The Personalization Imperative: Driving telecom growth with AI-powered marketing

Learned a lot lending an editorial hand here:

PwC Strategy& Middle East, April 2025

by GP Singh, Mahmoud Makki, Tarek Matar, and Ankit Kushwaha




Customers in every industry are demanding personalized, real-time engagement across channels, whether it is social media, mobile apps, or retail stores online and offline. They expect to be uniquely understood in the moment.

Marketers know that personalization is critical to relevance and differentiation, revenue growth, and brand value. Leaders are tackling this challenge head-on. First, they are gathering massive, proprietary data sets of customer information. Unilever, for example, is on a mission to create 1 billion one-on-one customer relationships by analyzing interactions across digital and in-store touch points for marketing insights. Second, such leaders are creating integrated marketing technology (MarTech) stacks to enable real-time personalization. McDonald’s integrated its MarTech to deliver personalized drive-through menus, mobile app offers, and in-store experiences, increasing its digital customer frequency by 10 percent and raising customer spending. Third, these leaders are exploiting real-time insights to get their concept into the market faster. Such agility allowed Coca-Cola to quickly move from a concept to the production of personalized bottles and cans in its “Share a Coke” campaign, which it launched in Australia and then expanded globally, and grow sales by 2.5 percent in a year in the competitive U.S. market.

Telecom operators are uniquely positioned to fulfill the personalization imperative. Their data sets, which include real-time location data, usage patterns, and customer service interactions, are broader and richer than those of industries such as finance or retail.

The problem is that many telecom operators are struggling to tap this gold mine of insights. In many cases, they are unable to deliver the right offer at the right place and the right time to their customers. We find that telecom companies typically utilize only 30 to 50 percent of their data. Senior telecom executives worry that disconnected MarTech stacks and skills gaps are holding them back.

Data-fueled, AI-powered marketing engines can unlock the potential for personalization. Such engines can produce the insights needed for personalized engagement, promote more informed decisions, and create the precision targeted strategies needed to enhance returns and deliver competitive advantage. Our analysis shows that for telecom companies in the early stages of their customer value management (CVM) journey, every $1.00 invested in AI-powered, data-fueled marketing can yield up to $5.90 in EBITDA (earnings before interest, taxes, depreciation, and amortization) gains over five years. (Read the rest here.)

‘I’m Sorry, Dave.’ When AI Writes a CEO’s Apology Letter.

This Week in Leadership, May 8, 2024

by Theodore Kinni




Generative artificial-intelligence programs are already helping professionals write compelling sales presentations, convincing emails, and other difficult business communications. It was only a matter of time before someone tried using it for one of the most sensitive documents of all: an apology from the boss.

Using the widely available GPT-4, researchers at the Korea Advanced Institute of Science and Technology (KAIST) recently built a model they call “Prompt Engineering for CEO Apology.” The model incorporates a number of variables, including type of event, structure and length of previous apologies, audience, delivery method, and the communication styles of the CEO and the company. The researchers used the model to create apologies for some recent high-profile CEO gaffes, and apparently, AI did pretty well: According to the KAIST study, the notes “conveyed empathy” and “mimicked the same structure and emotional language” of CEO apologies produced by human beings. Read the rest here.

Seeing, doing, and imagining

strategy+business, March 16, 2021

by Theodore Kinni


Photograph by Laurence Dutton

My vote for the foggiest assertion of the pandemic to date is that the U.S. has an abysmally high number of COVID-19 cases — more than 29 million as of March 8, 2021 — because of testing. “If you don’t test, you don’t have any cases,” former President Donald Trump said during a televised White House roundtable on June 15, 2020. “If we stopped testing right now, we’d have very few cases, if any.” I wonder how many people who heard that had the same first thought as I did: Correlation is not causation.

Though associations gleaned from big data drive recommendation engines and bolster corporate revenues, they have their limitations. Imagine trying to control a viral pandemic by refusing to test people for the virus.This isn’t to say that correlation — the idea that two or more things are associated in some way — isn’t valuable. Indeed, there is big money in correlation. In order to peddle subscriptions, Pandora doesn’t need to know what causes people who listen to The Grateful Dead to also listen to Phish. To bulk up its sales, Amazon doesn’t need to know what causes people who buy a Paleo diet book to also buy beef jerky.

