GAIn® introduces SCENE as the first personality framework originating in the world of Artificial Intelligence, built to let organizations succeed in their AI transformation by proactively overcoming the challenges of human change management.
Artificial Intelligence (AI) is omnipresent in our daily lives and continues proving its business value for enterprises. Other than with previous revolutionary innovations – such as the steam engine and automobile – AI is replacing the decision-making process of humans. As was the case with those previously emerging technologies, a one-on-one replacement will not result in the full potential of AI. Therefore, businesses should build processes where humans and machines collaborate.
However, transitioning to such a collaboration is not easy. The largest challenge does not lie on the machine side, but on the human side, since AI fundamentally changes the human expert way of working and redefines their role as humans. To better understand the human-side of this equation, one should start with understanding the personalities of those involved in an AI transformation. Adjusting to the expert’s willingness to change and what will trigger this same willingness, as well as promoting diverse personalities within project teams will foster AI adoption.
Consequently, a suitable personality test is needed. Although many personality tests are available, lots of them lack scientific evidence and a data-driven approach. The Big Five personality framework does possess these qualities, yet the absence of a typology – like for example the MBTI personality types – makes it difficult to use in a business context. Therefore, GAIn® developed SCENE: the first personality framework that finds its origin within the field of Artificial Intelligence, where the Big Five – backed with its extensive research – is well-translated into the field of AI. Through analyses and extensive domain expertise, eight key profiles are identified that all contribute in their own way towards a successful adoption of AI. SCENE is aimed at making AI transformations work for enterprises through understanding human personalities. In this article we introduce SCENE, discuss the need for an AI personality framework, the SCENE typologies, and how SCENE helps organizations succeed with AI.
Artificial Intelligence is changing life as we know it. According to Google CEO Sundar Pichai, AI is one of the biggest developments in human history. Bigger than fire and electricity. We are interacting with algorithms on a daily basis, whether we are looking something up on Google, or searching for the next series to binge on Netflix. Moreover, in organizations AI is no longer a futuristic notion, it is only used by a few Data Scientists tucked away in the corner of the office building. As Paul Daugherty and James Wilson describe in their book Human + Machine: Reimagining Work in the Age of AI, AI is transforming our way of working and redefining our role as humans.
According to Daugherty and Wilson, much value is expected to come from the collaboration between humans and algorithms, defined as Augmented Intelligence by Gartner. Gartner estimates the value created by AI augmentation in 2021 at $2.9 trillion of business value and 6.2 billion hours of worker productivity globally. (1)
So how do experts adopt AI? Different personalities have a different trigger for change. Experts who are akin to visionaries are moved by the innovative character of AI, whilst guardians are more protective towards the status quo and are inclined to choose proven solutions. Additionally, philosophers that are introvert thinkers would first like to understand the complete mechanism of the human-machine interaction before they are willing to adopt it.
Furthermore, on the organizational dimension, personality plays a key role to deliver on the promise of value creation with Artificial Intelligence. Visionaries are needed, who dare to change, inspire, and motivate others for continuous innovation. Critical analyzers are necessary to concretize the business processes where AI can potentially make the largest impact. Controllers who are detail-oriented have a leading role in creating high-quality algorithms that consistently show high performance. And managers are crucial to create the multi-disciplinary teams needed and guide them to realistic targets.
To understand and optimally profit from the talents and mindsets within an organization, personality tests can give a lot of insight. However, although there are many personality tests available in the world, such as the Myers Briggs Type Indicator (MBTI) or Insights Discovery, many of them lack scientific evidence and a data-driven approach. Most of the popular personality frameworks are based on Carl Jung’s psychological theory. And although the theory is easily and widely applicable, it lacks scientific evidence for its assumptions.
The Big Five framework seems to be the most fitting, as it has earned its credibility and popularity due to its scientific validity as well as the robustness of its conclusions regardless of gender, culture, and so on. However, the Big Five is also criticized for its negative connotation on some personality traits and labeling them as good or bad. Whereas we foresee an important role for all personalities within AI transformations. Also, the absence of personality typologies within the Big Five decreases the practical suitability of this framework.
