Enterprise modernization and agentic AI are the same strategy, not separate projects
Enterprise modernization agentic AI refers to a deliberate transformation of legacy systems into cloud-enabled, data-consistent and API-integrated infrastructure that is purpose-built to support AI agents in production, turning scattered, unreliable workflows into a coherent foundation that can safely run autonomous, AI-driven customer and operational experiences at scale.
The main takeaway is blunt: if you want agentic AI at scale, you cannot treat infrastructure modernization as a separate “IT clean-up” project that happens before the real innovation. Modernization is the innovation. Batteries Plus did not stumble into AI readiness by bolting agents on top of old platforms; it spent multiple years reshaping its core systems into cloud-enabled, data-driven architecture expressly described as “AI-Ready Enterprise Architecture.” That mindset shift matters more than any new model release. The companies winning the agent race are those that understand their AI agent readiness infrastructure is the product, not the pre-work. Everyone else is trying to pour autonomous agents into leaky pipes.
Inside Batteries Plus: modernization as a knowledge-centric AI foundation
Batteries Plus offers a concrete case study in legacy system transformation tied directly to agentic AI outcomes. Ranked No. 956 in a leading ecommerce database, it undertook a deliberate multi-year modernization of ecommerce, point-of-sale, payments, product information management, enterprise search and partner integrations while moving to cloud-native infrastructure. Importantly, this was framed not as a migration project but as building a “knowledge-centric architecture.” Instead of hoarding raw data, the team invested in structured, contextual, semantically rich information so AI systems can reason with reliable context.
That decision now pays off in measurable AI outcomes. After modernization, Batteries Plus reduced failed searches by 25% and achieved a 35%-40% customer inquiry containment rate in automated self-service, including AI chatbots and voice agents. Those are not marketing metrics; they are signals that the new foundation can sustain real agent workloads. The modernization created the consistent data and clean integrations that let AI agents understand products, customers and store operations without falling apart in edge cases. In practice, the retailer turned enterprise digital transformation into AI agent readiness infrastructure long before “agentic AI” was a buzzword.
From modern stack to agentic experience: how customers feel the difference
Modernization only matters if ordinary customers feel it. At Batteries Plus, they do. The retailer’s upgraded systems strengthened reliability and built a foundational data layer that gives its AI systems the structured context they need to deliver accurate, reliable results. That foundation now powers an AI-powered voice agent integrated with store phones, helping with product discovery, service scheduling and intelligent call routing in multiple languages while handing off to human associates when calls turn complex.
Store teams see the impact as well. An AI-driven search platform supports associates with complex product compatibility questions, improving conversions, while a conversational AI assistant helps with operational knowledge, troubleshooting and multilingual support. For a business where most online orders are buy online, pick up in store, that matters. Customers reach answers faster, call experiences feel smoother, and staff spend more time on higher-value interactions. According to Karthik Jambulingam, “this modernization work was not just about upgrading systems; it was about building what I call a knowledge-centric architecture.” This is what enterprise modernization agentic AI looks like when it is executed as one strategy: AI agents that behave like informed colleagues rather than brittle scripts.
Why legacy platforms are racing to buy AI-native capabilities
While retailers like Batteries Plus are modernizing from within, legacy customer service platforms are pursuing a different path: buying AI-native capability. In a recent interview, John Kim discussed Salesforce’s acquisition of Fin as a sign that established platforms are under pressure to move faster while AI-native companies reshape buyer expectations for automation, workflows and engagement. These incumbents have scale and brand, but their architectures and seat-based business models are often misaligned with the economics and speed of agentic AI. Acquisitions are an admission that they cannot build AI agent readiness infrastructure quickly enough inside their existing constraints.
Kim notes that AI-native providers are structured to test frontier models, push accuracy, reduce latency and cut service delivery costs more aggressively than traditional platforms. The window to lead in AI-powered customer service is closing as AI agents shift from pilots into live production. For CX leaders, the question is no longer whether to modernize, but whether their organization’s architecture can absorb agents safely and at scale. Kim advises teams to start with focused use cases in production, learning where AI can improve response times, reduce customer effort and support service teams without unnecessary risk. That advice aligns neatly with Batteries Plus: build the modern plumbing, then let agents run through real workloads, instead of treating AI as an isolated experiment.
Modernization ROI accelerates when agents arrive — if the groundwork is real
The lesson from Batteries Plus and the broader CX market is clear: enterprise digital transformation pays off faster when it is explicitly designed for agentic AI, not simply for “cloud migration.” Batteries Plus now runs AI-powered voice agents, store call analytics, operational assistants, commercial AI agents for sales outreach and churn prevention, and is focusing on Agentic Commerce to bring expert, personalized guidance into ecommerce using large language models. Those capabilities exist because the modernization work created a coherent data layer, integrated systems and knowledge-centric design. The ROI numbers on failed search and containment rates are early signals that modernization returns compound once agents start doing real work.
Enterprises that still separate legacy system transformation from AI strategy are missing this compounding effect. The forward-thinking approach is to design modernization as AI agent readiness infrastructure from day one: clean APIs, shared data models, and domain knowledge encoded in ways agents can reason over. The next competitive divide will not be who has access to the latest model; it will be who has an architecture that lets agents act reliably on behalf of customers and employees. Companies like Batteries Plus are already showing that when modernization and agentic AI adoption are treated as one interconnected strategy, the path from infrastructure investment to customer impact becomes much shorter — and far more defensible.