The passive observation of data has limited value, because, as Judea Pearl reminds readers several times in The Book of Why: The New Science of Cause and Effect, data is profoundly dumb. “Data can tell you that the people who took a medicine recovered faster than those who did not take it, but they can’t tell you why,” writes the director of UCLA’s Cognitive Systems Lab. “Maybe those who took the medicine did so because they could afford it and would have recovered just as fast without it.”

Association, which Pearl, a Turing Award winner, identifies as the first of three steps on his ladder of causation, won’t help executives answer many of the questions they need to ask when formulating corporate strategy, making investment decisions, or setting prices. To answer questions such as, “What will raising prices by 10 percent do to revenues?” you need to start climbing Pearl’s ladder. Read the rest here.

Why So Many Data Science Projects Fail to Deliver

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, March 2, 2021

by Mayur P. Joshi, Ning Su, Robert D. Austin, and Anand K. Sundaram

Image courtesy of Jean Francois Podevin/theispot.com

More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning. Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.

To better understand the mistakes that companies make when implementing profitable data science projects, and discover how to avoid them, we conducted in-depth studies of the data science activities in three of India’s top 10 private-sector banks with well-established analytics departments. We identified five common mistakes, as exemplified by the following cases we encountered, and below we suggest corresponding solutions to address them. Read the rest here...

The Transformational Power of Recommendation

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, November 24, 2020

by Michael Schrage




Image courtesy of Paul Giovanopoulos/theispot.com

Wikipedia defines recommendation engines (and platforms and systems) as “a subclass of information filtering system that seeks to predict the ‘rating’ or ‘preference’ a user would give to an item.” But as a tool, technology, and digital platform, recommendation engines are far more intriguing and important than this definition suggests.

In data-driven markets, the most effective competitors reliably offer the most effective advice. When predictive analytics are repackaged and repurposed as recommendations, they transform how people perceive, experience, and exercise choice. The most powerful — and empowering — engines of commerce are recommendation engines.

Recommendation engines have been essential to the success of digital platforms Alibaba, Amazon, Netflix, and Spotify, according to their founders and CEOs. For companies such as these, recommendation engines aren’t merely marketing or sales tools but drivers of insight, innovation, and engagement. Superior recommendations measurably build superior loyalty and growth; they amplify customer lifetime value. Computing compelling recommendations profitably reshapes human behavior.

The influence and purpose of recommendation engines are not limited to customers or consumption. Large employers, most notably Google, have adopted and adapted recommendation engines as internal productivity platforms to nudge workers to their best decision options. Indeed, in late 2016, Laszlo Bock, the senior vice president of people operations at Google, left the company to launch Humu, a recommendation-engine startup for advising workforce behavior change.

While data remains the essential advisory ingredient, the global recommendations revolution reflects profound and ongoing algorithmic innovation, enabling machine learning and AI to power improvements in deep learning and generative adversarial networks. Successful recommendation engines learn how to learn. The more people use them, the smarter they become; the smarter they become, the more people use them. Done right, recommendation engines enable virtuous cycles of value creation.

The networked nudges and prompts of recommendation engines increasingly influence people’s choices in clothing, entertainment, food, and medicine; they also influence the texts we send, which friends we contact, the customers and prospects we prioritize, the experts we seek, the job candidates we hire, the investments we choose, the memos we edit, and the schedules we follow.

But prompts and nudges shouldn’t obscure the subtle but vital design principle that makes the recommendation-engine value cycle more virtuous: Recommendation is about ensuring better options and choices, not obedience or compliance. Recommendation engines don’t seek to impose optimal, best, or right answers on their users. To the contrary, their point and purpose are greater empowerment and agency. Influence, not control, is the algorithmic aspiration. In this, successful recommendation-engine design depends more on how recommenders seek to influence than on how much they know.

Recommendation engines transform human choice. Much as the steam engine energetically launched an industrial revolution, recommendation engines redefine insight and influence in an algorithmic age. Wherever choice matters, recommenders flourish, and this profound digital transformation of choice will only become more pervasive as recommenders become smarter. Better recommenders invariably mean better choices. Read the rest here.

