Capturing the AI advantage through culture change

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Oil Review Middle East, May 30, 2025

by James Thomas, Shantanu Gautam, and Pavel Evteev



The GCC’s national oil companies (NOCs) must put AI to work if they are to keep delivering the world’s lowest cost and lowest carbon footprint barrels. To achieve this, NOCs need organisational cultures that can quickly produce many small, high-impact artificial intelligence (AI) applications.

AI-powered solutions are the next major cost and efficiency frontier in the oil and gas industry. Leading oil majors are already using them to produce oil faster, at lower cost and resource intensity. For example, AI can accelerate subsurface analysis, reduce uncertainty, and optimise capital allocation. Shell partnered with startup Avathon (formerly SparkCognition) and is using AI-powered deep learning to reduce seismic shots by 99%, maintaining image accuracy while cutting exploration time from nine months to just nine days.

Beyond exploration, AI is transforming well planning, automating drilling, predicting conditions, and streamlining workflows. ExxonMobil, collaborating with IBM, used AI to reduce well planning and design time from nine to seven months, and cut data preparation time by 40%.

Drilling optimisation is another area seeing major gains. AI can now analyse real-time downhole data, optimise rate of penetration, and predict failures. Machine learning can adjust drilling parameters dynamically, reducing non-productive time, cutting costs, and improving well economics. ConocoPhillips used three years of drilling data to develop a machine learning model that improved vertical rate of penetration by 20% and reduced premature drilling-motor failures by 65% – saving US$30,000 per well.

Environmental performance is improving too. AI can track emissions in real time, detect leaks, and increase carbon capture. Chevron deployed AI to optimise methane emissions reduction in upstream operations, helping cut methane emission intensity by 60%.

NOCs across the region are also making tangible progress in applying AI to boost performance. Aramco, for example, deployed 40,000 sensors across 500 wells, enabling AI-driven process control that increased production by 15% and halved troubleshooting time. ADNOC’s Emission X tool helped abate 1 million tonnes of CO2 in one year through AI-powered emissions prediction and optimisation. 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.

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.

The Future of Platforms

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MIT Sloan Management Review, February 11, 2020

by Michael A. Cusumano, David B. Yoffie, and Annabelle Gawer

The world’s most valuable public companies and its first trillion-dollar businesses are built on digital platforms that bring together two or more market actors and grow through network effects. The top-ranked companies by market capitalization are Apple, Microsoft, Alphabet (Google’s parent company), and Amazon. Facebook, Alibaba, and Tencent are not far behind. As of January 2020, these seven companies represented more than $6.3 trillion in market value, and all of them are platform businesses. 

Platforms are also remarkably popular among entrepreneurs and investors in private ventures. When we examined a 2017 list of more than 200 unicorns (startups with valuations of $1 billion or more), we estimated that 60% to 70% were platform businesses. At the time, these included companies such as Ant Financial (an affiliate of Alibaba), Uber, Didi Chuxing, Xiaomi, and Airbnb.

But the path to success for a platform venture is by no means easy or guaranteed, nor is it completely different from that of companies with more-conventional business models. Why? Because, like all companies, platforms must ultimately perform better than their competitors. In addition, to survive long-term, platforms must also be politically and socially viable, or they risk being crushed by government regulation or social opposition, as well as potentially massive debt obligations. These observations are common sense, but amid all the hype over digital platforms — a phenomenon we sometimes call platformania — common sense hasn’t always been so common.

We have been studying and working with platform businesses for more than 30 years. In 2015, we undertook a new round of research aimed at analyzing the evolution of platforms and their long-term performance versus that of conventional businesses. Our research confirmed that successful platforms yield a powerful competitive advantage with financial results to match. It also revealed that the nature of platforms is changing, as are the ecosystems and technologies that drive them, and the challenges and rules associated with managing a platform business.

Platforms are here to stay, but to build a successful, sustainable company around them, executives, entrepreneurs, and investors need to know the different types of platforms and their business models. They need to understand why some platforms generate sales growth and profits relatively easily, while others lose extraordinary sums of money. They need to anticipate the trends that will determine platform success versus failure in the coming years and the technologies that will spawn tomorrow’s disruptive platform battlegrounds. We seek to address these needs in this article. Read the rest here. 

Predictions for 2019: People analytics will augment the workforce and the workplace

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Deloitte Capital H Blog, December 4, 2018

by Kathi Enderes


Ninety percent of the data in the world has been created within the last two years alone, and the continued emergence of new technologies will likely increase that rate even more. HR leaders have been attempting for years to use people analytics to turn this vast amount of data into actionable insights, but many still struggle with how and where to apply people analytics to maximize the return on investment. In the coming year, more and more organizations will start to apply people analytics in a new way, with a direct focus on the individual, rather than through HR or leaders—a bottom-up approach, as opposed to just top-down.

