When Business Simulations Meet Machine Learning

“Disruption” is probably not a strong enough word for what is about to happen in talent development. In the very near future, the predominant method of learning for core employees will be through a convergence of curated content coming from an infinite number of sources of both good and bad quality including live and online, simulation-centric learning that enables learners to learn-by-doing, and machine learning that supports and enhances the entire process to shorten the cycle times from learning to meaningful results.

I am currently working on a position paper that will become the blueprint for our company and for how we will continue to work with our clients into the future.  This blog shares a few of the essential elements of the blueprint for the purpose of provoking thought and gaining further feedback.

What are Learning & Development Simulations and What is Machine Learning?

The first thing I’d like to do is set definitions and clarifications on the two main discussion points.

Learning & Development Simulations are cloud-based tools that present to learners a case study that comes to life.  The purpose is to build skills and competencies through a “learning-by-doing approach rather than being talked at or just reading something.  The best analogy is understanding how pilots learn to fly planes and deal with different situations that may arise; they go through flight simulators and practice different scenarios so that they know what to do back in a real aircraft.  Learning & development business simulations enable leaders to learn how to “fly a business” by participating in leadership simulations.

Machine Learning is artificial intelligence integrated into an application.  Machine learning tools can listen to a conversation and that provide feedback based on models and best practices.  For example, Machine Learning can listen to a coaching dialogue and provide feedback on the use of a coaching model and suggest ways of enhancing the dialogues for the future.

Integrating machine learning with learning & development simulations will present organizations with the most powerful and useful tools to push out skills, culture, and consistency.

4 Important Things to Know When Learning & Development Simulations Meet Machine Learning

Scalability

As someone who has trained groups of 25 participants at a time to make an organizational impact on thousands, simulations with machine learning creates instant scalability.  An online, cloud-based learner will have the opportunity to:

• Assess current skills through a simulation
• Be presented with a “playlist” of available content to close skill gaps
• Participate in a learning simulation to practice new skills
• Receive specific feedback and tips for improvement and further learning
• Participate in further reinforcement simulations supported by machine learning for continuous improvement

“Tailorability”

Machine learning is intelligent and adaptable.  Once you set the foundation and basic understanding of frameworks, models and application, it will continue to teach itself and get better.  For example, company ABC, Inc. has a specific coaching model it wants to use.  As learners develop and fine-tune their skills through simulations and machine learning, everything can be and will be easily tailored to the specific needs and business strategies of ABC.  In addition, the machine learning tools will then adapt to:

• Different learning styles of participants
• Baselines of knowledge and skills to lift everyone to their highest level of competence

Assessment

The combination of learning & development simulations plus machine learning lifts “assessments” to a new level of usefulness.  For example, a potential General Manager can be placed in a Future General Manager Assessment Simulation and be measured on ability to set and execute a global leadership and business strategy.  While the simulation results can quantify the business elements, the machine learning can view into the algorithms and best practices and identify trends and then recommendations for content to close gaps.

Reinforcement

After the assessments and after the learning, simulations plus machine learning can be incredibly useful for reinforcement of new skills.  Every learner, no matter how good they are, can continue to get better and further define and refine their skills.  Micro-simulations can present different and unique scenarios for learners to practice on and the machine learning then provides detailed and subtle feedback on continuous improvements.  For example, a sales professional who is used to giving an “elevator pitch” of their company and products could develop bad habits or emphasize the wrong thing as a marketplace evolves.  The machine learning can help identify the subtle differences, breakdown every aspect of the elevator pitch and provide instant coaching for improvement.

In summary, things are accelerating and changing quicker than anyone can truly comprehend.  While we can’t predict all the changes and emerging technologies, we certainly can be aggressive in learning and exploring all the potentials.  One thing is for sure is that simulations plus machine learning will be playing a dominant role in all future scenarios.