For several years process automation has been slowly deployed across organizations moving gradually from simple assisted robotic process automation (RPA) through unassisted, automated, and now in some cases, intelligent RPA. Covid-19 has accelerated RPA and a current focus within many organizations the next 12-18 months on cost optimization and customer experience, which will only further accelerate this trend. AI is increasingly becoming the focal point of RPA, but RPA initiatives lack a focus on AI governance. That is a potential business risk!
The focus on RPA drives several factors, such as process optimization, labor cost reduction, speed, resilience, and enhanced customer experience. RPA has shifted substantially over the last four years, with an increase in dynamic, ML-based algorithms growing and, as a result, embedding AI as the foundation of RPA. While AI, as part of RPA, is still limited from an implementation perspective, it is increasing rapidly. However, automated RPA environments, with static algorithms at the core, is becoming pervasive in some industries. Ultimately RPA drives an increase in static, and as we advance, intelligent algorithms, increasing AI-based environments as a result of automation. All of this is well documented and known by most organizations.
However, an increase in AI models across an organization begs the question, what the organization will do to ensure these models are actively managed? To broaden the question, how will this affect the organization's approach to AI governance? Especially since the deployment of RPA in larger organizations often is a more distributed effort, driven more enthusiastically by business unit managers and with very little central coordination and governance. RPA, especially intelligent RPA, will require the organization to create a centralized approach to AI governance that includes all RPA initiatives, indeed for anything that is automation RPA and intelligent RPA.
AI Governance must become a central part of the Automation effort in all organizations from the start, not as an afterthought. This focus requires all organizations to consider six critical aspects of AI Governance for your Automation initiatives to succeed long term:
Determine executive responsibilities: Led by the CEO, create a plan of accountability for all key executive roles, including the CIO. Ensure that it is cascaded to all critical individuals responsible for the different automation initiatives. Equally, ensure that key employees can provide feedback and ideas to the executives making it a shared responsibility.
Create an AI Governance Charter: An AI Governance charter should describe the principles and values that the company employs when adopting AI. It must cover the different internal and external constituencies served, such as employees, customers, and suppliers. And the AI Governance charter should be public, at least the elements relevant to each constituency. All Automation initiatives must comply with the charter.
Create measurable KPIs: Without metrics, you can’t determine whether you are achieving your objectives around AI governance. Establishing KPIs for AI can be complicated but will increasingly become both a legal and ethical requirement. Aligning the KPIs to the AI Governance charter is a good start and making them part of your Automation initiatives.
Implement an AI Governance software platform: As organizations increase the number of deployed models or algorithms, managing them becomes increasingly important. The increase in intelligent automation projects will only accelerate this. As a result, adopting an AI platform where it becomes possible to manage the governance of the algorithms and models will be critical.
Implement an AI communication plan: Automation drives the need for continuous communication throughout the organization. AI, as part of the automation effort, will merely accelerate that need. Because of the impact on jobs, tasks, ecosystems, and society as a whole of widespread AI usage, organizations need to create a communication plan for AI centered on matters such as AI governance, ethics, and the organization's values in relationship to AI.
Implement an AI education & Development plan: Not all AI is bad, but all AI will require a focus on education and skills or competency development. The combination of automation and AI makes this critical because it changes or absorbs tasks, in some cases, job categories, changes the leadership skills required to be a manager in an organization, and the organization's overall process and data flow. Therefore, the education and development of employees and specifically managers become critical and urgent.
These six points are critical for any organization walking down the automation path. Especially because AI increasingly becomes the center of that effort. AI Governance starts with the CEO and the board recognizing their crucial role from now on. Make Automation comply with your AI Governance charter.
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