Why your op model is key to successful automation at scale

When I talk to clients about intelligent automation, they already understand that automation and AI are critical capabilities. The real need? Practical advice on how to scale their efforts successfully. They want to know how do I move through the early stages faster and start realizing value sooner?

Our answer: for effective automation at scale, you need a structured plan for the automation journey — supported by a strong automation operating model. This acts as a management framework. It sits below the level of overall strategic goals for automation and above the level of supporting particular business operations.

Think of this operating model as the essential link between strategic intent and execution. It shines a light on all the elements that must be established, managed and working in harmony for automation to truly take hold and deliver business value.

So what makes a strong operating model? There are many things you need to consider. To help you focus your efforts, here are five factors key to success:

Identifying the right opportunities

Because there are myriad opportunities for applying intelligent automation, the operating model must include a centralized, ongoing way to choose the “best bets.” This is the essential starting point for successful automation at scale.

What should this approach look like? First, it identifies processes that are valuable candidates for automation. Second, it requires a clear-eyed assessment of your company’s automation maturity. Last, it supports a plan for undertaking initiatives in the right sequence. This helps your organization to grow its intelligent automation capabilities and achieve maximum business benefit.

Keeping track of benefits

How will you know if intelligent automation is on track and delivering the results you need? How do you measure the benefits? To answer questions like these, it’s crucial to establish measurable success metrics.

These must get right to the heart of what success really means to your business. Project managers tend to favor objective, easily quantifiable measures. But being easy to track doesn’t necessarily make a measure any better. You need to identify all the metrics that matter, based on your business objectives, and outline a standard method for reporting on them.

Take banking for example. Loan processing is a popular candidate for automation. The obvious metric to track there will be total effort and time saved through automation. But that’s not the only one. Measurable value can be delivered to the business in other areas. That could mean better customer experiences through faster, easier processes. It could mean better employee experiences through less cumbersome operations. Or it could mean cost savings and extra people capacity to be reinvested into innovation and growth.

There are various methods for consistent measurement. In large organizations, the “goal, question, metric” (GQM) approach is commonly used to attach useful metrics to software processes.

Delivering automation, managing change

Project teams often focus on solution development. But business impact hinges on smooth solution delivery. That’s not all. By embedding automation into an organization’s culture, change management also has a vital role to play.

This part of the operating model should specify agile best practices for project management and solutions deployment. It should also set out a plan for equipping all employees with the new technology skills they’ll need to adapt to automation and ongoing change.

Deploying the right technologies and platforms

Any large-scale automation environment needs multiple technologies and platforms to stay on the leading edge. These range from cloud and Internet of Things (IoT) applications to chatbots and business process management solutions.

Key questions to ask? How do you ensure the flexibility of the solutions and systems you put in place? Do your people have the skills they’ll need to make the best use of them? How will you know when a solution needs updating? A well-planned operating model will help you answer all these questions, matching the right technologies to business needs and deploying them effectively.

Harnessing AI’s power to accelerate automation

Principles and practices for data and information management, and asset and knowledge management are all foundational capabilities for success. You need to put them in place well ahead of any automation efforts.

Critical to AI-powered automation success? Data. Data scientists need to confirm that automation datasets are complete and accurate, with appropriate algorithms. They’ll run into a wall if the organization has not made data management a priority. Plus, data is proving to be a new form of strategic capital.

Another key to success? Learn from the past. Automation leaders should create libraries of reusable automation assets and knowledge — and actively add to them as their automation maturity grows.

By putting all these elements in place, you can drive a more holistic approach to scaling automation and driving value enterprise-wide. Once you can manage automation across the business, you’ll be able to give every new intelligent automation opportunity the best chance of success.

Dr. Bhaskar Ghosh, Chief Strategy Officer at Accenture, is co-author of “The Automation Advantage” from McGraw Hill with Accenture’s Rajendra Prasad and Gayathri Pallail. Learn more at accenture.com/automationadvantage.