Despite the promise of intelligent automation (IA), the reality of these technologies has yet to fully catch up to the hype. While there are many examples of successful robotic process automation (RPA) and advanced data and analytics (D&A) use cases, most organizations have yet to deploy artificial intelligence (AI) and machine learning (ML) at scale, or to use IA technologies to fundamentally change their core business operations.
There are a host of existing cases where organizations have used advanced automation technologies to fundamentally transform their businesses and, as the technologies continue to mature, the hype will likely increasingly transform into reality. As more and more tasks currently performed by humans can be turned into algorithms and accomplished by software, there will likely soon come a time when AI and other advanced technologies become widely ingrained in the most commonly used technology platforms, leading to a marked increase in adoption and associated business benefits.
IA has become an important strategic priority at an increasing number of organizations, who seek to exploit these technologies to reduce the time devoted to manual tasks, increase the quality of insights from D&A, and improve the quality of outcomes of their most important business processes.
The promise of some IA technologies, however, has yet to be fully realized at many organizations. While there are many cases where RPA and advanced D&A have been deployed at scale, most organizations have yet to broadly apply AI and ML to have a transformative impact on end-to-end processes.
An inability to build compelling and realistic business cases often hinders IA efforts, especially among organizations undertaking such initiatives for the first time. In particular, organizations tend to overestimate the cost savings from hours saved, and fail to properly quantify more intangible benefits such as the value of better analysis. The most sophisticated users of IA, however, have largely addressed this challenge.
Many organizations also struggle to pinpoint the areas they should target for automation and what specific technologies to use. What’s more, lack of a coordinating strategy often leads business units and functional silos to undertake efforts that are out of sync with one another.
Given these challenges, ensuring strong, centralized coordination of IA efforts across the enterprise and gaining access to scarce IA-related talent are among the most critical enablers of a successful IA strategy.