Cliff Brunette
September 2019
Reference as: Brunette, C.; Department of Higher Education and Training (DHET). (2019). Research Bulletin on Post-School Education & Training: Number 8. Pretoria: DHET.
Available on the Department’s website: www.dhet.gov.za
as well as https://www.researchgate.net/
Abstract
In this article, the author explores the notion that traditional learning architectures are too slow to keep up with the speed of learning required to match the rate of organisational adaptation.
The author posits that the rate of organisational adaptation within the fourth industrial revolution requires a new learning architecture that should enable an innovative view of organisational learning itself. However, such a learning architecture would depend on augmented leadership who can harness the collective intelligence, and enable multi-frame thinking, within their organisational teams.
Such a view of organisational learning, however, requires leaders to challenge their own – and their team’s – very human moral dilemma of holding a single truth. The new learning architecture will have to compensate for, and enable, multi-truth intelligence, or rather adaptive intelligence, which could be obtained through the embedding of axioms within the learning architecture.
Key Words
Augmented leadership; adaptive intelligence; adaptation; axiom; collective intelligence; Fourth Industrial Revolution; learning architecture; multi-frame thinking; multi-truth;
Introduction
In this global village called Earth, or what we call our world, there is a global rise in the consciousness and connectedness of its people. Through technology, information is daily reaching all the corners of this village. No longer is knowledge contained only in leather volumes and translated by a circle of select scholars.

According to Manuti, Pastore, Scardigno, Giancsapro and Morciano (2015), the place of learning – the place of work – has changed forever and is forever changing. Today, this world, this consciousness, this connectedness, is our reality.
Knowingly or unknowingly to most of society, the global village is transitioning from a time of everything accumulated, to everything experienced (Wadhera, 2016).
In a traditional era, which most organisations still find themselves in today, delivering training through teachers (trainers) as a tradition of the institution that holds the keys to the leather volumes of replicable outputs leading to business success, is still commonplace. A commercially viable product is produced to deliver the known and the knowable (Snowden, 2005) in the shortest time, which has the lowest impact on production.
Current learning approaches are mostly following an information age narrative based on the accumulation of knowledge (information). This approach implies that the information you own is your competitive advantage. However, it takes much time, to accumulate information and embed information as organisational wisdom within the collective people of the organisation as an accumulated information advantage.
In the new experience age, a shift from “instructional design” to “experience design”, using design thinking as the foundation, is required (Bersin, 2017). The ability of employees to co-create workplace realities (Brunette, 2017) may become one of the key focus areas of learning-experience design, with the understanding that at the heart of the new experience age sits 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 new expectations of both their employers and their customers. This new reality requires a depth of internalisation and a capacity to apply collective values (Neskovic, 2016) which cannot be taught, but can only be co-created.
The author posits that in a post-information age, where thinking is a critical skill, this industrialist, static, production-line mindset towards learning is nothing but an outdated myth, which can only lead to failure in a world that requires co-creation.
Through the awakening of the Fourth Industrial Revolution (4IR), a new economy of learning is required, with new relevance and understanding of learning, the architecture of learning experiences, and a new grand learning design, to co-create social-oriented learning experiences that are business impactful. A new learning architecture is required that harnesses the speed and impact of learning from augmented intelligence and multi-frame thinking.
Organisational Adaptation

Kontoghiorghes, Awbre, and Feurig (2005, p. 190) define adaptation as the extent to which the organisation can rapidly adapt to changes. Three further definitions of organisational adaptation through learning are: Pedler, Burgoyne and Boydell (1991) stating that learning organisations facilitate learning of all its members and continuously transform. Senge (1990) states that people continually expand their capacity to create results they truly desire, where new expansive patterns of thinking are nurtured, where aspirations are set free, and people are continually learning how to learn. Garvin (1993) states that people are skilled at creating, acquiring and transferring knowledge, and modifying their behaviour to reflect new knowledge and insights.
According to Lowe and Sandamirskaya (2018), learning can broadly be described as producing change within an organism, enabling more effective behaviour within its environment. Adaptation entails behavioural adjustments to environmental change that may be the direct product of learning (Lowe & Sandamirskaya, 2018, p. 1). These behavioural adjustments may be true in both human and machine learning today. Adaptation requires changes to routines which represent much of the on-going activity of the social agent, and they come to be challenged and adjusted through processes of learning (Nelson & Winter, 1982).
These changes to routines require intelligence – both human and artificial. The speed of adaptation is confined to the processing speed of the intelligence. Intelligence and intelligence of leadership require augmentation to facilitate organisational adaptation.
Augmented Leadership
Augmentation and augmented leadership are words that are easily used, and even more so as we forge forward on the 4IR journey. However, the word ‘augmented’ leadership should be contextualised. Augmented, per definition, means to make larger, to make bigger, or to increase intentional value (Merriam-Webster Editors, 2018).
Within the context of the 4th Industrial Revolution, it also means to make faster, quicker, and 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). One of the biggest of these challenges will be in the field of intelligence. Adaptive intelligence will become one of the significant discourses in navigating through the experience age. Moreover, the driving force of this discourse will be the speed of adaptation. Amongst the disruptors of intelligence will be 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.
Within the context of the above, the leadership challenge is the augmentation of leadership approaches in order to unlock and enable the speed of human