What people like you like

strategy+business, November 11, 2020

by Theodore Kinni




Photograph by Paper Boat Creative

I don’t set much store in the endless stream of recommendations offered by Amazon, Netflix, Spotify, and most other online businesses. Occasionally, a book, flick, or song pops up that delights me, but most of the suggestions I get either miss the mark or appear suspiciously advantageous to recommendation engine operators and their advertisers.

Michael Schrage, visiting fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy and s+b contributor, awakens us to the potential of delightful discovery in his latest book, Recommendation Engines. “Recommendation inspires innovation: that serendipitous suggestion—that surprise—not only changes how you see the world, it transforms how you see—and understand—yourself. Successful recommenders promote discovery of the world and one’s self,” Schrage writes in its introduction. “Recommenders aren’t just about what we might want to buy; they’re about who we might want to become.”

If this smacks of techno-utopianism, well, there is a strong strain of that ideology running through Recommendation Engines. For the most part, however, Schrage grounds this rosy view in the powerful effects that recommenders are already producing and balances it with acknowledgment of these systems’ potential for abuse. He also provides a short history of recommendations and a suitably technical description of how recommendation engines work and are built.

To date, the powerful effects of recommenders have manifested themselves mostly in commerce. Schrage cites a variety of facts in this regard: a survey that found recommendations account for approximately 30 percent of global e-commerce revenues; another that found online shoppers are 4.5 times more likely to buy after clicking on a recommendation; and research that “strongly suggests” recommendations drive roughly a third of Amazon’s sales. Read the rest here.

The algorithmic trade-off between accuracy and ethics

strategy+business, March 12, 2020

by Theodore Kinni



Photograph by Yuichiro Chino

Strava, a San Francisco–based fitness website whose users upload data from their Fitbits and other devices to track their exercise routines and routes, didn’t set out to endanger U.S. military personnel. But in November 2017, when the company released a data visualization of the aggregate activity of its users, that’s what it did.

Strava’s idea was to provide its users with a map of the most popular running routes, wherever they happened to be located. As it turns out, the resulting visualization, which was composed from three trillion GPS coordinates, also showed routes in areas, such as Afghanistan’s Helmand Province, where the few Strava users were located almost exclusively on military bases. Their running routes inadvertently revealed the regular movements of soldiers in a hot zone of insurgency.

The problem, explain University of Pennsylvania computer and information scientists Michael Kearns and Aaron Roth, authors of The Ethical Algorithm: The Science of Socially Aware Algorithm Design, is “that blind, data-driven algorithmic optimization of a seemingly sensible objective can lead to unexpected and undesirable side effects.” The solution, which they explore for nontechnical leaders and other lay readers in this slim book, is embodied in the emerging science of ethical algorithm design.

“Instead of people regulating and monitoring algorithms from the outside,” the authors say, “the idea is to fix them from the inside.” To achieve this, companies need to consider the fairness, accuracy, transparency, and ethics — the so-called FATE — of algorithm design.

Kearns and Roth don’t deal with the FATE traits in a sequential manner. Instead, they describe the pitfalls associated with algorithms and discuss the ever-evolving set of solutions for avoiding them. Read the rest here.

Need to Work Differently? Learn Differently

Learned a lot lending an editorial hand here:

Boss Magazine, October 2019

by Michael Griffiths


Digitization requires a new set of skills and a new set of training for employees

The next time your company holds an all-hands meeting, look around the room — or the arena — and consider this: It’s likely that more than half the people present will need reskilling or upskilling in the next three years.

This probably doesn’t come as a complete surprise to you. The forces of change are transforming every aspect of work, including what is done, who does it, and where it is done.

For example, emerging technologies — especially AI and machine learning — are among the most disruptive of these forces. In fact, 81 percent of respondents to Deloitte’s 2019 Global Human Capital Trends survey indicated they expect the use of AI to increase or increase significantly over the next three years. Unlike some, we don’t believe that AI will eliminate the need for a workforce. Instead, we anticipate the rise of hybrid jobs, which are enabled by digitization, technology, and the emergence of a new kind of job, which we call the superjob. A superjob combines work and responsibilities from multiple traditional jobs, using technology to both augment and broaden the scope of the work performed and involving a more complex set of digital, technical, and human skills.

