Is Your Contact Center Ready for AI?
In This Article
Artificial intelligence (AI) platforms can help address common contact center challenges, such as improving the customer experience and reducing costs. While there are many available solutions in the market, Google's Contact Center AI (CCAI) solution, a cloud-based artificial intelligence platform, is worth considering as it integrates seamlessly with Cisco contact center solutions.
Below are the top 10 things organizations should keep in mind when implementing AI in their contact centers.
The emergence of AI in the contact center is happening at warp speed. Eighty-three percent of businesses say AI is a strategic priority and, according to Forbes, AI conversational agents will handle more than 20 percent of customer service requests by 2022.
Customers expect multiple communication options and top-notch, consistent customer service across all communication channels. Simply put, a bad experience can mean losing valuable customers. In highly competitive industries, this issue can be especially detrimental to the bottom line. When implemented correctly, AI and machine learning can help streamline clunky and time-consuming processes and leverage customer journey data to proactively anticipate customer needs.
Many organizations struggle with the requirements of making sure their contact centers comply with laws and regulations (e.g., FedRAMP, GDPR, PCI-DSS). Unfortunately, it's not enough to identify gaps by replaying calls days after they've occurred or reviewing an escalated customer email. Virtual agents can be used to improve regulatory accuracy without needing to engage live agents to reply to general questions. For example, a simple online FAQ can be interpreted by a virtual agent to automatically answer basic questions.
When a live agent is required for more complex inquiries, Google Agent Assist coupled with Insights analytics can help address the compliance requirements of the enterprise by interpreting voice and chat interactions in real time. This enables the organization to escalate sensitive inquiries based on sentiment and guide agents to the right processes and procedures to provide accurate responses.
Before introducing Google CCAI or another AI solution, organizations should reevaluate their existing contact center platform prior to attempting to extend its capabilities. If a current on-premise solution requires an upgrade to implement Google CCAI, it might be time to reevaluate the contact center platform as a whole to enable new features or, perhaps, consider moving to a cloud-based contact center. If AI is being considered as part of a more comprehensive strategy, these questions become even more critical as the organization seeks to expand AI services across its footprint.
Technology for technology's sake is not a good plan. Sure, AI for the contact center sounds like a great idea, but where does it fit into the organization's overall strategy (digital transformation, self-service, omnichannel, analytics, etc.)? How well does the organization know its contact centers and the associated backend processes? Comprehensive discovery may be needed to truly identify requirements and current state "hot spots" that would benefit most from AI.
Additionally, organizations should consider the needs of all contact center personas. For example, have quality assurance resources provided input? Is marketing involved? Where is the contact center on the multichannel curve? Is the organization moving beyond the voice channel? Are non-voice interactions like e-mail handled by dedicated backend teams? Has chat been considered yet? Depending on the extent to which organizations want to leverage Google CCAI, these are important questions to answer.
AI solutions require a harmonious, cross-channel integration design to avoid rework and achieve maximum ROI. A centralized development platform can help organizations achieve these goals. Google CCAI leverages Google's Apigee API management platform, which is ranked as the leader in the upper, right-hand corner of Gartner's magic quadrant. Lifecycle API management is especially critical as support staff with limited development backgrounds transition from contact center administrative roles to AI platform subject matter experts.
It's also important to ensure your integration approach aligns with the overall strategic direction. On the backend, organizations must promote a shared model that allows teams to leverage newly developed services, as well as existing services on other self-service platforms such as CRMs, websites, mobile applications, etc. to reduce development timelines and ensure transaction consistency across all self-service channels.
Before organizations add AI technology to their toolkit, it's important they understand how the technology will fit into their long-term contact center roadmap. According to industry analysts such as Forrester and McKinsey, contact center AI can generate ROIs of more than 180 percent, depending on the implementation approach. There are many factors that should be considered when determining the ROI calculation model, including projected volume of interactions planned for transition to AI virtual agents, projected increase in first interaction resolution rate, reduction in security and compliance litigation, and increase in customer loyalty. It's also important to consider that other contact center costs -- agent outsourcing, contact center seat licensing, etc. -- may be reduced or eliminated as AI becomes ingrained with contact center operations.
Many on-premise contact centers have traditionally utilized a capital expense (CapEx) budgetary model with focus on server infrastructure, licensing costs, etc. Cloud-based contact center solutions like Google CCAI leverage a consumption-based (cost per interaction), pay-as-you-go pricing model which aligns more with an operating expense (OpEx) model. If an organization is used to managing contact center expenses in a CapEx model, it may need to adjust how business units are charged back for contact center services.
Since Google CCAI can be integrated into many environments impacting the contact center, it is important to prioritize and select a starting point. Once the starting point has been determined (e.g., interactive voice response applications), organizations must create a migration strategy that includes associated customer base demographics, risk level of interactions, interaction volume and platform readiness (e.g., upgrade needs, backlog of required enhancements and fallback options).
Organizations can also start with a proof of concept that targets a specific self-service "hot spot" issue (e.g., a new product, tax season, etc.) and expand from there. This allows teams to become familiar with the Google CCAI development environment, tools, reporting and analytics, and apply what they've learned as they expand usage across the organization.
As virtual agents are introduced to siphon off repetitive, quick-hit interactions, live agents will need to gain more expertise in handling escalated, omnichannel interactions. Live agents must be tech savvy, emotionally intelligent and have a high-level understanding of products in order to relate to customer experiences.
Administrators and developers will also need to be educated on the new solution so they can support and extend the platform. Since the Google CCAI environment can be shared across multiple applications and teams, it's critical to assess and realign contact center personas accordingly to effectively manage the platform. For example, which roles will need access to the environment and for what purposes (e.g., development, reporting, analytics)?
There are a lot of components to consider before, during and after adding AI into your contact center. Organizations must carefully choose an integration partner that can help them combat obstacles along the way, improve the agent and customer experience, reduce agent turnover and increase overall customer satisfaction. Our close partnerships with Cisco and Google, combined with our contact center consulting and implementation expertise, allows us to help organizations align strategy, develop roadmaps and deploy solutions so they can achieve their business outcomes.