By AJ Hanna & David Rombough
In our first three posts of this series, we’ve established how to describe scale, why to avoid shiny objects (aka quick wins), and the key attributes that make for an environment where scale can be effectively enabled. In our final post of the series, we’re putting it all together.
A scaled automation program involves far more than deploying a single technology and hoping the value rolls in. There are dozens of factors to consider and orchestrate to successfully drive adoption, deliver value to the organization, and manage the associated risks.
From a pure technology perspective, there are likely multiple platforms across different functional areas, and technologies that need to be integrated within any process (i.e. Optical Character Recognition, Natural Language Processing, Knowledge Bases, Machine Learning, Process Mining, Workflow, Analytics, etc.). These tools require the coordination of infrastructure and ongoing support. Further the movement of data and scheduling of workloads need to be considered.
Beyond tools, look at all the affected stakeholders. In addition to the workforce whose jobs are impacted and will require training to work alongside their new digital colleagues, consider the implications for HR – how will they support the transition, when and how will they deploy learning and development, how will performance management and career paths be impacted?
Consider the suppliers and customers of the process. Are they able to “fuel” the digital processes? Is the customer experience as expected? How do the new processes (and the enablement of those processes) affect the enterprise risks and controls?
Just as a melody takes form through sheet music, IA orchestration manifests itself in a well thought out strategy. Each group in the orchestra uses the sheet music to blend their individual talents into a coherent and complex whole. Successful, scaling and evolving intelligent automation programs employ a strategy that makes clear what the goal is by laying out the melody, describes the harmonies needed to fill out the sound, defines the roles for each of the players and outlines the structures for their interaction.
There are many views on what a strategy should look like and what the key elements are – ranging from simplified mission statements to tactical execution plans that are dictating the what and how, but not capturing the why. From experience, it’s not a simple document that describes an aspiration – it’s detailed, intricate and requires extensive time and skill. It also takes time and skill to properly put it all together. This is not something to be rushed or taken lightly. To have, and be able to communicate, a clear view of your automation program, the strategy must answer a core set of questions:
What are we trying to do?
Are you trying to be the market leader in use of intelligent automation technology? Are you trying to accomplish a fully automation-enabled finance, human resources, supply chain, etc.? Are you trying to absorb future growth through use of intelligent automation?
Why are we trying to do it?
Is our market position threatened due to higher costs than our competitors? Are we experiencing customer satisfaction concerns because of how difficult it is to do business with us? Are we at risk of fines and audit deficiencies because our control environment is lacking?
Who is going to be involved?
Which parts of the organization are key to success? Who is leading the effort – Ops, IT, a CoE? Will there be governance, and by whom? Will partners be engaged? How will we engage the broader organization?
How do we identify success?
What are the key indicators and measures?
It takes more than communication to the project team. Organizations that scale their IA programs understand that it takes involvement and cooperation across the organization, approaches that recognize the interconnected nature of operations, and engagement among all involved stakeholders.
Really, what we are suggesting here is that you take the necessary time to fully define your automation initiative. As a matter of fact, that’s probably a great addition to our recently explored D’s of Intelligent Automation:
As daunting as it may seem, this isn’t your first transformational rodeo and there is a lot you can learn from your past experiences. Those projects required this type of discipline and, if you want to scale your automation program to achieve maximum value, then you need to apply the same discipline here.
Helping to assure that you are going both deep and wide, avoiding the dissatisfaction that can come with “the quick win syndrome”, and understanding what attributes you possess to enable your program and what you need starts with defining what you want to do.