Gartner: How AI Is Rewriting the Rules of Business Process Efficiency

From manufacturing floors to financial services back offices, business leaders are grappling with an uncomfortable truth: the playbook that once delivered efficiency is no longer fit for today’s world. Traditional automation tools, particularly robotic process automation (RPA), have reached their limits. Designed to handle repetitive, rules-based tasks, they were never built to cope with constant change, shifting customer expectations, or unpredictable market dynamics. 

And yet that’s exactly the environment we’re operating in now. 

With supply chains under pressure, workforces stretched thin, and customer needs evolving rapidly, organisations need more than basic task automation. They need systems that can adapt, learn, and respond in real time. That’s where artificial intelligence (AI) is changing the game. 

The question  is not whether AI can enhance automation, but whether infrastructure and operations (I&O) leaders and business leaders can harness it quickly and effectively enough to stay competitive amid complexity and change. 

Discovery: Seeing the bigger picture 

The pressure to do more with less is growing. But traditional RPA often falls short when processes evolve. Bots can break if a user interface changes. Workflows stall when exceptions occur. The very efficiencies RPA is meant to deliver can quickly unravel.  

AI has changed the game by adding intelligence. Now processes are enhanced at every stage of the automation lifecycle.  

One of the most profound changes is in the discovery phase, identifying what to automate in the first place. Historically, this required significant manual effort: interviewing teams, mapping processes, and analysing logs. Now it uses advanced data mining and pattern recognition to uncover inefficiencies that often go unnoticed. It’s like putting on a pair of X-ray specs, you suddenly see where time and resources are being wasted and where automation could make the biggest difference. 

It can weigh task complexity, repetition, business value, and return on investment to help leaders focus on the automation initiatives that will move the needle. 

Design: Democratising automation 

Design is also getting a shake-up. With natural language processing, teams can describe what they want in plain English —“Assign all unassigned tickets created today to Level 1 support and send a notification”—and let AI help generate the workflows and documentation.  

This isn’t just about speed; it’s about accessibility. AI makes it easier for non-technical stakeholders to contribute, creating a more collaborative approach to automation. It also helps generate documentation, identify performance metrics, and ensure alignment across teams, all during the design phase. 

Development: From natural language to executable code 

In development, AI is moving from a helpful assistant to a genuine co-pilot. 

Developers can use natural language prompts to generate code, API connectors, exception handlers, and test cases. Rather than starting from scratch, they describe a desired function, say “Create a C# script to POST to a given endpoint and pass variables”, and the AI generates the underlying script. AI can even generate synthetic test data and suggest validation logic, improving quality assurance while cutting down development time.  

This generative capability also enhances standardisation. Expect to see centres of excellence increasingly relying on AI-driven prompt libraries to create and manage reusable components across teams. 

Deployment: Enter agentic automation 

Perhaps the most transformative impact of AI lies in the deployment phase, where RPA evolves from a static executor to an “agentic” system capable of learning and adapting on the fly. 

In this phase, AI introduces self-healing capabilities, where bots can detect and respond to changes in application interfaces without manual intervention. These bots don’t just follow rules, they interpret them. They predict failures using machine learning models, suggest alternative flows, and support complex exception handling. Additionally, AI-driven computer vision enhances the bots’ ability to interact with dynamic UI elements, improving compliance with service-level agreements.  

This is automation that’s resilient by design. It learns from failure, adapts to change, and performs reliably in complex, real-world environments. 

A strategic imperative, not just an IT upgrade 

Generative AI is now the number one innovation priority for enterprise application leaders, and the top area of planned investment for the next two years. AI-powered automation isn’t just an IT upgrade. It’s a strategic imperative. 

If you’re leading an enterprise application strategy, here are a few things to consider: 

• Start with discovery: AI can help you identify the right opportunities, not just the obvious ones. Use it to get a clear view of where automation will have the most impact. 

• Get the business involved: With low-code and no-code tools supported by AI, more of your people can contribute to designing and improving workflows. 

• Think beyond efficiency: Yes, automation saves time and money. But its real power lies in its ability to drive resilience, agility, and long-term transformation. 

Welcome to the age of intelligent automation 

AI is not just augmenting RPA; it is redefining it. By enabling systems to analyse, adapt, and innovate, AI-driven RPA is poised to become a critical enabler for businesses in an increasingly digital world. However, success requires more than just adopting new technologies. It demands a strategic mindset that balances innovation with governance. 

Organisations and their I&O leaders that can master this balance will not only unlock new levels of efficiency but also position themselves as leaders in the next era of automation.  

Gartner analysts will further explore AI and intelligent automation at the IT Infrastructure, Operations & Cloud Strategies Conference in London, from 17-18 November 2025