To build or not to build? Agentic solutions provide another path to transformation

Tightly regulated industries are struggling to escape the agentic AI pilot trap. For example, while 78% of P&C insurers have adopted the latest AI, only 4% have scaled it across their claims operations and just 27% have attempted end-to-end claims transformation. Claims, where AI should deliver the most value, remain stuck at proof of concept for almost everyone.

Today’s AI-powered coding tools have made everyone feel like a builder, with enterprises encouraging teams to prototype with the latest AI assistants. However, while many small-scale, personal productivity pilots have been successful, transforming even transactional processes with AI has been very difficult. Embedding AI into your most critical and complex processes isn’t just a coding challenge—it’s one of trust, consistency, and compliance. That’s where so many leading enterprises are struggling today.

In this blog, I’ll lay out the critical characteristics an agentic solution needs to succeed in an enterprise environment. I’ll explain why, for most regulated workflows and transactional processes, a purpose-built solution often provides governance, orchestration, and domain expertise faster and more reliably than building from scratch.

When to build or buy your agentic solution?

The build-first approach absolutely has its place. If the process you want to transform is something so specific to your business that no one else could package it, then building makes sense. The same is true if you have a mature AI engineering organization, strong MLOps discipline, and the risk and compliance muscle to govern non-deterministic systems in production. Experimental use cases at the frontier of what’s possible with AI often belong in the build camp as well.

However, what should you do if the process is a well-understood, repeatable workflow with similar processes at every company in your industry? Consider processing invoices, managing customer claims, know your customer (KYC), inventory management, order-to-cash. In these cases, every peer in your industry is wrestling with the same steps, exceptions, and regulatory obligations. Building your own agentic solution here is like commissioning a custom ERP in 2026: technically feasible but not worthwhile.

Time to value also matters, when the process is heavily regulated, and when the cost of getting it wrong can lead to fines and reputational damage rather than lost developer hours. MIT research found that purchasing AI tools from specialized vendors succeeds nearly 70% of the time, while internal builds succeed less than one-third as often. For most of the workflows business leaders want to transform, purpose-built agentic solutions prove their worth.

What are agentic solutions?

The term ‘AI agent’ has become one of the most overloaded phrases in enterprise software. It’s worth drawing a distinction here between agents and solutions.

An agent is a software entity that can reason, plan, and act on a specific task—like reviewing a document, screening a transaction, or drafting an appeal. A good AI agent is impressive. But when dropped into a complex process on its own, it struggles without the right grounding, tools, and team surrounding the process. It can do the work in isolation, but it can’t reliably deliver the outcome without proper orchestration.

An agentic solution is the full operating environment around that agent. It’s a purpose-built, pre-packaged offering that brings together specialized AI agents, deterministic automations, human-in-the-loop review, integrations, and the governance and audit controls a regulated enterprise needs. It targets a specific, high-value workflow and delivers the domain expertise, policies, and best practices already embedded.

Crucially, an agentic solution also orchestrates the entire process. Some of today’s most capable AI assistants can coordinate tasks and even string together multi-step workflows on the fly. But in practice, no two runs follow the same path and that lack of consistency is a non-starter for highly regulated work. Consistency and reliability require a dedicated control plane: one that coordinates AI agents, automations, and people end-to-end. One that can pause, resume, and keep state over days, weeks, or months. This is agentic business orchestration, and it’s how enterprises move from task automation to real process transformation.

Why agentic solutions thrive in regulated workflows

Purpose-built solutions compress the time to value that custom builds struggle with. The components are already assembled. Your team spends its time configuring your policies, proprietary rules, and data instead of building the framework. Industry benchmarks show that equivalent internal builds typically require many months to reach initial production, while purpose-built deployments are measured in weeks. For a process owner or decision maker under pressure to show fast ROI, that’s often the difference between a program that scales and one that’s shuttered.

Agentic solutions also carry embedded domain expertise and the accumulated experience of countless prior deployments. Edge cases have already been anticipated and policies encoded. Guardrails, explainability, audit trails, and compliance controls for regulations like HIPAA and GDPR are part of the solution from day one. In regulated industries, this is arguably the single most important argument for going prebuilt.

The other differentiator is durability at enterprise scale. The processes that matter most in a regulated enterprise are long-running and exception-heavy. They can’t afford to lose state. A good agentic solution is engineered to run reliably over months and years, with deterministic exception handling and people kept firmly in charge. The agents work and orchestration ensures they act within safe, approved boundaries every time.

The results are clear. In healthcare, medlitix deployed the UiPath Solution for medical record summarization and cut average review time from 70 minutes to six (a 90% reduction), freeing clinicians to spend more time on direct patient care. Similarly, Valley National Bank implemented an agentic solution for transaction screening and automated 61% of sanction hit reviews. They were able to handle 14,000 alerts monthly while accelerating payments and improving the employee experience.

How to get started with agentic solutions?

The first step is to identify your preferred solutions partner. The agentic market is crowded and the gulf between an impressive demo and an enterprise-ready deployment is vast.

Prioritize an enterprise-grade platform built for regulated markets. It should be backed by partnerships with domain experts in technology and process design, rather than a vendor that claims to do everything itself. The real differentiator is a platform that is compliant, proven at scale, and enriched by an ecosystem of customers and partners that delivers the specialized knowledge your workflows demand.

You should also consider enterprise-grade governance and trust. That means platforms with full audit trails, policy guardrails, simulation and evaluation tooling, and controls aligned to the regulations you operate under daily. Finally, seek out deployment flexibility and scale, because a partner that forces you into a single tenant will hit a wall in your most sensitive processes.

The enterprises that win with agentic AI won’t necessarily be the ones that built the cleverest agents. They’ll be the ones that choose the right deployment path for each process, whether it’s building where it truly differentiates them or partnering where there’s already a purpose-built solution for the job. For most enterprise workflows, that second path is the safest and quickest path to transformative value with agentic AI.

Go deeper

At last month’s UiPath Agentic AI Summit, we launched our latest purpose-built agentic solutions for mission-critical processes across financial services, healthcare, testing, and more. Check out my recap blog for a complete rundown of our new solutions or watch the summit on demand.

Sources:

• Bain & Company, “Scaled AI Is Transforming the Claims Process,” December 2025.

• Fortune, “MIT Report: 95% of Generative AI Pilots at Companies Are Failing,” August 18, 2025.

• TechAhead, “Build vs Buy vs Partner AI: The Enterprise AI Decision Framework for 2026,” March 30, 2026.