Publication

Five steps to raising the intelligence of a company

November 02 2017
Executive Education INCAE

To be successful in the long term, companies need to create some kind of sustainable competitive advantage and take advantage of it. This advantage can derive from traditional sources, such as lower costs of production or purchasing at scale, highly motivated employees, or visionary strategic leaders.

But, in the knowledge economy, strategic advantages will increasingly depend on the shared ability to make better judgments and choices.

Today's smart companies are being shaped by two different forces: the growing power of computers and Big Data, and our growing understanding of human reasoning, decisions, and choices.

How to combine human intelligence with the knowledge provided by technology to make smarter decisions in the face of uncertainties and difficulties?

To help your organization gain a cumulative advantage in business, we've identified five strategic capabilities that smart companies can use to outperform the competition through better judgment and smart decisions.

1. Find the strategic advantage

The starting point for becoming a smart company is knowing how to prioritize, assigning analytical effort where it pays most, the exact point where hard data is productively mixed with soft judgment. That sweet spot lies in combining the strengths of computers and algorithms with expert human judgment and sound questioning.

Building a truly smart business is neither quick nor easy and often involves recommendations that seem out of line with common sense. Few companies have been able to turn these ideas into transformative practices that make their business that much smarter. 

At the same time, executive teams that advance opinions are rarely asked to defend them in depth. In most cases, the results of the judgments or decisions are rarely checked against the initial assumptions. There is a clear opportunity to raise a company's "IQ" both by improving business decision-making processes and by leveraging data and technology tools.

2. Participate in prediction tournaments

One promising method of delivering better business forecasts is to use what are known as "prediction tournaments" to bring out the people and insights that offer the best judgments in a given field. The idea of ​​a prediction tournament is to encourage your participants to predict what they think will happen, translate their ratings into probabilities, and then track which predictions turned out to be the most accurate. 

Four key factors appeared as crucial for successful predictions: 

- Identify the qualities of analysts, such as their curiosity, open-mindedness and willingness to cultivate forecasting as a skill. 

- Train people in techniques that avoid common cognitive biases, such as overconfidence or overvaluation of preferred cues.

- Create stimulating work environments that encourage the best to engage in collaborative work and avoid groupthink

- Devise better statistical methods to extract wisdom from people; for example, giving more weight to analysts with broader points of view.

3. Imitate the experts 

Another way to build a smarter company is to draw on the insights of expert employees so they can be leveraged more effectively. 

Years ago, when banks applied computer models for loan officers, few believed that a simplified model of their professional estimates could make better predictions than the loan officers themselves. 

Their explanation was that customer loans contained many subjective factors that only lenders with more tables could adequately assess, thus generating great skepticism about whether distilling intuitive expertise into a simple formula could help new generations of customers. credit managers to learn faster. But here, too, the models performed better than most loan experts. 

Although they tend to have a deep understanding of the topics, they often do not make good predictions, because wisdom is mixed with so-called "random noise." However, humans are very assertive when evaluating predictor variables and deciding when the model is out of date.

4. Experiment with artificial intelligence

Resampling uses a simple input-output approach to model the expertise without delving into the processing models of human reasoning. Accordingly, resampling can be augmented by artificial intelligence technologies that allow more complex relationships between the variables extracted from human perceptions or from the mining of big data sets.

Humans simplify the complexity of the world around them through the use of various cognitive mechanisms, such as pattern finding and storytelling, but there is the risk that humans get caught up in their own initial stories and begin to value confirming evidence more favorably than information that does not fit your internal narratives.

Companies should closely monitor the development of artificial intelligence applications to determine which aspects are most worthy of being adopted and adapted in their industry.

5. Change the way the company works

The most powerful decision support systems are hybrids, which fuse multiple technologies. These decision aids will become more and more common, extending beyond more limited applications, such as prediction.

sales, to provide a foundation for broader systems, such as IBM's Watson supercomputer, which, among other things, helps clinicians do

complex medical diagnoses. 

As machines become more sophisticated, humans and businesses will also advance. To eliminate the excessive noise that often undermines human judgment in many companies and to amplify the signals that really matter, we recommend two strategies: 

- Keep a record of predictions to later check them against results.

- Keeping track of records can help determine your “reputational capital”.

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Excerpt from the article "Creating a Smarter Company" published in Harvard Business Review by researchers Paul J. H Schoemaker and Phillip E. Tetlock.

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