Why it’s time we put AI process on a pedestal 

When was the last time you heard someone get excited about process?  

 

The spotlight rarely shines on the inner workings of a business: the details of invoice approvals, policy compliance, onboarding workflows, or supply chain handoffs. But processes are the bedrock of some of the most successful businesses: think Ford’s production line, Toyota’s kanban methodology, or the agile software development workflows that have powered thousands of startups to success. Processes may not be the headline-makers, but they’re the backbone of how organisations create value, manage risk, and serve customers at scale.  

 

Yet despite its critical role, process is often sidelined in the rush to innovate. According to McKinsey’s State of Organizations report, creating end-to-end processes that drive value is an area where many organisations have fallen short. Nearly two-thirds of executives view their companies as too complex and inefficient due to fragmented, outdated, or non-existent processes. 

 

And when processes break, everything slows down. In the era of AI and automation, we can’t afford to treat them as an afterthought. We need to put process back on a pedestal as the most important driver of business success. 

 

 

Why process is king in the age of AI 

AI doesn’t operate in a vacuum. It needs the context, structure, and clear purpose that process provides.  

 

At its core, process is a map of how value flows through an organisation. It’s grounded in real business data and user needs. That makes it the ideal foundation for AI. 

 

Yet this foundation is too often overlooked. MIT’s recent research found that 95% of generative AI pilots fail, and a major cause is brittle or misaligned workflows. In too many cases, AI is being layered on top of broken processes, disconnected from how work actually gets done. 

 

Too often, organisations respond by bringing work to AI through copilots, chatbots, or standalone tools that sit outside the flow of real work. But AI only delivers “serious” results when the opposite happens: when AI is brought to work. That means embedding intelligence directly into the processes that run the business, where decisions are made, data is generated, and accountability already exists.

 

AI doesn’t know what (or why) to automate without process defining the guardrails. Think of process as the rails of a train system: AI is the engine, but without rails, it’s just spinning wheels. 

 

 

What to look for in AI-ready processes 

Designing AI-ready processes starts with understanding where intelligence genuinely adds value, rather than trying to automate everything at once.

 

MIT Sloan professor Rama Ramakrishnan has this important observation: “Jobs are collections of discrete tasks that vary in terms of how well they can be automated with generative AI.” 

 

Not all business processes are good candidates for AI. But for those that are, embedding intelligence where work already happens is the fastest and safest path to value. 

 

Businesses need to treat processes like a science: grounded in structure, data, and continuous improvement. Ideal candidates include jobs that are the most important and most frequent, or multi-step tasks that could be built into agentic workflows. 

 

Of course, a process is never static; AI applications should be modifiable and flexible to accommodate how processes naturally tend to evolve over time in healthy organisations.   

 

 

Embedding AI for scale 

One of the biggest missteps we see today is companies trying to build new AI workflows from the ground up, rather than embedding AI into the processes they already rely on. 

 

Take Japanese innovation leader Hitachi, for example. Facing an overload of fragmented systems and siloed data, Hitachi decided to create a unified process framework. They didn’t start by asking what AI tools to use or rushing to implement that month’s most popular solution. Instead, they asked a better question: how do we make our processes work better? 

 

By better connecting data and embedding intelligence into their processes, Hitachi was able to process over 50 million sales transactions more efficiently and accurately, drastically improving both time-to-value and customer experience. It’s a real-world blueprint for how to do AI integration right: start with the process, embed AI into it, and then scale. 

 

The lesson is simple but often missed: designing AI-ready processes means fixing how work flows first, then embedding intelligence into that flow, not the other way around.

 

 

The benefit of built-in guardrails 

Another benefit of process-led AI is that it comes with governance included. If your business has process set up right, then you already have the established teams, goals, rules, and accountability structures needed to deploy AI responsibly. This gives enterprises a safer, faster way to scale without sacrificing trust or compliance, which is especially important in regulated industries like healthcare and financial services, where the cost of AI missteps can be steep. 

 

Because process and governance are so tightly linked, they also create the foundation for flexible control. One of the most powerful aspects of AI in process is that it enables a spectrum of autonomy, where the degree of AI involvement can be calibrated based on risk and complexity. Organisations can apply deterministic rules and human intervention when the cost of being wrong is high, while allowing AI greater flexibility in the more routine, repetitive flows.  

 

This balance of governance and flexibility is already proving its value. Acclaim Autism, a behavioural healthcare provider, used AI to dramatically reduce the administrative burden on its clinicians. Previously, documentation, scheduling, and billing were all manual and compliance-heavy. By embedding AI and automation within governed processes, Acclaim created a system where AI could handle repetitive, rules-based work while staff focused on higher-value, patient-facing tasks. The result was a smooth, safe transition that freed clinicians to spend more time on care, and less on paperwork. 

 

 

Scaling AI through process, not pilots 

At Appian, we power 16 billion process executions every day across industries. And we’re seeing the same pattern again and again: the most successful organisations with AI aren’t just experimenting, they’re scaling by putting process first. 

 

Deloitte predicts that 50% of companies using generative AI will launch agentic AI pilots or proofs of concept by 2027. The big question is: how many of those will be baked into real processes? 

 

Because it’s only when AI is integrated into the process fabric that it can drive lasting, measurable impact. Anything else is just a shiny demo. 

 

If AI is the future, process is how we’ll get there.  

 

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Mark Talbot is Director of Architecture and AI, Customer Success at Appian

 

Main image courtesy of iStockPhoto.com and BlackJack3D