By Harkirat Degun, CTO, Resonate
Many organisations are approaching Agentic AI from the wrong direction.
They start by evaluating models, copilots and automation tools. They ask what AI can generate, what tasks it can automate or how quickly they can deploy it. Those are important questions, but the real opportunity starts somewhere else.
It starts with conversations.
Every day, customers contact organisations through phone calls, emails, chat, messaging channels and digital forms. Within those interactions is everything an organisation needs to understand intent, urgency, context and desired outcomes.
The challenge has never been collecting this information – it is in the conversations – the challenge has been connecting that intent to the systems, processes and people responsible for delivering outcomes.
That’s where Agentic AI has the potential to change customer experience fundamentally. It enables organisations to move beyond responding to customer enquiries and towards resolving customer needs. It bridges the gap between communication and action. And for organisations under pressure to improve service whilst controlling costs, that’s a compelling opportunity.
What Is Agentic AI?
At its simplest, Agentic AI is an intelligent decision-making layer that sits between customer conversations and the systems that run your organisation and historically, people have acted as that bridge e.g. a customer contacts a service team. An employee interprets the request, accesses multiple systems, follows policy and initiates the required actions.
Agentic AI helps streamline that process. Rather than simply answering questions, it can:
- Understand intent from natural language
- Evaluate context across multiple systems
- Determine the next best action
- Trigger workflows and processes
- Adapt when new information becomes available
- Escalate to humans when judgement or approval is required
This is an important distinction, while traditional automation followed predefined rules, agentic AI introduces reasoning – evaluating context, considering options and determining the most appropriate action within the boundaries set by the organisation.
The outcome isn’t simply a better chatbot – it’s a better operating model for customer engagement.
Why Agentic AI Matters Now
The idea of connecting systems and automating workflows isn’t new, what’s changed is that the conditions are finally right.
Firstly, customer interactions are already digital and organisations are capturing intent across voice, email, chat and messaging every day.
Secondly, enterprise systems are more connected with most platforms now exposing APIs, making it possible to orchestrate processes across multiple environments without the complexity that existed a decade ago.
Thirdly, AI has matured dramatically. Today’s language models are increasingly capable of understanding context, ambiguity and intent, making them far more practical in real-world service environments.
And finally, there is growing business pressure with organisations being asked to improve customer experience, increase productivity, maintain compliance and reduce costs simultaneously.
For the first time, connecting customer conversations directly to operational outcomes is both technically possible and commercially viable.
Not Everything Needs to Be Agentic
One of the biggest lessons we’ve learned from helping customers deploy AI is that not every process benefits from Agentic AI. Despite the current excitement around AI agents, some processes are still better suited to traditional automation. A simple status enquiry, appointment confirmation or account lookup doesn’t necessarily require sophisticated reasoning.
The real value of agentic AI comes when context and judgement influence outcomes. For example, a customer reporting a service issue may require information from multiple systems, assessment of urgency, identification of previous interactions and routing based on several factors. Those are the situations where Agentic AI can improve both decision quality and customer experience.
The goal shouldn’t be to make everything agentic but rather to identify where intelligence improves outcomes.
The Business Outcomes Agentic AI Can Deliver
AI projects often fail because organisations measure activity rather than impact.
The number of automated conversations or AI-generated responses tells you very little about business value. The organisations seeing the greatest success focus on outcomes instead.
1.Increased Operational Capacity
By reducing manual triage, repetitive administration and low-value tasks, Agentic AI allows employees to focus on the areas where human expertise delivers the greatest value. The goal isn’t simply efficiency; it’s creating capacity for more complex, higher-value work.
2.Better Customer Experiences
Customers want faster resolution, fewer handoffs and less effort. By understanding intent earlier and connecting customers to the right outcome more quickly, organisations can improve satisfaction whilst reducing repeat contact.
3.Reduced Risk and Improved Compliance
In regulated environments, consistency is just as important as speed. Agentic AI can help ensure policies are followed, actions are auditable and escalation processes are applied consistently across every interaction.
4.Greater Adoption of Digital Services
The most successful AI deployments are the ones customers actually choose to use. Tracking self-service completion rates, resolution outcomes and customer satisfaction provides a far clearer measure of success than interaction volumes alone.
Where Organisations Are Applying Agentic AI Today
Many of the most valuable use cases being deployed today aren’t futuristic. They’re practical improvements that remove friction from customer journeys and reduce operational effort.
1.Intelligent Routing and Triage
Instead of asking customers to navigate complex menus, organisations can allow them to simply explain what they need in their own words. AI can identify intent, assess urgency and route interactions to the most appropriate destination, improving both customer and employee experiences.
2.Automated Summaries and Follow-Up
Customer service teams spend significant time creating notes, updating systems and documenting interactions. AI can automatically generate transcripts, summaries and follow-up actions, and integrations and automation can post that information to business systems and triggers actions – reducing administration whilst improving consistency and auditability.
3.Utility Services: Prioritising Urgent Customer Requests
For example – imagine a customer contacting an energy or utilities provider because they’ve lost heating and hot water.
Rather than navigating multiple options and repeating information several times, they simply explain the issue. AI identifies the nature of the request, assesses priority, gathers relevant context and routes the customer to the most appropriate specialist. Advisors begin the conversation with the information they need already available, enabling faster resolution and a more seamless experience.
4.Public Sector: Identifying Vulnerability Earlier
The same principles apply in public services. A resident may contact a local authority regarding a housing issue. On the surface, the enquiry appears routine. However, the wider conversation may indicate a vulnerability or safeguarding concern.
Agentic AI can identify those signals, prioritise the case, provide employees with additional context and ensure follow-up actions are tracked appropriately. Automated summaries and escalation workflows reduce administration whilst creating a clear audit trail.
Different industries. Different services but the underlying principle remains the same: understanding intent earlier and connecting it to action faster
How to Get Started
Most organisations Just need a starting point not a large-scale AI transformation programme. The most successful projects begin by identifying a specific business challenge.
- Start with a customer journey that creates friction, consumes resource or introduces risk.
- Understand how the process works today. Gather data. Establish a baseline.
- Then prioritise opportunities based on business value, operational risk and implementation effort
- Once you’ve identified the right use case, design clear governance boundaries e.g. where human involvement is required and establish measurable success criteria.
Start small – organisations that prove value in one journey build confidence, learn quickly and create the momentum needed to scale successfully.
The Future of Customer Experience Isn’t Better Conversations
Most customer experience investments over the last decade have focused on making communication easier but now agentic AI creates an opportunity to make outcomes easier.
That’s an important distinction – customers don’t contact organisations because they want a conversation – they contact organisations because they want something to happen. So the organisations that gain the greatest value from Agentic AI won’t be those that build the most sophisticated assistants but the ones that connect customer intent, business systems and operational actions most effectively.
Because ultimately, the future of customer experience isn’t about answering more questions it’s about resolving more outcomes.
