Businesses are constantly vying to capture the attention of potential customers. It’s not easy to do. People are inundated with different brands as they stroll through the streets, scan through their social media newsfeeds, and binge television. The average American is exposed to more than 4,000 ads every day.
A simple concept can help businesses cut through the noise. It’s called psychological ownership. That’s when consumers feel so invested in a product that it becomes an extension of themselves.
Most hedge funds don’t make money. This hasn’t stopped a growing list of startups from trying their hands at employing machine learning to tip the scales in their favor. But Pit.ai, a new machine learning-powered hedge fund, adopted into the YC W17 class, thinks it can best Numerai, Quantopian and others with its own unique recipe for automating money making. Read More
If a picture is worth a thousand words, a video is worth that times the frame rate. Matroid, a computer vision startup launching out of stealth today, enables anyone to take advantage of the information inherently embedded in video. You can build your own detector within the company’s intuitive, non-technical, web platform to detect people and most other objects. Reza Zadeh, founder… Read More
Young management consultants may be novices, but they’re sold as experts. Conversely, even experienced consultants, who legitimately present themselves as experts, still feel like novices when they embark on a new project.
The challenge with effective consulting is that it depends on in-depth situational knowledge that consultants simply can’t have when they start an assignment. What’s more, they may not yet be completely clear on what the client — who’s paying top dollar and expects results immediately — really wants. So consultants must rapidly and discreetly gain knowledge of the client’s business while simultaneously giving an impression of competence and self-confidence. We call this challenge learning-credibility tension.
Traders who have an idea for a money-making algorithm have two choices: learn to code themselves, or hire a great engineer. But neither of these two options are realistic, especially for part-time traders who don’t have a large bankroll behind them. Meet Algoriz, a startup participating in Y Combinator’s Winter 2017 batch. Read More
Google and other tech companies have come up with glasses and contact lenses for the purposes of AR, but Omega Ophthalmics is taking a much more invasive approach by using surgically implanted lenses to create a space for augmented reality inside the eye. Read More
Source: TechCrunch Startup
There is no shortage of advice for those who aspire to be effective leaders. One piece of advice may be particularly enticing: if you want to be a successful leader, ensure that you are seen asa leader and not a follower. To do this, goes the usual advice, you should seek out opportunities to lead, adopt behaviors that people associate with leaders rather than followers (e.g., dominance and confidence), and — above all else — show your exceptionalism relative to your peers.
But there is a problem here. It is not just that there is limited evidence that leaders really are exceptional individuals. More importantly, it is that by seeking to demonstrate their specialness and exceptionalism, aspiring leaders may compromise their very ability to lead.
The simple reason for this is that, as Warren Bennis has observed, leaders are only ever as effective as their ability to engage followers. Without followership, leadership is nothing. As one of us (Haslam) observed in a 2011 book coauthored with Stephen Reicher and Michael Platow, The New Psychology of Leadership, this means that the key to success in leadership lies in the collective “we,” not the individual “I.”
In other words, leadership is a process that emerges from a relationship between leaders and followers who are bound together by their understanding that they are members of the same social group. People will be more effective leaders when their behaviors indicate that they are one of us, because they share our values, concerns and experiences, and are doing it for us, by looking to advance the interests of the group rather than own personal interests.
This perspective identifies a major flaw in the usual advice for aspiring leaders. Instead of seeking to stand out from their peers, they may be better served by ensuring that they are seen to be a good follower — as someone who is willing to work within the group and on its behalf. In short, leaders need to be seen as “one of us” (not “one of them”) and as “doing it for us” (not only for themselves or, worse, for “them”).
In a recent paper, we set out to test these ideas through a longitudinal analysis of emergent leadership among 218 male Royal Marines recruits who embarked on the elite training program after passing a series of tests of psychological aptitude and physical fitness. More specifically, we examined whether the capacity for recruits to be seen as displaying leadership by their peers was associated with their tendency to see themselves as natural leaders (with the skills and abilities to lead) or as followers (who were more concerned with getting things done than getting their own way).
For this purpose, we tracked recruits’ self-identification as leaders and followers across the course of a physically arduous 32-week infantry training that prepared them for warfare in a range of extreme environments. This culminated in the recruits and the commanders who oversaw their training casting votes for the award of the Commando Medal to the recruit who showed most leadership ability. So who gets the votes? Marines who set themselves up as leaders, or those who cast themselves as followers?
In line with the analysis that we present above, we found that recruits who considered themselves to be natural leaders were not able to convince their peers that this was the case. Instead, it was the recruits who saw themselves (and were seen by commanders) as followers who ultimately emerged as leaders. In other words, it seems that those who want to lead are well served by first endeavoring to follow.
Interestingly, though, alongside these results, we also found that recruits who saw themselves as natural leaders were seen by their commanders as having more leadership potential than recruits who saw themselves as followers. This suggests that what good leadership looks like is highly dependent on where evaluators are standing. Evaluators who are situated within the group, and able to personally experience the capacity of group members to influence one another, appear to recognize the leadership of those who see themselves as followers. In contrast, those who stand outside the group appear to be most attuned to a candidate’s correspondence to generic ideas of what a leader should look like.
This latter pattern tells us a lot about the dynamics of leadership selection and helps to explain why the people who are chosen as leaders by independent selection panels often fail to deliver when they are in the thick of the group that they actually need to lead. It also has the potential to complicate the picture for aspiring leaders. The reason for this is that in organizations that eschew democratic processes in their selection of leaders, employees who are seen as leaders (by themselves and by those who have the power to raise them up) may be more likely to be appointed to leadership positions that those who see themselves as followers.