Hybrid jobs and superjobs can enable your company to be more responsive to customers and adaptable to change. But it requires a more deliberate and agile approach to capability development. Already, many companies are responding to this need: Our research finds that 83 percent of organizations are increasing their investments in reskilling programs, and more than half (53 percent) increased their learning and development budgets by 6 percent or more in 2018.

But will more learning be enough at your company? It’s doubtful. To Work Differently we think your company should first Learn Differently. Read the rest here.

Conversational computing

strategy+business, June 13, 2019

by Theodore Kinni

Steve Jobs could be relentless when he wanted something. In early 2010, he wanted a small startup in San Jose, Calif. CEO Dag Kittlaus and his cofounders had just raised a second round of funding and didn’t want to sell. Jobs called Kittlaus for 37 days straight, until he wrangled and wheedled a deal to buy the two-year-old venture for Apple at a price reportedly between US$150 million and $200 million. The company was Siri Inc.

Wired contributor James Vlahos tells the story of how Siri took up permanent residence in the iPhone in his new book, Talk to Me. It’s the first nontechnical book on voice computing that I’ve seen and a must-read if you have any interest in the topic.

Vlahos spends the first third of Talk to Me describing the platform war currently raging in voice computing. It details the race among the big players, including Amazon, Google, and Apple, to embed AI-driven voices in as many different devices as possible, as they seek to dominate the emerging ecosystem. The fact that Amazon now has more than 10,000 employees working on Alexa provides a good sense of the dimensions of that race.

But voice computing is more than a platform play. It is likely to have ramifications and applications for every company, especially if Vlahos’s contention that “the advent of voice computing is a watershed moment in human history” turns out to be right.

“Voice is becoming the universal remote to reality, a means to control any and every piece of technology,” he writes. “Voice allows us to command an army of digital helpers — administrative assistants, concierges, housekeepers, butlers, advisors, babysitters, librarians, and entertainers.” Voice will disrupt the business models of powerful companies — and create new opportunities for upstarts — in part because it will put AI directly in the control of consumers, Vlahos argues. “And voice introduces the world to relationships long prophesied by science fiction — ones in which personified AIs become our helpers, watchdogs, oracles, and friends.” Read the rest here.

Using AI to Enhance Business Operations

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, June 11, 2019

by Monideepa Tarafdar, Cynthia M. Beath, and Jeanne W. Ross



Artificial intelligence invariably conjures up visions of self-driving vehicles, obliging personal assistants, and intelligent robots. But AI’s effect on how companies operate is no less transformational than its impact on such products.

Enterprise cognitive computing — the use of AI to enhance business operations — involves embedding algorithms into applications that support organizational processes. ECC applications can automate repetitive, formulaic tasks and, in doing so, deliver orders-of-magnitude improvements in the speed of information analysis and in the reliability and accuracy of outputs. For example, ECC call center applications can answer customer calls within 5 seconds on a 24-7-365 basis, accurately address their issues on the first call 90% of the time, and transfer complex issues to employees, with less than half of the customers knowing that they are interacting with a machine. The power of ECC applications stems from their ability to reduce search time and process more data to inform decisions. That’s how they enhance productivity and free employees to perform higher-level work — specifically, work that requires human adaptability and creativity. Ultimately, ECC applications can enhance operational excellence, customer satisfaction, and employee experience.

ECC applications come in many flavors. For instance, in addition to call center applications, they include banking applications for processing loan requests and identifying potential fraud, legal applications for identifying relevant case precedents, investment applications for developing buy/sell predictions and recommendations, manufacturing applications for scheduling equipment maintenance, and pharmaceutical R&D applications for predicting the success of drugs under development.

Not surprisingly, most business and technology leaders are optimistic about ECC’s value-creating potential. In a 2017 survey of 3,000 senior executives across industries, company sizes, and countries, 63% said that ECC applications would have a large effect on their organization’s offerings within five years. However, the actual rate of adoption is low, and benefits have proved elusive for most organizations. In 2017, when we conducted our own survey of senior executives at 106 companies, half of the respondents reported that their company had no ECC applications in place. Moreover, only half of the respondents whose companies had applications believed they had produced measurable business outcomes. Other studies report similar results.

