Back in 2008, Genentech’s top drugs were Avastin, Rituxan, Herceptin, and Lucentis, which generated sales of over $7 billion. As of 2018, the same drugs generated over $21 billion in revenues for Roche, and it appears that Roche’s acquisition of Genentech was a good deal, especially considering how it benefited Roche’s broader R&D direction.
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We conducted a 10-year study, which we call the CEO Genome Project, in which we assembled a data set of more than 17,000 C-suite executive assessments and studied 2,600 in-depth to analyze who gets to the top and how. We then took a closer look at “CEO sprinters” — those who reached the CEO role faster than the average of 24 years from their first job.
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When it comes to the fourth industrial revolution, it seems no capability has been touted more than the connected enterprise. For years now, the connected enterprise—where everything from factory floors to retail shelves are digitally linked—has been one of the main objectives of digital transformation. So much so that Business-to-Business (B2B) Internet of Things (IoT) devices such as industrial sensors and connected machines are expected to increase to 5.4 billion in 2020 from 2.5 billion in 2017.
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Artificial intelligence is reshaping business—though not at the blistering pace many assume. True, AI is now guiding decisions on everything from crop harvests to bank loans, and once pie-in-the-sky prospects such as totally automated customer service are on the horizon. The technologies that enable AI, like development platforms and vast processing power and data storage, are advancing rapidly and becoming increasingly affordable. The time seems ripe for companies to capitalize on AI. Indeed, we estimate that AI will add $13 trillion to the global economy over the next decade.
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Aspiring PMs should consider three primary factors when evaluating a role: core competencies, emotional intelligence (EQ), and company fit. The best PMs I have worked with have mastered the core competencies, have a high EQ, and work for the right company for them. Beyond shipping new features on a regular cadence and keeping the peace between engineering and the design team, the best PMs create products with strong user adoption that have exponential revenue growth and perhaps even disrupt an industry.
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AI and IoT devices are too complicated for their own good. Digitization, optimized processes, prescriptive analytics – these phrases may excite the C-suite, but for those actually using industrial machinery, following processes that have often been unchanged for decades, buzzwords that describe AI’s business potential are – well, just buzzwords.
For IoT and AI to be accepted and adopted on a grand scale, it is crucial that they solve a tangible problem on the ground that makes life easier for workers rather than giving them more headaches. Industrial environments are a perfect test bed for new technology, and may also be the best way to explain how AI and IoT work in a real-world scenario, so that the benefits are clear to everyone throughout an organization.
Keep it simple, stupid
It’s all well and good for a CEO, CTO, or CDO to proclaim a sweeping policy of IoT and AI adoption, but without tangible evidence of how these technologies improve things, the people that interact directly with connected equipment are unlikely to abandon their old ways just because the boss said so. Added to this, without promoting an understanding of the entire data chain from start to finish, data could be compromised, and a company could invest time and energy digitizing operations with very little return. Employing data officers and digital natives in the boardroom may well help executives understand AI and IoT better, but these technologies need to be properly understood at every stage so that all parts of the data chain run smoothly.
A recent report from Plutoshift on the number of manual processes that are still commonplace in the manufacturing sector found that 48% of respondents (all ‘mid-level manufacturing professionals’) collect data manually, and 23% didn’t know whether their manufacturing processes were outfitted with IoT or not. This shows not only that those in middle management or on the factory floor will stick to their tried and tested ‘clipboard and tick-box’ methods if undisturbed, but also that communication from the C-suite about automation is not as effective as it should be. IoT devices that feed AI systems should be easy to understand, and the benefits need to be communicated effectively so that employees choose to abandon time-intensive manual processes in favor of automated data collection and analysis. If AI and IoT are not made understandable for everyone involved, then new technologies simply won’t be embraced by the people they most affect, and the hype around these technologies will rise and fall without consequence.
Sounding it out
Equipment maintenance is an area that requires a high amount of knowledge and insight into how a particular machine works. Technicians need to understand the nuts and bolts of all the mechanisms at work, and where problems might arise in order to predict what could go wrong first. This is where IoT and AI come in. What was once a frustrating and time-consuming task can now be easily automated using sensors and predictive machine learning models, and brings benefits that anyone who has worked with machinery can grasp. Augury, a provider of IIoT sensors and AI software, uses ultrasonic vibrations to create an ‘unique acoustic fingerprint of machines’ and preemptively detect equipment failure, while working with technicians and plant leaders to ensure that their solution does not adversely affect those working on the factory floor.
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GE announced major upgrades to the edge-computing capabilities of its Predix industrial IoT (IIoT) platform, which enables manufacturers to connect and monitor their industrial equipment.
Edge-computing solutions allow data to be stored and processed locally rather than in the cloud or at a remote data center. The upgrades will be available for download on the industrial giant’s website sometime during Q1 2018. Here’s what’s new:
The updates are part of GE’s larger push to give Predix customers more flexibility in how they deploy the platform. Last week, the industrial giant unveiled a new software development kit (SDK) that allows developers to build Predix applications that run on iOS devices, such as the iPad or iPhone. GE aims to make Predix’s device management and data analytics tools accessible in as many settings and across as many devices as possible.
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Tomorrow is World Water Day, and in honor of that day, we want to recognize all that water does for us that we don’t usually see. Much of the world’s water is hard at work in the production and delivery of nearly all goods and services, from apples to zippers. And the amount of water needed to make products is only going to increase, with manufacturing expected to use 400 percent more water from 2000 to 2050.
Where is all of this water coming from? Do we have enough? And once it’s used, what happens to those gallons of water?
Read more about what the Ecolab and Microsoft Partnership: Water Risk Monetizer is doing to solve this problem.
The MIT Sloan Sports Analytics Conference celebrates its tenth year this weekend with its annual gathering being held in the Boston Convention and Exhibition Center. It has attracted sell out crowds of over 2000 the last few years with a price point of $575 this year (a discounted student rate of $200 was also available).
So how did this event, dubbed the Super Bowl of Analytics, get to this point and what can this year’s attendees expect to find in Boston on Friday and Saturday? Here are ten things to know about the history of the conference and this year’s version held March 10 and 11:
Growing revenue and profits is a core objective of most companies, and it is the responsibility of every function to contribute to the pursuit of this goal. Yet, in recent years technology startups have embraced a new role, Growth Manager — alternatively Growth Hacker, Growth PM, or Head of Growth — that focuses on it exclusively. By viewing product development and marketing as integrated functions, not silos, leading tech companies like Facebook and Pinterest are rethinking their approach to driving growth and achieving breakthrough results.
Yet, the Growth Manager role remains poorly understood, especially outside Silicon Valley. As part of an entrepreneurial research effort for Harvard Business School, we interviewed more than a dozen Growth Managers at fast-growing startups and explored what they are doing to design a growth function within an organization.
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