Wise Leadership and AI: New Intelligence, New Leadership

Intelligent behavior has long been considered a uniquely human attribute. But as computer science and IT networks progress exponentially, artificial intelligence (AI) is increasingly standing out as the transformational technology of our age. From industry 4.0 outwards, human and artificial intelligence may compete for jobs. But they will increasingly collaborate and complement each other.

What kind of leadership will be needed to make that happen? Here is our summary.

Wise Leadership And AI 1

New Intelligence, New Leadership

1 | New intelligence requires new leadership

Any debate surrounding AI requires a re-assessment of leadership itself. Without a transformation here, AI will never fully deliver: the problems and dilemmas of business cannot be solved by algorithms alone. The answers lie elsewhere, in a transformation from smart, to wise leadership. For wise leaders do not only create and capture vital economic value, they also build more sustainable – and legitimate - organizations. More than reasonable, they are responsible, honoring their fiduciary duty of loyalty and care to the organization and its sustainable/long-term value. Value that AI can enhance, erode, or destroy, depending on how wisely it is led.

In essence, wise decision-making is about broadening the contextual framework and providing a more holistic perspective. It means being able to understand and resolve contradictions, paradoxes, tensions. Wise leaders adopt a ‘multifold perspective’, and have the emotional maturity and generosity of soul to inspire and mobilize others. AI can help leaders to materialize an organization’s vision, but without  wisdom, it may endanger a more humane future.

2 | AI is creating wins across the board

AI (and machine learning) involve computers crunching vast quantities of data to find patterns and make predictions. Deep learning allows predictive modelling via an artificial ‘neural network’ – loosely modelling the way neurons connect in the brain. It supports 3 key business needs:

  1. Automating business processes, using robotic technologies
  2. Gaining insights through data analysis and enhanced, more precise and cost-effective prediction, based on algorithms that detect patterns in vast volumes of data and interpret their meaning
  3. Engaging with customers and employees with natural language-processing chatbots, intelligent agents and machine learning.

3 | Leveraging AI’s predictive power will take wise judgement

Any senior executive knows the importance of predicting the next market shift or great product, making sense of multiple factors. AI is already outperforming humans in rising above the noise, in capturing patterns and signals. Its precise predictive power is working with, and enhancing, human judgement. But leveraging AI means transforming key operational activities and collaborating beyond internal corporate boundaries to form an ecosystem of common (digital) connections and practices. Predictions, moreover, must lead to strategically-valid actions, with data analytics embedded as a core organizational capability, used to detect pain points, design solutions and enable decisions.

Business is about envisioning the unimaginable, innovating solutions, inspiring and mobilizing people. Shaping a future situation, rather than only predicting particular outcomes. Wise leaders will combine human and artificial intelligence, deploying AI as a very effective tool. Wise leaders use their creativity where they can make a difference, by establishing a new solution they can influence. AI is reaching far beyond industry 4.0 (where the popular imagination tends to place it). It has been transforming financial services for years, informing investment decisions. In healthcare, machine learning can analyze brain scans on hospital admission, filtering the most urgent cases, for example. Digital tech, and especially the predictive power of AI, can also help organizations to ease their pollution footprint. Philips is using it to capture more information on the product life cycle to reduce waste.

4 | AI is vulnerable to some very human flaws

If machine learning is designed to emulate the human brain, then it is also fallible - subject to bias. It tends to discount the possibility of significant change, operating within the world defined by the data used to calibrate it in the first place. For example, someone from a disadvantaged neighborhood applies for a mortgage. Her application is declined based on a system data bias. So AI, and particularly machine learning, must be de-cluttered, monitored and managed by wise, responsible leaders, with data integrity safeguarded, the right data inputs (“Garbage in-garbage out”), and verifiable, adaptable algorithms.

5 | AI is a black box, trust must be built

Mechanical systems learn from processing data. Each deep neural network layer progressively recognizes more complex features, which are captured in algorithms. Yet this accumulation of complexity obscures the decision process. In the 2017 PwC ‘CEO Pulse’ survey 76% of respondents cited a lack of transparency (and potential for biases) as impeding its adoption in their enterprise. 73% raised the need for governance and rules to control AI. Furthermore, the ‘soul’ of any AI or machine learning system remains the human mind that designed or manages it. AI needs explicit and appropriate goals: algorithms do as they’re told. And if they can identify patterns too subtle for human detection, generate accurate insights and allow better, more informed decisions, they don’t explain why they offer particular recommendations. Nor does prediction equal advice - this assumes a certain “socio-ethical value” dimension. So for the foreseeable future, AI may be smart, but still need humans to set the right goals and engage in creative interpretation. Human intelligence alone, and by association, artificial intelligence, do not equal wisdom.

