LSE - Small Logo
LSE - Small Logo

Helena Vieira

January 15th, 2018

AI and the democratisation of judgement and decision-making


Estimated reading time: 5 minutes

Helena Vieira

January 15th, 2018

AI and the democratisation of judgement and decision-making


Estimated reading time: 5 minutes

Qualitative judgement – the ability to make considered business decisions based on a personal interpretation of the context and facts — has never been more important (or overlooked).

There are three main reasons why judgement will remain central to the practice of management and leadership in the years to come. First, qualitative judgement is the last preserve of humanity in making decisions. Creativity, emotional understanding and pure imagination are things that humans excel at, and the availability of a huge amount of additional data or AI will not negate this fact of life.

Second, as the cost of prediction goes down, the demand for judgement will increase. AI is a prediction technology, so the cost of prediction will also become cheaper over time. This means that we will substitute other input factors (human skills) with the low cost and better technology to collect data and develop predictions. But at same time the value and demand of complementary factors will rise, like more decisions to be made due to more frequents insights and predictions. This, in turn, will lead to greater demand for the application of judgement and emotional understanding (provided by humans) to make these decisions.

Third, as data-prediction technologies are more widely distributed, judgement must also be more widely distributed. Big Data and AI technologies will provide managers and employees with accurate data and predictions at their fingertips. These technologies employ distributed IT architectures to allow employees throughout organisations to make the right decisions in a timely way. Distributed data will enable and demand the distribution of judgement-based decision powers.

Implications for organisations

These three factors indicate that now, and in the future, companies will require more rather than less human judgement for their innovation and market-related decisions. To get there, judgement will need to be democratised across the organisation.

How to get started? Below are four guiding principles to follow.

  1. Democratise judgement power

Companies tend to believe that innovation and market-related decisions are the responsibility of a few, highly-positioned people. There is a widespread autocratic view, which conceives that only these ‘elected ones’ are entitled to make decisions that affect customers and innovation. By way of contrast, consider the credo that Toyota embraced in its Toyota Production System (TPS). In the TPS, everybody is responsible for the search and implementation of ideas to improve operational performance. Responsibility is pushed down to the very lowest level in the organisation. In the TPS, two worlds — manufacturing and market innovation, which appear so remote from each other—share the same philosophy for success.

  1. Foster qualitative judgement skills

As soon as we push down the responsibility to identify issues and make decisions, we will want to increase the probability that our employees will chose the right course of action and execute on it properly. The second core principle of the TPS is to train everyone in the workforce in quality, lean/six sigma tools and techniques. Massive training on standardised tools is the means of increasing the probability that people will come out with the right insight, decision and execution to impact performance on the factory floor.

Other organisations should apply this same principle and standardise tools, methods and techniques to improve the skills of their employees in generating insights—based on both data and qualitative customer explorations—and applying judgement for innovation and market related decisions. Doing so will require a shift in perspective, to a mindset that views judgement as a key organisational capability worthy of investment.

  1. Provide data access to all

We define data democratisation as the capability to integrate data across the firm and enable a wide range of employees to access and use it at any given time. Data access raises the effectiveness of employees in using their judgement.

Of course, some companies are better than others at transforming data into actionable insights. Prior research has tended to emphasize the role of data scientists who have the skills to analyse data. This implies that companies with more data scientists have better chances of generating value. My own experience as a consultant, supported by recent research, indicates a different view: Firms that hire an army of data scientists do not always generate better value. Rather, it is the process of data management—and particularly the democratisation of access and use of data among managers and employees—which create tangible value.

  1. Loosen the reins of control

Organisations tend to be uncomfortable at the prospect of decision-making authority being pushed down the organisational hierarchy. The perception of risk as being related to loss of control has been the major barrier to true empowerment of the workforce. As a consequence, today most organisations still use a “Prevention Control Model”, i.e. checking on employees before they make any decision, by requiring preventive authorisations.

For example, in a bank, even when a loan applicant has a perfect credit score and fits with the bank’s policy, most likely the loan will need to be signed by the employee and her supervisor before being approved. Prevention Control Models are the greatest barrier to true empowerment and the main root-cause of bureaucracy and slow decision making.
The solution lies in shifting from a traditional ‘Prevention-Control Model’ to a ‘Post-Detection Model’. To better understand this, consider Affinity. The Minnesota-based credit union issued a framework to guide everybody in making decisions for loans. It is called MOE (Member, Organisation, Employee) and operates like a “Constitution” to free up the judgement powers of their employees and provide a guiding star when applying these powers. The employees have full latitude on rates and overriding bank’s policies based on their judgement of what is “right for the customer” while supported by customer analytics.

The MOE Constitution states: “No employee will ever get in trouble for doing what is right for the customer… There is only one operating policy or guideline you ever need. Trust your feelings – if it feels right and makes sense, do it on behalf of the customer. Do not consider the system capability, policy, or procedure – err on doing whatever is necessary for the customer and allow your manager or supervisor to take care of the rest. Finally, be prepared to defend your decision! If your intention is to do what is right for the customer, you have the support of management and your co-workers.”

Affinity moved towards a distributed judgement model. Every employee can decide on the spot to provide or not to provide a loan and at which rate by using her judgement with the MOE Constitution as a guiding star and customer analytics as supporting data. Employees can deviate from bank’s policies but they are required to justify their decisions and post their rationales in Affinity’s Touche system which stores all data and electronic records of members/clients as well as a full history of employee explanations for any lending. Charge off rates for higher risk clients dropped by almost 50 per cent (from 1.9 to 1 per cent) when employees started to make judgement-based decisions.

Employee empowerment has been a talking point and neat theory for too long. It is time to walk the talk with respect to innovation and market-related decision making authority. Low cost data-prediction technologies coupled with a democratisation of judgement can help to free employees from the shackles of hierarchy and create truly agile and customer-centric organisations able to adapt to the market with speed. Profitable growth is sure to follow.



  • This blog post appeared originally on Rotman Management, a magazine of the Rotman School of Business, University of Toronto. Available for download here (paywall). 
  • The post gives the views of its authors, not the position of LSE Business Review or the London School of Economics.
  • Featured image credit: Decision-making, by Daniel Foster, under a CC-BY-NC-SA licence 
  • When you leave a comment, you’re agreeing to our Comment Policy.

Alessandro Di Fiore is CEO of the European Centre for Strategic Innovation (ECSI) and ECSI Consulting ( based in Boston and Milan. You can reach him at and follow him on Twitter @alexdifiore



About the author

Helena Vieira

Posted In: Technology

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.