Computer networks are a new business model as technology speeds up data flow.
In the late 1990s, I developed the concept of Organizational IQ with my Stanford Research Team. We also devised a method to measure and improve it. Managers were experiencing an accelerated development cycle, intensifying global competition, and increased demands from investors, consumers, and regulators. Managers in all industries experienced a flood of new information due to increased connectivity, faster processing speed, better software, and integrated information flows. These trends continue today to redefine what organizational Intelligence means.
The technology at the time was in its early stages of exponential development. At the time, smartphones did not exist. GPS technology had just begun to be commercially available. Few organizations experimented with artificial Intelligence, so they failed to see the expected benefits. As a consequence, even fewer organizations chose to use AI. While firms and industries struggled with managing information, a select few were able to take advantage of it. I said that organizations needed to increase the efficiency of their digital and organizational nervous system to take advantage of the increasing clock speeds and information flow. I measured this by what I called Organizational Intelligence. At the time, I argued that the digital and organizational nervous systems were complementary. Integrating them was critical for success.
The research that led to the Organizational IQ concept was built on an information-processing model of the firm. I saw the design of integrated organizational architectures as analogous to a networked computer system, where digital and human processors perform processing. They engage in integrated feedback cycles where they combine information from the environment with computer-based and human knowledge to make fast and effective decisions. The two loops are an action loop that translates data into action and a learning one that uses data and experience to improve perception and understanding.
Using the analogy of networked computers as a guide, the challenge is creating an ” organizational network” to manage information effectively. Like a computer system, it must have sensors or receptors that take inputs from outside and send them to the network. The system also requires processors, including computers and human decision-makers, to quickly convert the information into valuable decisions. Design principles are needed to distribute the decision-making. It is essential that the system can store and share information effectively so that decisions are based on its entire knowledge base rather than each processor being limited to its local data. The system must also reduce bottlenecks in processing, or else it will be overwhelmed with information, leading to poor decisions and delayed responses. Finally, the organization network should not be viewed as a closed, standalone system but as part of an interconnected collection. The concept of interoperability is extended from digital strategies to the organizational nervous systems: Systems designed for different organizations should be able to work together (or at least to a certain degree of alignment) to achieve a common goal. Along with my research team, I translated these ideas into performance metrics that measured the information-management effectiveness of organizations and found, in a series of papers, that the resulting measure of Organizational IQ was a strong predictor of business success.
Our work in the late 1990s underestimated the importance of Organizational IQ. The key drivers of the change have now accelerated, and their effects are dramatically amplified. The clock speeds have increased so that actions that once took hours or weeks can now be completed in seconds. The Internet of Things, artificial Intelligence, and advanced analytics have all increased the power and speed of computerized knowledge management and decision support. Digital nervous systems are now at the forefront of action loops across various areas, from automated trading to self-driving vehicles to supply chain management. Digital nervous systems play an increasing role in the feedback loop for learning.
These effects can be seen in action if we focus on a simple example (see the chart). Call centers traditionally handled customer service, aiming to solve customer problemsiciently. Agents were evaluated based on their average hold time on the phone. The Organizational IQ approach views customer service differently: It is a way to differentiate the company from its competitors. Not only does customer service play a part in creating a superior experience for customers, but it also provides valuable input into the learning loop, which allows the firm to improve product design and customer journeys and identify new trends. Instead of urging customers to stop calling, we recommended that organizations focus on increasing the value created by every customer touchpoint. This is true for the customer who initiated the contact and future customers. Effective customer service is a combination of the action and learning loops.
Leading firms have adopted this view over the years. Their learning loops were still largely manual but supported by systems aggregating and presenting data for decision support. Technology is taking our approach to a new level. Computer-based Intelligence plays a significant role in the action loop as well as the learning loop. Computer-based Intelligence improves productivity and reduces the lag between learning and taking action. It aggregates data from multiple sources in seconds, transforming some organizational issues into engineering challenges. It allows for delegating customer service operations to the best in class while capturing the learning loop’s benefits.
As the digital nervous system gains ground, the role played by organizational nervous systems shifts from execution to business model design and strategy. The increasing capabilities of artificial intelligence create the need to design and evaluate new business models; something automation is not very good at. On the other, I have argued a new window, business models, will evolve so that they can be reconfigured through the complex interaction of automated agents. We’ll watch how the boundaries between organizational and digital nervous systems shift in the future.