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MIT - tech, AI, leadership

November | Organisational Transformation | Read time: 1 minute

MIT Series: How Tech Will Transform Organisations and Leadership

Margit Mansfield recently visited the 166-acre Massachusetts campus to meet with some of the world’s leading minds on leadership, AI and ‘real data.'

MIT has a long history of innovation. Active companies formed by living MIT alumni produce estimated annual revenues on par with the GDP of the 10th-largest economy in the world. Their ‘mind and hand’ philosophy pushes real world engagement to make a better world through education, research, and innovation. 

Students at MIT learn through a combination of lab work, making, building, experimenting, and scaling, a practical focus has led to some impressive results

Visiting the 166-acre campus, I felt the place had the energy of a small city, a heady integration of education and start-ups in various stages of development. The connection was made possible through a Keogh client, who is part of MIT’s Industry Liaison Program, and was facilitated by Ron Spangler, Program Director.   

MIT - tech and leadership - Keogh Consulting

Ray and Maria Stata Center, Boston. Photo by Mark Boss on Unsplash.

Making Sense of Successful Leadership

My first sit down was with Professor Deborah Ancona, The Seley Distinguished Professor of Management, Professor of Organisation Studies, and Founder of the MIT Leadership Center at the MIT Sloan School of Management.

A wealth of knowledge, Professor Ancona describes four capabilities that underpin successful leadership*:

  • Sense making. Fully comprehending the context in which you operate. 
  • Relating. Working with and through other people. 
  • Visioning. The capacity to paint a picture of what is possible
  • Inventing. Making the future possible – about implementation and execution.

While visioning and relating get a lot of airtime, Professor Ancona’s research shows that it is sense making and inventing that are becoming more predictive of leadership effectiveness.   

Sense making is about more than analysis of data, it is also an act of creativity. Ancona and her co-creators outline a number of steps involved in sensemaking in their HBR article “In Praise of the Incomplete Leader.”

Steps for sense making

  1. Get data from multiple sources: customers, suppliers, employees, competitors, other departments, and investors. (More on this with Sandy Pentland below).
  2. Involve others in your sense making. Say what you think you are seeing, and check with people who have different perspectives from yours.
  3. Use early observations to shape small experiments in order to test your conclusions. Look for new ways to articulate alternatives and better ways to understand options.
  4. Do not simply apply existing frameworks; be open to new possibilities. Try not to describe the world in stereotypical ways, such as good guys and bad guys, victims and oppressors, or marketers and engineers.

By way of example, Ancona describes how Microsoft’s CEO Satya Nadella broke tradition at his first strategy retreat by inviting the CEO’s of newly acquired companies to the table. Being interested in their perspectives allowed him to fully comprehend the challenges ahead.

Nadella ensures that his top management team gather data too, by encouraging in-person customer visits. He regularly conducts small discussions with senior leaders, focusing on the trends that are impacting their businesses, what’s keeping them up at night and what some of the positive indicators for businesses are. 

The article goes on to suggest that leaders who are strong in this capability are able to make sense of complexity and explain it clearly to others. Here we can take a leaf from famous Nobel winning physicist (and MIT alumni), Richard Feynman, who suggested that if you can’t explain it to a grade 6 student, you don’t understand it yourself.

Professor Ancona emphasised that sense making was key to innovation. She also emphasises that senior leaders must engage in sense making first hand – it is not something that can be delegated.  

The last point that she left me with was to give attention to leadership signatures – each individual’s unique way of leading. The quality of one’s personal leadership signature, along with the four capabilities, are important for effective leadership.     

* (In Praise of the Incomplete Leader Deborah Ancona, Thomas W. Malone, Wanda J. Orlikowski, Peter M. Senge FROM THE FEBRUARY 2007 ISSUE)

Readying Leadership for AI with AI

Founded by Harvard and MIT, edX is home to more than 20 million learners, the majority of top-ranked universities in the world and industry-leading companies. It is the only leading MOOC (Massive Open Online Courses‎) provider that is both non-profit and open source.

There, I met with Adam Medros (President and COO) who spoke about the organisation’s desire to offer affordable, high quality education to anyone in the world. A bold vision, but one that has in many ways become reality.

Adam Medros (left), myself and Lee Rubenstein (right) at edX.

My time at the Singularity Summit in San Francisco and subsequent conversations at MIT certainly emphasises the need for lifelong learning. It also begs the question of what role organisations should play as exponential technologies demand new skills from our workforce. 

Medros’ piece in the Boston Globe answers part of the question: “…the way to address the skills gap is to think about reskilling and corporate learning through the lens of corporate social responsibility, or CSR. Corporate leaders have the power to be the driving pressure toward a collective solution for the future of education in the workplace by empowering their employees with opportunities for continuing their education.”

Companies like Amazon are already forging ahead, making education a workforce and CSR priority. Their Career Choice program pays up to 95-percent of tuition and fees toward a professional certificate or diploma in qualified areas of study. More than 10,000 employees have participated, gaining knowledge and credentials to advance their career, often with Amazon.

