Moving From GenAI To Agentic AI In Within Customer Experience

Forbes

23-07-2025

As customer service leaders prepare for agentic AI, they should apply what they've learned from GenAI so far.
By Reza Soudagar, SAP
In many companies, generative AI (GenAI) is either fully automating some aspects of customer support or helping service reps with repetitive tasks. Now, agentic AI has arrived on the scene.
Agentic AI systems and models act autonomously to reach complex goals without constant human guidance. For example, an agentic system could spot a customer delivery that's behind schedule, alert the customer, and offer a discount to hedge against disappointment.
As the business world advances from GenAI to agentic AI, leaders can apply what they've learned from their GenAI pursuits. Here are four key lessons that will help guide businesses into the agentic AI era.
Lesson 1: GenAI copilots and conversational assistants may take you only so far.
GenAI-driven copilots can undeniably improve productivity for customer service reps. However, depending on the use case, these gains may be incremental and could max out quickly.
For instance, according to a 2024 report by McKinsey & Co., off-the-shelf GenAI systems that translate customer communications or summarize customer interactions are relatively easy to integrate into existing service processes, making them low-risk investments. However, McKinsey says, there are limits to these types of use cases, and the captured value is modest. The total value is only about 3% to 5% of the whole customer operation.
At the same time, according to Simon Bamberger, managing director and partner at Boston Consulting Group, some customer service centers have reduced the time to issue resolution by 50% when using a GenAI assistant to resolve issues requiring access to product or diagnostic solutions or to individual customer information.
To achieve even greater results, customer service leaders are contemplating the move to agentic AI. Beyond acting as a copilot or assistant to address a customer billing dispute, for instance, autonomous AI agents could take meaningful, independent action.
They could route the issue to a cash collection AI agent, which would kick off a dispute resolution workflow. With cross-functional AI agents working together, the dispute could quickly be resolved, increasing process efficiency and boosting customer satisfaction.
Lesson 2: To achieve greater productivity gains, the data has to be right.
GenAI outputs are only as good as the data they use. This poses a particular problem in customer service, which needs to pull insights from a variety of structured and unstructured data sources. According to McKinsey, this makes data quality one of the top challenges for implementing GenAI in customer service—especially when businesses want to identify context-relevant responses, which requires formatting internal data and incorporating it into the large language model, or LLM.
Ensuring data quality is even more important when it comes to agentic AI. Since the AI in this case is taking autonomous action, the data has to be right.
The trouble is that many companies are carrying a data debt in the form of inconsistent, incorrect, outdated, or incomplete data across systems. Customer service reps, especially those with experience, are adept at handling these data discrepancies. They know, for instance, that one data source might be more reliable than another, or they might easily recognize when the data seems inaccurate, then take measures to validate it.
Such is not the case with an autonomous AI agent. Because the agent is unable to make that distinction, it's even more important to make sure the data is high quality.
Lesson 3: Address new information security threat vectors.
According to Deloitte, cybersecurity is a top area of GenAI investment. Still, Deloitte says that '58% of businesses are highly concerned about using sensitive data in models and managing data security' and 'only 23% say they're highly prepared for managing [GenAI] risk and governance.'
Early GenAI adopters have learned that they need to do their own adversarial tests of their GenAI systems, while also limiting liability for hallucinations.
Security has an even more important role in agentic AI as the stakes are much higher. Organizations evaluating the use of autonomous AI agents need to consider and protect against scenarios in which the agents interface with outside contacts (such as through a chatbot) and can be duped into completing actions beyond their intended use. They will also need to fully test current security controls to ensure that they work with the agentic AI technology.
As companies identify strong guardrails that protect agentic AI, AI use and adoption will grow and mature. Humans must have an oversight role, ensuring that the system is working as planned and not leaving room for breaches.
Lesson 4: Service reps will need to learn new ways of working.
When incorporating GenAI into a customer service center, training is essential for ensuring that customer service reps know how to work productively with the technology. According to Bamberger, GenAI implementations involve a heavy dose of change management.
'We tell our clients that 10% of the success is around the algorithm, 20% is around the data and the technology, and 70% is around the operational transformation,' Bamberger says. The bulk of the work consists of operational tasks such as change management, people management, process reengineering, and orchestration of a cross-functional team.
The need for AI training and change management will increase with agentic AI. Customer service reps will need to learn new ways of working as process flows change to map with the autonomous behaviors of these systems.
As Deloitte says in a recent report on agentic AI, processes will need to be redesigned to remove unnecessary steps. While autonomous agents can help each other navigate their environments, 'cluttered and sub-optimized processes could deliver disappointing results.'
Applying lessons from GenAI to agentic AI
If you've spent the last year or two working on pilot projects, or even a full deployment of GenAI in customer service, have no fear. The work you've done so far will be put to good use as the industry shifts to offering agentic AI customer experiences.
A version of this story also appears on SAP.com.