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    Research analysis

    Analysis: what hyperautomation means once you strip the vendor language

    Author

    AI Cubed

    Published

    August 9, 2026

    Read time

    7 min

    Gartner defines hyperautomation as a business-driven, disciplined approach that organisations use to rapidly identify, vet and automate as many business and IT processes as possible, using a coordinated set of tools. That definition is Gartner's.

    Our analysis is about which half of it a $10M–$100M operation should actually adopt — and which half reliably produces a portfolio of half-working automations that nobody owns.

    The useful half: discipline

    The genuinely valuable idea inside hyperautomation is that automation should be identified and vetted through a repeatable process rather than requested ad hoc by whoever shouts loudest. A standing intake, a scoring model, and a review cadence are all worth adopting at any size.

    The dangerous half: coverage as a target

    'As many processes as possible' is a target that enterprises with dedicated automation teams can survive and mid-market operations generally cannot. Each automated process carries ongoing cost: monitoring, exception handling, and someone who understands it well enough to change it. Multiply that by thirty and the operating burden exceeds the hours reclaimed.

    We have inherited these portfolios. The pattern is consistent — a long list of workflows, a short list of ones still running as designed.

    What we would do instead

    1. Adopt the intake discipline: a single queue, a scoring model, a quarterly review.
    2. Cap the active portfolio — three to five automated processes in year one.
    3. Instrument each one so its contribution is measurable in isolation.
    4. Only add the sixth when the first five are provably stable and owned.

    When hyperautomation is the right frame

    Once an operation has a functioning automation practice — an owner, a monitoring layer, a change process — breadth becomes the correct next objective. The sequencing is what matters: discipline, then depth, then coverage.

    Frequently asked questions

    This page is AI Cubed's own analysis of independently published research. The underlying study belongs to its publisher, credited and linked in full below.

    Sources

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