Ready for agentic AI? Adopt strategy before following hype.

What you need to know before jumping on the bandwagon.

With its ability to complete complex tasks and meet objectives with little or no human supervision, agentic AI is a trending topic in the contact centre industry as operators embrace ways to streamline operations, improve the customer experience (CX) and reduce costs.

Adoption is accelerating.

According to findings from an enterprise survey on AI, Deloitte predicts that in 2025, 25% of companies that use generative AI (GenAI) will launch agentic AI pilots or proofs of concept, with this figure growing to 50% in 2027.

Among these companies, those in the customer service sector are some of the most prolific early adopters of agentic AI.

Gartner predicts that agentic AI will autonomously resolve 80% of common customer service issues without human intervention by 2029, leading to a 30% reduction in operational costs, with 50% of all service requests likely initiated by customers powered by agentic AI systems by 2030.

Agentic AI refers to artificial intelligence systems designed to act autonomously and pursue goals with minimal direct human supervision. 

This capability is achieved by combining the control, reliability, and integration of conversational AI with the language prowess of large language models (LLMs) to create autonomous, goal-oriented AI agents.

Moving beyond traditional AI systems.

Unlike traditional systems, which are often task-specific, follow rigid programming and are restricted to responding to user prompts, offering a limited range of responses and actions, agentic AI systems exhibit agency.

Agentic AI customer service agents are actively engaged in their tasks and environment, leveraging sophisticated models to quickly grasp customer intent and emotions, with the ability to actively evaluate and predict customer needs in real-time and fully resolve more complex customer queries, providing personalised and responsive experiences at scale and speed to enhance CX, rather than simply providing a basic response.

Even if agentic AI systems are guided by goals that humans initially define, they can act alone and are capable of free-form interactions with the real world, all while operating 24/7 to ensure consistent and efficient support.

This autonomy also enables them to adapt actions based on changing conditions to optimise defined goals or objectives by handling multi-step processes and carrying out complex sequences of activities, independently searching databases or triggering workflows to complete activities while learning from each engagement.

These capabilities make agentic AI extremely valuable in the contact centre environment as agentic AI agents can handle more of the complex, decision-intensive tasks that were previously handled by agents, freeing a company’s human resources to focus their energy and expertise on strategic initiatives that add greater value, such as creative problem-solving and building stronger customer relationships.

Technology alone isn’t enough.

Given the potential benefits, many operators are jumping the gun by rushing to implement it. However, while most operators have access to the technology, they don’t take a strategic view of how to implement and use it.

The ability to train agentic AI rests on access to structured data via a unified platform or data lake that integrates data from various sources. This data holds relevance when training AI engines for accuracy, reliability and latency, while keeping within LLM context windows.

Furthermore, fully autonomous capabilities only become possible when contact centre operators integrate agentic AI systems into multiple third-party systems, such as CRM and CSM solutions, and omnichannel platforms.

Achieving this level of autonomous decision-making and action also requires the complex integration of different machine learning, natural language processing, and automation technologies.

In addition, operators require deterministic workflows for repeatability and control, which is where systems of record and workflow management solutions add value for agentic AI tools.

Taking a strategic view.

Understanding what capabilities an operator has in place and what it still requires to fully leverage agentic AI requires a comprehensive top-down business analysis.

The Connect360 services-led consultative engagement model embraces this approach, where operators benefit from the strategic advisory, consultancy, and business analysis services offered by the Connect360 Business Enablement Services function.

By gathering as much data as possible and engaging with the customer to listen and learn, consultants gain deep insights that give them a holistic understanding of the business’s existing capabilities and future needs.

The consultant team uses this information to develop the foundational understanding needed to recommend and design solutions and define the AI toolset that will deliver measurable and meaningful business impact.

By bridging the gap between strategy and technology, these services deliver actionable plans that support business growth and innovation.

Connect360 Delivery Services then focus on providing expertly designed technical solutions based on these insights and work on the integration required to fully embrace agentic AI.

By ensuring operators can leverage the solutions they already have in place and supplement them with the additional capabilities needed to address the gaps, operators can integrate with LLMs and orchestrate agentic AI experiences, maximising it’s impact to realise a return on investment (ROI).