Key idea: AI will be a decisive competitive advantage only if companies are able to upgrade theirs managers' skills to become learning leaders aka foster collective intelligence.
Artificial Intelligence promises seem big enough to make executives dream of new competitive advantages. The disruptive nature of AI will indeed widen the competition gap between companies around the world. But AI could first be - technically - difficult to introduce in companies’ operating model due to the prevalence of legacy IT systems. Say it passes this first test, then organizational and psychosociological factors get in the way. Filling the competition gap with advanced engineering talent is only one step towards solving the problem. Increasingly, companies seeking to take advantage of digital disruption are falling short of the social, interpersonal skills needed to speed up adoption and smart use of new technologies, highlighting the need for a new kind of managers: the learning leaders.
There is an increasing disruption on human resources: these learning leaders are in short supply. Upskilling is the name of the next game. In manufacturing for example, the “knowledge worker” has replaced the traditional blue collar worker for years now. Industry 4.0 goes even further. It holds big potential for manufacturers – from predictive maintenance of machinery to enhanced device interconnectivity – but successful implementation of these solutions will require a large degree of even more upskilled workers which calls for an upgrade in management too. More generally speaking, within the workplace, the growing reach of data and technology is being felt across various departments. Practically any of them could tap into the treasure trove that are analytics. Increasingly, this work requires collaboration between technical and non-technical roles. For example, technical professionals like data scientists, data analysts, and data engineers are more frequently needed to partner with functional managers in order to help them understand the data that is relevant for decisions related to internal business problems (the basics of data interpretation). “Ok, bring me the smartest!” could be the logical answer to this new situation. Indeed, before the digital age, pure IQ performers were the usual high performers. Thanks to Command & Control management, organizations demanded a few people with high cognitive skills. This elite could be low on social skills: they were relying more on their knowledge than on human relations. Hierarchies worked in bi-directional flows up and down chains of command. Managers were just a convenient relay then. Now 5G is coming; it allows Internet of Things and cloud-based AI almost everywhere: more data you need to make sense about aka turn it into information that collective intelligence turns then into knowledge that is your competitive fuel! But collective intelligence is not a given. When seen through these lens of information processing, today corporate features have serious limits:
Structures. Silos were designed to allow us to have a grasp on things. It worked well but we lost the global picture in the same time and silos became fiefdoms. It’s difficult to talk about collective intelligence in such a context.
Culture. Does your corporate culture value data and analytics? There is no point in laboriously gathering data and running sophisticated machine learning models if the analysis will be ignored. Many of the world’s largest enterprises have historically grown through gut decisions from influential executives, not from collaborative, data-driven decision-making. Gut decisions are by definition made by the One who knows.
Decision making. Decision making is not engineered as a known and shared process. For routine decisions, team management is OK. For big ones, everybody seeks to protect oneself and one’s logic, starting with Boards and C-suite. Joint decisions then, are watered down compromises that everyone can live with or top down unilateral decision. We lost somehow the collective intelligence between these two points.
Performance. The values of an organization are revealed through its performance metrics. A company that uses only compliance focused metrics is very different from one that uses some aspirational ones. And imagination doesn’t work well with compliance.
Data access. Closed access to data is about keeping them away from people, because doing so makes your life easier. You don’t have to feel discomfortable about potentially being criticized and questioned. And of course financial information is God’s words and approaching them as data is a sin ;)
The way we think. How we think can be a powerful roadblock to collective intelligence too. Take “frozen thinking” which occurs when you have a fixed orientation that determines the way you frame or approach a problem. For instance, when relying only on traditional IT executives, like the chief technology officer, a company’s approach to AI tends to be restricted to current operating procedures. Or take groupthink: the more homogeneous your team, the stronger your frame (and your bias) of a problem. A group of IT guys will find an IT solution to a problem curing the symptoms of it without attacking the real cause they can’t see. When confronted, they tend to even radicalize their positions.
