Key takeaways:
- Most AI initiatives stall — not because of the technology, but because of how work is built around it.
- Agentic scaffolding gives you a structured, governed way to build new AI workflows from the ground up.
- One insurer cut processing time by up to 80%. Your roadmap starts with just three decisions.
The best way to improve an old process is to get rid of it: Stand up a new process that does many old ones at once—or takes a different, faster path to value. Some companies are achieving this transformative value. But most aren’t. In our experience, four obstacles are holding them back:
- Workflows are linear and rules-based—built for people assisted by simple automation, not for people governing intelligent, autonomous AI agents
- AI doesn’t have governance built in, so your stakeholders won’t trust or use it
- “Pilot theater”: Pilots work in the lab but don’t scale enough for real-world value
- Controls and audit take place after AI has done its work, adding costs and delays
Agentic scaffolding can help overcome these obstacles. It combines tools, people, and processes so you can safely design, simulate, visualize, stress-test, and stand up new agent-driven processes. It embeds decision rights and governance. And it helps you keep enhancing what you’ve launched.
One Fortune 500 insurer that we work with, for example, is using agentic scaffolding to expand beyond its core business and grow in a different market: smaller companies, which typically provide coverage to 100 or fewer employees, spouses, and dependents. Scaffolding has helped create—and validate—agentic workflows for intake, quoting, underwriting, enrollment, and billing in the market. That’s cut the quote-to-bill time by 50% to 80%, depending on case complexity. The system keeps improving. Customers love it. It’s already enabling better penetration and a lower cost structure. And it’s ready to scale.
It can all add up to be the start of a new operating model—and a tightly-governed change system. Here’s how it works.
Choose your outcome. Create a product that adapts in real time.
Let’s say you have key functions or value streams where you’d like to boost productivity, agility, and strategic capacity. You can proceed in AI-enabled, risk-managed stages.
Meet your new unit of transformation: scalable workflows built to meet your needs
Agentic scaffolding delivers the missing link for AI initiatives: a way to plan, structure, organize, and visualize new agentic workflows and processes, designed and built to address your goals and needs. You can run the new agentic workflow in parallel to existing processes, so you can test it, enhance it, and get your stakeholders comfortable with it—before you move to a full build.
Since a scaffold contains detailed and stress-tested specifications, on which your stakeholders have aligned, you can stand up these workflows with confidence—then monitor and evolve them in response to real-time inputs. As these workflows spread, your company can become as nimble as any AI native, with your selected decision rights, controls, explainability, and user experience built in.
Build your AI native operating model: 3 decisions to make today
Since AI can speed up so many previously manual tasks, agentic scaffolding can deliver value fast. It starts with three key decisions:
- Choose the triad. Pick three executives—a value owner, a technology leader, and a risk or controls officer—who will own your first few scaffolds. Assign them measurable business outcomes, give them authority over the related systems and data, and make them directly accountable.
- Choose where to focus. Good places to start are typically enterprise processes or functions rich in manual effort or coordination (such as frequent handoffs), offer a clear path to new revenue or lower costs, and feature fragmented data and repetitive work that hasn’t been standardized (so simpler automation can’t handle it.)
- Choose your outcomes—and constraints. Based on your priorities, set KPIs such as faster cycle times and lower costs, or greater throughput, accuracy, and precision. Also set “must-never-happen" constraints around key risk domains, such as compliance or data governance, AI performance and explainability, financial exposure, or the user experience.
Decisions made, you’ll be ready to start scaffolding: Gathering more detailed objectives and process requirements as needed, embedding your unique value proposition and security needs, refining and stress testing a realistic simulation, then executing and continually governing, improving, and scaling.
