Creating AI-native workflows with PwC's agentic scaffolding: Your top questions answered

Key takeaways:

• Speed or control? You can have both. Agentic scaffolding embeds governance in the design, so your agents move fast and stay inside policy.
• Prove it before you build it. Simulate the happy path—and everything going wrong at once—to see the business case, costs, and risks up front.
• Production-ready agentic workflows in 90 days, with the right scoping. And each one you stand up makes the next one faster.

Whether your company is large or small, you likely see the writing on the wall: AI is poised to enable small, intelligent enterprises to outperform even established industry leaders, because they’re “AI native.” To keep or take the lead, your company may have to be AI native too. But how do you do that? What does going AI native even mean?

In a workflow or business process, AI native often means:

• AI does everything that it can do well.
• Humans validate and govern that work.
• Pre-AI processes are either retired or don’t get in the way.

That may sound utopian. But in our experience, there’s a way to reach this AI-native state seamlessly and cost-effectively with governance that’s rigorous but doesn’t slow you down. It’s agentic scaffolding, a PwC AI-powered operating framework that can design, simulate, visualize, stress test, and stand up all-new agent-driven processes with new and usually higher-value roles for your employees.

Agentic scaffolding allows agents to identify an effective AI-native path to achieve a desired outcome. Since it works with almost any AI platform and model, you can sequence technology planning around the value drivers that matter most and link AI investments to measurable business outcomes.

We’re having great success with agentic scaffolding in real business scenarios, both in-house and with clients. But when we talk about it with CEOs, CIOs, and transformation leaders, we find ourselves answering some common questions.

If you just layer AI on top of an old workflow, you’ll get the old workflow—just a little faster. Real value comes when AI agents streamline several old workflows into one. Or reduce the number of steps in a workflow from 12 to two. Or when you can find new ways to increase revenue.

But how do you figure that out? Where do you even start?

Agentic scaffolding offers a “blank sheet” approach. You start by selecting a process—such as customer onboarding, cash-to-cash (or quote-to-cash) cycles, or getting a new product to market—and then defining your desired outcomes and measurable KPIs: generally faster, more economical, and more accurate results.

Scaffolding doesn’t ask about your current workflows when you get started. At this stage, they’re not relevant. Instead, the framework guides you through a conversation to gather input about your business outcomes (including compliance) and capabilities (including data). It then provides an easily understood visualization, showing which agents can do the job and how, and where to consider human intervention.

By starting with your desired outcomes, enabling agents to determine how to reach them, and only then adding people as needed for oversight, compliance, risk, and strategy, agentic scaffolding can deliver smoother, faster, simpler, AI-native workflows.

If you use AI agents to execute key processes in all-new ways, you’re likely to wonder:

• How to ensure agents stay within policy boundaries.
• Where and how people should intervene, and if you can audit AI agents’ actions.
• If risk, compliance, and audit functions get a look at each new agentic workflow or process (and understand what they’re seeing) before it goes live.

When agentic scaffolding shows you a potential AI-native process, it also embeds suggested governance on where and how policies can be enforced, decision rights granted, and evidence logged. For the first draft (intended for your specialists to consider and revise), it draws on industry standards, PwC compliance and risk expertise, and your own policies and risk appetite.

Since this governance is embedded into the design and automated wherever risk levels permit, it doesn’t slow the process down. And since it’s built into scaffold design from the start—easily visualized, with explainability and observability also built in—your stakeholders can understand, stress test, and adjust it as needed before it goes live.

AI transformation can be a big-ticket item. So, before giving the green light to wholesale transformation, you and your stakeholders might want to see proof points like these:

• A quantifiable, verifiable business case
• Reliable estimates for future cycle times, cost, capacity, quality, and risks
• Pre-deployment testing
• An easy-to-understand vision of the expected future state with a business value roadmap, workforce and organizational impacts, and governance components

Agentic scaffolding presents how AI agents can execute new workflows. You can then run scenario simulations. One simulation can be the “happy path,” another might be “everything goes wrong at once.” Each simulation provides data on impact, integration and data challenges, costs, risk, business value, and so on. To improve outcomes, within this sandbox you can move agents around, add more human oversight, or try a new process.

Since strategy, design, build plans, and future-state visualization are all in the scaffolding framework, it’s straightforward to assess, stress test, and change what you’re planning to build before you start.

This is a common challenge with agentic AI. You implement some agents here and there, doing something useful, but they don’t build on each other and don’t scale. They don’t even talk to each other. To avoid this trap and achieve value at scale, you need to pick the right spots to focus efforts, let agents themselves determine the best path to achieve your chosen outcomes, then design and build so your agents can collaborate across workflows and functions—giving you modular, reusable capabilities.

Since agentic scaffolding creates planning artifacts for multiple potential agentic workflows, you can both pick your preferred starting point and set a sequenced roadmap for what to do next. To help you make the right decision, it assesses your platform requirements and data readiness, then creates a build plan that sequences implementation into phased work with timelines, staffing, and risk visibility.

Since workflows are designed within the shared scaffolding context, insights from your first workflow can connect directly into the next, letting you reuse agents, data pipelines, and governance patterns. This creates the modular, connected capabilities you need to achieve value at scale.

Agentic scaffolding can make it easier to speed up data modernization initiatives. It does this by ingesting information about your data sets and data integrations to identify any key gaps. It then helps you assess how long it might take to close those gaps and how much it would cost.

The scaffolding framework can almost always find ways for agents to help with this data work—lowering costs and saving time. And since the framework also maps out business value from a potential agentic workflow, you can make a more educated decision. You can compare data costs, timelines, and likely business value, and then start with the workflows where you can benefit most.

With the right scoping, agentic scaffolding can help deliver production-ready agentic workflows in 90 days. And by enabling more data-driven prioritization and greater agentification, agentic scaffolding can be a key part of your AI data strategy, where each success builds on what came before and value keeps accelerating.

If you’re going to reinvent workflows, processes, and even whole functions and lines of business with agentic AI, you’ll need to do more than add agents. You’ll likely also need to upgrade architecture, update your strategy, and find new, higher-value roles for people.

Agentic scaffolding helps make this easier.

When you use the scaffolding framework to design and visualize a new workflow, you can share your current capabilities and workforce. The framework’s planning artifacts can then map out the likely impact: which roles may shift, what new skills will be needed, what new responsibilities leadership may now have, and how your organizational structure might evolve. It can also spot technology gaps: where new data integration or an upgraded ERP module might be needed.

That can make agentic scaffolding an important aid not just for productionizing AI-native workflows but for bringing your whole organization into the AI age as well.