How then to foster collective intelligence in your company? Some conditions are necessary for having learning leaders in your staff:
Capability - knowledge and practice necessary to effectively analyze inputs and develop/implement solutions. 1. Organizations are increasingly networks rather structured silos. Indeed, networks fit well with complexity. The information in the network is much better than the information available in any individual node. Make access to data easy (providing the fact that your company obviously can collect data). To better steer this kind of organization, a manager has to give up the illusion of control on data to improve the overall manageability. 2. Grow managers cognitive skills: analytical capabilities (included data interpretation), problem solving.
Diversity - differences in thinking needed to distinguish good ideas from bad and counter natural human tendencies for homogeneity and group think. 1.Increases diversity (more women for instance) + “social sensitivity" of group members, that is, the ability of each to decipher the mindset of others by capturing the unspoken and the nonverbal cues + Equal speaking time. 2. Create dynamics of confrontation of ideas capable of facilitating contradictory debates: pros and cons subgroups in a meeting or someone playing the devil’s advocate, ask for analysis when delegating decisions to your team.
Independence - counter human and group bias, foster creativity and resist conformity by taking a stand against the group. On individual level: Promoting self awareness using tests eventually. Understanding the nature of bias and how it operates through categorization of data. Understanding this important concept helps individuals approach their own biases. Organizing training sessions on bias literacy have been proven effective in minimizing bias (Molly Carnes, 2012). On organizational level, you have to measure the outcomes of these previous efforts: develop concrete, objective indicators & outcomes for hiring, performance evaluation, and promotion to reduce standard stereotypes for instance.
Interdependence - utilise processes and systems to enable collective focus and action toward common objectives. On organizational level, establish a common destiny between all managers, to organize interdependence and the equitable sharing of the benefits of collective action and resources.Know the difference between coordination and cooperation. Collective intelligence needs a smart combination of both. On one hand, coordination refers to the organisation of all the activities in an orderly manner, to achieve unanimity of individual efforts in the pursuit of group goals. Individuals don’t need to amend their way of thinking/working. Cooperation on the other hand supposes altering thinking, working ways to include others in the party aka achieving the deliverable. It is a joint effort for accomplishing a defined target. Cooperation is more “costly” (energy, emotions) than coordination. Foster delegation down to managers with hierarchies more decentralized to the point they turn into organizational networks. As with the Internet, the network takes precedence over the computers (the individuals) connected to it.
How to make managers at ease in such an environment? Grow theirs social skills. A report* covers key insights showing the workforce trends that are prompting companies to switch from tech-focused to people-focused strategies. Learning leaders: Accurately draw conclusions about social and emotional state and determine appropriate response / action (social and emotional reasoning) Interact with others to coordinate group activity (coordination with many people) Identify social and emotional states (social and emotional sensing) and act accordingly in emotionnaly appropriate ways (e.g. speech, body language). Accept seeing errors of judgment and arise questioning very early on without seeking to rationalize at any cost. It’s cheaper psychologically. In the event of a conflict, intervene as soon as possible using a pre-established relational protocol.
Capability, diversity, independence and interdependence lead to an uncommon capacity for learning leaders to let go of comfortable ideas and become accustomed to ambiguity and contradiction; a capability to rise above conventional mindsets and to reframe the questions they ask; the ability to abandon their ingrained assumptions and open themselves to new paradigms; the propensity to rely on imagination as much as on logic and to generate and integrate a wide variety of ideas; and the willingness to experiment and be tolerant of failure. Learning leaders are also tenacious about teaching and helping colleagues … and be taught by them ! They know the limits of their knowledge and who ask for help when they need it ; they rely on others and their own flexible attitude to constantly improve. Today, real AI capability building demands more than just ‘plug-and-play’ solutions. It requires to redesign workflows, improve workforce training, enhance communication between teams, and overcome cultural resistance to automation. New applications of AI will create fundamental and sometimes difficult changes in workflows, roles and culture, which managers will need to shepherd their teams through carefully.” The shift in focus from technology to organisational behaviour comes as awareness of the difficulty involved in scaling the implementation of AI initiatives grows. Companies need learning leaders as AI facilitors and “social engineers”
* GetSmarter’s 2019 Disruptive Tech Survey