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Workflow Automation: How to Automate Repetitive Business Tasks with AI
·4 min read
Work that moves slowly through a business rarely does so because people are slow. It moves slowly because information has to be gathered, checked, passed on and confirmed at each stage, and every one of those handoffs depends on someone having a moment to do it. As a business grows, the volume of those handoffs grows with it, and the time they consume is not always visible as a cost until it becomes impossible to ignore.
AI workflow automation addresses that accumulation directly. It can carry work through a connected sequence of steps: pulling information from one system, applying a rule or a judgement to it, routing the result to the right place, and logging what happened. The global market for AI workflow automation is estimated at USD 10.8 billion in 2026 and is projected to reach USD 97.9 billion by 2036.
Workflow automation works best when it is applied to sequences of steps, not individual tasks. A sequence automated end to end changes how a team works.
Research published in MIT Sloan Management Review in April 2026 makes this point precisely: AI delivers the most value when organisations redesign workflows around it. AI's biggest impact comes from how it reshapes entire workflows, specifically how tasks are sequenced, grouped and handed off between humans and machines. A company that automates one step while leaving the surrounding process unchanged captures only a fraction of what is available.
Sequences worth looking at closely are those where employees spend most of their time collecting data from multiple sources, reformatting it, checking it against a threshold, notifying someone, waiting for a response and updating a record. These steps are often spread across several systems and several people, which puts them across several calendars too. Automating the connective tissue between steps tends to be where the time is recovered.
Nearly nine in ten organisations now use AI regularly in at least one business function, and 44% say AI is scaling across the enterprise, up from 38% a year earlier. What varies considerably is how much of the available value organisations actually capture.
A pooled analysis across three randomised controlled trials covering 4,867 developers at Microsoft, Accenture and a Fortune 100 company found a 26% increase in completed tasks among those given an AI coding assistant, with less experienced developers gaining the most. The pattern appears in other settings: more than 57,000 team members at Telus are regularly using AI and saving 40 minutes per AI interaction. Across a large workforce, that figure compounds quickly.
Two-thirds of organisations report productivity or efficiency gains from AI, according to Deloitte's 2026 enterprise survey. The same survey identifies insufficient worker skills as the single biggest barrier to integrating AI into existing workflows, which points to where the gap between adoption and value tends to open up. Deploying a tool and redesigning the workflow around it are different exercises, and most of the organisations that have done the first have not yet fully done the second.
Three questions shape how much value an organisation extracts from workflow automation, and settling them before implementation begins determines what the system actually does.
The first is where software should act without review. Routine, well-defined tasks with low consequences for error are the natural candidates: scheduling, data entry, document routing, standard notifications. These can run continuously and free up time immediately.
The second is where a person should review before anything proceeds. An automated process that routes a customer commitment, a financial figure or a compliance-related output without a review step is a process where errors reach the outside world before anyone catches them. The level of scrutiny should reflect the cost of a mistake.
The third is who owns each automated workflow. Automation without a named owner tends to drift: the process runs, exceptions accumulate, and nobody is clearly responsible for noticing that something has changed. Assigning ownership is also how an organisation ensures that the workflow is reviewed when the underlying process changes, which it will.
Should You Give AI More Autonomy? covers how to think about expanding what automated systems can do once the basics are working. The Best Workflow Automation Tools in 2026: The European Buyer's Guide covers the tooling available to businesses choosing where to start.
Businesses that capture the available gains from workflow automation tend to have designed their workflows carefully, defined their review requirements clearly, and assigned ownership before the system went live. The time savings are real and the tools are mature; the design work is what determines whether those savings materialise.