How to Make Mentoring Work PODCAST


How to Make Mentoring Work PODCAST

Don Pare is a successful serial entrepreneur and President of Real Venture Counsel. Mentoring is being promoted as a tool for knowledge transfer and for those mentored to be able to benefit from the experience and expertise of a mentor. In this podcast, Don discusses the structure needed to make mentoring work including motivation, metrics and training that mentors need.

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Gamification: Manufacturing Example with Raven Telemetry


Gamification uses the principles of games to create intrinsic motivation for individuals within organizations. We often see badges, points, stars, leader boards, etc to indicate achievement within an environment. In addition, levels are used to have participants learn new skills. Higher levels usually require more time, effort and skill as the participant progresses.

Within an organizational environment, participants can achieve higher types of intrinsic motivation according to Maslow’s hierarchy including recognition, self actualization and self transcendental.

As an example, consider manufacturing, it is an environment rich in data, decisions and evolves over time. Individuals within that environment are looking for constant improvement for both themselves and their machines. Raven Telemetry provides an AI based improvement approach using gamification. With Raven, equipment and machine operators look for improvement in a gamified structure accumulating badges, and recognition as they progress, for example, the software can note operator accomplishments such as:

1. Shared filling best practices with colleagues.

2. Explored ways to improve first 30 minutes of your shift.

3. Maintained exceptional tagging rate

While a supervisor can focus on the people side activities such as:

1. Worked with Operators C1, C7, C8, C10, C11 to improve the last 30 minutes of their shift .

2. Worked with Operator C9 to improve the first 30 minutes of their shift.

3. Recognized operator C3 for exceptional performance in December exceeding performance in all areas.

Key considerations in gamification are that participants need to be able to progress, have metrics of success and gain value from achievement such as recognition.

In a manufacturing environment, we have the data and the need to improve. A game based approach linked to intrinsic motivation can make intangibles such as decisions tangible through the use of tokens or badges which can then be recognized by others. Obviously traditional operator metrics of improvement alone can be recognized without gamification, but a gamified approach also brings in the personal sense of achievement of accumulating tokens over time.

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Using Technology to Broaden Students’ Understanding

I recently spoke with Professor Kyle Anderson of Clemson University. Professor Anderson is a unique intersection between accountancy and technology, and, between practitioner and academic. In this podcast he discusses his approach to use technology to move his students beyond the mechanics of accounting and to develop a bigger system-level understanding. He includes practical ideas for implementing such an approach in other courses.

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Podcast: AI for Actionable Results, Manufacturing Example

In this episode of my podcast, I interview Martin Cloake, President of Raven Telemetry. Martin has a back ground in manufacturing, engineering and is a data analytics AI guy. As we enter the era of Internet of Everything, the world is changing to data driven decision making. However, as Martin discusses, this provides the opportunity to move beyond analytics and presentation of data into the world of actionable analytics results.


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Diversity of Thought in an AI World



Diversity is often touted as a desirable attribute in the modern organization, an interesting focus is diversity of thought, that is often expressed as desirable in entrepreneurship courses. Diversity of thought is having people around you that think differently thus helping to incorporate new ideas into today’s solution approaches.

What about when we are working with Artificial Intelligent (AI) systems? Where will diversity of thought be? An AI system could possibly analyze the data, make and implement a decision or parts of it. An AI system could learn ethics and even human emotional responses to decisions and thus incorporate these factors into the decision. So where does that leave us humans? And the concept of diversity of thought?

An AI system can extrapolate patterns and inferences from its data and thus apply these patterns to new situations perhaps even considering 2nd and 3rd order consequences of its decisions. We humans have experiences that we have used to make inferences, as AI becomes more prevalent in the organizational environment, humans can continue to add value by learning from experiences not captured in data available to an AI system, for example, based on past data who would have thought 20 years ago that people wanted to text on their phones?

Humans can focus on asking the right questions for the AI to answer.

So humans might still have a role in the new AI era by approaching a decision as if there is no box while AI will consider the box. As well as understanding what the world should be going and thus ask the right questions to get the organization moving in the right direction.

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Followership is the New Leadership


Followership is a relatively lesser used term than leadership. Lots of organizations have leadership training very few have followership training, in our modern organizations, this is changing.

Effective followers tend to be those individuals who challenge their leader’s decisions and implement those decisions keeping in mind the intent of the decision as well as the explicit directions given in the decision. Challenging the leader’s decision rationale helps ensure that the decision incorporates the realities of implementation and doesn’t become bogged down in old ways of thinking. Understanding intent or the “why” of the decision helps the follower adapt the decision nuances to the reality of the implementation situation as it evolves.

Today’s business environment tends to have more real time attributes than in decades past and thus the value of followership has increased. Gone are the days in which autocratic leaders made decisions and followers merely implemented them. Today’s followers have to be part of decision making in a real time environment partly because there isn’t time for the leader to be the bottle neck in a decision. This suggests that leadership is more about creating the setting or tools such that appropriate decisions can be made rather than making the decisions by themselves.

The top takeaway for creating effective followership:

Provide followers the decision making toolkit which includes:

–         Actively participating in the decision,

–         The rationale of the decision so they can adapt the decision to the realities of the business environment this means an implementation that can exploit nuanced opportunities and reduce mistakes arising from the decision,

–         Motivation,

–         Positive supportive organizational culture,

–         Necessary relevant information; and

–         Decision making guidelines.

This environment includes an understanding of calculated risks taken, a follower centric leadership, the information, analysis tools and decision making guidelines that the follower needs to adapt and evolve the information quickly as the decision implementation meets reality.

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