The emergence of AI automation and large language models (LLMs) has given way to advancements that have the potential to change the way we do business going forward. Agentic AI, a goal-driven AI model that acts with more autonomy than traditional chatbots, is no longer an idea considered viable only in the distant future. It is already here and reshaping how enterprises engage with their customers, streamline services, and make decisions in real time.
With autonomous technology evolving at warp speed around the benefits of agentic systems, leaders who wait to deploy the technology may find themselves playing catch-up.
The reality of business in the AI agent age is taking shape
For Shalima Bhalla, a global customer experience and AI executive with more than 20 years of experience helping enterprises navigate digital transformation and the host of the “Customer Signal” podcast, the shift to agentic AI work has been about a business reality taking shape across industries. In her current role leading Global Customer Experience Solutions and Go-to-Market Strategy for the telecommunications sector at a Fortune 500 cloud and enterprise technology company, she helps translate digital transformation into customer value.
“I have watched the same pattern repeat across technology cycles,” Bhalla explains. “The companies that come out on top are the ones that can connect innovation to actual customer data about positive outcomes and thoughtfully use AI to get ahead.”
The urgency around agentic AI is growing, especially at the intersection of executive strategy and market conversations. The ability of the technology to complete complex end-to-end workflows with minimal human oversight has allowed enterprises to further design, development, execution, and workflow optimization at rapid speeds.
The telecommunications proving ground for AI-driven customer satisfaction
Telecom matters in the agentic AI conversation because it has become one of the most significant proving grounds for transforming the customer experience through AI. Telecom sector support teams deal with a massive amount of customer data. As connectivity is seen as a utility and telco customer churn is one of the highest in any industry, customer experience is the biggest differentiator.
“Telecom is uniquely positioned as the proving ground for agentic AI systems that transform behavioral signals into revenue,” says Bhalla.
Meanwhile, customers want faster answers, fewer IVR or self-service trees, fewer handoffs, and more personalized support.
“It doesn’t matter what the industry is. The lessons we learn from telecom use cases can stretch into finance, healthcare, retail, and beyond,” Bhalla explains. “AI has become the tool that can deliver what customers want out of customer service at scale.”
Today’s telecom companies are already utilizing agentic AI to greatly improve customer service, reduce operating costs, and optimize their operations across the board.
Successful Agentic AI use begins with the customer support journey
Effective use of generative AI and agentic AI doesn’t begin with a conversation about technology. It begins with the customer journey.
“So many enterprises treat AI as a tool they can just ‘bolt on’,” Bhalla says, “something they add after workflows for human agents have already been designed.”
However, Bhalla also encourages leaders to try to better understand the customer touchpoints that matter most. Leaders should analyze customer behavior before enabling AI agents if they expect higher customer satisfaction. Considering complex customer preferences, meaningful customer interactions, and better customer data can all contribute to a better use of agentic AI overall.
“Onboarding, resolutions, retention, upsell — these are the moments that matter in customer relationships,” Bhalla says. “AI tools need to be integrated into these touchpoints from the beginning. That way, leaders can remove friction and create a more seamless experience for both customers and employees. It’s a significant step in reshaping customer service.”
The agentic AI approach encourages leaders to redesign workflows around outcomes rather than fixed steps, making AI not just an add-on tool for service, but an ingrained part of the entire customer journey from start to finish.
What should enterprise leaders know now, and how do they take action?
The biggest mistake Bhalla sees enterprise leaders making in the current business space is treating agentic AI like a future-state experiment.
“Agentic AI is already redefining customer expectations,” she says. “The pace of adoption should accelerate to meet those expectations.”
For leaders, the challenge comes not with exploring what agentic AI can do, but with employing the technology responsibly and strategically. Leaders should take time to identify parts of the customer journey that are high-friction, test AI in controlled environments, and, despite agentic AI being known for not needing human intervention as frequently as other forms of AI, ensure that AI integration empowers human agents to intervene when judgment and empathy matter most.
Agentic AI is not just changing the way companies automate tasks; it is also transforming customers’ expectations. Enterprise leaders who move early on agentic AI will create more responsive, intelligent, and, ironically, more human customer service experiences.
For Bhalla, transformation is never just about technology or just for transformation’s sake. Movement is about leadership, timing, and a willingness to rethink the customer experience based on the technology we have available to us now.