This suggests that generating value from ECC applications is not easy — and that reality has caught many business leaders off guard. Indeed, we found that some of the excitement around ECC resulted from unrealistic expectations about the powers of “intelligent machines.” In addition, we observed that many companies that hoped to benefit from ECC but failed to do so had not developed the necessary organizational capabilities. To help address that problem, we undertook a program of research aimed at identifying the foundations of ECC competence. We found five capabilities and four practices that companies need to splice the ECC gene into their organization’s DNA. Read the rest here.

Will Manufacturers Rule the Global Economy Once More?

strategy+business, February 1, 2019

by Theodore Kinni

We’ve been treated to various versions of manufacturing in the past couple of decades. There’s manufacturing as an exercise in financial arbitrage — a link in a global supply chain that is reforged whenever and wherever people will do more work for less pay. There’s the maker movement, populated by hordes of entrepreneurs laboring away in shared shops. There’s Industry 4.0, where we inefficient humans need not apply. There’s the revitalized rust belt, polished to a mirror’s shine with tariffs. And now there’s Tuck School of Business professor Richard D’Aveni’s vision of manufacturing, detailed at length in The Pan-Industrial Revolution, a book that starts out strong but eventually bogs down in speculation.

D’Aveni’s version of manufacturing could be labeled “when dinosaurs rule the Earth once more.” He thinks that hulking tyrannosaurs like battered General Electric are going to rise up and roar in the years ahead. If he’s right, the Dow Jones Industrial Average might actually become industrial again.

D’Aveni weaves this new vision on an intricate loom. Its weft is composed of additive manufacturing (AM), which includes all the evolving forms of 3D printing in combination with other production technologies, such as lasers and robotics; its warp is digitized, AI-powered management systems and platforms.

AM is a game-changing family of technologies, and D’Aveni illustrates them with a host of gee-whiz examples. Lockheed Martin can 3D-print the entire body and interior of its 12-ton, 50-foot-long F-35 fighter jets in about three months, compared with the two to three years it takes to make them using traditional technologies. (It’s now working to cut production time to three weeks.) Electronics parts supplier Lite-On is using 3D printers to make 15 million smartphone antennas annually, demonstrating the technology’s potential for cost-effective mass production. The medical device company Stryker, which is already 3D-printing joint implants, is developing machines that can be installed in hospitals to produce customized implants while surgeons and patients wait. Local Motors has demonstrated its ability to 3D-print a car — reducing the number of parts needed from 30,000 to 50. Read the rest here.

How to embed data-driven decision-making into your organisational culture

Learned a lot lending an editorial hand here:

InsideHR, November 20, 2018

by Jeff Mike



Thanks to the increasing sophistication of analytics, data and algorithms can inform and improve management, business, and HR decision-making throughout companies. But, the tools of data collection and decision-support algorithms are only one element in the quest to attain the full potential of analytics.

Another is the ability of employees at all levels to use these tools, a challenge that will require a broad-based upskilling of the workforce. And, there’s an additional element – the willingness to employ analytics to make decisions. Compounding all of this is the fact that organisations today are becoming social enterprises, where the ability to manage social, environmental and governance concerns are as important as financial returns. In this environment, workers have more influence than ever. Their voices are amplified through social media and other means, meaning errors made by an organisation can have far-reaching consequences.

So, what does all of this mean? To create a willingness to use more data and unbiased decision-support algorithms, a mindset of data-driven decision-making should be embedded in the organisational culture in a way that benefits employees in their work as well as other stakeholders. The need for a data-driven culture is important and shouldn’t be underestimated.

In fact, this need is one of the top findings in Bersin’s High-Impact People Analytics research, which revealed that a company can fully utilise people analytics only if – and when – using data to make decisions becomes part of the culture, or “how we do things around here.” In fact, the research determined that organisations that have achieved the highest levels of people analytics maturity are three times more likely to have such a culture than organisations at lower maturity levels.

