Don’t jump the Gen AI gun to realise returns on AI investments.

Moreover, contact centre operators will fail to realise the full return on investment (ROI) in AI by looking to Gen AI to solve issues and challenges that basic AI, predictive AI and conversational AI can effectively address with lower cost and less complexity.

For instance, basic AI is adept at determining intent and finding and accessing relevant information from back-end systems to automate front-end engagement and offload repetitive, low-value tasks from agents to streamline CX and improve efficiency.

These AI models can also streamline data analysis in the contact centre, with the ability to rapidly process large sets of unstructured and inaccessible data in seconds to find patterns that help improve performance by informing data-driven decision-making.

AI-based decisioning can also field and prioritise inbound calls through intent recognition, which advances previous capabilities related to speech analytics based on exact phrases. By orchestrating first-line customer engagement with intelligent routing, the solution can allocate basic engagements and repetitive tasks to automated AI solutions like chatbots or conversational virtual agents.

In this way, basic AI augments the human role to support effortless experiences and handle higher call volumes by serving as an adaptive tool that helps to process calls faster, reducing average handling times (AHT) and improving FCR rates.

When used to augment the agent role, predictive AI provides the tools, insights and capabilities needed to help agents assist customers in the moment and address their specific needs and requirements with greater relevance and speed to boost customer satisfaction (CSAT) scores.

With the appropriate training, AI will super-charge live agents as they work alongside the technology in a manner that enhances their skills and empowers them with insights and intelligence to handle higher priority, more complex or sensitive tasks and engagement with customers.

AI-powered agent assist technologies can also understand the customer’s intent and context and leverage data analytics and big data to serve up relevant profile information, dashboards and historical interaction data to support agents in the moment.

By looking up appropriate responses within its knowledge base and delivering recommendations on the best actions to take next via the agent desktop, these solutions create super-agents and improve AHT to enhance CX and service.

Furthermore, applying conversational AI empowers chatbots and virtual agents or assistants to simulate agent conversations with customers, enabling them to actively respond to questions, queries and requests in real-time and in a way that feels natural and human-like.

Conversational AI solutions leverage natural language programming (NLP) to process and understand human language or text by recognising grammar, words and phrases. By linking to a Customer Relationship Management (CRM) application or other back-end data source, the system can anticipate a customer’s need and pre-empt a recommendation or response when engaging with a customer without sounding robotic.

As such, contact centre operators must acknowledge that Gen AI can solve certain challenges but does not yet have practical applications in every instance. Given the diverse applications in the business, AI strategies must consider which models currently offer the most credible cases for investment amid all the hype and excitement.

As such, the first step on the journey to advanced AI capabilities starts by defining the need and role that AI will play in the operation. It is critical to identify the relevant models to implement in areas where AI-driven innovation can drive RoI and deliver transformational benefits while mitigating the risks and inherent limitations that organisations face when implementing more advanced AI-based solutions.

Working through this needs analysis will reveal that Gen AI is not the most suitable solution to the business problem or challenge that contact centre operators currently face, with potential applications for other AI iterations potentially offering more relevant benefits and RoI.

Ultimately, if the requirement is simple, start with basic AI and build out your capabilities. Operators can then implement elements of each AI type and do not need to necessarily progress from one stage to the next in an iterative process.

Using a mix of basic, predictive, conversational and Gen AI in the right places will deliver the greatest RoI, particularly for those embarking on their AI journey.

Partnering with a business-outcome-driven, technology-independent expert in contact centre AI enablement can achieve similar outcomes that Gen AI can deliver today across various use cases at a lower cost using predictive and conversational AI.

As such, the potential exists to focus on building out basic AI capabilities within your contact centre operation to cover 80% of your requirements, with a Gen AI strategic roadmap in place for the other 20% of AI-enabled engagement to create an optimised cost model and future-proof your operation as Gen AI gathers pace as a transformative trend in the contact centre industry.