Just five years ago, automation in companies usually meant a single RPA bot clicking through an accounting system’s interface. Today it’s an entirely different game. According to Precedence Research, the global hyperautomation market reached $76.86 billion in 2026 and is projected to grow to $306.21 billion by 2035, at an average annual growth rate of 16.64%. This isn’t a technology fad — it’s a shift in how enterprises design their processes from the ground up.
What Hyperautomation Actually Is
Hyperautomation isn’t the next stage of RPA — it’s the combination of several technologies into one coherent system: artificial intelligence, robotic process automation, process and task mining, and low-code platforms. Instead of automating individual, repetitive tasks, companies map an entire business process from start to finish — from a customer order through to the invoice and the financial report — and eliminate the manual touchpoints that actually generate cost and delay. Gartner has named hyperautomation one of the key strategic trends for years, and according to industry-cited data, as many as 90% of large enterprises now consider it a technology priority for the coming years.
Adoption Is Growing Faster Than Expected
Data from Duke University’s Fuqua School of Business shows that 60% of companies have already implemented some level of process automation, and among large organizations that figure reaches 84%. Forrester adds another significant signal: as many as 87% of enterprise developers now use low-code platforms, which radically cuts the time to build new workflows — from months to weeks. Mordor Intelligence also highlights the process and task mining segment, growing at 28.10% annually, faster than any other automation technology category. This shows that companies want to understand what a process actually looks like in the data before they start automating it.
Why Single Tools Are No Longer Enough
RPA works great for tasks with fixed rules, but it breaks down with exceptions, unstructured data, or decisions that require context. That’s why mature organizations combine RPA bots with AI models that understand document content or customer intent, with process mining that detects bottlenecks in real time, and with low-code that lets business teams modify workflows themselves without waiting in an IT queue. According to Mordor Intelligence, large enterprises now account for nearly 68% of hyperautomation spending precisely because only this combination of technologies lets them handle the complexity of processes spread across multiple systems and departments.
Where Hyperautomation Projects Most Often Go Wrong
Market size doesn’t mean implementations are simple. Boston Consulting Group estimates that as many as 70% of digital transformations fail to meet their stated goals, and only 26% achieve the expected return on investment. Formstack goes further, finding that only 4% of companies can claim fully automated end-to-end processes — the rest get stuck somewhere between pilot and scale. VegamAI’s analysis points to poor change management as the cause of 35% of failures — not the technology, but a lack of preparation among people and processes for a new way of working. This is the most common mistake: companies buy tools before they map the process and build organizational readiness for change.
What the Numbers Show Where It Works
Where implementation is well thought out, the results are measurable. McKinsey & Company reports that 60% of business process automation initiatives achieve a positive return on investment within the first 12 months, and 73% of IT leaders report cutting process completion time in half. Camunda finds that 95% of IT specialists report a productivity increase after deploying end-to-end automation. A concrete market example, described by Mordor Intelligence: Ring Container cut annual freight-documentation processing costs by $102,000 and sped up customer inquiry handling by 96% by combining document automation with intelligent task routing. This shows that hyperautomation pays off when it addresses a real, measurable pain point in a process — not when technology is deployed for its own sake.
At Unomage, we’ve observed the same pattern for years among our B2B marketing and sales clients: the greatest value doesn’t come from a single tool, but from combining automation, data, and AI into one coherent process — exactly how platform.unomage.com works, pairing campaign automation with intelligent customer data analysis. If you’re wondering where to start building a hyperautomation strategy in your own organization, our team in Warsaw is happy to set up a free consultation and help map the processes that deliver the biggest return on automation.
This article was created with the help of the Unomage AI platform.