Through a thought-provoking workshop on Strategic Workforce Planning, our team uncovered valuable insights to help guide businesses’ staffing decisions. 

Mustafa Gajibo from Nigeria embarked on a remarkable journey to create an all-electric vehicle completely homemade.  After considerable hard work and dedication, the great case study of his success inspires others to pursue their dreams!  Gajibo dropped out of university in his third year to run it.  His first project was converting the internal-combustion engines of commonly used vehicles in the city to electric versions.  He focused on two types of vehicles that residents often pay to ride: seven-seat minibuses and the motorized tricycles known as kekes.  (Source – Article Written by Valentine Benjamin in MIT Technology Review – May/June 2023 edition).  Gajibo learned most of what is required through hyper-personalized content. 

The hyper-personalization is expected to get further accelerated through technologies like Generative AI.    In Skills enhancement and Innovation, this is very important.  In fact, we have known this for a very long time, but the dream is getting closer.

Let us look at this graph from Benjamin Bloom’s 2 -sigma problem constructed in 1984.  The Benjamin Bloom 2 Sigma Problem refers to a research finding by educational psychologist Benjamin Bloom, which found that students who receive one-to-one instruction using mastery learning techniques perform two standard deviations, or “two sigma,” above students who receive traditional classroom instruction

The concept has a lot of applicability in enterprise innovation and skill building.  If we can time the right amount of learning and the skills at the correct execution point, the mastery of our employees will exponentially grow (not a statistical term here as I am showing normal distribution curves in graph ?)

Workforce Planners, Recruiters, and the L&D team have a significant role in this journey. 

Incorporation of Generative AI Scenarios in Workforce Planning:  We need a concept paper that includes the following components.  We can build this with you as per your requirements

Component 1:  Use cases where generative AI may help Innovation and optimization. 

Example:

• In sales, generative AI can create personalized marketing content and provide sales teams with intelligent recommendations on engaging with leads and prospects.  This aspect can improve the efficiency and effectiveness of the sales process, leading to increased revenue and customer satisfaction.
• In marketing, generative AI can create content such as ads, social media posts, and blog articles.  Generative AI can generate content that resonates with the target audience by analyzing customer behavior and preferences data, resulting in higher engagement and conversions.

Component 2:  Map the tools and the potential impact that they may bring in by sub-functions.  An example snapshot is given here

This mapping gives an order of magnitude that may happen by Job role.  (Draup also has developed DRQ – Digitally Replaceable Quotient that may be helpful)

Component3:  Develop a hypothesis around new roles/skills and models that may come into play (please note that we have expanded now into Roles/Skills/Models)

Component4:  Develop Proactive Learning Scenarios

Let us say we identify Bias in Decision Science as a key skill all employees should learn.  In such instances, we need to map out scenarios where we need to learn and apply.

Proactive learning scenarios can be created to equip employees with the knowledge and skills necessary to mitigate bias in decision science.  The failure points are proactively understood and mapped in advance.  I recently read that the Japanese Bullet Train Shinkansen has exceptional safety records because of this anticipation of failure and having plans to mitigate the same proactively.  The Japanese bullet train is renowned for its safety record, with no passenger fatalities since it was first introduced in 1964.  For every minor issue faced, they deconstruct and develop an action item.  Something all of us could learn.

Proactive Learning Scenarios are a sort of risk mitigation plan from a positive construct. 

For example, training programs in healthcare could be designed to educate medical professionals on recognizing and mitigating biases in diagnostic and treatment decision-making.  Then employees can be trained in any confirmation or anchoring bias that can be expected

How great will it be if we can develop proactive learning scenarios by Job role?