Augmented leadership through adaptive intelligence

by Cliff Brunette
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Abstract

Organisational adaptation is critical for the survival of companies, the challenge being that traditional learning architectures are too slow to meet rapid environmental and market changes. In the fourth industrial revolution, the rate of organisational adaptation requires a new learning architecture that enables an innovative view of organisational learning. Such a learning architecture would depend on augmented leadership who can harness the collective intelligence and enable multi-frame thinking within their organisational teams. To achieve this, leaders need to challenge their own – and their team’s – very human moral dilemma of holding a single truth. The new learning architecture needs to enable multi-truth intelligence, or adaptive intelligence, which can be obtained by embedding axioms within the learning architecture.

Introduction

In this global village called Earth, the consciousness and connectedness of people is growing. Knowledge is no longer contained in leather volumes and translated by select scholars. Now, through technology, information reaches all the corners of our global village in seconds.

According to Manuti, Pastore, Scardigno, Giancsapro and Morciano (2015) , the place of learning – the place of work – has changed forever and is forever changing. Connectedness and change have become our reality. Whether we are conscious of it or not, the global village is transitioning from a time of everything accumulated, to everything experienced (Wadhera, 2016) .

Current learning approaches are based on the accumulation of knowledge (information). For this traditional, information age approach to succeed, time is required to accumulate information and also to embed it as organisational wisdom within the collective of people comprising a company, so and for it to can serve as an advantage. However, in the new experience age, a shift from “instructional design” to “experience design” is required, using design thinking as the foundation (Bersin, 2017). The ability of employees to co-create workplace realities (Brunette, 2017) may become a key focus area of learning-experience design, given that at the heart of the experience age is the organisation’s ability to deliver value and an enhanced customer experience (Ngubane, 2017).

The new learning paradigm should address the human employee’s ability to understand deeply, facilitate, and create value-experiences to satisfy the expectations of both their employers and customers. This new reality requires a depth of internalisation and a capacity to apply collective values (Neskovic, 2016) which cannot be taught, only co-created. 

In a post-information age, the industrialist, static, production-line mindset towards learning is no more than an outdated myth leading to failure. Thinking and co-creation are now critical skills. 

Through the awakening of the Fourth Industrial Revolution (4IR), a new economy of learning is required. To make an impact on business, this learning must be relevant, and the experience of learning has to be socially-oriented and co-created by the organisation as a collective. It also needs a new architecture of experiences and a new grand learning design that favours the speed and impact of learning through augmented intelligence and multi-frame thinking.

Organisational Adaptation

What is organisational adaptation? Kontoghiorghes, Awbre, and Feurig (2005, p. 190) define it as the extent to which the organisation can rapidly adapt to changes. Pedler, Burgoyne and Boydell (1991) believe adaptive organisations facilitate the learning of all their members and continuously transform. Senge (1990) poses that people continually expand their capacity to create results they truly desire, and continually learn how to learn. An environment supporting this is one where new, expansive patterns of thinking are nurtured and where aspirations are set free. According to Garvin (1993), people are skilled at creating, acquiring and transferring knowledge, and modify their behaviour to reflect new knowledge and insights.  

Lowe and Sandamirskaya (2018) hold that learning produces change within an organism, enabling more effective behaviour within its environment. Adaptation requires change to the organisation and to what we might call the human ‘being’ and ‘doing’. This, in turn, requires intelligence – both human and artificial. The speed of adaptation is confined by the processing speed of the intelligence, so to increase organisational adaption, intelligence and intelligence of leadership must be augmented.

Augmented Leadership

By definition, augment means to make larger or to increase intentional value (Merriam-Webster Editors, 2018). Within the context of the 4th Industrial Revolution, augmentation also means to make faster, and to increase the intentional impact. 

