Summary: In today’s global business environment, market share is increasingly lost by those unable or unwilling to adapt. As technology advances at a faster rate, the dynamic of the global workforce is changing. Mundane tasks are being automated, and employees want more meaningful work and more empathetic leaders. This post will explore two areas often positioned at opposite ends of the “soft” and “hard” skills spectrum—collaborative leadership and implementing artificial intelligence. We will address each individually, explore where they intersect, and delve into a framework for how leaders can fuse them to meet today’s challenges.
According to Dr. Edward Marshall of Duke University, collaborative leadership is defined as “an ethical, principle-based philosophy of service that builds a leadership culture of psychological safety, ownership, and trust that empowers the workforce to be their best selves so they can do their best work and produce superior results.”
These “superior results” can be seen at the task level, as it relates to an individual employee’s performance. However, there’s the potential for the organization as a whole to collaborate and perform better. Renowned Harvard Business School professor Linda Hill links collaborative leadership with sustainable innovation at the organizational level:
Put differently, collaborative leadership is a process in which individuals use their skills and expertise within a network to contribute to the overall leadership function and work toward shared goals. In other words, teams assign responsibilities for each project based on the unique leadership abilities of each team member, to best meet the needs of that particular project.
With the term defined, let’s look at how collaborative leadership differs from traditional leadership models.
In a traditional approach to leadership, individual managers lead day-to-day operations, while top-level management makes large-scale decisions. In this model, information can flow sporadically from the top, often shared out with broader teams. Sometimes this information flow occurs at inopportune times, when acting on information may be difficult for the doers, the mid-level employees.
With this traditional approach, there’s an assumption that only a few individuals are skilled enough to surface brilliant ideas and move them forward in a meaningful way. This model of consolidating leadership at the top often also results in less diverse leaders. Although research continues to point to how diverse teams are more innovative teams, it’s still a rarity to find full-spectrum diversity across top-level management teams. Additionally, research is beginning to show that diversity alone is not enough—pointing to further issues with traditional, consolidated leadership. A 2021 study published in the Review of Public Personnel Administration found the following:
“[G]reater team diversity does not automatically yield an inclusive climate. Inclusive leadership is needed to support an inclusive climate in which different team members are valued for what they bring to work practices.”
On the other hand, in a collaborative leadership model, organizations operate as communities. In contrast to the structure we often see in a traditional top-down leadership model, these communities are made up of a connected network. Everyone on the team leads in some capacity, not just an individual manager.
In this type of leadership, accountability is shared, problem-solving is collective, every team member is empowered to share ideas and concerns, and communication is more transparent and inclusive.
While transitioning to this model is far from simple, one easy way to understand the core of collaborative leadership is to see leadership as a culture, not a purely individual function.
Once you make that mindset shift, other elements often thought of as one-person jobs (like problem solving and decision making) begin to take on a new collective dimension.
In one way or another, those who study how collaborative leaders operate often come to the same conclusion: They are successful because they harness the collective power of their teams.
And to harness the collective power of your team, you first must believe in the collective genius of teams. This can take some work, especially because many business schools and business publications frame successful leadership through an individualist lens.
It goes without saying, but the modern workplace is changing more rapidly than ever before. More complex problems require more creative problem solving, and the solutions to one problem may create a set of new challenges to overcome.
Consider the role of automation, which can, on the one hand, simplify a particular workflow, but on the other hand, create new workflows—like managing, optimizing, and analyzing the automation itself.
The management of this changing landscape and the problems it presents is something Wayland Baptist University’s Dr. Nick Ejimabo unpacked in a study entitled, “An approach to understanding leadership decision making in organization” (European Scientific Journal). When examining decision-making and organizational leadership in today’s workplace, he notes:
“Too often we confuse things like personal style and a position of authority with leadership. [Leadership] must be all-inclusive, ongoing, strategic, systemic, productive, positive, as well as influential and goal orientated.”
The assumption that our leaders will have the answer to a given problem just because they have an impressive title can discourage collaboration.
As stated earlier, top-down leadership models assume that answers will come from a small cohort of leaders. If that’s the assumption, that’s where you’ll look for answers.
This assumption can deter collaboration and stifle the resulting innovation. As Professor Hill says in the video above (I highly recommend you scroll up to watch it if you haven’t already):
“Innovation is not about a solo genius having an ‘aha moment,’ it’s actually a collaborative problem-solving process, usually with people quite different in their points of view.”
The move to a more inclusive, collaborative, innovative, and whole-team approach necessitates leaders willing to embrace those ideals and have the courage to try to implement them.
