Business leaders and chief information officers around the world are jumping on the robotic process automation (RPA) pilot bandwagon to start their companies on the automation journey. Some RPA pilots are evaluating software designed to stitch together known technology concepts—such as screen scraping and macrobased automation—through user-friendly tools to take process automation to the next level. Other pilots are venturing into the use of machine learning and cognitive automation to unleash new business insights.
These pilots—or proof-of-concept programs—help leaders set a foundation for their understanding of RPA, while at the same time introducing new ideas for how automation can help scale operations or define new business strategies. And now the pilot was successful, and leaders are seeing the possibilities. So what happens next?
When performing RPA pilots many companies get stuck in basic automation and stop there. Other companies have basic and cognitive automation pilots going on simultaneously.
Aligning the goals of basic RPA with cognitive computing and artificial intelligence can seem improbable. But are the objectives really that different? Leaders want to use all levels of automation to
- drive business growth,
- manage risk,
- and increase value.
The trick is having a strategy for getting from pilot to program, and putting in place a comprehensive structure looking beyond the RPA pilots to intelligent automation (IA) as an across-the-board investment. This ensures IA ventures become more than speculation and remain significant to the business.
- But how can leaders ensure that IA is more than a one-time cost play?
- How are future automation opportunities identified and evaluated for both risk and benefit?
- How is “electronic employee” service performance monitored?
- How do leaders ensure the optimal mix of basic, enhanced, and cognitive automation?
- How is business continuity maintained if the IA solution fails?
- How is
- system security,
- change management,
- system processing,
- and authentication control
- maintained as automation risk becomes more complex?
- How will IA be used to transform the business?
Leaders know technology is changing rapidly, and IA is a moving target. Implementing a “bullet-proof” value-based program is critical to managing the automation revolution and ensuring it delivers positive business impacts over time. Robust program management balances risk and reward with structures driving sustainable IA value. An IA program model delivers these ideals.
An Intelligent Automation program can help enhance and expedite the implementation of IA throughout an organization. Here are four critical characteristics for success:
- It is strategically positioned – Positioning IA on par with other business strategies as integral to enterprise objectives is the best place to start. Similar to outsourcing (OS), these dependent IA vendor relationships are treated as strategic. Global processowners (GPO) use IA to transform end-to-end services. Global teams engage in IA opportunity evaluation to ensure bad processes are not automated.
- It uses a “center of excellence” service model – Establishing a center of excellence (CoE) demonstrates a commitment to IA success. Focus drives effectiveness, and CoEs drive transparency to IA results. CoEs have varied formats (virtual, centralized, regional, etc.) and engage cross-functional teams. CoE governance guides IA strategy and validates results. Clarifying decision rights balances governance and operations accountabilities. Incorporating IA support roles (e.g., HR, IT Security, Internal Audit, risk) in decision-making ensures change integration is well managed.
- It has a robust delivery framework – Integrating technologies, toolkits, and tactics into IA program execution safeguards sustainability. Including relevant designers, IT professionals, and operations teams in testing makes sure solutions work. Socializing and managing life cycle compliance (e.g., intake, approvals, testing) ensures team interaction is clear. Program management, repository, and workflow tools makes oversight effective.
- It incorporates a proactive risk management structure – Making IT risk and security control oversight a part of IA development ensures solutions are sound. Like any technology integration, change control is critical to implementation success. An IT security risk and control framework provides this support. Risk mitigation strategies linking security reviews to IA validation ensures business goals and technology risks are appropriately considered.