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Why We Ask Every New Employee to Code an App Their First Week on the Job

harvardbusiness.org 23 December 2016
In the early days of Twilio, the cloud communications platform company where I work, the company’s founders decided that priority number one was customer empathy. That’s not unique, of course. But as a company created by developers for developers, early on we all had the job of talking to customers constantly. When the company was younger, we worked in a small office where everyone knew everything that was going on.
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What It Will Take for Us to Trust AI

harvardbusiness.org 29 November 2016
The early days of artificial intelligence have been met with some very public hand wringing. Well-respected technologists and business leaders have voiced their concerns over the (responsible) development of AI. And Hollywood’s appetite for dystopian AI narratives appears to be bottomless. This is not unusual, nor is it unreasonable. Change, technological or otherwise, always excites the imagination. And it often makes us a little uncomfortable.
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Bots That Can Talk Will Help Us Get More Value from Analytics

harvardbusiness.org 24 November 2016
Over the past few years, much has been made of the rise of big data. And yet research from TDWI states that at organizations where 50% of employees have access to business intelligence tools, only 20% of that group actually use them. Part of the problem is that systems are often hard to use. Another challenge is low rates of data literacy.
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The Simple Economics of Machine Intelligence

harvardbusiness.org 17 November 2016
The year 1995 was heralded as the beginning of the “New Economy.” Digital communication was set to upend markets and change everything. But economists by and large didn’t buy into the hype. It wasn’t that we didn’t recognize that something changed. It was that we recognized that the old economics lens remained useful for looking at the changes taking place. The economics of the “New Economy” could be described at a high level: Digital technology would cause a reduction in the cost of search and communication. This would lead to more search, more communication, and more activities that go together with search and communication. That’s essentially what happened.
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Hiring Your First Chief AI Officer

harvardbusiness.org 11 November 2016
A hundred years ago electricity transformed countless industries; 20 years ago the internet did, too. Artificial intelligence is about to do the same. To take advantage, companies need to understand what AI can do and how it relates to their strategies. But how should you organize your leadership team to best prepare for this coming disruption? Follow history.
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What Artificial Intelligence Can and Can't Do Right Now

harvardbusiness.org 9 November 2016
Many executives ask me what artificial intelligence can do. They want to know how it will disrupt their industry and how they can use it to reinvent their own companies. But lately the media has sometimes painted an unrealistic picture of the powers of AI. (Perhaps soon it will take over the world!) AI is already transforming web search, advertising, e-commerce, finance, logistics, media, and more. As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu’s AI team of some 1,200 people, I’ve been privileged to nurture many of the world’s leading AI groups and have built many AI products that are used by hundreds of millions of people. Having seen AI’s impact, I can say: AI will transform many industries. But it’s not magic. To understand the implications for your business, let’s cut through the hype and see what AI really is doing today.

You Don't Need Big Data - You Need the Right Data

harvardbusiness.org 3 November 2016
Marion Barraud for HBR The term 'big data' is ubiquitous. With exabytes of information flowing across broadband pipes, companies compete to claim the biggest, most audacious data sets. And businesses of all varieties - old and new, industrial and digital, big and small - are getting into the game. Masses of social, weather, and government data are being leveraged to predict supply chain outages. Enormous amounts of user data are being harnessed at scale to identify individuals among a sea of website clicks. And companies are even starting to leverage huge quantities of text exchanges to build algorithms capable of having conversations with customers. But the reality is that our relentless focus on the importance of big data is often misleading. Yes, in some situations, deriving value from data requires having an immense amount of that data. But the key for innovators across industries is that the size of the data isn't the most critical factor
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The Competitive Landscape for Machine Intelligence

harvardbusiness.org 2 November 2016
Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we’ve tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term “machine intelligence” over “AI.”)
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How Artificial Intelligence Will Redefine Management

harvardbusiness.org 2 November 2016
Many alarms have sounded on the potential for artificial intelligence (AI) technologies to upend the workforce, especially for easy-to-automate jobs. But managers at all levels will have to adapt to the world of smart machines. The fact is, artificial intelligence will soon be able to do the administrative tasks that consume much of managers’ time faster, better, and at a lower cost.
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Machine Learning Is No Longer Just for Experts

harvardbusiness.org 26 October 2016
If you’re not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms’ accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build.
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New Evidence Shows Search Engines Reinforce Social Stereotypes

harvardbusiness.org 20 October 2016
In April, an MBA student named Bonnie Kamona, reported that a Google image search for “unprofessional hair for work” produced a set of images that almost exclusively depicted women of color. In contrast, her search for “professional hair” delivered images of white women. Two months later, Twitter user Ali Kabir’s report on an image search for “three black teenagers” resulted in a good deal of mug shots, while “three white teenagers” retrieved images of young people having fun.
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Finding the Sweet Spot Between Mass Market and Premium