However, just making the decision to implement a mindset of data-driven decision-making into an organisation’s culture won’t work. In an analytics-friendly culture, data-driven decision-making isn’t an afterthought, an add-on, or a justification; rather, it is a shared mindset in which:

• Everyone recognises that data and analytics are essential to sound decision-making;
• They use data and analytics in their decision processes for all aspects of the enterprise including financial, social and environmental well-being;
• They use data and analytics to monitor – and adjust – decision outcomes to ensure desired results and to prevent bias.

Read the rest here...

When Prediction Gets Cheap

strategy+business, August 8, 2018

by Theodore Kinni


I don’t usually write mash notes, but I recently sent one to Waze via Twitter. I figured the navigation app had helped me avoid more than 100 hours of traffic jams over a couple of years, and I felt compelled to declare my undying gratitude.

After reading Prediction Machines, by three Rotman School of Management professors, it turns out I’m not so much enamored with Waze as I am with the technology that powers it: artificial intelligence (AI). It is AI that enables the app to predict the best routes for its users.

According to Ajay Agrawal, Joshua Gans, and Avi Goldfarb, who are also, respectively, founder, chief economist, and chief data scientist of the Creative Destruction Lab, prediction is the essential output of AI. “The current generation of AI provides the tools for prediction and little else,” they write. “Today, AI tools predict the intention of speech (Amazon’s Echo), predict command context (Apple’s Siri), predict what you want to buy (Amazon’s recommendations), predict which links will connect you to the information you want to find (Google search), predict when to apply the brakes to avoid danger (Tesla’s Autopilot), and predict the news you will want to read (Facebook’s newsfeed).”

This is the key insight of Prediction Machines, and it is an extraordinarily useful one for any executive who has been grappling with the implications and ramifications of AI. AI will automate prediction, and as a result, prediction will become cheap. “Therefore, as economics tells us,” explain the authors, “not only are we going to start using a lot more of it, but we are going to see it emerge in surprising new places.” Read the rest here.

Wait-and-See Could Be a Costly AI Strategy

Learned a lot lending an editorial hand here:

MIT Sloan Management Review, June 15, 2018

by Jacques Bughin

From the dexterity of Amazon’s Kiva robots to the facial recognition in Apple’s iPhone X, artificial intelligence is increasingly sophisticated and accessible. It also promises to be a rich source of profit uplift — up to 10% of revenue, depending on your industry.

Nevertheless, more than 95% of companies have not embraced AI technology to reinvent how they do business. Even though there are many unknowns regarding AI’s capabilities and uses, our research at the McKinsey Global Institute suggests that following a wait-and-see strategy for too much longer could be a costly mistake.

How costly? When we collected more than 400 use cases in 19 industries and simulated the dynamics of AI diffusion (based on current corporate intent to adopt, the technology’s impact on cash flow, and the profit growth linked to adoption), we found significant divergences in the patterns of economic growth between early adopters of AI at scale and non-adopters. In the simulation, early diffusers — that is, companies that will use a full suite of AI technologies in the next five years — doubled their normal profits by 2030, bringing in an additional 4% of gross profit growth annually at the expense of their competitors. When we extrapolated this on a global basis, it equated to a shift in corporate profit to early AI diffusers of approximately $1 trillion by 2030, or 10% of the current profit pool. Read the rest here.

It's Time to Make the People Side of Business Data-Driven and Evidence-Based

Learned a lot lending an editorial hand here:

Boss, June 2018

by David Mallon



These days data is front-page news. It’s use—and misuse—has precipitated a host of corporate crises. In many of these stories, data is being demonized. But data, per se, isn’t bad or good; it’s what people do with it that matters.

We live in an era of digital technology, in which more and more of what we do generates data. A few years ago, IBM estimated that we were creating 2.5 quintillion bytes of data daily, and that something like 90 percent of all the data in the world created in the prior two years alone. The data we generate is collected, analyzed, and served back to us constantly, whether we realize it or not.

Companies and individuals must become masters of data or they may risk being mastered by it. And to master data, companies and individuals need to be mindful. What data will be collected? How can they ensure its accuracy? Who will collect it? Where, when, why, how, and for what purpose will it be used? Where will the data go? Who will have access to it? What privacy and security controls will protect it?

If that seems like a lot of questions, well, it is. We need to be active stewards of our data. We need to actively seek insights from it. We need to use those insights in positive, productive ways that drive organizational and personal value. The headlines tell us that these tasks shouldn’t be left unaddressed—especially when it comes to our people.

