Rejoy Nair

Hyperautomation was at the top of Gartner’s list of strategic technology trends for 2020, and again in 2021.

And with good reason, the adoption of automation has been rising ever since, and the tools and technologies available are evolving and increasing at a rapid pace.

So, what’s Hyperautomation anyway?

In simple terms, hyperautomation is the orchestration of advanced technologies to automate human tasks of business processes. With RPA/BPM at its core, the advanced complementing Artificial Intelligence (AI)technologies include:

  • Machine Learning
  • Natural Language Understanding
  • Computer Vision, Image Recognition
  • Conversational technology
  • Business Process Mining
  • OCR
  • Big Data Analytics, Workflow Analytics
  • Artificial Neural Networks
  • Deep Learning

The complementing AI tech can be made available natively as a single-platform platform solution or can be a combination of point-solutions.

Hyperautomation will extend back-office RPA functionality into adaptive systems that automate functions like sales, accounts payable, and customer service. Hyper-automation is profoundly transformational - No more will executives have to manually approve loans, file insurance claims, or do compliance checks.

  • Expand the depth and breadth of your automation capabilities to as many functional areas.
  • Makes automation even more intelligent and actionable through technology orchestration and data insights.
  • Improves operational and service performance, Customer Experience (CX), Employee Engagement.
  • Helps in cost-effectiveness and overall organizational intelligence.

Applying hyper-automation in a fast-moving, complex business is a mighty endeavor even for the best and the most experienced.

Key Challenges in the adoption of Hyperautomation

The limitations of AI get transmitted to the realm of Hyper automation viz.

  • Knowing how to measure success.
  • Developing and adhering to a realistic project timeline
  • Sourcing comprehensive datasets, pre-processing and labeling data.
  • Choosing the right solution from the ever-growing and evolving product marketplace.
  • Societal concerns (e.g., data privacy) and regulatory constraints.
  • Improper handling of technology, processes, and people can slow or impede adoption of hyperautomation.
  • Processes that cannot attain full digital maturity can also become an impediment to successful adoption.
  • Ensuring that you have an end-to-end leadership for the entire project – right from initiation to delivery.
  • People competencies – decision-makers need to have an in-depth understanding of the varied technologies as well as the business.
  • Fully comprehending and underestimating the complexity of the planned solution.

Decision Matrix

It also becomes necessary to have a prior indicative value assessment of hyper-automation across functions applicable within the organization’s business context which in practice is a really hard task.

Hyperautomation Technique

A few guidelines for an effective Roadmap

Synchronizing and orchestrating a diverse set of AI technologies is overwhelming and fraught with business risks. Value realization depends on top-notch implementation which in turn draws from a clear blueprint/ roadmap.

The key phases in rolling out Hyperautomation:

1) Planning Stage

This stage has more to do with organizing priorities around Hyper Automation initiatives viz.

  • Automation CoE practices and procedures
  • Use-case identification and an automation pipeline that satisfies target RoI, is based upon relative priority to business and leverages digital technologies in the best way to orchestrate processes across the enterprise.
  • Due diligence of business systems.
  • Vetting of Technical Architecture and related considerations like data acquisition, ingestion, security, etc. by internal consultants and even experts.
  • Flexible and scalable tech stack.
  • Vendor/Partner selection, building the ecosystem.
  • Change Management processes.
  • Talent Management Processes.
  • Bring stakeholders including employee together through an excellent internal communication drive

2) Initiation Stage

  • Data curation, creating comprehensive data sets.
  • Process discipline and redesign, and process governance.
  • Infrastructure for development and deployment – Specialized hardware, proprietary technologies, data centers, or cloud-based solution requires considerable CAPEX.
  • Tooling for end-to-end management. Fragmented solutions compromise scalability.

3) Solution Stage

  • Agile methodology.
  • Knowledge acquisition, coding practices reusable workflows,
  • Quality Assurance, Ethical assurance*

4) Support Stage

  • Orchestration – Scheduling, Load Balancing.
  • Repository for deployed assets.
  • Bug fixing, DevOps for continuous release management.
  • Model retraining to adapt to changes in downstream processes.
  • liability and legal framework of risk owing to AI deployments.

Ethical assurance* - There has been a demand for explain-ability of AI models and ownership structure to legally distribute business risks and fix accountability amongst stakeholders, partners, and vendors alike. Algorithms that are backboxes will no more be acceptable.

It is imperative the companies invest in:

  • Diverse set of AI technologies that address many use cases.
  • Building a strong leadership to evangelize enterprise-wide acceptance of hyperautomation.
  • Seeking scalability and flexibility in their operations and service innovation efforts.
  • Building systematic development and deployment competencies.
  • Measuring costs and benefits – RoI, TCO, and Process specific KPIs.
  • Elevating the role of IT from an auxiliary one to an associative for business purposes.


An effective Hyperautomation blueprint is not a mere ask – due to the almost intractable process and technical interdependencies across organizational units.

Plus, there are new-found ethical and legal issues triggered by automation that compound matters. It is not just about assembling a solution, but about raking in many heads – business leaders, technology experts, process owners, etc. to visualize the best end-outcomes for the business and strategize the means to deliver it in a way that doesn’t compromise the vitality of hyperautomation into the ensuing future.

Rejoy Nair

Rejoy Nair

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