harvardbusiness.org 19 October 2016
Persuading consumers to pay more for a product by introducing some kind of “premium” element into it has always been a challenging task—but it was one that big, established brands had managed with a reasonable amount of success until recent years. For example, Gillette has successfully encouraged consumers to trade up again and again by continually introducing razors with the latest and greatest shaving technology. A decade ago, the Mach 3 razor was Gillette’s premium offering for men, until the Fusion line was launched in 2006 at a 40% price increase, followed by the Fusion ProGlide in 2010 and the Fusion Proshield Flexball in 2016—to name a few of the brand’s major releases.
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Leaders Need Different Skills to Thrive in Tech

harvardbusiness.org 17 October 2016
You accept your first job as a manager in a fast growth tech company, thinking: “How much different could this be from my former company—a financial services firm? Management is management, right?” But by the end of the first month you feel confused and disoriented. A series of jarring experiences have taught you that...

Why Better Technology Can Be Slower to Take Off

harvardbusiness.org 12 October 2016
Find your place on the automation frontier.
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The 'Maximize Profits' Trap in Decision Making

harvardbusiness.org 19 September 2016
What's the right way to make hard business decisions? We all know the standard answers: Obey the law and do whatever maximizes profits or produces the greatest shareholder value. This logic and the institutions that reinforce it, like competitive markets and the rule of law, have transformed the world and lifted billions of people from poverty. But, for gray area decisions, the standard answer isn't the right answer. Gray areas are situations with high uncertainty and serious human stakes. In these situations, you have to look hard at the economics, but you can't stop there. You have to approach these problems as a manager and do the best analysis you can, including hard-headed financial analysis. In the end, however, you have to rely on your judgment and resolve these problems as a human being. In these cases, running the numbers and grasping what they tell you is important, but it isn't enough.
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Global Companies Need to Adopt Agile Pricing in Emerging Markets

harvardbusiness.org 19 September 2016
One day in December 2014, Sergey, the Russia general manager for a multinational consumer goods company, was up early in the morning, watching the ruble's value slide by the minute. As the currency was crashing, he found himself facing a painful dilemma: either raise prices to recoup the losses and hit his annual target - set in U.S. dollars - or wait it out for another two months and hope that the ruble recovers, since that would give him a leg up on his competitors. But with the currency changing every day, how much of a price increase should he consider? The 30% that the ruble had dropped since his last quarterly review? Or should it be more, to compensate for the likelihood of further depreciation to come? That would make his product unaffordable for most of his core customers, and they would almost certainly switch to his competitors' cheaper alternative. There was no good solution.

A Quick Guide to Value-Based Pricing

harvardbusiness.org 9 August 2016
In my 15-plus years of working with companies & teaching courses on pricing strategies to MBA students, I have found value-based pricing (also known as 'value pricing') to be the most commonly discussed concept that's also the most misunderstood one. It creates more confusion among marketers, even many pricing experts, than any other pricing concept. What is more, these misconceptions often lead companies to shy away from using it, instead settling for cost-based or other pricing methods that leave money on the table.
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Figuring Out How IT, Analytics, and Operations Should Work Together

harvardbusiness.org 3 August 2016
A new set of relationships is being formed within companies around how people working in data, analytics, IT, and operations teams work together. Is there a 'right' way to structure these relationships? Data and analytics represent a blurring of the traditional lines of demarcation between the scope of IT and the responsibilities of operating divisions. Consider the core mission of the modern IT department: Taking in all the technology 'mess' (often from several different divisions), developing the necessary competencies, and delivering savings and efficiency to the company. Many IT organizations, having achieved this original mission, now are turning to the next thing, which is innovation. Enter data and analytics, which provide an opportunity for such innovation. However, data traditionally is owned by the business, and analytics is valuable only to the extent that it is used to make business decisions, again 'owned' by the business.

Why Technologists Should Think Like Biologists

harvardbusiness.org 20 July 2016
Our technologies are far from pristine constructions. Frankly, they're a mess. Our software evolves over years, or even decades, with bits and pieces being added over time. The IRS uses technologies from the 1960s, and the Space Shuttle used computer chips that were decades old. The code in our automobiles is fantastically baroque, and in many cases may be too complex to understand. Everything from our kitchen appliances and medical devices to our legal codes and bureaucratic structures are, in a word, kluges. A kluge - a term from the engineering and computer science world - refers to something that is convoluted and messy but gets the job done. Think Rube Goldberg contraption meets MacGyver, but without the playfulness. Kluges often have been adapted and constructed over a period of time, with band-aids upon band-aids, serving their function. But woe betide the person who must maintain or fix such as monster.
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When Old Technologies Create New Industries

harvardbusiness.org 18 July 2016
People understandably get excited about new digital technologies, whether it's the digital camera that is cheaper than developing rolls upon rolls of film, or the photo-sharing apps that - in turn - make your iPhone camera easier to use than your old digital camera. New technology is so much fun that it can be easy to forget that new business models are what drive industries forward, and that old technology can still be valuable as long as it is paired with a smart strategy. Of course, every innovator knows that new technology doesn't always translate into profits. In The Innovator's Dilemma, Clayton Christensen explains how incumbents actually pay a high price for actually doing a great job and constantly improving their technological offerings and creating overserved consumers.

