By replacing feature-poor Chatbots, agentic AI can streamline customer service technology, thereby granting human agents more time to interact meaningfully with customers.
Where traditional chatbots, rule-based, scripted and repetitive, are so often inflexible and thus unable to handle complex, multi-step queries, agentic AI can act on behalf of a human, utilising autonomous systems and human-like reasoning to make decisions and take the required actions to satisfy whatever service demand is at hand.
Where agentic AI is deployed, whether for internal or external customers, anyone requiring service can initiate and complete operational, action-based tasks simply through natural language. This development is set, not to replace human effort, but to scale and augment effort by taking on repetitive processes, accelerating workflows, and by freeing employees to focus on higher-value work. In this way, agentic AI will help organisations save time, improve productivity, and drive a competitive edge.
Agentic AI as Service Agent
In many ways, the first promise of agentic AI is convenience; in the field of service delivery, anything that can promise customer convenience whilst also promising a lower cost profile, is likely to attract attention. Indeed, agentic AI is set to be invaluable in its ability to serve customers and deal with service enquiries efficiently: according to Gartner, “by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention, leading to a 30% reduction in operational costs.”
But how exactly is agentic AI so well adapted to front-end service delivery? This class of artificial intelligence can break down queries into sub-tasks to achieve a holistic solution, adapt to individual queries effectively based on context, analyse data across multiple systems such as CRM and user behaviour, and improve rapidly by learning from each interaction. AI's flawless memory of even very specific detail, and its ability to work 24/7without fatigue, makes it the perfect support tool for service operations.
Organisations that focus heavily on developing agentic AI are seeing a significant “reduction in average handle time…. but more importantly, from a customer point of view, the first contact resolution is improving, meaning that people don’t need to call back to try and resolve their issue because they got it resolved the first time” (Brian Blackader, partner in McKinsey’s Düsseldorf office).
Agentic AI first gathers data from different sources and uses natural language processing (NLP) to interpret the request. It then analyses the information to uncover its goal and determine the most effective course of action. Then, the agent operates across systems to initiate multi-step workflows, entirely independent of any human input.
Agentic AI can help customers solve problems in real time, resolving incidents quickly and saving costs by automating multi-step, repetitive and time-consuming tasks. The technology can also handle a high volume of tasks, and be configured to continuously monitor for threats, escalating any suspicious activity for human intervention where necessary. With this type of technical capability in place customer support teams can turn their attention to higher-value work.
More specifically, agentic AI can take on Level 1 ITSM tasks, using intelligent triage to route issues and resolve common requests. Traditionally, when a customer calls IT support or opens an IT service chat, a human agent reads and makes a judgment on the language of the user to gauge urgency and sentiment. The agent responds with clarifying questions, concerning, for example, the application or enquiring as to when the problem started, before deciding whether to point the end-user to a knowledge base article, run a scripted fix, or escalate to Level 2. Agentic AI transforms this interplay into an automated, data-driven process: it can identify the issue, collect the relevant evidence, and remove the need for users to provide screenshots or other manual inputs. Once the problem is established, the AI simply asks for confirmation, streamlining resolution and saving considerable time.
Agentic AI is revolutionary because it can take action end-to-end. By launching automated workflows, agentic AI is able to call underlying APIs (such as password resets) as a skilled agent would, and monitor outcomes carefully before proceeding.
Agentic AI can also upgrade RPA with “operator” integration: got a bot that’s too rigid to be useful? Agentic AI can solve for that. Instead of breaking when something unexpected happens, the AI can interpret the situation, adjust the process, and keep things moving — much like a human operator would.
This quality of interaction can also go beyond ‘type onscreen’; voice-enabled AI agents can greet customers naturally before performing authentication through adaptive voice biometric checks or conversational MFA. Moreover, AI can translate any language in real-time, preventing language barriers and maintaining global customer satisfaction levels. Given 75%of customers are more likely to purchase from companies that offer support in their native language, efficient translation technology is now a necessity.
When and how should you adopt Agentic AI?
Companies will surely drive high value from adopting agentic AI early. Early adoption is likely to promote first-mover market advantage, with the net result being better agentic service outcomes, that promote deeper customer relationships, and lead to streamlined operations and cost reduction. It is feasible that these early agentic practices will go on to shape industry standards, and in so doing, create barriers for lagging competitors.
An early forerunner of this trend was Bank of America’s ‘Erica,’ launched in 2018. While not agentic AI in today’s sense, Erica showed how virtual agents could handle millions of enquiries each week, provide 24/7customer support, and reduce service costs by 10%. Agentic AI now extends this model further, with flexible reasoning and end-to-end automation that Erica’s rules-based design could not achieve.
But how can companies most effectively implement agentic AI? To depend on such systems for large-scale customer interactions, the right infrastructure must be in place. Implementing agentic AI is not simply an extension of past AI deployments; it requires new methods. Companies must be able to simulate and test potential conversation variants, evaluate how the system performs, and define robust compliance layers to ensure security, privacy, and regulatory standards are upheld.
Once simulations and testing are fully complete, and beyond the ‘go live’ event, the day-to-day must also adapt. As agentic AI takes a greater role in control of workflows, the Chief Information Officer and Chief Operations Officer and their respective teams must cooperate closely to formulate a “performance management framework that provides real-time feedback” to “allow both teams to track performance gaps, refine or redefine processes, and ensure human oversight maintains the right balance with automation”, explains Oana Cheta, partner in McKinsey Chicago, in charge of generative and agentic AI in North American service operations. In addition, the CIO must ensure that the agentic AI is secure, and that it integrates with other systems.
Safeguards are in short: essential. Companies should aim to define confidence thresholds, escalation logic, and fallback rules pre-launch. Input information, reasoning, output, and the outcome should all be tracked. Keeping tabs on the agent(s) will drive better understanding, inform trouble shooting and ultimately, improve its behaviour. If you’re reading this and thinking about operational role redefinition, you’re on the right track: McKinsey 2024research shows that the most successful AI adopters don’t just train their teams to use AI, but redesign roles around it.
Agentic AI is set to revolutionise how companies design and operate software-driven systems. It brings new levels of flexibility and autonomy, taking much of the repetitive busywork off employees’ hands. However, because the technology is still in its early stages, organisations must carefully assess whether it fits their needs. Success is dependent on disciplined testing, monitoring, data management, profiling, and governance to ensure the AI behaves in a way that fits (and continues to fit) with the service ideals the company has. Ultimately, a well-trained and fully tested AI will operate like a well-trained and fully tested workforce. Some things don’t change: the principles of preparation, oversight, and accountability will still define success.