Leadership will be hugely challenged, as leaders begin to understand and experience the impact of the 4IR (Schwab & Sala-i-Martin, 2010). Adaptive intelligence, and the speed of adaptation, will become significant discourses in navigating the experience age. Disruptors of intelligence will include big data, big data analysis, artificial intelligence, and collective human intelligence. The slowest of these is human intelligence even though a human has the fastest processor – the human brain.  

It’s no wonder that the approaches to augment leadership will unlock and enable the speed of human intelligence. These approaches demand a fresh look at all enablers of organisational strategy, including organisational learning – a closely related enabler of adaptive intelligence. 

Within organisational learning, two elements should be reviewed: the utilisation of collective intelligence, and the use of axioms embedded within the organisational learning architecture.

Collective Intelligence

Within the challenge of augmenting leadership (improving leadership and intentionally increasing its value) in the 4IR, organisations will need to adapt quickly and often, which calls for leaders that enable adaptation. 

Organisational adaptation requires the adaptation of human behaviour. This is not simply a “change of behaviour”: mere change will not be enough. Given the nature of the 4IR, change will be constant, ongoing and complex. Snowdon (2003) speaks about the order domain and the emergent-order, or un-order domain. Within the un-order domain are chaos and complexity, and within the order domain are known and knowable cause and effect. Adaptation is the complete change from one state (or domain) to another through the emergent properties formed between relationships within the complex. Adaptation starts within a state, within the complex, and ends once new relationships are accepted as a known, or knowable, within the domain of order. 

Industry disrupters are adding new relationships to the complex and require leaders to navigate the emergence of new properties. Leadership is required within the co-creation of new order, within a highly adaptive, highly iterative project of organisational adaptation. 

Within organisations, behaviour must shift completely. Organisational shift, or behaviour shift, is not simply a shift from unorder to order. It is a shift to more order, better order and intentional order. Speed is critical so that organisations can align with an environment that is competitive, and which has industry disruptors and shifting consumer expectation. In the 4IR, the augmentation of organisational behaviour has to meet the speed of the adaptation challenge. 

Traditional approaches to shifting organisational behaviour include training that focuses on the intelligence of the individuals within the organisation. This is premised on a machine-like paradigm that all individuals should be at the same level of knowledge or competence, and have the same level of intelligence. The leadership in such organisations applies standardised units of learning, assessments, and curricula. With this approach, change is slow. 

By contrast, a 4IRO, where speed is essential, requires leadership that focuses on harnessing the collective intelligence of teams. This implies an acceptance that individuals are not at the same level of knowledge; that they have different competence and insights. Here, intelligence is seen as adaptive, something that can be uniquely applied to a team’s specific challenges. Harnessing each individual’s unique knowledge and skills within the collective system is key to the speed of adaptation. 

Leaders do not have to wait for the slow learning process of getting everyone on the same level of competence in order to achieve effective organisational behaviour, as argued by Kirkman, Li, Zheng, Harris, and Liu (2016). However, it does require the leader to rely on, and trust, the collective consciousness of the team. For the leader, the challenge might be accepting that there are multi frames of reference, thinking, and truth. An adaptive intelligence is driven by an adaptive truth. 

The 4IRO leader must learn to work authentically with diversity, and the truths that each individual brings to the team. As humans, we have the fastest computer processor on the planet, yet we are the slowest to learn. This is mainly due to the human’s moral dilemma: despite having a collective social consciousness (Laszlo, 2004), we became single-truth societies over millennia of human development and the separation of societies. 

Insights and collective consciousness must be built around collective intelligence for leadership to augment, which requires organisations to move away from single-truth to multi-truth thinking.

Multi-frame Thinking

Multi-frame thinking refers to the cognitive ability to process more than one thought at a given moment (Resnick, 1987). All humans are capable of this; it is a natural process. However, traditional training favours single-frame thinking, where a learner develops one viewpoint that is either right or wrong. This correlates with lower-order thinking and is found in procedural thinking approaches (Richland & Simms, 2015). 