Accenture developed “Job Buddy,” software that tells employees what percentage of jobs are likely to be lost to automation, which skills are adjacent to their skills, and recommends the additional training they could enrol in. After the pilot program 85% of employees (it was made available to) used it to assess their jobs and enrol in new training.

IBM’s Blue Matching program uses predictive analytics to generate a list of jobs currently available based on an employee’s location, pay grade, job role, experience and other factors. Once an employee opts in to the service, they receive weekly notifications to view potential job matches. 

To level up, AI Skills Academy boasts a suite of learning tools and resources which can empower IBM employees with tools and information to make the most of AI technology.  

AI and IBM’s Watson analytics are also being used to power Mica, the virtual career advisor behind the company’s Watson Career Coach, a program that helps employees identify skills gaps.  Citizens Bank uses Career Coach for its own employees to suggest new jobs and recommend training, videos to watch and materials to read based on employees’ career interests.

According to IBM’s estimates, Blue Matching and Career Coach have saved the company more than $100 million last year alone –  a result of reducing the need for sourcing, recruiting and training new, outside talent.   

There is a mild irony in the fact that AI advancements are shaping the demand for new skills, while also being a powerful tool being used to reskill the workforce.

Real Data with MIT’s Sandy Pentland

In 2012 Forbes named Alex `Sandy’ Pentland one of the ‘seven most powerful data scientists in the world’, along with Google founders and the CTO of the United States, and in 2013 he won the McKinsey Award from Harvard Business Review. 

Pentland directs MIT’s Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program, and co-leads the World Economic Forum’s Big Data and Personal Data initiatives. 

He’s a pioneer in computational social science, organisational engineering, wearable computing (Google Glass), image understanding and modern biometrics. It’s no wonder that Sandy is among the most-cited computational scientists in the world. 

A large focus of Sandy’s current work revolves around how to use big data to understand patterns of employee interaction and communication in organisations. One way his team has managed to do this is through the use of wearables, or “sociometric badges.” 

Pentland’s badges measure movement, proximity to other badges, and speech (volume and tone, but not content). They indicate who talks with whom, where people communicate, and even who dominates in conversations.

With them, Pentland and his team can convert millions of face-to-face interactions into data that can be analysed to improve group communication. 

He argues that this sort of data yields a far more accurate picture of behaviour. Mining Twitter or Facebook feeds, or asking consumers to complete a survey, will at best reveal our socially constructed selves, rather than indicate what we actually do. 

A study conducted in 2017, analysed the use of email communication, meeting schedule data and sociometric badge information. The analysis of the data revealed the difference in promotion rates between men and women was not due to differences in behaviour between men and women, but to how they were treated.  The discrepancy was due to bias, not differences in behaviours.

I anticipate that engagement and customer surveys may well fade into obscurity, increasingly replaced by AI powered organisational analytics. After all, it’s often possible to explain away survey results (snapshot in time, the “fickleness” of human emotion), it’s a lot harder to explain away cold hard data. How much of that data people will be comfortable offering – and the full potential of gathering the digital breadcrumbs we leave behind – remains to be seen. 

These insights were front of mind during my visit with Kathleen Kennedy, Executive Director for the Centre of Collective Intelligence.

At its simplest level, the Centre is interested in how decisions are made in groups and how technology can be used to augment this.

Thomas Malone (CCI founding director), has demonstrated that rather than individuals, it’s groups of people working together in super minds that have been responsible for almost all human achievements in science, business, government and beyond.

Technology is now able to boost this through the use of AI, but it’s the ability of technology to connect human minds at speed and scale – and in new ways – that has incredible potential. These advances may soon enable us to solve the biggest issues and answer the biggest questions of them all.  

Key Takeaways from MIT

1. In an increasingly complex world, executives need to spend more time on Sense Making and Inventing.  How often (and how) do they gather leaders from different parts of their businesses to share their perspectives? How well are they explaining their sense making to the people they lead? Perhaps a mini revolution is required, one where Senior Executives take charge of their diaries, leave the tactical and operational to their team, and take time out to make meaningful maps of the complex world around them.

2. While the current rhetoric on the impact of AI on jobs may well be alarmist according to the MIT Task Force’s Work of the Future interim report, organisations must be proactive in preparing their workforce for the future. Organisations can create environments where employees are encouraged to take charge of their learning, and deploy AI tools to help employees make good choices about where to invest their reskilling efforts.   

3. Organisations would benefit from taking an augmentation, rather than automation, approach to implementing AI. What can leaders do now to liberate their employee’s full talents? How can they deploy technology so that their employees can turn their attention to generating ideas and solutions that will take the organisation forward.

4. Organisations already have a wealth of collective intelligence at their disposal that they can harness – not just from their employees – but their suppliers, strategic partners and customers. All organisations need to think more broadly about how they can tap into this.

Jump to a section

1. MIT Overview
2. Making Sense of Successful Leadership
3. Readying Leadership for AI with AI
4. Real Data with Sandy Pentland from MIT
5. Key Takeaways from MIT