How enterprise AI is reshaping the customer edge

As AI changes how customers discover, evaluate, buy and get service, companies are rethinking the commercial operating model-connecting marketing, sales, commerce, pricing and service around the customer journey.

Key takeaways

• AI is pushing companies to move beyond individual use cases and rethink how customer-facing functions operate together.

• An intelligent customer edge connects marketing, sales, commerce, pricing and service around the customer journey through shared intelligence, coordinated workflows, AI agents and human judgment.

• Client examples show how embedding AI into workflows-not layering it onto existing processes-can improve customer and employee experiences and drive business outcomes.

Customers aren't waiting for companies to figure out AI. They're already using it to research products, compare options and make purchasing decisions. Yet many companies are still deploying AI inside marketing, sales, commerce, pricing and service structures designed for a different era.

That creates a growing mismatch-and a pressing question for business leaders: What happens when the customer becomes AI-enabled faster than the company serving them?

At PwC, we're seeing the answer begin to take shape, and it starts with moving beyond isolated AI use cases to redesign how the front office operates.

Moving beyond the AI use case

The first phase of enterprise AI largely focused on individual use cases: automate a task, add a copilot, deploy an agent. Those efforts can create value, but they don't necessarily address the friction that exists when customer-facing functions still operate with different data, workflows, incentives and measures of success.

The companies that get the most value from AI won't simply automate the front office they have today. They'll redesign it around what AI now makes possible.

The opportunity is to create a more intelligent customer edge: a commercial operating model that connects marketing, sales, commerce, pricing and service into a coordinated system organized around the customer journey, supported by shared intelligence, coordinated workflows, AI agents and human judgment.

The shift is from optimizing individual functions to orchestrating the commercial enterprise. Customer signals can move across the organization in real time, decisions that once depended on handoffs can happen in parallel, and employees can spend less time navigating systems and more time applying judgment where it matters. AI becomes part of how the business operates-not another layer added on top of existing complexity.

Building a more intelligent customer edge reflects a broader shift PwC sees across the enterprise: functions that once operated largely on their own are beginning to work as parts of a connected system, with AI helping coordinate decisions, execution and governance.

The shift isn't theoretical

Client work is already showing what this can look like across service, healthcare and sales, and why the operating model matters as much as the technology.

For one large multinational company, PwC helped redesign customer service around a unified, AI-powered contact center using Salesforce, Agentforce Service, Agentforce and Amazon Connect. The model brings customer context, AI assistance, and workflows together at the point of service rather than requiring agents to navigate disconnected systems. The deployment went live across three global locations without business disruption, and approximately 45% of frontline agents were actively using Agentforce assist capabilities in the first week.

At Rush University System for Health, the same principle is playing out in a different setting: patient access. Patients were navigating disconnected calls, messages and websites while care teams were stretched by administrative work. PwC helped Rush rethink the patient journey and connect experiences using Salesforce, Agentforce Health, Data 360 and AI-enabled workflows, while also establishing an AI governance framework to evaluate and scale use cases responsibly.

The new model uses AI agents for high-volume requests such as prescription refills, clinic hours and provider lookups, while giving staff better visibility into patient history, preferences and next steps. The result is a different division of labor between technology and people: AI handles more routine requests while care teams can focus on the interactions where human judgment, empathy and expertise matter most.

Early results include a 15% reduction in patient calls for routine requests, 5% to 15% faster responses to common patient questions with agentic AI, and 25% more patients using digital tools to get care information and avoid call-center wait times.

For one global financial technology company, PwC helped move Agentforce beyond a traditional conversational assistant into a guided-selling experience embedded directly in the quoting workflow. Using historical purchasing behavior, the solution surfaces relevant product, promotion and document template recommendations to sellers at the point of decision.

The production-ready solution moved from pilot definition to deployment and business ownership in approximately seven weeks. The bigger shift was moving AI from a standalone assistant to part of the selling process itself, helping create a more consistent, data-driven experience for sellers.

Taken together, these examples point to a larger lesson: The value of AI increases when companies redesign the work around it. The common thread isn't a single agent or platform. It's connecting data, decisions and workflows across the customer journey while preserving the human judgment, governance and industry context that make those decisions useful.

The platform matters-but it starts with the operating model

Technology is central to this shift, but the platform decision should follow the business problem-not the other way around. The right architecture depends on the customer journey, existing technology environment, and outcomes an organization is trying to achieve.

Salesforce can anchor service workflows, customer data and agentic experiences across significant parts of the journey, while other cloud, data and AI capabilities can play important roles elsewhere. The goal is to create an architecture in which those capabilities work together as one commercial system.

That changes the starting question. Instead of asking which AI tool to deploy, leaders can begin with where customers experience friction, where valuable information stops flowing and which decisions still depend on disconnected systems or manual coordination. Technology choices then follow the operating-model design and the business outcomes the company wants to create.

"Partners like PwC play an important role in helping customers translate technology into operating-model change and real business outcomes. From driving a unified contact center where nearly half of human agents adopted Agentforce in week one, to helping Rush University System for Health free up care teams for higher-judgment work through Agentforce Health and Data 360, we can see what's possible when agentic AI becomes part of how the business operates rather than just a separate technology layer. By connecting the data, workflows, and governance of the Salesforce platform, companies can move beyond individual use cases and rethink how work happens across the customer journey."

From the customer edge to the intelligent enterprise

Customer experience is only one part of this shift. The same fundamental question, "how would we design the function differently if AI were built in from the start?" is emerging across the enterprise. In finance, AI can support continuous decision-making and execution under codified governance. In operations, connected demand signals can help leaders link physical execution more directly to growth, resilience, and P&L outcomes. In cybersecurity, trust and controls can be embedded from day one instead of added after innovation is underway.

Together, these models illustrate the larger idea behind an intelligent enterprise: strategy, technology, operations and governance working as one integrated system, with AI at the core.

The next phase of enterprise AI won't be measured by how many tools a company deploys. It will be measured by whether the enterprise can turn intelligence into coordinated action-and whether that action improves performance.

At the customer edge, that shift is already underway.

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Advisory External Communications, PwC US