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Application of RPA & AI Technologies in Learning BPS


My colleague, Gary Bragar, recently discussed RPA and AI initiatives in HR, including payroll, recruiting, and learning. Within learning BPS, the majority of RPA investments have been made at a basic level within learning administration, specifically around training scheduling. For example, it previously took ~40 FTEs to manage the entire scheduling process for ~1k classrooms, including identifying classrooms based on availability, identifying onsite facilitators for training days, sending notifications, etc. Through RPA, the same workload can be completed in 15 minutes.  

Vendors such as Raytheon Professional Services (RPS) and IBM, however, have used more advanced applications of RPA and AI throughout the learning lifecycle. IBM, for example, is currently expanding RPA to the design and development of learning content via its Cognitive Content Collator (C3). IBM is leveraging Watson to interpret structured and unstructured data to drastically reduce the number of man hours spent annually on tagging and chunking content and then matching it with curriculum, competence, and goals. Specifically, it takes ~50k man hours to tag, chunk, curate, and map structured courses for ~10k hours of learning content; with IBM’s C3, these activities are completed in 55 hours.

With respect to AI and cognitive, IBM has launched ‘Personalized Learning,’ which offers a consumer-grade experience for learners that provides recommendations to employees based on job role, business group, skill set, and personal learning history to encourage continuous employee development and skill growth. The experience includes ‘content channels’ that support a variety of needs and interests to facilitate simpler browsing, as well as a five-star rating system, and will include virtual job coaches that pull content for an individual to help them develop certain skills.

While interest in RPA and AI technologies by organizations is high, overall adoption rates for these technologies in learning BPS has been low for two reasons. First, RPA requires investment by organizations, which is often problematic since a company’s learning budget is typically low. In addition, RPA requires that an organization exposes its technology and data to the service vendor, which they are often hesitant to do, since learning technology relationships are often separated from service relationships.

Current adopters of RPA in learning BPS tend to be from heavily regulated industries, including financial services, healthcare/pharma/life sciences, oil and gas, and automobile manufacturing. These organizations are realizing a significant reduction in training resources, which is creating more time for value-added activities.

Over the next year, adoption rates for RPA within learning BPS will increase and still be applied mainly to learning administration services. To be successful, vendors will not only have to demonstrate the business case, expected ROI, and previous successful deployments of RPA, but will also need to have a consultative partnership in place within the client organization.

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