Colville, Hennestad and Thoner (2013, p. 4) refer to Groffman (1974) and Schutz (1960), asserting that frames of thinking represent the organisation of past experiences as culturally-based recipes that function as schemes of interpretation and guides to future action. Colville, et al., (2013) also refer to Bruner (1990), stating that frames of thinking serve as the retention system for past organisational learning which become more ingrained with age. 

Learning occurs in the moment that the balance between order and un-orders is disturbed. From a sense-making perspective, the significance of the moment of realisation within the learning process resides within the relationship between frames of thinking and cues of new realities. 

Multi-frame thinking encompasses the general concept of thinking, as well as learning how to think, as essential application to learning (Brunette, 2017). Learning how to think in a learning process is one of the most discussed topics in the educational space (Fink, 2013). Within the concept of general thinking, Fink (2013) refers to three types of thinking: critical thinking, creative thinking and practical thinking, all of which are important in the application of learning. 

Critical thinking entails analysing, evaluating or judging something (Glaser, 2015). Creative thinking occurs when new schemes are formed through the creation of new ideas (Sternburg & Sternburg, 2011). In practical thinking, the individual applies their thinking to solve problems or make decisions. Practical thinking is how one adapts both to people and circumstances, or the manner in which one changes one’s environment to pursue essential goals (Gladden, 2015).

According to Brunette (2017), a fourth type of thinking exists within the construct of multi-frame thinking: contextual thinking, which refers to the relationship between context and content (Bateson, 1987, p. 410). According to Allen, Kilvington and Horn (2002, p. 17), in behaviourist learning theories, knowledge is viewed as passive, mostly automatic responses to external environmental factors. Cognitive theories view knowledge as abstract symbolic representations in the mind of the individual. Constructivist learning theories view knowledge as non-transmittable from one person to another, seeing knowledge as a construct made by each individual through a learning process (Varghese, 2015), whereby knowledge is reconstructed based on the information that is shared. 

In behaviourist and cognitivist approaches to thinking, the focus is on the promotion of pre-determined options for change, whereas within the constructivist approaches there is more reliance on the contextual world where the individual reconstructs knowledge. 

Allen, et al., (2002, p. 24) refer to Parnell and Benton (1999), asserting that contextual thinking within a learning programme should focus on increasing knowledge through awareness and reflection or consciousness, and how the person contributes to the specific problem. Furthermore, the focus ought to be on the relations between options or the relevance of the enabling environment in developing a consensus on the different options within a specific situation wherein the knowledge is reconstructed. 

One possibility is the development of multi-frame thinking and the development of 4IROs as multi-truth organisations. This is locked within the learning architectures we apply within organisations (Brunette, 2017). Here, we can learn from artificially intelligent machines. 

Multiple truths are coded into the algorithms that drive a machine’s learning (Jiang, et al., 2017). These truths are coded in as axioms, or truth points. This allows the machine – when it encounters a new or unknown situation – to go back to the truth-point, or axiom, allowing the axiom to guide the reprocessing, or new decision path. 

However, being equipped with multiple axioms, the machine can consider multiple truths and find multiple ways to react to the new situation. It also uses that same axiom and the new situation to adapt its intelligence, thus becoming more intelligent. 

If we can ‘code’ axioms into organisational learning architectures, the co-creation of workplace realities and the adaptation of 4IROs might become more rapid. This is based on having an adaptive intelligence, based on multiple truth points or axioms, built into our learning architectures to achieve a collective adaptive intelligent workforce. 

The use of axiom learning could guide a collective consciousness when applied to the unique knowledge, skills and individual consciousness of team members. A diverse team using axiom learning could make directional decisions and problem-solve faster than a standardised single-truth team could.

Conclusion

With a directed collective consciousness, leaders of 4IROs have access to an adaptive intelligence that might be superior to artificial intelligence. Moreover, the combination of an adaptive human intelligence with an ethical artificial intelligence might be the solution to the speed of the adaptation challenge that leaders in the 4th industrial revolution must achieve. This would be a truly augmented leadership. 

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