Going forward, we predict people analytics will become a principal supporting factor in the growing autonomy (and productivity) of the individual, empowering each person with the insights to help them do their best work. Last year, Bersin’s High-Impact People Analytics research revealed that only 2 percent of surveyed organizations are highly mature in people analytics. That tiny percentage has granted us an advance look at how to put people analytics to work. The most mature functions not only integrate it throughout their enterprises but also focus it on addressing business problems, enhancing the quality of day-to-day decision-making, and expanding its accessibility and use through robust insight delivery systems. The purpose of people analytics in these few high-performing companies? Enhanced workforce productivity and performance. Read the rest here.

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

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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.

Why AI Isn’t the Death of Jobs

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MIT Sloan Management Review, May 24, 2018

by Jacques Bughin

When pundits talk about the impact that artificial intelligence (AI) will have on the labor market, the outlook is usually bleak, with the loss of many jobs to machines as the dominant theme. But that’s just part of the story — a probable outcome for companies that use AI only to increase efficiency. As it turns out, companies using AI to also drive innovation are more likely to increase head count than reduce it.

That’s what my colleagues and I recently learned through the McKinsey Global Institute’s broad-based research initiative aimed at understanding the spread of AI in economies, sectors, and companies. We polled 20,000 AI-aware C-level executives in 10 countries to compile a sample of more than 3,000 companies (mostly large), identified distinct clusters within that pool, and ran a variety of scenarios on those clusters to project the effects of AI on employment, revenue, and profitability.

This research and analysis suggest that although AI will probably lead to less overall full-time-equivalent employment by 2030, it won’t inevitably lead to massive unemployment. One major reason for this prediction is because early, innovation-focused adopters are positioning themselves for growth, which tends to stimulate employment. Read the rest here.

Meeting the Challenges of Global Mobility

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HR Technologist, March 2, 2018

by Jonathan Pearce and Mark Solow





A well-developed capability for global mobility is essential for companies seeking to develop and manage top talent, achieve business objectives, and foster a global mindset. Of the 10,400 businesspeople in 140 countries who participated in Deloitte’s 2017 Global Human Capital Trends survey, 68 percent agreed that “a mobile workforce is an enabler of business and talent strategies.”

The problem? Only 3 percent of the respondents rated their companies as “world class” in global deployments.

There are good reasons for this gap: global mobility is a complex, risk-laden, and disruptive undertaking. Moreover, it’s costly to move employees around the world. Our experience working with multinationals tells us that it costs approximately three times an employee’s salary (and typically, these are executive and professional salaries) to deploy someone on a traditional long-term global assignment. And that does not include the productivity losses commonly incurred as employees move themselves and their families to new and unfamiliar locales.

That’s needed is a way to manage global assignments that is simple, personalized, and predictive, in a manner that better serves the needs of workers and the companies for which they work. Read the rest here.

TechSavvy: Beware the Paradox of Automation

MIT Sloan Management Review, October 20, 2016

by Theodore Kinni
Earlier this year, Facebook exorcised those pesky human editors who were introducing political bias into its Trending news list and left the job to algorithms. Now, reports Caitlin Dewey in The Washington Post, the Trending news isn’t biased, but some of it is fake. Turns out the algorithms can’t tell a real news story from a hoax.

Facebook says it can improve its algorithms, but errors of judgment aren’t the only pitfall in transferring human tasks to machines. There’s also the paradox of automation. “It applies in a wide variety of contexts, from the operators of nuclear power stations to the crews of cruise ships, from the simple fact that we can no longer remember phone numbers because we have them all stored in our mobile phones, to the way we now struggle with mental arithmetic because we are surrounded by electronic calculators,” says Tim Hartford in an excerpt published by The Guardian from his new book, Messy: The Power of Disorder to Transform Our Lives. “The better the automatic systems, the more out-of-practice human operators will be, and the more extreme the situations they will have to face.”

Hartford borrows William Langewiesche’s harrowing description of the crash of Air France Flight 447 to illustrate three problems with automation: “First, automatic systems accommodate incompetence by being easy to operate and by automatically correcting mistakes. … Second, even if operators are expert, automatic systems erode their skills by removing the need for practice. Third, automatic systems tend to fail either in unusual situations or in ways that produce unusual situations, requiring a particularly skillful response.”

The excerpt is worth a read — especially if it prompts you to ask if your company’s automation initiatives might entail similar risks. Read the rest here.

TechSavvy: Does Social Media Enhance Employee Productivity?

MIT Sloan Management Review, July 7, 2016

by Theodore Kinni


Do you know what your employees are doing online? Come next May, Singaporean prime minister Lee Hsien Loong won’t have any trouble answering that question. That’s when 100,000 computers used by the city-state’s civil servants will be disconnected from the Internet. The government is taking this drastic action to “tighten security,” writes tech editor Irene Tham in The Straits Times.