The Iceberg of Ignorance
In 1989, Sidney Yoshida, a consultant working at Calsonic, a Japanese car manufacturer, discovered a disconnect between managers and front-line workers. His research, famously known as the “Iceberg of Ignorance,” revealed significant knowledge gaps between senior management and the rest of the organization. Yoshida concluded:
“Only 4% of an organization’s front-line problems are known by top management, 9% are known by middle management, 74% by supervisors, and 100% by employees.”
While this study is over 30 years old and only focuses on mid-sized organizations, the issue is still relevant today. Traditional, top-down hierarchies in the workplace tend to create a disconnect between leaders and employees.
A strong leader must have the tools and skills to anticipate, recognize, resolve, and learn from problems that happen in the workplace.
As we consider leadership in the context of AI, that means knowing how to tap the insight of everyone in the organization to provide a wider perspective for solving those problems.
This collaborative approach to managing teams is best suited for today’s more complex workplace, where newer, less-defined problems need agile solutions. The old model, where top-down leadership created less inclusive teams, is not equipped to solve the challenges of the modern workforce.
According to the Oxford Dictionary, artificial intelligence (AI) is defined as “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”
The team at World Wide Technology (WWT) visualizes the relationship between AI, machine learning and deep learning like this:
Artificial intelligence is impacting practically every industry. And with AI’s growing reliability and expanding use cases, the trend will likely continue upward.
International Data Corporation (IDC) predicts spending on AI technologies will double over the next four years, reaching $110 billion by 2024. And McKinsey’s State of AI in 2020 report revealed that organizations are already seeing significant cost benefits from their adoption of AI:
“The use cases that most commonly led to cost decreases are optimization of talent management, contact-center automation, and warehouse automation. Over half of respondents who report adopting each of those say the use of AI in those areas reduced costs.”
Stanford’s 2021 AI Index Report (PDF here) presents nine key takeaways that can help round out our understanding of AI’s impact:
1) There is a significant increase in AI investment for drug design and discovery. “Drugs, Cancer, Molecular, Drug Discovery” received the most significant amount of private AI investment in 2020, with more than USD $13.8 billion, 4.5 times higher than 2019.
2) The role of AI development is increasing. 65% of graduating North American PhDs in AI entered the industry in 2019, compared to 20.6% in 2010, amounting to an increase of 44.4%.
3) AI is abundant. It’s hard to tell the difference between artificial and non-artificial since AI systems can produce high text, audio, and images.
4) AI has a diversity challenge. AI Ph.D. graduates in 2019: 2.4% African American; 3.2% Hispanic; 45% Caucasian.
5) China surpasses the US in AI journal citations. In 2020, China surpassed the US in AI journal citations per capita by .9%.
6) Foreign AI PhD graduates are remaining in the US after graduation. International PhD students in North America rose 4.3% to 64.3% in 2019 from the previous year. Among foreign graduates, 81.8% stayed in the US.
7) AI surveillance technologies are on the rise and are everywhere. 2020 saw significant advancements in large-scale surveillance due to increased approaches to image classification, face recognition, video analysis, and voice identification techniques.
8) AI ethics lacks benchmarks and consensus. The AI field largely lacks standards to measure or assess the relationship between technology itself and the societal implications of these technological developments.
9) AI is gaining attention from the US Congress. Mentions of AI in the congressional record tripled for the 116th Congressional session compared to the 115th Congressional session.
Also of note, the report dedicates an entire section on international collaboration and AI, highlighting the development of multilateral AI strategies across intergovernmental initiatives, working groups, summits and meetings, and bilateral agreements.
The increase in international agreements and initiatives shows how global collaboration skills are critical for the future of AI development and for addressing issues of equity and fairness in AI.
Discussions about the interplay between humans and machines in the workplace are often dominated by various technological topics. While advances in machine learning and deep learning can mean faster processing speeds for applications and more effective solutions in general, one could argue that collaboration underpins it all. Collaboration across disciplines and researchers led to those technological advances. And collaborative leaders will be key to ensuring these technological advancements are integrated in a way that improves the human experience at work.
AI is often categorized into three primary waves: handcrafted knowledge, statistical learning, and contextual adaptation.
The first wave enables computer models to use logical reasoning over narrowly defined problems (without learning and perceiving capabilities).
The second wave has to do with statistical learning, where computer models perceive the surrounding world through classification and prediction by being trained on big data. However, there is no contextual ability, and there is minimal reasoning ability.
In the third wave of AI (the current wave we are in), systems build contextual explanatory models to explain and drive decisions over time to solve real-world situations. In this wave, AI moves from being a technology that feels relatively separate to becoming a kind of sidekick that leaders must learn to collaborate with. In this sense, leveraging AI becomes far less about what new insights the technology can surface and far more about how leaders pair their own and their team’s reasoning with AI’s reasoning.
