How contact centres are transforming through artificial and augmented intelligence

There’s no question that contact centres have undergone a dramatic transformation in recent years, driven by evolving customer expectations and technological advancements. Modern customers demand quick, efficient, and personalised service, and they expect to connect with contact centres through multiple channels, including phone, email, social media, and asynchronous messaging. Contact centres must continually adapt to meet these expectations, integrating advanced technologies to enhance operational efficiency while handling customer interactions with increasing
levels of sophistication and personalisation.

It’s imperative for contact centres to evolve alongside these technological shifts to provide exceptional customer service in a digital age. Now, artificial intelligence (AI) and augmented AI are transforming contact centre operations even further. While both technologies enhance efficiency and customer experience (CX), they serve distinct roles.

Artificial versus augmented: What’s the difference?

Many savvy contact centre organisations have already made the leap to AI. Chatbots, automated systems, and other AI-driven solutions typically operate as the first point of interaction for their customers and are used to handle high-volume, repetitive queries. This approach benefits contact centres by managing simple tasks, such as account enquiries, status updates, or frequently asked questions (FAQs), while freeing up human agents to handle more complex issues. The result is a more streamlined process where initial customer interactions are quick and efficient.

Augmented AI supports contact centre agents, rather than take tasks off their hands entirely. In other words, it doesn’t replace human agents, but it does enhance their capabilities. Augmented AI tools, like real-time contextual response and knowledge suggestions, assist agents during customer interactions by analysing the context of the
conversation to suggest appropriate responses to agents, reducing the mental
effort required to handle each query. This is underpinned by brand-aligned
knowledge articles, tonality, process documentation, and product information.

Agents shoulder a significant cognitive load, especially when dealing with complex or emotional customer issues. Augmented AI systems ease this burden by providing information and suggestions in real time so agents can focus more on empathy and problem-solving rather than recalling information or procedures. Importantly, one of the key benefits of augmented AI is its ability to learn from human interactions. As agents use suggested responses and tailor them to specific situations, the system learns
to provide better agent support and automate certain types of interactions even
more effectively. Augmented AI can also help to standardise responses across
various channels and agents. The resulting consistency improves overall customer
service quality through a high standard of response regardless of the agent or
channel.

Two heads are better than one

It has become clear that combining huma n agents with the power of AI delivers real benefit to businesses, and the same is true when it comes to augmented AI. Contact centre organisations should operate AI and augmented AI in tandem with their skilled human agents for the best chance of success. This synergy is crucial; while AI handles routine queries directly, augmented AI equips human agents with the tools to offer
personalised and empathetic responses for the more complex queries they manage,
delivering a combination that ensures efficiency and quality in customer service.

Using AI and augmented AI together also ensures better data collection and analysis. Contact centres can track common issues, agent performance, and customer satisfaction for continuous improvement of both technologies and agent training programs. The integration of these technologies into contact centres can also lead to significant cost savings as contact centres can handle more queries without additional staff. This scalability is essential for businesses facing fluctuating contact volumes.

Risky business versus innovation

One of the main sticking points for many organisations is trusting AI to handle conversations with customers unattended and having confidence that the answers given to human agents are accurate and relevant. When generative AI (GenAI) models get this wrong and create incorrect or completely made-up answers, this is referred to as ‘hallucination’ or ‘confabulation’. It occurs when the AI tries to be helpful at the expense of accuracy and compliance. The best practice and broadly accepted mitigation for this is to use retrieval-augmented generation (RAG), where GenAI tools take the input (question or prompt), produce an answer, and use the knowledge base to improve that response. While this approach does not solve the hallucination problem
entirely, it is better than letting large language models (LLMs) be helpful yet incorrect.

However, a more successful solution is the emergent approach of something I have termed ‘RAG+’. This strategy restricts AI to answer questions only using information that exists in the knowledge management system (KMS) and is far more efficient and effective at mitigating risks.

RAG+ relies on multiple parallel processes, technologies, and machine learning (ML) approaches, including vector databases, multi-tiered knowledge structuring, effective prompt engineering, and natural language understanding. This combination delivers an effective way to mitigate risk around brand damage, reputational risks, compliance, and legal issues while simultaneously fostering contact centre innovation.