The Future Belongs to the Intelligent Enterprise: Combining AI and Human Insights

In the age of digital renaissance, innovation is no longer a buzzword but a necessity. Everything around us is getting smarter by the day. Our cars, phones, homes, schools and cities – all moving towards a ‘smarter’ avatar. Obviously, much of this digitization has percolated to the enterprise data environments as companies seek efficiency and flexibility from available resources. But tough economic climes have made the business ecosystem more competitive and cut-throat. Inordinate operational costs, uncertain markets and record deficits have forced businesses to a new direction – a faster, leaner and more intelligent enterprise to succeed in the unpredictable landscape.

“Intelligent Enterprise” is a management approach that applies technology and new service paradigms to the challenge of improving business performance. The concept was introduced in James Brian Quinn’s influential book Intelligent Enterprise – it stipulates that intellect is the core resource in producing and delivering services. Businesses need to create and leverage a competitive edge that is sustainable to succeed in the long run. While the conventional wisdom of lower costs, intellectual property, and strategic leadership still holds true for a successful enterprise, they need something extra. Survival of the fittest in the knowledge economy will depend on the ability to make superior judgments and choices.

Intelligent businesses are molded by two separate forces.

1. The first being the increasing power of computers and big data. These provide the foundation for business research and business intelligence, forecasting models and AI.
2. The second is our renewed perspective on human judgment, reasoning and decision making. It is now that decades of research unravels to show what human beings do well or poorly.

Based on these forces, there are a few strategic capabilities that can help your company gain a cumulative advantage in business. These ideas help organizations to be “future ready” – prepared for whatever tomorrow may bring. Let’s take a look at these capabilities:

1. Find Your Leverage
   Before starting the journey to become an intelligent enterprise, it makes sense to analyze where you should use the leverage. Focus on areas where improving subjective forecasts can really move the needle. Be strategic about the problems you decide to deal with head-on. Using data analysis and judgment, you can find the sweet spot where the results are most likely to be beneficial. Creating an intelligent business is neither quick nor easy. You need to find your strategic edge and raise the company’s IQ by improving corporate decision-making processes as well as leveraging data and technology.
2. Run Prediction Contests
   You can refine the forecasting methodology by inculcating more practice. One way of doing this is by encouraging competition, experimentation and creativity among teams to accelerate learning cycles. Such tournaments help in finding the common traits of consistently superior forecasters, training teams in methods for avoiding common cognitive biases, practicing techniques like accurate questioning and constructive confrontation, and formulating better statistical processes to mine wisdom from the crowd.
3. Follow The Forecasters
   By running prediction tournaments, organizations can find who the expert forecasters are. Using the knowledge of these experts, enterprises can model their predictions and forecasts to spread the knowledge and perfect the science of projection. This can be achieved with a decision-making technique known as bootstrapping. For instance, when banks launched computer models for the first time for loan making decisions, they leveraged the knowledge of the best loan officers who had a simplified model for predictions. Once they fed this model of their professional judgments and the factors that help them decide, the models started performing better than most loan experts.
4. The Use Of AI
   Bootstrapping can also be improved by AI technologies that allow for more complex relationships among variables extracted from human insights or from mining big data sets.
   In the early days of AI, deeper cognitive insights drove computer modeling of master chess players. Modeling human thinking with all its cognitive biases is complex and has its limits. Often computers are able to develop an edge by simply using advanced computing power to study historical data. Enterprises need to go beyond simple linear models and learn to deploy deep neural nets in limited task domains to outperform human experts.
5. Changing The Operational Model
   A pre-requisite for an intelligent enterprise is an inquiring culture that is constantly looking for ways to combine human intelligence with technology. Enterprises that have managed to be truly efficient will use human intelligence to build intelligent enterprise by blending the soft side of human judgment, including its known flaws and biases, with the sharper side of big data and business analytics to create competitive advantages.
   Despite numerous challenges, the science of subjective forecasting is increasingly getting better, even if the external world has become more complex. With approaches such as wisdom-of-crowd, prediction markets, forecasting tournaments, big data and business analytics, and artificial intelligence, there is a lot of optimism about identifying the best approaches.

The cognitive-science revolution is both an opportunity and a challenge for businesses. For most companies, the struggle will be in choosing which human versus machine approaches to apply and how to get the best of both worlds. Once an organization learns to adopt hybrid approaches for making subjective probability estimates and keeps improving them, it can be truly intelligent and develop a sustainable strategic edge over the competition.