HR is the area that is full of data for the senses. Humans are effective in organizations due to their sensemaking and attachment.
Founding theories of sense making (Piaget) and attachment (Bowlby, Ainsworth) in my research showed statistically significant correlations to success in adulthood.
Sensemaking in organizations is how we make sense of the Organisation, business problems, opportunities and the wider macro environment. This is from the stories you here, the symbols you see visuals, offices or role models, the smells and even tastes all five senses.
Attachment is how we relate and connect to others which optimizes a persons success and their organizations. In childhood that's the attachment with parents, as we grow that moves to friends, teachers, mentors, partners and when we enter the adult work world colleagues.
When you apply this depth of understanding to variables that can be used in talent and learning interventions in machine learning, it has enormous potential. The supervised and unsupervised learning can have an increased chance of success to develop and evolve in the things that matter. This has been shown in the development of a child to an adult, and what is critical to their success. Think learning the traditional learning taxonomies are knowledge, skills and behaviour. However, the success of talent is the ability to deal with the unknown, figure out solutions to knew problems and form new learning pathways - that's sense making. As well as the ability to increase the size of the learning and it's impact with a super network - that's attachment.
That right now is my focus. I am testing hypothesis and the mathematical calculation of theta and the exponential cost function. This is identifying patterns in what makes the difference, and machine learning that connects learners to personalised offers. Going further in the future machine learning is not just one sense, but also for example visual data (here mathlab is great).
What does this mean for machine learning. Well if you go back to the very first example of machine learning when Arthur Samuel in 1959 battled a computer against itself in chequers it learnt from wins and loses.
The people data that is in our HRIS, ERP, HCM however we want to term it is rich with wins and losses, and remember failure is part of the path of learning how to be successful. Examples of the data (typically I am hypothesising this data as 'Y' with a number of variables):
- Performance data
- Potential data
- Successful hiring data
- Leaving Data
- Promotion Data
- Moving Data
- Size Network data
It is important to understand the independent variables. Unassisted learning is useful to classify and look for patterns in the data.
For example, traditionally you would design job roles, a catalogue and competencies.
In an AI environment you could understand what skills are high in certain roles, in a group of a certain performance, potential and geographic region. The data starts to then learn and can then be programmed to unassisted learning to form and be more agile to what an Organisation and person needs.
Machine sense learning in HR is an area ripe for development.
It does require some precursors:
- data architecture and readiness
- a cultural shift to responsive design and transformation
- the right talent to shift to data science and machine learning
There is a sixth sense missing. In humans we call it the 3rd eye. That instinct, knowing what is right or wrong and emotion is not even there in the Business to Consumer Artificial Intelligence. So AI sensemaking is there to empower the heart and consciousness of the Organisation 'People.'