AI Adoption And Change Management Go Hand In Hand

Fabio Sattolo, Chief People and Technology Officer, Covisian.

A desire for greater efficiency is driving companies to implement AI across many different facets of business. But, as any leader knows, implementing a new technology and embracing it as part of the business mindset are two entirely different things.

In customer service and support, there has been a long history of technology innovation. From the old days of interactive voice response (IVR) adoption and screen pop to advancements in self-service automation, sentiment analysis, call analytics and AI, contact centers are often at the forefront of adoption.

While many of these technology shifts have required a change in human roles and responsibilities, AI is transforming how we work in ways we never expected. Offloading many traditional administrative or labor-intensive manual aspects of the customer service and support role to automation and AI is opening opportunities for customer experience (CX) representatives to uplevel their skills, handle more complex requests and practice greater empathy in communications. In short, CX agents must become better versed in intelligent problem-solving that builds brand loyalty, working in concert with AI tools that improve efficiency.

To achieve this, leaders must embrace change management strategies, modify the roles CX representatives play and provide the right resources and support to ensure success.

Training humans is as important as training AI.

Well-trained AI models, generative AI tools and AI agents can solve increasingly complex queries based on available data. The same is true for humans, but we have the added benefit of intuition, perspective and emotion, among other soft skills. Agents who were previously trained to follow a customer support script may struggle to embrace a new role that encourages them to focus on the customer relationship and make in-the-moment judgment calls based on conversations.

Leaders should focus on developing and rewarding these skills, helping agents become more comfortable with their new relationship-based role. Tools such as live sentiment analysis and AI recaps of conversations can help guide agents toward the right language and answers that lead to customer satisfaction, but agents need to understand how to leverage these tools to optimize interactions.

The days of pushing customers into closed-loop, frustrating automated or self-service interactions are coming to an end. More brands are adopting a human-first approach, where humans set the foundation for the relationship and then determine when and where AI plays a role. For this to be successful, representatives need to be trained on when to use AI versus when human intervention is needed.

For instance, if a customer is contacting support because they are already frustrated with a product or service, it’s best for a human representative to be the first point of contact. A human can listen and provide immediate support. When it comes time to gather data, agents can then opt to transfer a customer to AI. If customer support teams are experiencing high volumes of calls that could cause long wait times, representatives can be trained to delegate more tasks to AI as needed.

Find new ways to measure success.

In addition to changing training models, companies may need to measure and report on success differently. For instance, instead of sending customer satisfaction surveys after the fact, consider using AI to help determine the level of a customer’s satisfaction during the interaction itself.

Using real-time analysis and algorithms, analysis of conversations can determine satisfaction, even during an interaction.  Transitioning to this method of measurement allows employees to modify their language and behaviors during interactions to preserve the relationship. But again, helping teams understand this shift and leverage AI analysis during interactions can help ensure success.

For example, if repeated dissatisfaction is reported regarding AI’s support when answering a particular customer question, AI can flag the pattern. Then, businesses can determine if human intervention is best served to address this question or if optimizations to the AI tool are needed. Continuing to measure the customer’s satisfaction in this situation can help resolve this identified pain point and mitigate potential hurdles between brands and their customers.

Another key performance indicator (KPI) is comparing the amount of AI used during the experience compared to the total time a customer spent interacting with the brand. This metric allows managers to see the negative or positive correlation between AI and human interaction.  If customer interactions are reported to be positive when AI is also heavily used, it can be determined that AI tools are working how they should be and customer service representatives are delegating work properly.

Continue integrating AI and people.

To fully utilize the best that AI has to offer requires a new mindset that goes hand in hand with the technology. For industries and companies to effectively realize the benefits of AI, they must be open to trying new things. This doesn’t mean deploying the technology and simply hoping for the best; it requires a thoughtful approach, new training for employees impacted by AI adoption, a different way of measuring success and continual modifications based on what is working and what needs adjustment.

With the right strategy, companies can find a balance between human support that drives loyalty and AI tools that drive efficiency.

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