“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” – Bill Gates, co-Founder of Microsoft
Automation can have a profound impact on the quality of our lives. Since the dawn of time, the human race has been pushing the boundaries of innovation. Automation has always been part of this journey – helping people to achieve more in faster and more effective ways.
In the stone age, the creation and use of tools to speed up manual tasks aided our survival. During the industrial revolution, humans automated at scale. Then, in the digital age, barriers across space and time were broken down in a shared economy where we amplify intelligence to automate and connect people, processes, data, and devices.
Science, engineering and economics are all agents of change and enterprises must use automation to optimize resources, increase capacity and prepare for the future. To help, I have curated some lessons learnt and best practices from my experiences leading transformative automation programs and projects.
|Lessons learnt||Best practices|
|– Don’t let current inefficiencies get carried over into new processes
– Avoid capturing incorrect, insufficient and unstructured data around peak volume, average volume, average and peak handling times, frequency
– Make sure service level agreements are in place for bot access provisioning and you document access requirements
|– Consider a business process reengineering exercise prior to robotic process automation (RPA). You’re looking for a clear understanding of complexity, scale and feasibility
– Capture and validate information in a structured template – volume, schedule, frequency, handling time, TAT
– Capture access requirements in a structured template and track throughout the project
| – If you don’t formally define test data requirements, otherwise you’ll be faced with a scramble for data
– Forgetting to plan your test data management strategy can be costly
– Look for top-down messaging to your business for provisioning test data
– Don’t forget regression test packs
|– Ensure a solution design captures test data requirements across different scenarios, geographies, etc.
– Create a simple spreadsheet for a test data management framework to streamline roles, requisition and provisioning
– For large processes, make sure time is devoted to creating test scenario packs modularized for specific functions
|– Without an end to end estimation framework, automation estimation becomes siloed with a lack of granularity
– Don’t skip the need to collect quality data through the delivery and support cycle for the right insights and metrics
– Headcount related metrics should not be the major focus; instead think about the end user experience
– Don’t ignore the need to put in place a solid business continuity plan
|– An end to end estimation framework should generate a plan and timeline using your inputs
– Leverage a business intelligence tool to visualize automation metrics across delivery, support and business outcomes:
That’s a lot to remember. But, if you keep these three key takeaways in mind, you’ll be on the right path:
- Use automation to build capacity. There won’t be a loss of jobs, instead we’ll need a hybrid workforce of human and machine to solve the problems of the future.
- Governance is critical. Pay attention to the small details, planning and execution. And, let’s not fear automation – it’s here to stay and the benefits are huge.
Nothing is perfect, but stability is key. Focus on amplifying strengths and overcoming challenges through continuous teamwork and stakeholder collaboration.