The HR Function Needs to Build Data Muscle

One finding rings out clearly in Bersin’s High-Impact People Analytics study: Companies that are proactively building an organizational muscle around people data and analytics are getting ahead.

Some corporate functions, like marketing, are already well along in the data and analytics race. But the typical HR department is not nearly as evolved. Our study revealed that only two percent of companies surveyed have a fully mature (Level Four) people analytics capability; meanwhile 83 percent of companies are operating at Levels One and Two.

The people side of business must become more data-driven and evidence-based. Data can and should inform decisions around performance, people, and talent, if for no other reason than the fact that relying on tradition and prior experience are neither sufficient nor prudent in today’s digital world...read the rest here

Ethics Should Precede Action in Machine Intelligence

MIT Sloan Management Review, June 29, 2017

by Theodore Kinni


As analytics and Big Data continue to be integrated into organizational ways and means from the C-suite to the front lines, Josh Sullivan and Angela Zutavern believe that a new kind of company will emerge. They call it the “mathematical corporation” — a mash-up of technology and human ingenuity in which machines delve into every aspect of a business in previously impossible ways and produce insights that will allow previously unimaginable solutions — new businesses, strategies, products and services, and so on.

Sullivan and Zutavern don’t think the mature mathematical corporation exists yet, but they do have a privileged view of the forces that will produce it. Both are executives at Booz Allen Hamilton Inc., where Sullivan organized and leads the company’s data science and advanced analytics capabilities, and Zutavern is developing applications of machine intelligence to organizational leadership and strategy.

In their new book, The Mathematical Corporation, Sullivan and Zutavern explore how company leaders can prepare for and accelerate the transformation to a new corporate model. The following excerpt from Chapter 7, edited for space, examines the inevitable ethical conflicts that will arise — and have already arisen — as the ability of companies to collect and parse personal data explodes. And more important, it points out the need to proactively anticipate those conflicts. Read the excerpt here.

TechSavvy: Competing for Talent in the Platform Economy

MIT Sloan Management Review, October 24, 2016

by Theodore Kinni

Platforms are all the rage these days. Companies are being urged to create their own — à la Uber and Airbnb. But platform advocates often take one thing for granted: a seemingly infinite supply of workers who will happily do the platform operator’s bidding in return for, well, whatever the operator is willing to give them.

That may not be a sound assumption, especially as the competition heats up in platform markets that prove viable. Witness Sheelah Kolhatkar’s article on Uber’s fast-growing rival, Juno, in The New Yorker. “Juno’s business model is to take what Uber has created and appropriate it,” writes Kolhatkar. “Most of what Juno does is predicated on the fact that many drivers feel mistreated by Uber. … If Uber seems cold and impersonal, Juno will smother its drivers with attention. If Uber has raised its commission — the part of each fare that the company keeps — Juno will set a much lower one.”

As the folks at Uber think about how to frame a response to the wooing away of its drivers, they might want to read the new report on platform workers from the Institute for the Future. The IFTF did an ethnographic study of a select group of platform workers. It found the workers fit into seven distinct archetypes and that there are seven qualities that define the platform working experience.

The study also found out what platform workers care about. Their top three concerns: income potential; control over choosing which jobs to take; and work frequency, immediacy of payment, and convenience.

Uber isn’t the only company that should be reading the IFTF report. Its battle for drivers suggests that eventually all successful platform companies will have to compete for contract workers. So they better get to know them. Read the rest here.

TechSavvy: Why Digitization Won’t Put Operations Managers Out of Work

MIT Sloan Management Review, October 13, 2016

by Theodore Kinni

On Oct. 3, ING Group joined a growing number of big European banks when it announced a big investment in digital technology (800 million euros) and a big reduction in force (11% or 5,800 jobs). “Unfortunately digital transformation means less jobs,” CFO Patrick Flynn told Bloomberg Television not very ruefully.

But perhaps not fewer management jobs. “Even as organizations balance lower investment in traditional operations against greater investment in digital, the need for operations management will hardly disappear,” write McKinsey consultants Albert Bollard, Alex Singla, Rohit Sood, and Jasper van Ouwerkerk in a new article in McKinsey Quarterly. “In fact, we believe the need will be more profound than ever.”