Therefore, to facilitate and support successful AI transformations, GAIn is launching SCENE – the GAIn® personality framework that is specifically tailored for use in an AI context. Based on analysis and extensive domain expertise, eight key profiles are identified that all contribute in their own way towards a successful adoption of AI.
Yet, we have been quite abstract about the changing role of business experts, where they make decisions with algorithms, jointly. To make it more concrete, let us illustrate an example from the role of specialists in medical diagnostics. When a patient comes to the hospital with symptoms of asthma or COPD, several tests will be done, and a chest X-ray will be made. This X-ray results in an image that is sent to a pathologist who specializes in lung diseases and makes a diagnosis by looking at these images. However, with modern image recognition techniques, the classification of lung tissue and subsequent diagnostics can be enhanced by an AI algorithm, where the pathologist and the algorithm collaborate to make the most accurate diagnosis. In this new situation, only a selection of images for which the algorithm is too unsure will have to be judged by the pathologist. As a result, the algorithm does the frequent diagnoses accurately, while leaving the edge cases to the pathologist. This drastically changes the role of the pathologist. On the one side, the pathologist must trust the algorithm. On the other hand, he or she must understand when the algorithm is off, why it is off, and how to improve this. We see the same developments in businesses, where the role of business specialists is changing as more and more of their knowledge is accommodated in AI algorithms.
The eight SCENE typologies.
Mapping of the eleven clusters from a clustering algorithm on the eight SCENE typologies.
As mentioned before, the largest impact within AI is created through building systems where humans and machines collaborate in a decision-making process. In the success of building such AI solutions, personal diversity plays a key role, for two reasons. First, creating impactful AI solutions asks for personality traits that are unlikely to be found within a single individual. Second, the adoption of AI within an organization requires a culture shift that all different personalities must accept and embrace different approaches towards this change.
When building AI solutions, multiple disciplines are needed: The business expert who will work in the decision-making process jointly with the algorithm, the Data Scientist building the algorithms, and the Data Engineer who guarantees the algorithm is fed by data and its results are shared with the business experts. Each of these roles have different responsibilities, and different personality types thrive better within certain roles. For instance, Data Engineers excel in a more deterministic environment where the solution must be extremely robust since it must operate effortlessly day in, day out. Whereas the work of a Data Scientist is less predictable since their field is about distinguishing what is certain and what is not, which can change unexpectedly in the near future. Apart from their respective expertises, a Data Engineer needs qualities such as pragmatism, an eye for detail, and discipline to excel in his or her job. Data Engineers are therefore oftentimes Guardians, Controllers, or Managers. On the other side, a Data Scientist must be curious, creative, and innovative to find the best model solution for the challenge at hand. More often, a Data Scientist will be a Visionary, Challenger, or Philosopher.
Moreover, in the adoption of AI within organizations, personality also plays an essential part. A survey by O’Reilly(2) shows that the number one bottleneck for AI adoption is the company culture that does not recognize the needs for AI. Drivers of culture are manifold, but usually the willingness to change starts from the top and cascades down to the rest of the company’s hierarchy, from where the business will pick up an idea and move to execution.
Personality plays an important role whether and in what way people adopt a new way-of-working: Different personalities have a different tendency towards embracing new developments. Some are very open to new experiences and jump on new ideas (too) quickly – these are people with a high score on curious - whereas others first need to be convinced about the added value of a new approach over an existing approach – these are people with a tendency towards pragmatism.
To illustrate the power of SCENE as a tool to nourish the success of AI transformations, we look at the process of developing impactful algorithms, which lies at the core of AI solutions. Typically, there are four challenges that AI encounter in the model creation process, that also show why working in multidisciplinary teams is so important.
For people who want to experience the power of SCENE themselves or together with their team, GAIn® offers several badges on this topic in our Leadership & Change domain. These badges cover the topics of stakeholder management, team management, and effective communication. During the training you learn and experience how to use your own SCENE profile, and that of the people you are working or interacting with, to improve the effectiveness of your communication to reach your objectives.
This article was written by Lara Buimer, Bart Maassen, and Marcel Mol. All being part of the SCENE team that further develops the framework and aims at incorporating SCENE into GAIn's Leadership and Change badges. Please reach out to GAIn if you have any questions to us. We're happy to have a chat!