6 | Too few companies are realizing the current potential of AI

Early movers can realize significant benefits from AI – also in one of the most critical business areas in this data-driven age - security. Government (detecting potential cyberattacks, in traffic control systems, military drones), banks (detecting suspicious behavior) or retail (theft detection) will all benefit. And big data analytics correlated with core digital technologies are all powerful AI techniques: virtual agents, natural-language generation and processing, image recognition, decision-making, robotic process automation, robotics, and speech recognition. Yet AI is following a slow-moving S-curve. Only 10% of companies have tried to diffuse it throughout their organizations.

7 | Machines are unlikely to replace humans any time soon

For the foreseeable future, AI and machine learning may radically alter how work gets done, complementing and augmenting human capabilities. Cognitive systems can perform specific tasks, becoming more intelligent by the minute via feedback loops. But entire jobs remain beyond their scope. If automation and outsourcing will change the labor landscape, ‘upgraded’, AI-enabled humans will still be in charge. Mercedes-Benz is one example: ‘cobot’ arms (smart, context-aware robots), guided by human workers, manipulate heavy parts in an extension of the worker’s body. In this sense, AI may spark a need for new roles and talent, enabling a beneficial collaboration with smart machines. Productivity can grow thanks to digitization processes that encompass data analytics, AI, robotics and automation. Human creativity still surpasses computer power, especially because abstract symbolic reasoning cannot (yet) produce meaning on its own. Maybe neuromorphic computers (mimicking important aspects of biological brains by being energy efficient, resilient and able to learn, in the words of the European Commission) may perform such a feat in the distant future. But leaders need to make decisions today that will affect the immediate, more global environment –and hopefully provide ROI. Wise leadership will acknowledge the enormous opportunities and prowess of computer learning, enlightened by insights from neuroscience, whilst emphasizing human creativity. Not attempting to compete with computers, but developing our human qualities – creativity, discernment, fairness of judgment, social collaboration, and a holistic vision of the future.

8 | Human weakness is our ultimate strength

The  biased, emotional decision-making of the human brain has led to an upside - the installation of moral and ethical principles that transcend calculating, utilitarian thinking. We can only hope that managerial wisdom will be able to address current challenges and create a more “conscious”- and purpose-driven future, something machines cannot and should not do by themselves. A meaningful future requires that corporate leadership takes responsibility - a socio-economic phenomenon that only a conscious mind can perform. One that is not located in the brain, but is a social contract between humans aiming to progress in a commercially effective manner whilst holding a clear, broader, inspiring (social) purpose in mind.

Conclusion: New intelligence – new leadership

The new breed of wise(r) leaders will:

  1. Embrace and cultivate the collaboration between human and artificial intelligence: transforming operations, markets, industries - and the workforce - with new skills.
  2. Envision a more meaningful future: show organizational stakeholders what it can look like, and guide and enable their organization to pursue that goal
  3. As a result, instill profitable progress whilst making society a better place to live: upholding their fiduciary duty to the organization, its share- and stakeholders, and the community at large.

9 Facets of the AI-Wise Leader

The future ‘cognitive company’ will look very different to anything we know today. Yet data are just a bunch of numbers that are meaningless without context. So AI-wise leaders should facilitate innovation, embracing collaboration between human and AI, transforming operations, markets, industries - and the workforce - with new skills. An AI-wise leader will combine the following traits:

  1. Mobilizer: Inspiring people towards an envisioned future
  2. Social builder: Upholding human interaction
  3. Humanist: Valuing the creativity of people
  4. Mediator: Uniting humans and AI in a common quest
  5. Navigator: Building bridges in the AI eco-system
  6. Explorer: Using AI to sharpen the competitive edge
  7. Sense maker: Emphasizing clarity in AI design and processes
  8. Architect: Analyzing, diagnosing, designing
  9. Guardian: Safeguarding the integrity of AI design and maintenance

AI-wise leaders – like wise leaders in general - can envision a more meaningful future, show organizational stakeholders what it can look like, and guide and enable their organization to pursue that goal. In this way they will instill profitable progress, making society a “better” place to live – all whilst conforming to the fiduciary duty to their organization, their stakeholders, and the community.

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