Leading Large Scale Change

Not about digital, which I see is not even mentioned.  Why not?  But I like some of the points made:

A Better Way to Lead Large Scale Change in McKinsey

In Beyond Performance 2.0 (John Wiley & Sons, 2019), McKinsey senior partners Scott Keller and Bill Schaninger draw on their 40-plus years of combined experience, and on the most comprehensive research effort of its kind, to provide a practical and proven “how to” guide for leading successful large-scale change. This article, drawn from the book’s opening chapter, provides an overview of this approach and explains why it works. Future articles will deal with specific topics such as uncovering and shifting limiting mind-sets during change efforts, as well as how to create the ownership and energy needed to succeed.

Neville Isdell took the helm as CEO of Coca-Cola during troubled times. In his words, “These were dark days. Coke was losing market share. Nothing, it seemed—even thousands of layoffs—had been enough to get the company back on track.”1 Its total shareholder returns stood at minus 26 percent, while its great rival, PepsiCo, delivered a handsome 46 percent. Isdell was clear eyed about the challenge ahead; as he put it, “There were so many problems at Coke, a turnaround was risky at best.”

Isdell had a clear sense of what the company needed: to capture the full potential of the trademark Coca-Cola brand, develop other core brands in noncarbonated soft drinks, build wellness platforms, and create adjacent businesses. These weren’t new ideas, and Isdell’s predecessors had failed to make change happen at scale. No matter which direction he set, the company couldn’t make progress until it improved its declining morale, deficient capabilities, strained partnerships with bottlers, divisive politics, and flagging performance culture.  ... " 

How, Where is Machine Learning Evolving?

We were long used to analytics staying static in  method and application.   Well thats not the case, at least not yet.   Some good comments here in this excerpt:

Four ways machine learning is evolving, according to Facebook's AI engineering chief
Yangqing Jia, director of engineering for Facebook's AI platform team, on the changing field of machine learning.    By Nick Heath in TechRepublic

Machine learning is slowly changing the world — helping cars to "see" the world around them and virtual assistants to understand our questions and commands.

Driving forward machine-learning research are companies like Facebook, Google and Baidu — each of which are identifying new applications for the technology.

But how is the field of machine learning changing and what factors are shaping its future direction?

Yangqing Jia, director of engineering for Facebook's AI platform team, spoke about the changing nature of the field at the recent AI Conference presented by O'Reilly and Intel AI in London.

Training datasets are getting too big for humans to handle

In supervised learning, the system learns by example, typically by analyzing labelled data, for example, photos annotated to indicate whether they contain a cat.

The size of training datasets is often massive and continues to grow, with Facebook recently announcing it had compiled 3.5 billion public images from Instagram, labelling each image using attached hashtags.

"Data becomes a super important part in this AI ecosystem," said Jia.

"We know that lately, due to the internet era, we have a huge amount of data. That gives us a mass of data we can deal with."

The difficulty when datasets stretch to billions of images or videos is that manually labelling each one becomes too expensive and time-consuming.

"Data has become a gold mine but can we actually mine gold out of it?" said Jia.  ... "

Implementing and Sustaining Organizational Change

Back to a topic I have been following.  Its process that sustains business, but also the organization and resources that support the process ...

How to implement and sustain organizational change

The fundamentals of change implementation are crucial for not only top-level executives but also frontline managers who are involved in the day-to-day work. ... 

In this episode of the McKinsey Podcast, McKinsey senior implementation leaders Blake Lindsay and Nick Waugh speak with Simon London about the hard work of implementing and sustaining change in organizations—a priority that needs attention from the executive leadership and the frontline managers of each team. ... " 

Charting Change

Last year taught a course at Columbia on change management.  To my students who are still following, here is a nice piece out of Innovation Excellence on charting change.  Some very useful points included.

