Implications for theory and practice

A number of recommendations are provided for inclusion in the conversation on sensemaking. Firstly, the view of Weickian sensemaking on ICTs is outdated. This warrants more attention to be paid in exploring the potential impact of UEMI technologies on sensemaking. This study is an attempt to do so.

Secondly, sensemaking has an economic aspect (or property); it is reliant on scarce means, namely attention.

Thirdly, sensemaking is a production process; it produces knowledge (meaning) of a certain quality within a certain time. This quality is determined by its fit to reality, which matters when risky reality prevails, and which unlike other spheres of life, is pervasive in business organisations and other high reliability organisations. The high proportion of fire fighters assigned to lookout posts to make sense of the progress of a fire in the wild attests to this. This does not contradict or invalidate current sensemaking theory which considers the achievement of a point of plausibility that enables action as the only measure of success, irrespective of how it fits to reality, because that reality is individually or socially constructed. In the absence of risky reality, this poses no problem, e.g. when an individual realises that an alternative career is more congruent with his or her identity and should thus contribute to a more fulfilling remainder of his or her life. Nevertheless, consideration should be given to risky reality, notwithstanding the fact that it is not measurable, if not unknowable so as to consider the welfare of those sensemakers living in it.

Fourthly, like other production processes, sensemaking is open to increases in productivity, given a supportive environment comprising appropriate organisational structures and procedures, human skills, mindsets and crucially, any technology which lowers the requirement for scarce attention. Due to their attention efficiency, UEMI technologies enable this. They do so by reducing ignorance, and if properly designed and used, by reducing equivocality. The latter is enabled by UEMI’s capability for rich audio-visual communication which closely approximates that of face to face communication, but which exceeds human ability in recall to enable retrospective sensemaking, and with augmented reality. Crucially, through sophisticated algorithms embedded in software, ICTs are increasingly delivering the same results which humans produce when making sense, hence relieving humans of their sensemaking overload. The case of IBM’s supercomputers winning the game of Jeopardy! and chess against a grand master, as well as the capability of Google’s cars independently navigating traffic on public roads attests to that. On a less dramatic level, software costing a

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few dollars is putting highly skilled people out of work, e.g. tax consultants. The macro evidence of this is provided in figure 4 in section 1 of chapter 1, which shows the decoupling between productivity growth and job growth. In order to enhance the attention efficiency of UEMI, the key challenge to still overcome is speech recognition, despite the commercial success of speech recognition on the latest smartphones and the performance of a supercomputer during a TV quiz game. The goal is attention efficient (i.e. ubiquitous) speech recognition, to the level where it compares to human speech recognition without the need for contextualised settings, which is the case in smartphones and quiz shows.

However, given that much of what is envisaged in UEMI-based sensemaking is technically possible, the most important considerations in practice include the following: firstly, governments should redouble their facilitation efforts to speed up the availability of omnipresent broadband. In certain regions of the world that means the provision of the key general purpose technology of the Second Industrial Revolution: electricity. Secondly, education systems should be overhauled to optimise the digitisation of the process. For example, the methodology harnessed by the Khan academy293_{, which involves extensive use} of audio-visual teaching material, with teachers as moderators, provides useful pointers to what should be aimed for. Perhaps more importantly, ICT literacy and other skills which complement the productive capacity of ICTs should be increased with particular emphasis on the current and future labour force. This is necessary to reduce the unemployment caused by the substitution of ICTs for human workers.

Finally, the leaders of organisations should start paying attention to the optimum ratio between human employees and ICTs. A good example is Jeff Bezos, the founder of Amazon.com. The policy and supporting organisational culture, structure, employee skills and ICT infrastructure enables normal business to be conducted with customers without the direct involvement of a single employee. However, when problems and other exceptions arise, humans, backed by their ICTs take over to solve them. All the leading edge software is developed in-house with highly skilled employees. Amazon employs people and ICTs according to their strengths. This echoes Brynjolfsson’s example given in section 1 of chapter 1 of chess teams of computers and humans outperforming teams of only humans or only computers. Organisations, whether for profit or not, can do so much worse than trying to

293_{https://www.khanacademy.org/}

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learn from Amazon. (Organisations, whether for profit or not, can do well to learn from Amazon.

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Appendix i