Being of a cynical bent, I think that eliminating employee access to Facebook and Twitter and other social media platforms might give Singapore’s government a nice bump in productivity, too. But I might be wrong, according to a report from the Pew Research Center that delves into the use of social media in the workplace.

“Today’s workers incorporate social media into a wide range of activities while on the job,” explain Pew Center researcher Kenneth Olmstead and University of Michigan School of Information professors Cliff Lampe and Nicole Ellison. “Some of these activities are explicitly professional or job-related, while others are more personal in nature.”

Sure, their survey says — ding! — that the number one reason why American workers use social media at work (34% of respondents) is “to take a mental break from their job.” Moreover, reason number two (27% of respondents) is to “connect with friends and family while at work.” But then comes a list that might make your inner CEO perk up a bit: 24% of the respondents use social media at work to foster professional connections; 20% to help them solve work problems; 17% to foster relationships with co-workers and/or learn more about them; and 12% to ask work-related questions of people outside their organization and/or inside their organization.

So, maybe your company shouldn’t follow Singapore’s lead. Anyway, aren’t all those civil servants simply gonna go all Hillary Clinton with their personal devices? Read the rest here

Tech Savvy: What AlphaGo Means to the Future of Management

by Theodore Kinni

MIT Sloan Management Review, March 24, 2016

AI as management assistant: The artificial intelligence program AlphaGo got a lot of attention for beating 18-time Go world champion Lee Sedol four out of five games last week. The significance of this achievement is rooted in the extraordinary number of possible moves in Go: 2.08168199382 … × 10170, reportedly more than the number of atoms in the universe.

That’s too many possibilities for brute computing force to handle (which is how IBM’s Deep Blue beat chess master Garry Kasparov 20 years ago). Yet AlphaGo, created by Google DeepMind, formerly British AI company DeepMind Technologies, mastered the 2,500-year-old board game on its own in a matter of months. “It started by studying a database of about 100,000 human matches, and then continued by playing against itself millions of times,” reported science correspondent Geoff Brumfiel at NPR.

Go bragging rights are nice for Google, but what does AlphaGo’s victory mean for management? “These machine-learning methods will also have significant impact on how we perform unstructured and complex business processes and decision-making tasks in day-to-day work,” explains Lei Tang, chief data scientist at Clari, a sales analytics company, in VentureBeat. “AI routinely considers options ignored by human beings … In this sense, AI is creative, helping humans achieve more.”

Tang says that “self-driving” enterprise applications will be managerial assistants that “detect relevant context changes (location, target customer, timing) and deliver relevant information at the moment it is most helpful.” Better yet, like AlphaGo, they will “become smarter as they analyze the results of ongoing operations, such as marketing campaigns, lead conversions, sales meetings, email flows, interactions with customer success teams, or customer churn.” ...read the rest here

Tech Savvy: When to Hire a Robot

by Theodore Kinni

MIT Sloan Management Review, March 10, 2016

When to hire a robot: Robotics have reached their tipping point, according to International Data Corp. In a newly-released research report, the firm forecasts a near doubling of the worldwide robotics market over the next 4 years — from $71 billion in 2015 to $135.4 billion in 2019. Almost simultaneously, President Obama sent The Annual Report of the Council of Economic Advisors to Congress. It says advances in robotics technology are “presaging the rise of a potentially paradigm-shifting innovation in the productivity process.”

Tongue-twisting alliteration aside, this feeds fears that robots may eventually replace most employees (a thesis argued persuasively by Mark Ford in his award-winning book, Rise of the Robots). But how should your company use robotics between now and then? One answer, highlighted in two recent stories, is to hire robots for supporting, rather than primary, roles.

Mercedes-Benz came to this conclusion in a backward sort of way. As the company expanded the number of models and options it offers, it discovered that its existing assembly-line robots could not be adapted quickly and economically enough. So it’s hiring people to replace some of its robots, report Elisabeth Behrmann and Christoph Rauwald inBloombergBusiness, and equipping them “with an array of little machines,” a solution that the car maker calls “robot farming.”

Mark Rolston, the cofounder and chief creative officer of argodesign, sees the design industry following a similar strategy. “It’s easy to see how an AI-infused computer algorithm such as the future Netflix — after a human has completed the initial design and programming — could do the hard work of improving and evolving to accommodate user preferences largely on its own,” he writes in Fast Company’s Co.Design. “Moreover, 90% of product design today happens in the ‘fat middle ground’ between purely aesthetic and purely technical — incrementally tweaking designs, optimizing column widths, and experimenting with color schemes. These tasks are bread and butter for much of the design industry, and they are progressively being automated.” ...read the rest here