The 4 Mistakes Most Managers Make with Analytics

harvardbusiness.org 12 July 2016
There is a lot of hype surrounding data and analytics. Firms are constantly exhorted to set strategies in place to collect and analyze big data, and warned about the potential negative consequences of not doing so. For example, the Wall Street Journal recently suggested that companies sit on a treasure trove of customer data but for the most part do not know how to use it. In this article we explore why. Based on our work with companies that are trying to find concrete and usable insights from petabytes of data, we have identified four common mistakes managers make when it comes to data.

WhatsApp Grew to One Billion Users by Focusing on Product, Not Technology

harvardbusiness.org 1 July 2016
At a time when digital technology is transforming one industry after another, large companies tend to view innovation and disruption as the result of breakthrough discoveries or technological wonders. They look at the explosive growth of companies such as WhatsApp or Instagram and assume that true innovation is the realm of digital wonks and ambitious entrepreneurs. The corollary, of course, is 'we don't know how to do that.' But when Mubarik Imam, head of growth and partnerships for WhatsApp, told the company's extraordinary story to a group of high-level executives and technology experts at a conference in Palo Alto last year, the narrative was conspicuously free of digital breakthroughs or 'aha!' moments. For those who hoped to hear the secret of how digital wizardry turned two disgruntled Yahoo veterans into overnight billionaires, the real story was an eye-opener. Transforming a relatively simple idea into a $19 billion windfall, it turns out, was more
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Why Companies Are Becoming B Corporations

harvardbusiness.org 17 June 2016
jun16-17-nypl-b-corpFrom the New York Public Library The landscape of American corporations is changing. Since the financialization of the economy in the late 1970s, corporate governance practices have tightly linked the purpose of business with maximizing shareholder value. However, as the 21st century pushes on, there has been an increased emphasis on other stakeholder values, particularly social and environmental concerns. This trend in corporate governance - which has led to the growth in 'triple-bottom line' thinking - has fueled the emergence of a new organizational form: the Certified B Corporation. Certified B Corporations are social enterprises verified by B Lab, a nonprofit organization. B Lab certifies companies based on how they create value for non-shareholding stakeholders, such as their employees, the local community, and the environment. Once a firm crosses a certain performance threshold on these dimensions, it makes amendments to its corporate charter to incorporate t
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Why Salespeople Need to Develop 'Machine Intelligence'

harvardbusiness.org 10 June 2016
Artificial intelligence (AI) is on quite a run, from Google’s AlphaGo, which earlier this year defeated Go world champion Lee Sedol four games to one, to Amazon’s Echo, the voice-activated digital assistant. The trend is heating up the sales field as well, enabling entirely new ways of selling. Purchasing, for example, is moving to automated bots, with 15%–20% of total spend already sourced through e-platforms. By 2020 customers will manage 85% of their relationship with an enterprise without interacting with a human. Leading companies are experimenting with what these technologies can do for them, typically around transactional processes at early stages of the customer journey.

The Downside of the FCC's New Internet Privacy Rules

harvardbusiness.org 27 May 2016
There may soon be a new cop on the privacy beat - the Federal Communications Commission. Last month, the FCC issued a 150-page document proposing sweeping new rules and regulations for broadband Internet Service Providers (ISPs). But in my analysis, this is not good news for those who genuinely care about promoting consumer privacy. To understand why the FCC's involvement would create more problems than it would solve, it helps to understand a massive shift in web security over the last few years: the overwhelmingly successful campaign to encrypt data flowing to and from consumers over the Internet. Encrypting data traffic ensures that information you send and receive can't be decoded by anyone - including criminals, government snoops, and even the ISPs who provide your access to the internet. The latter group includes home and mobile broadband providers, and anyone - your cable provider or a coffee shop - who offers a Wi-Fi connection.
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Where Predictive Analytics Is Having the Biggest Impact

harvardbusiness.org 25 May 2016
The big data revolution is upon us. Firms are scrambling to hire a new brand of analysts dubbed 'data scientists,' and universities have responded to this demand by introducing data science courses into degrees ranging from computer science to business. Survey-based reports find that firms are currently spending an estimated $36 billion on storage and infrastructure, and that is expected to double by 2020. Once companies are logging and storing detailed data on all their customer engagements and internal processes, what's next? Presumably, firms are investing in big data infrastructure because they believe that it offers a positive return on investment. However, looking at the surveys and consulting reports, it is unclear what the precise use cases are that will drive this positive ROI from big data.

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