intelligence. Augmenting leadership approaches will demand a fresh look at all the enablers of organisational strategy – including organisational learning. Organisational learning is one of the closely related enablers to the adaptive intelligence discourse.
Within organisational learning, two specific – but not necessarily mutually exclusive elements – should be reviewed. These are the utilisation of collective intelligence, and the use of axioms embedded within the organisational learning architecture.
Collective intelligence
Collective intelligence is a view that requires some attention within the context of augmented leadership. Within the challenge of augmenting leadership, making leadership more, making leadership better, and intentionally increasing the value of leadership; speeding up the leadership quest – organisational adaptation – should be one of the turnkey focuses. Whenever the conversation is around the 4IR, the conversation is somehow always about speed. In the 4IR, organisations will need to adapt quickly and often, and this calls for leadership. Leaders must enable adaptation.
Organisational adaptation can be achieved through the adaptation of human behaviour. The author uses explicitly the words adaptation of human behaviour, rather than “change of behaviour”, because mere change will not be enough.
In many organisations, change is often seen as a project, but given the nature of the 4IR, the 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 state (or domain), through the emergent properties formed between relationships within the complex. Therefore, adaptation is the project, not the change. 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.
In organisations, this means that organisational behaviour must completely shift. Organisational shift, or behaviour shift, does not just mean a shift from unorder to order. It means to shift to more order; better order, and intentional order. Organisational behaviour must augment. In a 4IR organisation, or 4IRO, the speed of augmentation of behaviour is turnkey. The speed of augmentation can be contextualised within the nature of competitiveness; the nature of industry disruptors; and the nature of consumer expectation. The 4IRO leadership requirement is to

meet the speed of the adaptation challenge through the augmentation of organisational behaviour.
One of the approaches to adaptation, or dealing with the change required within organisations, is that of training or organisational learning, focusing on the intelligence of the individuals within the organisation. However, in most non-4IROs, traditional learning architectures are applied to shift the overall behaviour of the organisation.
The premise in these traditional approaches is that all individuals should be at the same level of knowledge, or the same level of competence, and have the same level of intelligence — a machine-like paradigm. The leadership in such organisations will apply standardised units of learning, standardised assessments, and standardised curricula. This might be sufficient in academic development or higher education environments, or in a slow-to-change, archaic organisational system.
However, for a 4IRO, or within an adaptive organisational learning system, this premise is just too slow. In a 4IRO, with an adaptive organisational learning architecture, leadership should focus on harnessing the collective intelligence of teams. Focusing on the collective intelligence of teams’ implies an acceptance that everyone is not on the same level of knowledge, or possesses the same competence and insights. It is required to accept that intelligence is adaptive and can be uniquely applied to the specific problems or changes facing a team. Harnessing the unique contribution of each individual’s knowledge and skill within the collective system is the 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 team members regarding the world of work and the problem-space that teams are responsible for. For the leader, the challenge might be accepting that there are multi frames of reference; multi frames of thinking; and multi frames of truth. An adaptive intelligence, driven by an adaptive truth.
The 4IRO leader must learn to work authentically with real diversity, and the truths that each diverse 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 when it comes to dealing with multiple truths.
It is natural for humans – despite having a collective social consciousness (Laszlo, 2004) – to accept one truth. 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, and for organisations to adapt, to multi-truth organisations; to

develop multi-truth societies within which there is a much greater reliance on collective consciousness than there is on standardised test and curricula.
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 that, and it is quite a natural process. However, in the field of traditional training, it often stands to reason that the approach towards thinking is that of single-frame thinking, where a learner is required to have 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 chase experience into memory and serve as the retention system for images of past organisational learning that become more ingrained with age.
Learning occurs in the moments of disturbing the balance between order and un-order in the form of frames derived during and retained from moments in the past, and cues in the form of current moments. Viewed from a sense-making perspective, the significance of the moment of realisation within the learning process resides in the relationship between frames and cues (Colville, et al., 2013, p. 3). Therefore, it is suggested that learning or sense-making is to be found in multi-frame thinking.
Multi-frame thinking is not only frames and cues for reference, but encompasses the general concept of thinking, as well as learning how to think, as an essential application in 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, namely, critical thinking, creative thinking and practical thinking; all of which are important in the application of learning.
In critical thinking, the thinking process is focused on analysing, evaluating or judging something (Glaser, 2015). Creative thinking occurs when new schemes are formed in the creation of new ideas through imagination (Sternburg & Sternburg, 2011). In practical thinking, the individual learns how to apply something under the pressures of solving problems or making decisions. Practical thinking is how the person adapts to an environment, both people and circumstances, or the manner in which the person changes their environment in adapting it to pursue their essential goals (Gladden, 2015).

According to Brunette (2017), a fourth type of thinking is apparent, and within the construct of multi-frame thinking: Contextual thinking 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 nothing more than passive, mostly automatic, responses to external factors in the environment. 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 constructed entity made by each and every learner 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 of the individual reconstructing the 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 situation. Furthermore, the focus ought to be on the relations between options or relevance of the enabling environment in developing a consensus on the different options available 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 and is locked within the learning architectures we apply within organisations (Brunette, 2017). Here, we can learn from our smart consort; – artificially intelligent machines.
With machine learning, multiple truths are coded into the algorithms that drive the 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 reconsider multiple options of truth and find multiple solutions for reacting to the new situation. It also uses that same axiom and the new situation to adapt its intelligence, thus becoming more intelligent.
The author posits that 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, and 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 team of unique intelligence with a consciousness built on the same axioms could make directional decisions, and problem-solve, within the same problem space much 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 still be – and continue to 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 adaptation challenge that all leaders in the 4th industrial revolution must achieve. This would be a truly augmented leadership.
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