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3 things on the Digital Transformation Radar for 2018


2017 has had it's challenges for HR, and this year I've been constantly working on:

  1. The speed and agility to recruit, develop and re-skill with digital employee operations, digital learning operations and digital talent.
  2. Enabling organisations to move to simple, automated and connected HR primarily enabled by Human Capital Management Systems (HCM) like Workday or SuccessFactors
  3. Facilitate organisations with the tools to manage an increasingly transient workforce.
  4. Design and update outdated and outmoded target operating models, with what roles are really needed for organisation success. 
  5. Fix poor employee experience in getting the basics right.
  6. Bringing consumer experience in to the business to employee space.

It's time to take a pause and look at what that means for 2018 as most organisations start to plan their budget cycle there are 3 things that leading organisations should consider, if they are already on the digital transformation journey.

  1. The old chestnut still holds true.  Technology only does what you tell it to do, the purpose you set out to achieve.  If you did not have one, or it is not aligned to the business then take a pause.  The capability for a business to grow is in Organisation Development its people, processes and technology.  However, even with a new HCM system growth requires real transformation, transformation of the digital employee offering and of the target operating model.  Assess where are you now versus where the business needs you to be. 
  2. Data and AI.  Investment in simplification and connection of systems starts to give you data.  The power of data in the consumer market has led to disruptive models and industries just look at: Facebook, AirBNB, Uber, Lyft, linkedin.  In 2018 data will be a focus as the basis for machine learning.  The focus for the last part of my year is machine learning in talent, where machines learn what makes success in a company from the data on performance and potential with algorithms.  This is a game changer and will disrupt Organisation Development.  Instead of designing competencies, career paths and programmes machines will start to define the offerings for employee success.  Digital transformation will require data transformation for AI.
  3. This year I worked on the first virtual agent in HR.  Analysts were surprised when yes we have one!  This is in early adoption in some companies, but in 2018 I really see a push for that business to consumer experience (B2C) into business to employee (B2E). More disruptive technologies will be fused into the B2E space like Watson, Blockchain, Commerce with a Employer Branding orientation. Digital transformation will require employer branding transformation.

HR's full of Colourful Sense Data for Machine Learning

My Doctoral Research identified statistically significant correlations of moderate strength for success in adulthood with Sensemaking and Attachment from childhood.  The Talent Development we undertake needs a shake up, and machine learning is evolutionary in the application of an increased understanding of success. Chantelle Brandt Larsen

My Doctoral Research identified statistically significant correlations of moderate strength for success in adulthood with Sensemaking and Attachment from childhood.  The Talent Development we undertake needs a shake up, and machine learning is evolutionary in the application of an increased understanding of success. Chantelle Brandt Larsen

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.' 

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