for the business and their agents. This is reflected as a disjointed experience for the customer. For Generative AI to be successful, businesses need to integrate it into their core systems workflows otherwise, it will become just another point of friction and disorientation for the customer experience.


Understand the unique strengths and weaknesses of technologies

The other lesson we can learn from the adoption of chat in the contact center is to consider the unique strengths of a technology. Most businesses today treat chat in exactly the same way as voice — as an synchronous channel with the same opening hours and success metrics. Doing it this way misses out on a lot of the benefits a blended live and asynchronous chat provides. Although it may start out that way, the most successful uses of Generative AI will not just try to replicate what we are already doing. Business and technology providers will need to learn and understand the unique characteristics of the technology and build use-cases around them.

The final insights come from a technology outside of the contact center but one that shares a lot of similarities to the hype and end goals of Generative AI. In the first half of the 2010s, self-driving cars started to be tested on real roads. By around 2015 there was a lot of talk that these fully autonomous vehicles would become widely available to consumers within the next 5-10 years. We are coming up to that 10 year mark now and you still can't go to your local car dealership and buy a self-driving car.


The last part is always harder than you think

The companies developing autonomous vehicles have solved many of the core problems required for these to work — you can ride in a driverless taxi in a few cities today. But the world is a complex place and driving a few blocks in San Francisco is different from driving hundreds of kilometers across regional Australia (Volvo realized in 2017 that kangaroos are different than elk or deer). Building the systems necessary to deal with all the variations in driving conditions across the world takes a lot of time, data, and processing power. We will face the same kind of challenges if we try to automate all the tasks in a contact center. There will be common simple tasks that will be automated quickly, but people and problems are varied and complex. It will be far longer than expected before human agents are completely unnecessary.


We’re living in a society

Technology is rarely developed and used in isolation. It is made to be used within a broader system that is mostly outside of its control. If we could replace all the cars and put up fences along the road tomorrow, then the self-driving car problem would be basically solved. But instead, we have millions of cars driven by humans, humans crossing the road, humans in government making laws and centuries of societal expectations for how things should and shouldn’t work. Autonomous Vehicles have to work within that system. Generative AI is being developed and used in this same broad system, so we need to consider that when thinking about how it could be used and how fast its spread will be.

It's hopefully obvious by now that I don’t think Contact Center Agents will disappear anytime soon. But I’m not saying businesses should ignore Generative AI or take a wait-and-see approach. Even in the early days of this new technology, the potential benefits are clear. If you are a business that has or runs a contact center, move faster than you did with Cloud or Chat (its not as hard as you think to start). If you are a software vendor, think about how you can incorporate this technology into the core of your platforms rather than trying to tack it on. And for everyone, don’t lose sight that we are doing all of this in service of real people.