Is Process Cool Again? The Most Innovative AI Teams Think So

Several years into widespread AI adoption in the workplace, one thing is becoming clear: AI can significantly improve how teams work. Teams using AI are three times more likely to deliver breakthrough ideas as they complete tasks faster, and they are more enthusiastic about their work.

However, while many organisations are seeing these benefits, a much larger share are still struggling to unlock them at scale. Integration into day-to-day workflows remains inconsistent, and without a clear operational foundation, even the most advanced tools fail to deliver consistent impact.

The path forward depends less on the technology itself and more on something often overlooked in fast-moving teams: process.

The hidden cost of speed over structure in AI implementations

Prioritising speed over structure in AI adoption creates gaps that are often invisible – until they’re not. Agent drift, where AI systems operate beyond approved knowledge or workflows, can produce untraceable outputs that may introduce compliance risks, security issues, and even erode consumer trust. 

The organisations most impacted are those that moved fast without a clear operational map. Lucid Software’s AI Readiness Report found that undocumented processes hinder team efficiency most of the time (79%), showing that speed without governance is the real liability teams are facing.

Closing this gap requires documenting and optimising operations so AI systems have the context they need to function reliably at scale.

Making process cool again

AI adoption depends largely on organisational and behavioural change, not just technology implementation. Change management is often the hardest part of any transformation. With AI, that work happens upfront: building the structure that enables systems to operate with confidence and consistency.

Yet according to Lucid’s research, only 16% of knowledge workers say their workflows are extremely well-documented, with 80% relying on institutional knowledge to complete their work. That creates a clear constraint for AI maturity, as human teams can operate with informal knowledge, but AI systems cannot.

The key is making documented context widely accessible and building governance into AI adoption from the start, rather than adding it afterwards. Here’s how to get started:

Map everything out. The most effective AI transformations start with a clear understanding of how the business operates today. Once you define the key systems, processes, data, and roles that will work alongside AI, you can document the workflows, integrations, and data flows needed for AI agents to operate effectively. It also helps to clearly define your AI agents—their capabilities, roles, and how they make decisions. Building in documentation, monitoring, and traceability from the start creates a strong foundation for scale.

Establish clear accountability. Every AI agent should have an owner responsible for its performance and outcomes. It’s important to ensure someone is actively managing, reviewing, and continuously improving how each system performs.

Create feedback loops. Governance shouldn’t be a one-time exercise but an ongoing process of monitoring, learning, and adjusting. Regular audits, performance reviews, and stakeholder feedback help you catch agent drift early.

Make governance enablement, not enforcement. When done well, governance helps teams move faster with more confidence. Framing it as a support system for innovation rather than a blocker makes it easier to embed into day-to-day work.

As these foundations strengthen, AI maturity increases and so does the impact teams are able to achieve.

Process is the backbone of agentic AI

For years, process has been an afterthought or slows things down. It’s time to retire that story. In the age of AI agents, process enables higher levels of productivity, efficiency, and scale. It also distinguishes organisations that experiment with confidence from those that experiment without direction.

Jamie Lyon is Chief Product and Strategy Officer at Lucid Software

Last Updated on May 9, 2026 by Jamie Lyon