Automation 101: Our five element design framework

By Ivan Sean Pulley

Learn the five elements of designing and deploying successful enterprise automation 

Robotics and process automation technologies are empowering organizations to approach operations in entirely new ways. You could even call it a revolution. Executives are asking fundamental questions about business transformation, such as:

  • What should our operating model look like in the next three to five years?
  • What is achievable with these technologies?
  • What is the future of work within our organization?

While few firms have progressed beyond narrow implementations, that’s changing. Now, companies are seriously looking at larger-scale paybacks through value levers that have tremendous potential to shift enterprise value, realign the workforce and achieve economies of operational scale.

Executives need to create appropriate strategy and governance models for automation decisions—and to evolve into a cognitive-oriented enterprise. Automation benefits extend beyond cost reduction to encompass greater control and sophistication in service operations. Yet few organizations understand the upfront and ongoing cost drivers. To work towards automation delivery methodology and benefits realization, companies need to understand those cost drivers, including:

  • Automation management
  • Technology environments
  • Skills development
  • Analysis and controls

As organizations release labor capacity, they should run operational scenarios based on new, automated operating models and a dramatically altered future of work. Assessing new skill set requirements is critical. For example, analytics, automation management and service management are quickly emerging as high-value skills.

Where to begin? Start with this five-element framework. Developed by IBM, it addresses a plethora of management questions concerning the evolution, adoption and benefits of automation.

1. Create a holistic automation strategy with client-centric goals

This holistic automation strategy should focus on articulating the optimal client and stakeholder experience. Goals should include exploiting the economies of operational scale, with increased firm-wide adoption and deployment. Clearly articulated automation goals are more effectively orchestrated across the service delivery organization.

2. Always put data at the core of your strategy

Large-scale data integration across structured and unstructured data sets is also necessary. This involves intelligent data capture, data governance and an understanding of data journeys as the foundation for process transformation. From extraction to processing and generation of data, quality and lineage are integral to an operating model powered by robotics, autonomics and cognitive capabilities.

3. Think broadly with end-to-end process leadership and design

Process owners should develop automation strategies that extend beyond task automation within functional silos. This requires using:

  • Key principals of design thinking
  • End-to-end business process management
  • Cross-functional use cases
  • Change management
  • Communication strategies

4. Transform the knowledge work in your organization

Robotic and cognitive processes drive value in knowledge work, augmenting the current workforce by reducing “generic” activities, lowering requirements for additional full-time equivalents (FTEs), achieving otherwise impossible monitoring functions and enabling the extension of customer services to generate new revenue.

5. Ensure consistency with innovation governance

You’ll need to align business cases for automation programs to strategic imperatives, as well as risk and regulatory demands. This helps ensure consistency in how the organization will optimize value and adapt to increasing analytical and automation skill sets.

In defining an enterprise process automation strategy and governance model, you’ll require a full spectrum of capabilities to execute and define end-to-end processes. Business process management techniques can determine critical steps to help avoid automating inefficiencies—and to enable new agilities. You can then effectively create end-to-end process automation with consistent approaches to redesigning operations combining robotics, autonomics and cognitive.

While autonomics represents the self-managing aspects of process automation, cognitive robotics is much broader. It involves artificial intelligence disciplines such as perception, attention, anticipation, planning, memory, learning and reasoning. To develop cognitive capabilities, organizations will need to acquire, build and partner. They’ll also need to interact with new applications, application programming interfaces (APIs), process data mining, process orchestration and expert systems.

Business-driven virtual workforces must align to and be supported by dedicated technology functions, maturing through narrow implementations toward centralized teams, managed centers and fully embedded enterprise capabilities. In other words, a vast array of processes can be automated within an organization. But what functions and process should be automated? How should they be automated, and how should their adaptive capacity be created?

Automation is revolutionizing work and operations, but few organizations are adopting automation technologies. These five elements can help your organization make real progress in your automation journey

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