After 60 years of computing power growth driven by Moore’s Law and 60 years of artificial intelligence (AI) development, we have now entered the Fourth Industrial Revolution, with networks to connect everything, including data. The speed of innovation, competition and change is unprecedented; the pace of iteration can be as short as hours, thanks to machine learning’s ability to automate it. The question for every organization is: How fast are you iterating toward your goals?

In the midst of a global pandemic and highly volatile markets, it is more urgent than ever that modern organizations be agile and efficient in responding to a rapidly changing environment. A learning organization is a company that supports its employees’ learning and undergoes continuous transformation. Now, the speed of organizational learning needs to accelerate, and an organization’s learning and development (L&D) department plays a key role in success.

Facilitating that acceleration requires iterations of data. In a recent interview, Nate Hurdo, vice president of global learning product sales and strategy at SuccessFactors, emphasized that fast iterations loops are fundamental to an intelligent enterprise and said that when intelligent organizations are able to combine data from multiple systems, a “transformation” occurs.

Learning Engineering and Organizational L&D

Data is power, but without good modeling, it’s useless. Marketing technology professionals have used granular personalized or persona-targeted recommendations for many years to influence user behavior. When it comes to driving an organization to accelerate learning through helping every employee learn more quickly, there is still much to be done. We need better data-gathering and modeling processes in place. The macro- and micro-iterations in current enterprises, even in a lot of learning management systems (LMS), usually aren’t built with the science of learning in mind.

Enter learning engineering, which IEEE ICICLE (the Institute of Electrical and Electronics Engineers Industry Connections Industry Consortium on Learning Engineering) defines as “a process and practice that applies the learning sciences using human-centered engineering design methodologies and data-informed decision making to support learners and their development.”

Learning engineering, combined with an algorithmic approach and coupled with machine learning, leads to power and speed in pushing organizational learning forward and could also be called “AI for learning.” This combination is already happening in other engineering disciplines.

Considerations for L&D in the Organizational Context

In considering how learning engineering can apply to the organizational learning context, let’s reexamine corporate L&D, which has similar but sometimes more complex needs in comparison to the K-12 and higher education contexts:

    • L&D is a means to an end and is part of a larger, more complex organizational system.
    • Employees often have little time to learn, and real learning often happens on the job, in just-in-time formats.
    • As Alban Jacquin, learning experience and innovation director at Schneider Electric, said in an interview, “Saving employee’s time is the golden KPI [key performance indicator], and what is desired is enabling learning in the workflow.” The key question is how to enable time-saving, effective and relevant learning on the job.
    • Jacquin also pointed out the importance of being able to measure the impact of L&D. His team measures and monitors over 100 metrics so that executives can correlate L&D and business outcomes and know the return on their investment (ROI) in training.
    • “Better skill assessments are much needed,” added Jacquin.
    • L&D professionals can’t neglect privacy, data integration and safety can’t be neglected. As an example, Hurdo said, “We are a privacy-conscientious company, so we don’t log granular behavior events.”
    • New knowledge is constantly discovered and created at work and in interactions between people, across many tools or systems. Organizations need to consider how they can capture this knowledge.
    • AI has already started to make an impact in corporate L&D, as Avinash Chandarana, head of the MCI Institute at MCI Group, pointed out in an interview.
    • L&D departments need to become performance consultants and learning experience architects, not just order-takers, according to Jacquin. Businesses must consider what their L&D department needs in order to catch up with the latest innovations and how they can support and augment L&D’s work through technology.
    • Enterprises have been facing the challenges of digital transformation as well as skill gaps for the AI/automation age. Current global conditions make digital transformation more urgent than ever.

AI in the Learning Organization

Alban notes that Schneider Electric has used an AI engine to recommend learning activities for learners. The foundation of this engine is a set of skill models for over 800 job roles. Learning in the workflow is new for Schneider Electric, and Alban’s team is trying to push in that direction. Alban hopes to improve their ability to correlate learning investment with business outcomes and better assess skills — something that’s on many chief learning officers’ wish lists.

Behavioral scientists have developed interventions to promote a variety of prosocial behaviors, such as healthy eating habits, physical activity, medical check-ups, voting and educational achievement. Learning engineering incorporates solid learning science, pedagogical best practices based on decades of research, empirical approaches, design thinking and teamwork. To learn about cognitive principles for designing effective remote learning, for example, check out “The Science of Remote Learning,” edited by Jim Goodell, a senior analyst at Quality Information Partners, and Aaron Kessler, a senior learning scientist at MIT.

In a Wiley survey, 55% of employers said they “believe artificial intelligence will help them fill their skills gaps.” There are several possibilities when solving workforce challenges with AI:

    • “AI for learning” can help make training more efficient.
    • AI can augment worker performance. (Imagine an AI assistant that can integrate learning, performance support and actionable data).
    • AI can complete some aspects of workers’ jobs, so where there is not enough talent, the technology can help fill the gap.

From a business perspective, if a machine can complete a task, it can result in significant savings and improved speed — and machines’ capabilities are growing every day. They even perform certain types of tasks better than humans. All they need is great algorithmic modeling and problem-solving strategies, which is why we encourage L&D teams and solution providers to leverage AI.

Robotic process automation (RPA) can complete repetitive tasks, but higher-level tasks can be automated, too. For example, there is not currently enough talent capable of building and deploying machine learning models, so many “AI builds AI” tools are emerging. They reduce the demand for data scientists and enhance the efficiency and quality of modeling and AI deployment. Machines can carry out trial and error steps, a workflow of best practices, and logical reasoning on data and knowledge, among other tasks.

Key Factors for Success in the AI-enabled Learning Organization

The speed at which an organization can effectively iterate toward goals is the decisive factor for thriving in these uncertain times. Key factors for that success are data and integration, good modeling and an effective approach to problem-solving, and valid measurement. There is a lot of opportunity at the intersection of learning engineering, AI and automation, and data-driven enterprise operations. But, AI is often seen as a black box, and stakeholders need to learn how it works, what its potential is, how to implement it, how to evaluate an AI solution and what its limitations are.

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