We are witnessing a time of seismic change in the service workforce landscape. Experienced workers are nearing retirement, agent turnover is at an all-time high, and remote work and decentralized operations are becoming the norm. These trends create a unique challenge for enterprises: the dilution and loss of critical knowledge and know-how that is stored within the minds of your service and support workforce.
Trends that demand attention
- An aging workforce
As experienced professionals step into retirement, we’re facing a shortage of young talent willing to step into their shoes. - The price of churn
High turnover rates in customer service roles put us in a perpetual state of catch-up. Most contact centers, for example, experience average annual turnover rates of up to 60% each year. - Remote realities
The rise of remote work adds another layer of complexity, requiring new work and technology paradigms to train, onboard, and enable agents to deliver consistent levels of customer service and support.
The hidden threat: Knowledge and IP are leaving the building
Expert knowledge learned on the job by experienced agents and techs carries huge value but is intangible, hard to capture and share, and leaves with them as they leave the organization. Additionally, service data resides centrally in multiple different tools and systems. Making it accessible and usable to a network of remote agents, many being new and with limited experience, is challenging and carries the risk of further fragmentation and dilution across various locations.
Lost institutional knowledge: When seasoned professionals leave, their accumulated knowledge and expertise – often residing in their minds or personal notes – departs with them. This IP drain can impact the ability to solve complex issues and affect service levels.
Escalating costs: High levels of workforce turnover strain resources and inflate operational efficiency. The constant cycle of recruiting, onboarding, and training new team members consumes ever more time, creating a significant burden on margins.
Diminished productivity: New hires, lacking the experience and knowledge of their predecessors, take longer to reach peak performance, inevitably leading to decreased productivity, impacting resolution times and overall service quality.
The ‘knowledge gap’ presents a significant challenge for service organizations, however, it also presents a significant opportunity to create a new paradigm where institutional knowledge can be captured, connected, searched, enhanced, and made accessible through the deployment of specialized AI solutions.
Bridging the knowledge gap: Unifying knowledge with AI
Over the last few years, working with customer service and IT organizations to build and deploy specialized, equipment-focused AI solutions, we’ve seen that the need to capture, distill, and distribute knowledge is a recurring challenge. The key to unlocking the solution has been to apply AI to unify disparate service data streams to make knowledge accessible, searchable, intelligent, learning, and in some cases prescriptive. This unified view is the foundation that powers specialized AI applications that can deliver the right knowledge, in the right place, at the right time across multiple use cases.
AI delivers the right knowledge in the right place at the right time
Retains critical IP to stem the knowledge loss: AI-powered solutions integrate all the information that resides in the minds of experienced workforce over the years; from comprehensive data on equipment repair, parts, warranties, service issues, and even personal notes on customer interactions. Capturing this valuable information ensures that no data is lost when an employee retires.
Reduces onboarding costs: With step-by-step generative installation guides, new technicians can learn at their own pace, reducing reliance on peer training and accelerating their ability to complete jobs effectively. Virtual assistants offer guidance and support as new hires learn on the job, helping them build proficiency and confidence faster.
Turbocharges remote working models: AI data integration and service copilots ensure that real-time knowledge is available to service agents, regardless of location or technical background. Armed with this wealth of information, even first-level agents can handle complex issues that would typically require senior intervention. Serving as decision guides, this approach harnesses the collective knowledge of all technicians, ensuring best practices are consistently applied.
Proactive AI deployment is essential to close the knowledge gap now
For service leaders, taking immediate action to mitigate the risk of knowledge loss and dilution is crucial, not only in the short term to stop the bleeding but in the future to be able to harness service knowledge and make it an enterprise-wide IP asset. If done right, this has the potential to become a competitive advantage by significantly increasing levels of customer support and satisfaction, while also reducing costs through AI automation and the development of virtual tools and assistants that are trained on the knowledge base.
It is important to note that not all AI solutions are created equal. Specialized, domain-specific AI tailored to connect the dots between customer service, technical resolution and maintenance data as well as parts, inventory, warranty, and connected equipment data is a must-have. Establishing this specialized AI foundation that can unify and apply intelligence across all of these disparate data points is the most critical success factor to creating relevant, timely knowledge that can be delivered through any service application such as virtual trainers and assistants, agent co-pilots, chatbots as well as existing tools such as CRM.
Protect your knowledge with a unified view
Bruviti specialized Equipment AI is the engine that powers a unified knowledge view. Built for seamless integration with your existing IT infrastructure, it focuses on enhanced security and rapid deployment. It operates securely within your organizational firewall, ensuring that sensitive data stays within the organization. Equipment AI is designed from the ground up to be deployed within 5-7 weeks, allowing your team to focus more on leveraging knowledge instead of managing data logistics.
Discover the power of Bruviti’s AI—get a live demo today.
