The future of customer interaction and internal operations is rapidly evolving, with businesses channeling significant investment into AI-powered agents. This shift signals a future where initial customer contact for inquiries or purchases may very well be with a sophisticated chatbot.
Retail giant Walmart is at the forefront of this transformation. In a landmark deal with OpenAI, the company is enabling shoppers to discover and purchase items directly within ChatGPT. This initiative extends to Walmart’s own app, where an AI agent now assists customers with product recommendations and answers queries. Walmart’s CEO, Doug McMillon, has identified agentic AI as a key growth driver for the retailer’s e-commerce division, emphasizing its ability to enhance the shopping experience by saving customers time and making it more enjoyable. Further integrating with evolving AI technologies, Walmart announced plans to leverage Google’s Gemini AI assistant to streamline product discovery and purchasing on its platforms.
These investments in agentic software are not solely focused on customer-facing applications. Internally, these AI agents are being deployed to augment employee productivity by assisting with tasks such as drafting emails, summarizing meeting notes, and streamlining workflows. This widespread adoption presents a critical challenge for companies: ensuring these AI solutions deliver tangible benefits across all stakeholders.
Michael Weening, CEO of Calix, a telecommunications software and services provider, recently highlighted this challenge at his company’s annual customer conference. Addressing an audience of broadband service executives, he posed a stark question about workload. “I asked if any of them were sitting around lazily, waiting for their jobs to be displaced because they didn’t have anything to do. No hands,” Weening recounted. “The message I hear from everyone all the time is ‘I have way too much to do,’ so my message was how do you free up time to do more and how do you add capacity so you can grow.”
In response, Calix deployed AI agents across its platforms to support its broadband service provider customers. These agents are designed to assist marketing teams in creating subscriber offers, empower customer service representatives with enhanced troubleshooting capabilities, route subscriber inquiries efficiently, and enable field technicians to automate diagnostics and optimize installations.
From Weening’s perspective, agentic AI should be viewed as a valuable asset. However, he acknowledges the apprehension stemming from pronouncements by larger tech companies, where the narrative often leans towards AI leading to job displacement.
## Integrating AI Agents into the Workforce
The discourse surrounding AI’s impact on employment has been amplified by significant layoff announcements in 2025, with AI cited as a contributing factor in over 55,000 job cuts across the U.S., affecting major companies like Amazon, Microsoft, and Salesforce. This trend is further fueled by industry leaders who predict AI’s potential to disrupt various sectors. Anthropic CEO Dario Amodei, for instance, has posited that AI could instigate a more profound labor market shock than previous technological advancements, functioning as a “general labor substitute for humans.”
This ongoing narrative is contributing to a noticeable decline in worker sentiment towards AI. A January 2026 poll by Mercer indicated that 40% of employees expressed concerns about job loss due to AI, a significant increase from 28% in 2024.
Weening believes that the pervasive “demonization and freaking out” surrounding AI executive messaging is a legitimate concern that risks overshadowing the technology’s true potential. He defines agentic AI as fundamentally a workflow enhancement, where each task within that workflow is performed by an agent. His counsel to businesses is to proactively demonstrate how AI agents can function as “new teammates to help you do a better job.”
To facilitate a smoother integration of AI agents, Calix adopted a strategy of transforming these agents into “really non-aggressive, very friendly, Teletubby-like characters.” Weening elaborated, “My view is they’re becoming part of your workforce. You think of them as part of your team.” This approach is gaining traction, with some organizations, like consulting firm McKinsey, now incorporating AI agents into their workforce counts. McKinsey reports having 25,000 personalized AI agents alongside 40,000 human employees.
This sentiment aligns with Calix’s internal adoption of Microsoft’s Copilot AI companion. Weening noted, “My thought is if we used Copilot ubiquitously, the benefit is we’ve got data protection, but more importantly, we can signal to the entire employee population we are serious about innovating.” Calix has seen over 700 employee-generated agents created. The company also identified 40 workflows where AI could significantly boost productivity, with the IT team formalizing and deploying these tools across the organization. “Are all those change-the-world agents? No, it may be something as simple as a tool to write an email faster, but at least they’re playing with it,” Weening remarked.
In his internal communications, Weening emphasizes a crucial balance between speed and risk, particularly concerning sensitive data. He observes a spectrum of reactions, from deep concern to a relentless focus on speed that overlooks potential risks. He suggests that many organizations are struggling to navigate this, underscoring the necessity of “very clear guidelines with regard to protecting data, whether that’s your customer data or how your partners are managing our data.”
Acknowledging the inevitable impact on jobs, Weening often repeats the adage: “80% of jobs will change 20%, 20% of jobs will change 80%.” His internal message is that these tools will empower employees to take on new responsibilities, supporting Calix’s growth trajectory. While headcount growth may not accelerate at the same pace due to AI-driven productivity gains, overall growth is still projected to be substantial.
“We’re in this disillusionment phase with AI right now where everyone is asking, ‘Where’s the ROI?'” Weening stated. “What we have to build is a mindset of change inside the company to embrace AI and look at it pragmatically so that we can evolve where we are.” He anticipates a significant acceleration in AI adoption throughout 2026.
## Ensuring Effective AI Agent Impact
From the perspective of Jimit Arora, CEO of Everest Group, the evolution of enterprise systems has progressed through distinct phases: systems of record (like ERPs), systems of engagement (like CRMs), and systems of insight, where data is translated into actionable intelligence. Arora categorizes AI agents as belonging to a new paradigm: “systems of execution.”
“When you use a combination of deterministic machine learning, AI, generative AI and agentic AI as currently defined, that’s when value happens,” Arora explained.
While widespread experimentation is underway, Arora describes the current stage as “pre-agentic.” He elaborates, “We still don’t have true agency with the agents; we are building agents that can do actions, and there’s a difference. We’ve reached autonomy in some ways, but we haven’t given them agency.”
Arora anticipates a surge in efforts to imbue agents with true agency starting in 2026, particularly within the “three biggest use cases” for agentic AI: the software development lifecycle, service desk applications across HR, IT, and finance, and customer experience.
As these developments unfold, Arora advises organizations to actively mitigate what he terms “PTSD”—”process debt, tech debt, skills debt, and data debt.” He warns, “If you have the right data, but you’re trying to identify a broken process, you’re going to amplify the brokenness. You also need the right skills, because applying yesterday’s skills to tomorrow’s problems won’t work. And through all of this, technology can be the easy part.”
Arora also cautions CEOs and executives expecting immediate, substantial returns from agentic AI. Drawing parallels with the cloud computing revolution, he noted, “AWS came out in 2006, Google Cloud Platform in 2008 and Azure in 2010. It took us a good 15 years to get to 50% public cloud adoption. That true unlock is going to happen in the next three to five years, but we’re gong to see some meaningful progress. We have to think of it as a capex project; that’s when you’ll get the true unlock, otherwise you’ll be stuck in the valley of incrementalism, or pilot purgatory.”
Bruno Guicardi, co-founder of CI&T, advocates for a phased approach to developing agentic AI, emphasizing a structure that “gives autonomy to the agents gradually in systems where there is a level of supervision that you can define when you actually retract the supervision.” He illustrates this with an example of automated client responses: initially, every AI-generated response would undergo human review. Over time, as the quality of AI responses proves consistently acceptable, the level of human oversight would decrease, eventually allowing the AI to send responses autonomously.
“We think that this will be a way to build confidence,” Guicardi stated. “It’s about building a system that earns control, that earns the trust to be autonomous.”
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