Intrapreneurship Case Study at the Foreign Trade University (FTU), Vietnam

Students of the Creativity, Innovation and New Value course at Colorado State University discuss the popular teaching case study “Boehringer Ingelheim: Leading Innovation” (available at Harvard Business Review (HBR) and the Ivey Business School) with intrapreneur Stephan Klaschka.


Intrapreneurship Case Study at the Foreign Trade University (FTU), 

As a cooperation between Colorado State University (CSU) and the Foreign Trade University (FTU) in Hanoi, Vietnam, FTU students are tasked to develop an opportunity in a team and conduct a feasibility analysis on the opportunity that they present to the class on June 4, 2018.

I will join the students and visiting CSU professor Robert Mitchell for a live discussion of the teaching case study Boehringer Ingelheim: Leading Innovation, which is available at Harvard Business Review (HBR) and the Ivey Business School,

In this teaching case study, the case writers Professor J. Robert Mitchell, Ph.D., and Ramasastry Chandrasekhar, of Ivey Business School, follow the footsteps of Stephan Klaschka’s intrapreneurial approach to innovation within a global pharmaceutical company (FORTUNE Global 500, Top 20 Pharma).

This intrapreneurship teaching case study is used by staff and students of Intrapreneurship and Innovation in business schools around the world. and features my career as an Intrapreneur at a major pharmaceutical company.


Collective Intelligence: The Genomics of Crowds

Group intelligence beats individual brilliance – and businesses are willing to pay for the crowd’s wisdom in the social sphere.  The MIT’s ‘genetic’ model allows combining social ‘genes’ to harness the collective intelligence of crowd wisdom successfully and sustainably, for example in scientific research or business/employee resource groups.

We use collective intelligence every day

Whenever we face a big decision, we turn to our friends, our family, or our confidants. We seek information, guidance, advice, confirmation, or an alternative perspective.  No matter if we make a life decision (partnership, job, picking a school, etc.), a purchasing decision (house, car, mobile phone) or a less monumental decisions (which movie to watch, which restaurant to go to), we make our decision more confidently and feeling better informed after reaching out to our personal network.

What we do is tapping into the collective intelligence, knowledge, or wisdom of a crowd that we know and trust: we are ‘crowd sourcing’ on a small scale.  We do this because we instinctively know that the focused collective intelligence is higher than the intelligence of individuals.

What is collective intelligence or the ‘wisdom of the crowd’?

Wikipedia, the iconic product of global collaboration and collective knowledge, brings it to the point:

“The wisdom of the crowd is the process of taking into account the collective opinion of a group of individuals rather than a single expert to answer a question.  A large group’s aggregated answers to questions involving quantity estimation, general world knowledge, and spatial reasoning has generally been found to be as good as, and often better than, the answer given by any of the individuals within the group.  An intuitive and often-cited explanation for this phenomenon is that there is idiosyncratic noise associated with each individual judgment, and taking the average over a large number of responses will go some way toward canceling the effect of this noise.”

Scaling up to a ‘crowd

When we read a movie review and rating on Netflix or customer ratings of a product on Amazon, for example, we tap into a larger and anonymous crowd.  On the other end, Netflix and Amazon know how they get people like you and I to deliver them free content (reviews, ratings) that runs their business.

So, let’s take this to a level where it really gets interesting for you!  How can you get a crowd to do your work?  How do you build a framework in which strangers work on your business problems and deliver quality result for free.

Crowd Wisdom

Genetics of Collective Intelligence

MIT professor Tom Malone dissects the mechanics of collective intelligence in his groundbreaking article (MIT Sloan Review, April 2010).  The MIT Center for Collective Intelligence researched to understand this matter better and identified a number of building blocks or ‘genes’ than need to come together to engage and tap into the ‘wisdom of crowds’ successfully and sustainably.

Since these ‘genomic combinations’ are not random at all, we can also combine genes to build a collective intelligence system.  Depending on what it is that you want to achieve, the genes can be combined to a model that suits your specific purpose.  This is ‘social genomics’ made easy, and you don’t need a biology major!  🙂

Interestingly, this social genomics can be used independently for social projects you have in mind but also in relation to Employee or Business Resource Groups (ERG/ERG).  – The common link lays in the organizational design that is similar to the generic BRG/ERG business model discussed previously.  Thus, collective intelligence systems need to address the same questions as a business model:

  • Strategy or the goal: what needs to be accomplished?
  • Staffing or the people: who does the work?  Are specific individuals doing the work or is there collaboration within a more or less anonymous crowd?
  • Structure and Processes or how to organize and conduct the work?  How is the product created, and how are decisions made?
  • Rewards or why do they do it?  What are the incentives, what is the measure for success?

Motivation is Key

It is crucial to get the motivation right, i.e. why people engage and continue to come back to contribute more to the cause or project.  It comes down to finding the basic drivers for human motivation.  This explains why people invest much of their time and resources to crowd sourcing.

The famous $1million Netflix Prize was a 5-year open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings.  The winner had to improve Netflix’s algorithm by 10%.  The million-dollar reward in 2006 gives a flavor of just how valuable the crowd’s wisdom is for a company!  In contrast to common belief, money is not always the driver.  If it was, how do you explain the popular virtual ‘farming’ on Facebook, for example, where players pay hard cash for virtual goods?

In the more clandestine intelligence community, recruiting individual operatives plays to four motivational drivers: Money, Ideology, Conscience, and Ego (easy to remember as ‘MICE’).
The drivers for attracting collective intelligence are a bit different, as Tom Malone found out.  Nonetheless, there are parallels: He calls the key motivators Money, Love, and Glory.

