It’s been an amazing couple of years for contact centers. They are benefiting from major enhancements and innovation in the form of artificial intelligence (AI), speech technologies, analytics, and the cloud, in many of their 45 systems and applications. AI and related technologies are in their infancy and are already making significant contributions to the companies that use them.

Most of the contact center technology and application providers say they have incorporated AI into their solutions, but this can mean many different things. In some cases, it means that they have enhanced their rules engines; in others, it signifies the use of machine learning to identify and improve the performance and benefits of their solutions; and for many organizations, it’s about the use of natural language understanding and processing (NLU/NLP). However, while the vendors are strongly marketing their adoption and use of AI technologies, for companies to realize the greatest benefit, they need to re-imagine how they apply the solutions.

To optimize the benefits, companies must evaluate the new offerings and then update their technology roadmaps to incorporate the use of the AI family of products. After attaining a good handle on the offerings, they then need to identify current business challenges and determine which ones can best be addressed with AI.

Below are proven examples where AI is making significant improvements in contact centers and some back-office operating departments. The following examples address both systems and processes:

• Speech Analytics – uses machine learning to discover and refine system findings and for analytics-enabled quality assurance;
• Workforce Management – uses machine learning to identify the most appropriate algorithms for each set of forecasting and scheduling data;
• Adaptive Real-Time Scheduling – uses a variety of AI technologies, including machine learning, to improve agent scheduling;
• Voice of the Customer – uses AI to kick off the appropriate survey for consumers who interact with a business;
• Knowledge Management – used to identify knowledge gaps and automatically kick off a workflow to address each one;
• Interaction Routing – used to match each inquiry to the optimal agent to address it;
• Sales – used to discern customer intent, such as likelihood to buy, as well as their needs and wants, if appropriate;
• Reducing Customer Attrition – used to determine the propensity to attrite and, if a customer is at risk, routes the interaction to the right person for retention;
• Hiring Solutions – used to assess and qualify candidates;
• Agent Coaching – uses AI and machine learning to identify performance trends and skill gaps to kick off skills-based coaching and e-learning sessions;
• Intelligent Virtual Agents – used to improve the voice and web self-service experience with conversational (NLU/NLP) interfaces;
• Desk Analytics – used to identify automation opportunities in the back and front offices;
• Robotic Process Automation – uses machine learning to automate routine tasks;
• Customer Journey Analytics – uses machine learning to identify relationships, patterns, and trends, and predictive modeling to identify the likelihood of each customers’ future behavior;
• Voice Biometrics – uses machine learning to support an adaptive tuning process; it recalibrates the voiceprint each time there is new data.

These are just a few examples of the power and benefits that AI is bringing to front- and back-office departments. The ease of delivering new capabilities via the cloud means that vendors are constantly introducing enhanced capabilities, and most of them welcome input from their customers. When it comes to AI, the fun has just begun.

The answer to the question presented in the title is yes. The variables are when, how, and which technologies warrant an investment. Artificial intelligence is a term that covers a broad set of advanced technologies that make solutions smarter. And most contact centers need smarter solutions.

IT and business leaders need to work together to pick the optimal systems for their companies. There are many considerations to take into account, including the deployment model (cloud, on-premises, hybrid or managed service). Ease of learning and use is also very important; while IT will be fine with solutions that require programming, business users prefer offerings that come with well-defined interfaces that make system administration easy for them. Lastly, it’s time for most organizations to start adopting these solutions, as these AI-based systems improve quality, productivity and the overall customer journey, and companies that do not use them will be at a strategic disadvantage.