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How AI and Automation Impact the Future of Work

How AI and Automation Impact the Future of Work

How AI and Automation Impact the Future of Work

As part of Solutions Review’s Contributed Content Seriesa collection of contributed articles written by our enterprise tech thought leader communityNicole Kyle, the Managing Director and Co-Founder of CMP Research, talks about the ways AI and automation technologies are affecting the future of work across customer-facing teams.

Generative AI and automation will transform the workforce. As customers become increasingly digitally dexterous and prioritize interactions that save time, companies are quickly transitioning to automate self-help, increasing efficiency and innovation, especially within customer service call centers. Most customer contact organizations already invest in improved self-service for customer and employee-facing tools, particularly agent knowledge bases. AI, particularly conversational and generative AI, deployments for employee-facing tools are widely considered lower risk for organizations.

It is better to experiment with tools internally and apply lessons learned to customer-facing pilots later. That way, disruption to customers is limited. Ubiquitous AI-enabled tools to improve employee and customer contact agent experience are interactive agent knowledge bases. These are directories that employees can interact with to find answers to their queries, ideally faster than consulting with a supervisor or peer. Customer-centric divisions consistently remind executives that AI is a spectrum, from rules-based to conversational to generative AI. The correct form of AI for a given tool has everything to do with the use case; generative AI is only sometimes better purely because it is the newest and most advanced. 

Customer contact executives cite reconciling internal technology systems as a top barrier to delivering effective self-service. To increase customer adoption of self-service and improve outcomes, customer contact executives evaluate the technology that underpins self-service interactions, particularly artificial intelligence.

Executives’ confidence in the current technology marketplace to deliver on AI use cases and requirements is variable. Rules-based chatbots and conversational interactional voice responses are the technologies that executives are most confident the current marketplace can provide. In contrast, they feel least confident in the market’s ability to support infrastructure for entirely self-service customer portals on websites and mobile apps and plug-ins for messaging platforms like WhatsApp and Meta Messenger. This makes sense since some of these applications are newer. Likewise, it explains why many organizations are building in-house portals instead. 

Risks to artificial intelligence in customer contact organizations are two-fold: risks to employees and risks to customers. With risks to employee experience, generative AI will reduce volume-induced burnout since it automates low-complexity, high-volume tasks. But when all that’s left over for employees is complex work, that can spark complexity-induced burnout. This is why proactive workforce and workflow management has never been more critical.

Similarly, AI tools, especially a generative agent knowledge base, will make training and onboarding easier in virtual contexts. The generative knowledge base is akin to a coach! If this results in employees, especially new ones, reaching out less often to their managers and team members, that can exacerbate silos. As far as risks to customers, it’s evident that poor self-service experiences make it less likely that a customer will seek self-service again. If customer-facing AI tools are rolled out before they are mature, they could harm CX and cause customers to avoid self-service in the future, which is not the outcome executives want. 

Across both categories of risks, artificial intelligence—like algorithms and most technology—have bias built into it. There are many examples of artificial intelligence being biased toward perfect English, toward men, or white people. These tools, especially generative ones, are still in their infancy and need human intervention and company-specific customization to mitigate harmful biases. 

A significant amount of customers do not know the difference between generative AI and conversational AI. Good conversational AI is more than sufficient for many customer contact use cases, and a strong contact center will advise executives not to get caught up in the buzz and noise around generative AI purely for generative AI’s sake. Only a tiny percentage of customers embrace generative AI, like ChatGPT, in their personal lives, so while customers are increasingly digitally dexterous, their expectations and experience with generative AI are still limited. This should give executives confidence that there is time to reevaluate their technology stack and to exert experiments and patience with their technology roadmaps. 

As we navigate the evolving landscape of AI and automation, one thing is clear: the key to success lies in a thoughtful, balanced approach that considers the unique needs of both employees and customers. With continued experimentation, customization, and a commitment to mitigating biases, organizations will truly harness the potential of AI and automation, paving the way for a future of work where processes become more efficient and human-centric. 

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