In the near term, the challenge will be the ability of companies “to embrace digital innovation and operations-management discipline at the same time.” That, the authors say, will require figuring out how to combine digital and human resources, modify employee roles to showcase and sustain digitization, support customers as they figure out how to work with the organization, and develop leaders and managers with “much stronger day-to-day skills in working with their teams.” Read the rest here.

TechSavvy: A Code of Ethics for Smart Machines

MIT Sloan Management Review, September 8, 2016

by Theodore Kinni


Smart machines need ethics, too: Remember that movie in which a computer asked an impossibly young Matthew Broderick, “Shall we play a game?” Four decades later, it turns out that global thermonuclear war may be the least likely of a slew of ethical dilemmas associated with smart machines — dilemmas with which we are only just beginning to grapple.

The worrisome lack of a code of ethics for smart machines has not been lost on Alphabet, Amazon, Facebook, IBM, and Microsoft, according to a report by John Markoff in The New York Times. The five tech giants (if you buy Mark Zuckerberg’s contention that he isn’t running a media company) have formed an industry partnership to develop and adopt ethical standards for artificial intelligence — an effort that Markoff infers is motivated as much to head off government regulation as to safeguard the world from black-hearted machines.

On the other hand, the first of a century’s worth of quinquennial reports from Stanford’s One Hundred Year Study on Artificial Intelligence (AI100) throws the ethical ball into the government’s court. “American law represents a mixture of common law, federal, state, and local statutes and ordinances, and — perhaps of greatest relevance to AI — regulations,” its authors declare. “Depending on its instantiation, AI could implicate each of these sources of law.” But they don’t offer much concrete guidance to lawmakers or regulators — they say it’s too early in the game to do much more than noodle about where ethical (and legal) issues might emerge.

In the meantime, if you’d like to get a taste for the kinds of ethical decisions that smart machines — like self-driving cars — are already facing, visit MIT’s Moral Machine project. Run through the scenarios and decide for yourself who or what the self-driving car should kill. Aside from the fun of deciding whether to run over two dogs and a pregnant lady or drive two old guys into the concrete barrier, it’ll help the research team create a crowd-sourced view of how humans might expect of ethical machines to act. This essay from UVA’s Bobby Parmar and Ed Freeman will also help fuel your thinking. Read the rest here.

TechSavvy: Four Lessons from IoT Early Adopters

MIT Sloan Management Review, August 12, 2017

by Theodore Kinni

To paraphrase the late Roy Scheider in one of the greatest of all summer movies, you’re gonna need a bigger router. In 2025, Machina Research predicts, the Internet of Things is going to be a $3 trillion market of 27 billion devices generating more than 2 zettabytes of data. Two zettabytes of data is something like twice the total global IP traffic we’ll generate this year, according to Cisco.

The IoT data deluge is, by the way, the first of four lessons drawn from early IoT adopters by contributing writer Howard Baldwin for his article in Computerworld. IoT initiatives at ARI Fleet Management, for instance, generate the same amount of data every two weeks as the company previously collected in two decades. “Understand where data is coming from, and determine how you’re going to analyze it,” writes Baldwin.

The second lesson is that IoT will require cross-functional collaboration. Because IoT is deployed and used in factories and fleets and products, the IT department is going to need to partner with other functions and business units. “Determine how and when to combine operations and information technologies for maximum data insight,” writes Baldwin.

Baldwin’s third lesson for early adopters is that IoT is likely to require working with and coordinating across multiple vendors. The new Kansas City streetcar line, for instance, required collaboration with Sprint, Cisco and other vendors. “In orchestrating the many moving pieces of an IoT rollout, make sure you know who plays what part,” writes Baldwin.

Fourth and finally, as applies to forays into any young, fast-emerging technology, watch out that you don’t get caught out too far on the IoT growth curve. “Some early IoT adopters have reported reliability issues with either sensors or vendors or both, and others have struggled to reconcile competing protocols,” writes Baldwin. “Be prepared for setbacks in an immature market, and try to select a protocol that has long-term industry support and a sound security footprint.” Read the rest here