Digital Pillars of Change

I taught a class at Columbia University on making change happen ....  This is a digital view:

Evolve Or Die: Why Digital Transformation Is More Important Than Ever  by Mike Bainbridge  

Digital transformation: The four pillars of change:
"Why is digital transformation so important and topical? Quite simply, if your organisation is not modernising then you'll be left behind, and the threat of disruption becomes very real." Here's a clear-eyed look at four pillars of change: customer experience, product digitization, employee engagement, and process optimization. .... "

Adaptive Machine Learning

Good points in DSC article below on adaptive machine learning.    I often make the case that you should often consider if your modeling should be modeled adaptively.    Data and context can be changing even if we don't expect it.  Time often drives change.  Even if your data is not necessarily streaming.  With an eye towards risk and change management:

Adaptive Machine Learning

Posted by PG Madhavan on May 20, 2016 at 5:30amView Blog
Machine Learning today tends to be “open-loop” – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called “In-Stream Analytics (ISA)”. Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an example of a “closed-loop” system – we will call this approach “Adaptive Machine Learning” or AML. ISA is a precursor to AML.  .... " 

On AI Adoption

Good thoughts.  really for any kind of  'automation'   adoption.  And the complexities of introducing any kind of non trivial change.  I recently taught this at Columbia.

AI adoption at the atomic level of jobs and work
O'Reilly Radar Podcast: David Beyer on AI adoption challenges, the complexities of getting an AI ROI, and the dangers of hype.  By Jenn Webb     ...

Predictions for Jobs in Emerging Information Tech

Reasonable thoughts here.  But even more important, jobs that will deliver on these innovation areas and sustain the process of their use and value.  Its change management all over again.

2017 Predictions For AI, Big Data, IoT, Cybersecurity, And Jobs From Senior Tech Executives by Gil Press in Forbes ...

" ... ‘Tis the season for the public relations exercise known as “here’s what we think (or hope) will happen in the tech sector next year,” flooding my inbox with predictions for 2017. No one knows what will happen tomorrow, let alone over the next 12 months, but the exercise yields interesting insights into what’s hot (and what’s not) in technology today. Artificial intelligence (and machine/deep learning) is the hottest trend, eclipsing, but building on, the accumulated hype for the previous “new big thing,” big data. The new catalyst for the data explosion is the Internet of Things, bringing with it new cybersecurity vulnerabilities. The rapid fluctuations in the relative temperature of these trends also create new dislocations and opportunities in the tech job market. .... " 

Making Transformations Work

Just covered this in my Columbia course, where it was about getting analytics change to work.    Here a much less technical and more organizational view, but still very useful.  In McKinsey:  Transformation with a capital T   By Michael Bucy, Stephen Hall, and Doug Yakola   " ... Companies must be prepared to tear themselves away from routine thinking and behavior.  ... " 

Technological Change: Harvey Nash Tech Report

To register for the entire report.    Fascinating piece by a participant.  Links to my recent Columbia course on managing change, this is part of that change.

Almost half of tech professionals expect their job to be automated within ten years – Harvey Nash

Technology Survey 2017
Technology is 'eating itself' / Continual skills development key to career success
Forty five per cent of technology professionals believe a significant part of their job will be automated within ten years, rendering their current skills redundant.

The change in technology is so rapid that 94% believe their career would be severely limited if they didn’t teach themselves new technical skills.

This is according to the Harvey Nash Technology Survey 2017, representing the views of more than 3,200 technology professionals from 84 countries.

The chance of automation varies greatly with job role, with Testers and IT Operations professionals most likely to expect their job role to be significantly affected in the next decade (67 per cent and 63 per cent respectively), and CIO/VP IT and Programme Management least affected (31 per cent and 30 per cent respectively).

Columbia University Course on Analytics Change

Next week I will be giving a course in Columbia University's School of Professional Studies entitled:   Applied Analytics PS5700 Analytics and Leading Change.    Any comments on what you might like to see emphasized in such a course, let me know.