Here is an example of what a complex system from a training dataset looks like:
In addition to the three waves, there are three schools of thought on the role of AI in the workplace.
While robots taking over the world have long been depicted in science fiction, a report from PwC titled Will robots really steal our jobs? (PDF here) predicts that AI could automate 30% of jobs by the mid 2030s.
Some leap to negative conclusions, suggesting this is game over for humans. Others see this shift ushering in an immense wave of human creativity and innovation due to the steep reduction we will see in mundane tasks.
Professor Matthew Mason, a roboticist and the former director of the Robotics Institute at Carnegie Mellon University, puts it like this:
“AI will present new opportunities and capabilities to improve the human experience. While it is possible for a society to behave irrationally and choose to use it to their detriment, I see no reason to think that is the more likely outcome.”
Most thinkers in this space agree that the more likely scenario is that AI will support humans in their tasks while simultaneously making routine tasks obsolete. Still, this won’t just spontaneously occur. It will demand and arise from a new type of collaborative leader, one who can build bridges between the collective creative genius of a human team and the cognitive genius generated by technology.
Here’s a TEDx talk from Pedro Uria-Recio, in which he makes the argument that artificial intelligence will make the workplace more human, not less:
Still, it’s important to double-down on this point: AI will not in itself make the workplace more human. That takes humans, and it will demand that we humans shift the type of skills we build. For collaborative leaders, building the future workforce will mean understanding the new landscape of these skills (both technological and social/emotional) and ensuring that their teams are in a continuous state of learning. This will also help ensure that change feels more like an opportunity than a burden.
In The roles of artificial intelligence and humans in decision making: Towards augmented humans?, researchers Claudé & Combe (PDF here) said it like this: “One of the future challenges of management will rely on the adaptability of the organization to handle change and transform themselves. The organizational challenge will be handled by managers using soft skills and new ways of human-human interaction and collaboration.”
The research of Professor David De Cremer, a behavioral scientist, provides a look at how leadership will need to change to adapt to the future of automation and AI:
“The more algorithms take over management, the more we will need leadership to set priorities. When automation goes up, so do a number of other factors: our need to have leadership in place that knows what it wants to achieve; leadership that offers judgment when decisions have to be taken; and leadership that can reflect effectively on the goals to be pursued.”
Today’s leaders may rightly want to enhance their understanding of artificial intelligence as they adapt to this new future, but they will be far better equipped if they also work just as hard to improve upon their fundamental collaboration skills.
Organizations that adopt the collaborative leadership approach typically see:
Overall, collaborative leadership can spark higher levels of commitment and engagement, more productivity and a more inclusive workplace.
Collaborative leadership starts with all members of a group being motivated by a shared goal. This style requires that each team member can honestly recognize their strengths, understands how to create a psychologically safe environment, and believes in the power of collaboration.
One way to conceptualize collaborative leadership is to place the leader at the bottom of the model to help involve others in decision-making. This type of leader grounds themselves on ethics, and their kindhearted behavior tends to increase the team’s growth while improving the quality of the organizational environment. Such leadership, according to this paper by Gonzaga University’s Larry Spears, is often referred to as servant leadership.
Servant leaders act in service to their team first and then ladder their focus up to the organizational level. In this way, change is enacted at the team level, subsequently leading to change at the organization level. Servant leaders lead through empathy, foresight, stewardship, and awareness.
Philosopher Ken Wilber’s Four Quadrant model is an instructive example of how organizations can build collaborative leadership. The integrative model, also known as the All Categories, All Levels (ACAL) model, examines people’s perspectives, mindsets, innate qualities, and levels of consciousness to explain reality and relationships.
In this model, there are four primary quadrants, broken up into two frameworks. The first framework breaks into interior and exterior. The second framework includes individual and collective. Any view of any issue has these four dimensions working simultaneously, as parts of a whole—knowingly or unknowingly. These four dimensions can apply to models, applications, change processes, analyses, and so on.
Applied to leadership, the ACAL model can be used to disclose degrees of awareness, behavior, system (social), and culture. Over time, a collaborative leader can begin assembling the pieces in this model (for example, by understanding how each individual on their team finds purpose).
Collaborative leaders can align the information in these quadrants with the following points to begin to improve workplace collaboration, especially as AI increasingly begins to feel like another member of the team:
Although it may seem counterintuitive, as more teams begin working with and relying on AI, leaders will need to advance their understanding of core collaboration skills. Collaborative leadership can be a bridge between both domains, allowing people and technologies to come together for the sake of inclusivity and innovation.
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