Why contact centre operators need to start thinking about AI with an enterprise-wide lens.

Artificial intelligence (AI) is transforming the contact centre environment, offering a wide range of applications that enhance efficiency, improve customer experience (CX), empower agents, and reduce costs.

By Jack Godfrey, Sales Director at Connect

The early stages of discovery.

Numerous vendors compete in a crowded market, looking to carve out a niche by providing solutions for different points in the customer journey and various back-end operational tasks.

Front-end solutions include automated engagement via intelligent virtual agents and chatbots, Interactive Voice Response (IVR) tools, and agent-assist solutions like AI copilots.

Operational back-end applications include workforce management (WFM), quality assurance (QA), knowledge management, sentiment analysis, and security and fraud detection solutions.

The dynamic hype cycle around new and evolving iterations of AI, like conversational AI, generative AI (GenAI) and, more recently, agentic AI, also complicates the decision-making process for operators looking to deploy these intelligent technologies into the contact centre environment.

Given the available options and the need to embrace AI to remain competitive and relevant, many operators choose to embrace a piecemeal approach to implementations.

While siloed implementations can unlock cost savings, create operational efficiencies, and elevate CX, these benefits only scratch the surface of the true value that AI can deliver.

Preliminary engagements with AI.

Delivering tangible value and a significant return on investment is only possible when operators consider AI as an enterprise application and use it to support more complex interactions.

However, this is only possible when the journey is fully automated from end-to-end, yet only 18-20% of companies are currently thinking about true end-to-end automation.

Developing digital transformation strategies that consider the technology’s potential impact across the enterprise is the key to building future-ready AI-powered contact centres.

Rather than simply initiating an automated response to a customer query to reduce call volumes or average handling time (AHT), AI solutions should trigger a flow of actions across the organisation that automates the entire customer journey.

Whether implementing conversational, generative or agentic AI, this process begins by determining which customer journeys an operator can automate across the whole ecosystem, and how AI can enhance every touchpoint to deliver tangible value.

AI impact is organisation-specific.

This may not look the same for different operators as some are further along in the adoption cycle than others. Often, an approach that initially seeks to automate the simplest journeys unlocks the most value, as the resultant cost savings can fund subsequent automation projects.

Operators may also measure the success of automation projects differently, looking at metrics like reductions in full-time equivalent (FTE) employees or cost to serve, increased call volumes, or improved employee advocacy.

Whatever the goal and measure of success, correctly structured data is a prerequisite for delivering the end-to-end automation that gets you there.

Deriving tangible value from automation becomes harder to achieve without structured data and an existing knowledge base because resource order tools rely on this data format to manage interaction flow.

Structured data provides the clarity, consistency, and machine-readability necessary for automation, efficient decision-making, predictable outcomes, and integration with other systems, such as CRM and CSM solutions.

Structured data securely stored in a centralised location is another critical element, as it ensures AI language models – large or small – can ingest data and access a knowledge base that shapes their understanding of the business’s products or services.

The impact of structured data.

This is necessary to support conversational AI for basic responses to customer prompts or queries, GenAI to action upstream and downstream tasks, and agentic AI, which is goal-driven and action-oriented, able to make decisions about the outcome it believes the customer wants.

For example, operators can automate simple customer journeys, using conversational AI to field and respond to frequently asked questions (FAQs) in the contact centre. However, this must be designed in a way that the customer will use, otherwise it will fail.

Building more sophisticated self-service automation requires the ability to interpret wider data sets, along with access to a deeper knowledge base.

This is particularly relevant given the emergence of real-time voice AI as contact centre operators look to replace agents with voice AI chatbots to significantly reduce their cost to serve and remove language barriers around translation.

Voice AI can handle the conversation and offer a valid response, no matter how a customer asks a question. Eventually, these tools will provide emotive responses, as a human agent would.

A future-focused AI strategy.

However, these toolsets are only valuable when the AI bot can trigger the task or action requested by the customer, which requires end-to-end automation.

Operators must also monitor and report on these journeys to determine the success rate and measure the tangible value AI delivers in the business.

Bringing these solutions to life and realising tangible value requires a solution partner that knows how to structure data and can design the AI engine in a way that interprets what customers are saying or asking for.

The solution provider must also know how to automate workflows to benefit other upstream and downstream resources, such as order management or billing systems.

Finally, a solution provider must have the ability to continually optimise and evolve these automated journeys according to changing business, operational or regulatory requirements, and work with AI frameworks that are still maturing.