Hybrid 2.0: Why your customer support team needs both human empathy and AI efficiency

The term ‘hybrid’ has been brandished around quite significantly since the pandemic. Full of good intentions (and sometimes results) and strategies to better serve the needs of a more distributed and diverse workforce, employers have been unsure about how it works. Of course, it existed long before the COVID-19 pandemic, when customer service operatives needed to serve the needs of their end users well outside traditional 9-5 parameters. I’ve witnessed this industry evolution first-hand over the past few years, and I know we’re all tired of hearing about hybrid work models that are nothing more than loose, grey areas describing vague working parameters. It’s time to address the hybrid elephant in the room and redefine what hybrid means in the context of customer support.

In the post-pandemic world, businesses are grappling with the same challenge: how to align the availability of support with the 24/7 demands of customers. The solution isn’t just about where your team works; it’s about how they work. Enter Hybrid 2.0 – a tangible shift beyond the workplace, to employee-centric, human-AI collaboration.

Hybrid 2.0 isn’t just another buzzword – this time around it is the next level up. It’s an approach that we’ve only seen unlocked since the advent of AI and the ability to understand the complexity of working patterns and nuanced customer needs. AI isn’t taking customer operatives’ jobs – it’s helping to elevate the customer experience while optimising operational efficiency. But what does this mean for your customer support team?

Let’s revisit and dispel a common misconception: AI is not here to replace your human agents. Instead, it’s here to augment their capabilities and free them from mundane, repetitive tasks, equipping them with the customer and contextual knowledge needed to solve the problem in the best way. Just as commentators in sports don’t always rely on encyclopedic knowledge (well, some do) – AI tech is there to serve up the most relevant information to enhance the commentary in the match – the same is true for customer support. In the Hybrid 2.0 model, humans and AI each play to their strengths.

The case and point for Hybrid 2.0 is of course July’s Crowdstrike patch update. Nobody could have (fully) predicted what had happened (although some claim that they did!), when a Microsoft Windows 10 ‘blue screen of death’ outage of historic proportions stalled TV broadcasting, grounded planes, defected supermarket checkouts, and caused widespread, global chaos, following a ‘defect’ in an update. This incident, reputably one of the biggest IT disasters in history, hit headlines broadly due to its impact on the end user. But it’s important to also consider what that meant for every professional sitting in a support position. It’s these ‘unknown’ events that you can’t train a bot for, and you do need to react to them in a hyper-flexible way.

It’s for reasons such as the ramifications of Crowdstrike that you need a hybrid team, and not a fully automated team – but you also need a flexible one, supported by a quick-response system. That way, it’s possible to reschedule and reshuffle staff to address a massive spike in demand and invite more agents to attend the phones, emails, or chat in the hours and days following such tremendous disruption. The human agents manage the immediate aftermath of the unknown, armed with the context and immediacy of AI insight.

That goes for forecasting, too. AI makes forecasting more precise by integrating multiple data inputs outside of historical case volume. This makes it hyper-precise but also allows agents to be available in exactly the right moment and context. Even though nobody could’ve predicted Crowdstrike, this level of forecasting can at least allow for the right structures, processes, and people to be in place to handle the ramifications.

Human agents bring empathy, complex problem-solving skills, and the ability to handle the unknown. They can navigate nuanced conversations, defuse tense situations, and provide that personal touch that builds customer loyalty. They can adapt to sudden changes that AI might struggle with – but they’re still equipped with the information at their disposal to understand the customer’s needs.

On the flip side, AI excels at efficiency, data analysis, and 24/7 availability. It can handle routine inquiries, process vast amounts of data to identify trends and provide instant responses at any time of day. AI doesn’t need sleep, or coffee, doesn’t take vacations, and doesn’t have bad days. It’s also your right-hand-agent for when you might need coffee or are short on sleep and can pick up the slack.

The magic happens when these two forces work in tandem and I’m not just spouting the adage we’ve read 1000 times about humans + machines. It’s more than that now.

Imagine an AI system that can handle initial customer inquiries, gather relevant information, and solve simple problems. Almost like a triage system in a hospital. For more complex issues, it hands off to a human agent, providing them with a complete context of the customer’s history and current issue. The human agent can then focus on what they do best – solving complex problems and building relationships – rather than wasting time on data entry or searching for often siloed and inconsistent information.

We know that Hybrid 2.0 isn’t without its challenges. One of the biggest hurdles is resistance to change. Many organisations are comfortable with their existing systems, even if they’re inefficient. There’s a fear that implementing AI will lead to job losses, but this is a shortsighted view. In reality, it creates new opportunities for human agents to upskill and focus on higher-value tasks.

Another challenge is technology integration. Many legacy systems weren’t built with AI in mind, making integration complex and potentially costly. AI also evolves so quickly, that it’s important to future-proof. However, the long-term benefits far outweigh the initial investment.

To successfully adopt Hybrid 2.0, organisations need to rethink their team structure and workflows. This might involve creating new roles, like AI trainers or hybrid team managers, who can bridge the gap between human and AI capabilities. It also requires a shift in mindset, viewing AI as a collaborator rather than a threat.

The data speaks for itself, and the increase in job satisfaction is particularly noteworthy. Contrary to fears about AI taking over, human agents reported feeling more fulfilled in their roles. They were able to focus on challenging, interesting problems rather than repetitive tasks, leading to a sense of growth and accomplishment. That passes on to the end customer too. For example, a mid-sized e-commerce company saw a 25% increase in customer satisfaction scores, a 60% decrease in average response times, and a 35% increase in agent job satisfaction.

The decreased turnover is a significant benefit, as it reduces training costs and helps maintain a more experienced, knowledgeable team. I’m hopeful that this will become the norm, rather than the exception, and brands that embrace this way of thinking will be better positioned to understand and meet the demands of customers across contexts, while also providing a more fulfilling work environment for their agents.

It’s not the robot takeover that everyone’s fearing – we’ve finally reached the ability to create a symbiotic relationship where each enhances the other’s capabilities, and understands their limitations.

Customer service technology has evolved significantly. Just a decade ago, the idea of AI-powered bots handling customer service was considered futuristic. Today, it’s AI-driven customer service, across chatbots and human operatives able to handle elements of the process and increasingly complex tasks and provide instant, 24/7 support.

As it stands, the notions of hybrid work seem to be either vague or distinct, when in reality, the definition is hybrid too. It’s not just about defining AI capabilities vs.. Human nuance, but it’s not a chatbot takeover either. Hybrid (2.0) now should be about hyper-flexibility for human agents, which is driven and enabled by AI. While the human side of the team is freed up to work in the way they want to work, advanced scheduling can take it even further to help people work in the way they want to work, too. By enhancing the model collaboratively, we help humans a little more genuinely, driving up engagement and productivity, and drilling down on churn.

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Posted in AI Journal

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