Real-World Examples

Everyone knows Wikipedia, arguably the best-known social collaboration and crowd-sourcing project thriving from an intellectual competition over Love and Glory, no monetary incentives involved for the authors.

How powerful Glory and Honor are we see also in areas away from the mainstream where you may not expect to find crowd-sourcing and gamification: in scientific research.  The following two impactful examples reflect successful implementations for large crowds collaborating and competing to solve scientific problems:

  • Seth Cooper’s AIDS research challenge  on the “FoldIt” online platform challenged players to find the best way of folding a specific protein.  We will not dive into the science behind it and its medical significance; here are the details for those who are interested to dig deeper: MedCrunch Interview with Seth Cooper at TEDMED 2012.  For our purpose, we establish that a relevant scientific problem in AIDS research, which remained unsolved within the scientific community for a decade, took the crowd 10 days to solve!
    You may find it surprising that there was has no monetary incentive involved whatsoever – yet FoldIt attracted over 60,000 players(!) from around the world.  The winner of the AIDS-related challenge was later recognized and honored at the 2012 TEDMED.  It was not a Nobel-prize laureate from an Ivy-League institution but a laboratory assistant from Britain – who, well, enjoys folding proteins and collaborating on the puzzle with think-alike from other countries.  This is the power of Love and Glory!
  • Another example is the ongoing “Predicting a Biological Response” on, a geeky online platform for people who like developing descriptive models.  My friend and colleague David Thompson of Boehringer Ingelheim (a major yet privately held bio-pharmaceutical company) designed this scientific competition to compete for the best bio-response model for a given data set of scientific relevance.
    The challenge offers a $10,000 prize for the winning model and lesser amounts for the models coming in second and third.  The monetary award together with a time limit of three months helps to speed up the process and keep up the competitive pressure.  Last time I checked, 467 teams competed and have already submitted 4,300 entries with another month to go.  The quality of the model is summarized in a single number (‘log loss’), so competitors can compare their results directly and immediately, the same quantifier determines the winner.
    Note that the Kaggle participation is not driven by the monetary incentive primarily; otherwise, the number of participants should correspond directly with the amount of money offered for a particular challenge, which is not the case.  Thus, participants are in it more for the challenge and fun than for the cash.  (If you are a participant and disagree, please correct me if I am wrong!!)
    On the other hand, don’t underestimate the business value of the gamification of science either: another ongoing competition in Kaggle offers a serious $3million reward!

The bottom line

Social collaboration, crowd-sourcing, and collective intelligence all rely and depend on humans collaborating to make things happen.  What holds true in the real world seems to hold true also in the virtual world: the magic formula is all in the genes…

Driving the ROI – where to start your projects metrics?

The most compelling metrics focuses on the business impact of an ERG rather than on ‘measuring the ERG’. Here are the rationale and a generic approach to deriving meaningful and business-relevant metrics for ERG projects.

Driving the ROI – where to start your projects metrics?

So you have started your ERG and done your homework on what the business strategy of your organization is. You also found areas of need in your organization that you want to address with some serious projects. – But where to start building a project metrics? What is important, what makes sense and is meaningful?

Establishing metrics can be stressful and confusing. What metrics persuade your stakeholders? Less is often more, so focus on just a few parameters that are to the point rather than drowning in a myriad of complicated and detailed measurements that will quickly suck your precious time and bore your audience to death.

In general, your project metrics can reflect the ERG or focus on the business results that the ERG achieves – I opt for emphasizing the latter.

ERG focused metrics

Let’s look at the ERG focused metrics first. It seems the traditional approach for most ERGs that may have evolved from affinity and network groups: The basic idea in establishing this kind of metrics is to help justify the ERG by demonstrating its growth and maturity over time. The typical metrics are, for example, the number of active and passive members, the participants in meetings, how many new faces (=potential recruits) show up and how many of them signed up as members, etc. These figures are helpful to explain that there is an interest in the ERG, what happened to funds (often spent on catering) or if the organization met demographic goals of diversity, for example.

However, if you measure along these lines alone you may miss out on leveraging your ERG to get recognized and valued as a credible business resource to the organization.

Business-focused Metrics

Question for you: which message does an executive find more compelling? “The ERG has 300 members and meets monthly for two hours.” or “The ERG contributed to $260 million in sales last year.”

Now, this kind of metrics takes a different approach, doesn’t it? It aims at driving business results, the famous return-on-investment (ROI), the ‘bottom-line’. It easily grasps a stakeholder’s attention because it demonstrates a significant and direct value proposition for the company.

By the way, the above example is real! According to, Ford Motor Co. directly linked the sales of $260m in one year to an initiative of its InterFaith ERG!
– Look it up yourself if you like:

Not all goals are high rolling and they also depend on the business you are in. The spectrum of possible success metrics is broad and ranges from obvious business goals such as increasing revenue, profit, market share, quality, speed and customer satisfaction to –perhaps‑ less obvious ones such as increasing employee satisfaction, intellectual property created, employee acquisition and retention or reducing turnover, waste or business risks, just to give some examples.

How to get started

For many ERG leaders, the most difficult question is how to establish a metrics when the targets appear fuzzy and are not as easy to grasp as a sales figure that was either met or not.

To find your bearings, try this: Relax. Breathe deeply. Then take a step back and use your imagination… Envision a picture of what the results look like when the project completed successfully. What do you see when you have reached the goal, what are the visible and tangible results, what has changed?

Now describe this envisioned picture in words in a demonstrative way using clear and unambiguous terms such as “By September 1st I want to be able to touch X and use to do Y with Z!”

This provides you with a great starting point to refine more specific requirements and also leads quite naturally to meaningful metrics in a simple but effective way such as the tangible deliverable (X), the target time until completion, some required feature (Y) and some input (Z) requirements in the example.