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Decision-making in the enterprise is undergoing a massive shift. Today, PwC announced the launch of agentic scaffolding, a tool for implementing AI initiatives in the enterprise.
The move comes as part of PwC’s attempt to modernize its professional services operations, with a representative for the company estimating that most of its teams were implementing its agentic transformation tool which uses Claude 4.6 and GPT-5.5/5.4.
As one of the big four professional service networks, PwC’s pivot toward supporting AI-driven change management represents a significant shift in how pilots are developed and deployed in the enterprise.
With industry leaders like Mustafa Suleyman, CEO of Microsoft AI estimating that “most, if not all, professional tasks” for lawyers, accountants, marketing professionals and others, “will be fully automated by AI within the next 12 to 18 months,” businesses are looking for better ways to deploy AI and drive outcomes.
Fixing Failing AI Pilots
Extracting value from AI has been a challenge for many organizations during the AI race. A widely cited report released by MIT in 2025 found that 95% of AI pilots failed to provide any notable return on investment. Part of the reason for this was the poor integration of tools like ChatGPT with enterprise workflows.
PwC’s approach represents a shift in the industry where AI adoption needs to be supported by agile change management processes. According to PwC’s announcement blog post, agentic scaffolding “combines tools, people, and processes so you can safely design, simulate, visualize, stress-test, and stand up new agent-driven processes.”
“Agentic scaffolding is one of the tools we’ve built to really help companies move from pilots to actually scaling their AI efforts across the enterprise,” Rima Safari, a partner in PwC’s US data, analytics and AI practice, told me in a video interview. “At this point, every major AI transformation that PwC takes on with a client, they’re using scaffolding for it as a tool to drive that operating model change.”
How Agentic Scaffolding Works
With agentic scaffolding, business leaders can input strategic objectives and process requirements into an agentic scaffolding tool. The scaffold is an application layer that can visually orchestrate AI workflows while generating the underlying code needed to execute and manage those agents.
“Agentic scaffolding allows you to think about, what are those bottlenecks that don’t allow you to scale, and how do you surpass them? So one bottleneck oftentimes is the change management required…in terms of the evolution of roles, so agentic scaffolding helps with that,” Safari said.
In short, scaffolding provides an early look at what a new process would look like automated with AI. For instance, a team can create a visual simulation of how a process would work, including simulated steps, required tasks, handoffs, exceptions, validators, evidence requirements, data flows and documentation.
The scaffold can also be stress tested to identify ways to improve performance before the initiative moves to production. This approach helps to fine-tune policies and controls for each agent.
But how effective is this approach? PwC claims that one Fortune 500 insurer it works with has used agentic scaffolding to help create and validate agentic workflows for intake, quoting, underwriting, enrolment and billing, cutting the quote-to-bill time by 50% to 80% depending on case complexity.
Reimagining Change Management
As more companies look to leverage AI agents alongside human employees, enterprises require a more agile approach to change management to keep up.
“Previous change management playbooks assumed time. Long planning cycles, sequential rollouts, structured resistance management. That model is now broken. Agentic AI does not give you a stable target to manage change toward because the system keeps evolving after deployment,” Shashi Bellamkonda, principal research director at Info-Tech Research Group, told me via email.
“What I am seeing is that the enterprises making real progress have stopped treating change management as a phase that follows implementation. It has to be the foundation the implementation is built on. The compressed timelines are not a temporary condition. They are the new operating environment,” Bellamkonda said.
Bellamkonda argues that traditional change management was designed for a bounded transformation with a defined end state, whereas agentic AI has no end state. Agentic systems learn, the workflows evolve and now the workforce also has to evolve with them continuously.
Building AI Initiatives
Rebuilding workflows to accommodate AI agents is a tall task. Chai Atreya, CPO and CTO of the autonomous marketing platform ActiveCampaign and an ex member of the Amazon Alexa team, also shared some fundamentals for rolling out AI pilots with success.
“Companies need three things: strong foundations, clear guardrails and operating-model alignment. The foundation is data, context, and integration. AI pilots fail when agents do not have access to the right business context or cannot operate inside real workflows. The guardrails are equally important: enterprises need verification, human oversight, governance, and clear rules for what agents can and cannot do,” Atreya said.
He notes that AI is forcing enterprises to move from program-based transformations to capability-based transformations. Under the old model, leaders could build a transformation program, define a roadmap, roll out the system and then increase adoption. In the AI era, such approaches are “too slow and too brittle”.
Companies that want to implement pilots effectively need reusable capabilities, context layers, governance models, agent infrastructure, evaluation loops and teams that can continuously apply AI to new business problems. Beyond that, there needs to be a company-wide change to support success.
“The biggest requirement is organizational. AI pilots cannot sit off to the side as innovation theatre. Product, engineering, design, data, legal, security, and business teams need to work together around a clear outcome,” Atreya said.
PwC’s deployment of scaffolding, alongside Bellamkonda and Atreya’s insights into modern workflows, points toward a shift in the tech industry in terms of how AI pilots are built and brought to production.
