Hyperautomation

Many organizations have automation, but it is often scattered. A finance team has a few bots, IT runs scheduled scripts, customer service uses a chatbot and a workflow tool handles approvals in one department. Each delivers some value, but no one has an overall view of what has been automated, what could be automated next or how the pieces fit together. Hyperautomation describes a different approach: treating automation as an organization-wide discipline, using many technologies together and systematically working through processes to automate them as fully as makes sense.

Research firm Gartner introduced the term and named hyperautomation a top strategic technology trend for 2020. Gartner emphasized that it is not a single product but an approach combining tools such as robotic process automation, low-code platforms, workflow and business process management, integration platforms, AI and process mining. Process mining and task mining play a key role in finding automation opportunities, while governance and a center of excellence keep efforts coordinated. With AI agents now able to handle more complex work, the scope of what can be automated continues to grow. For executives, operations and IT leaders, automation teams and finance, hyperautomation offers a way to scale automation beyond isolated projects.

Definition of Hyperautomation

Hyperautomation is a disciplined, business-led approach in which an organization systematically discovers, evaluates, automates, monitors and improves business and IT processes across the enterprise, orchestrating multiple technologies, including AI, machine learning, robotic process automation, low-code tools, workflow, integration and process and task mining.

It brings together intelligent automation, process mining and workflow automation under a single program.

Technologies Used in Hyperautomation

Discovery tools. Process mining and task mining to find where work happens, how long it takes and where automation would help. Task mining adds detail on work done on desktops, outside core systems.

Automation tools. Robotic process automation, workflow, business process management and low-code platforms to automate tasks and processes.

AI. Document understanding, natural language processing, machine learning and agents to handle unstructured inputs and decisions.

Integration. APIs, integration platforms and event streams to connect systems directly where possible.

Monitoring and analytics. Dashboards and process intelligence to measure results and find further improvements. This links automation to wider operational excellence efforts.

Elements of a Hyperautomation Program

Strategy and governance. Clear goals, priorities, standards and decision rights for automation across the organization.

Opportunity pipeline. A continuous process for identifying, assessing and prioritizing automation candidates.

Center of excellence. A team that provides expertise, platforms, standards and support to business units.

Value tracking. Measures of costs, benefits and outcomes for each automation and for the program overall. Results should be reported regularly to sponsors, so the program keeps its funding and support.

Importance of Hyperautomation in Enterprise Operations

It scales automation. A coordinated program delivers far more value than isolated projects. Shared platforms, skills and components make each new automation faster and cheaper to build.

It targets the right work. Discovery tools show where automation will have the most impact. This avoids spending effort on processes that are visible but not valuable.

It also builds resilience. Automated processes can handle higher volumes and continue operating when staff are scarce. Automated processes can scale up during peak periods without hiring temporary staff.

Hyperautomation and Financial Management

A central view of automation spending and benefits helps leaders invest where returns are highest. It also prevents different departments from buying similar tools separately.

Combining many tools can increase licensing and integration costs, so platform choices should be rationalized.

Benefits include lower processing costs, faster cycle times, fewer errors and capacity redeployed to higher-value work.

Maintenance of automations is an ongoing cost that should be included in business cases. Maintenance can be a substantial share of total automation costs over time, so it should not be ignored.

Common Challenges

Organizations accumulate many tools from different vendors, creating complexity and overlap. Each tool needs skills, licenses and support, and overlapping tools make it unclear which to use for a new process.

The pursuit of automating everything leads to automating processes that should be simplified or eliminated first. Automating a broken process locks in its problems and makes later improvement harder.

Automations break when underlying applications change, creating maintenance burdens. Screen-based bots are especially fragile, which is one reason to prefer APIs and integration where possible.

Business units bypass governance, building automations that are not documented or supported. These automations can fail without anyone noticing, or create risks when they handle sensitive data.