However, as our Marines data suggest, this elevation of those who seek to distance themselves from their group may actually be a recipe for failure, not success. It encourages leaders to fall in love with their own image and to place themselves above and apart from followers. And that is the best way to get followers to fall out of love with the leader. Not only will this then undermine the leader’s capacity to lead but, more importantly, it will also stifle followers’ willingness to follow. And that can only ever be a path to organizational mediocrity.
Innovation is famously difficult — many projects end up losing money, frustrating employees, and going nowhere. And yet corporations and governments spend billions of dollars annually pursuing innovation. This huge spending would generate more value for businesses and societies if the innovation success rate were just a little higher. Is there a way to increase the success rate without spending more?
We think there is. Innovation projects often fail because the resources are spent on the wrong kind of innovation. Too much money is spent on attention-grabbing activities that are straightforward to do, like hiring new people, procuring new technologies, and buying more facilities. It is much less obvious, and usually harder, to change the design of a current service system, introduce new customer experiences, or build a better business model — but the return on those investments may be much higher.
Innovation needs to be considered in two ways: innovation capacity and innovation ability.
Innovation capacity is the organization’s potential for innovation. This is the stuff that’s easy to buy, and that organizations tend to spend too much on: assets and resources. This includes technology and people, as well as tangible, intangible, and financial assets. Most innovation investments, such as product improvement, technological innovation, and research and development (R&D) traditionally aim at strengthening the innovation capacity of the organization. Today, every company, small or multi-national, new or incumbent, can obtain innovation capacity. People can be hired through the sharing economy; technology can be rented by the hour; finance can be sought for any prototype, and assets bought. But capacity alone is insufficient to create new, significant, sustainable value for customers — no matter how huge the capacity.
That’s where innovation ability comes in. This term describes the more difficult aspects of creating value, like new customer experiences, a revised service system, or new business models. An organization may have many people providing innovation capacity, but may still struggle to increase innovation ability, because capacity by itself does not invent nor implement a new business model or a better customer experience. Yes, an organization requires a certain amount of innovation capacity, but there is no increased value creation through an increase in innovation capacity alone.
We’ve come to these conclusions after completing case study analyses of a range of companies, including Nokia, Kodak, Borders, Amazon, Apple, and Xerox. Together, these companies have spent billions on innovation. But although the latter three spent relatively less on innovation, they spent their innovation budgets more wisely, choosing to invest in innovation ability rather than capacity.
Nokia during 2007-2010 was an example of a corporation with great innovation capacity. Nokia always offered technologically feature-rich mobile phones — in fact, Nokia invented the smartphone. Nokia actually offered a touchscreen smartphone two years before Apple’s iPhone. Yet Nokia hung on to the Symbian operating system despite knowing its weaknesses in the eyes of the consumer. Nokia did have resources to develop a new operating system, but chose to stick with Symbian. As a result, Nokia became less and less able to create new value. At one point Nokia manufactured 90 different mobile phones. Their functionality was developed slightly from one model to the next, but most phones were examples of innovation driven by the company’s innovation capacity. In short, technology was a strength for both companies, but Apple did a much better job connecting its technology to a service system delivering new customer experiences through a relevant business model. Developers outside of Apple were allowed to sell apps through iTunes and the App Store. Apple kept 30% of the sales made by outside developers. The huge number of apps created provided customers with a very wide selection of new customer experiences.
Nokia launched the OVI Store globally in May 2009. The company was however unable to match the service system provided by the iPhone in combination with iTunes and the thousands of applications that had already been developed. The then Nokia CEO Stephen Elop was quoted in Wired of February 2011 stating: “The first iPhone shipped in 2007, and we still don’t have a product that is close to their experience.” The Ovi Store was discontinued in 2015.
Kodak is another example of a company that spent most of its resources on drivers of innovation capacity. The company famously spent over four billion dollars developing the digital camera, but chose not to develop a new business model to convert that innovation capacity into innovation ability — and as a result, failed to capture the value of what they’d invented. By contrast, Xerox invested in customer experiences, creating increased value for customers by expanding its platform, resulting in increased revenues. As Xerox’s CEO Anne Mulcahy said in the Dean’s Innovative Leader Series at MIT in 2006: “In trying to rebound, we spent the vast majority of our time talking to customers.” By 2011, two-thirds of Xerox’s revenues came from products or services it had introduced within the last two years. Put simply, Xerox embraced the digital era and developed a host of technologies enabling the firm’s ability to transform to a services business . Kodak — in contrast — tried to delay that transformation as long as possible, avoiding developing its service system, customer experiences, and business model.
Three lessons for value creation emerge here.
First, organizations should spend less on building the capacity for innovation. In other words, even if your organization increases the number of people working on innovation initiatives by 10% or even 20% — while at the same time no other changes are made internally — there is simply no legitimate reason to believe that the organization will create even greater value.
Second, to succeed with innovation initiatives, corporations need to consider the value drivers that change through innovation ability — the business model, customer experiences, and the service system. Even if an organization has a new idea, a new technology, a new product, or a new service, none of these will necessarily increase the organization’s innovation success rate unless innovation ability changes one or more of the value drivers.
Finally, the thinking and practice of innovation should start from the premise that successful innovation is driven by the shared value created. Innovation should be value-driven; corporations, and governments, need to create value for a network of stakeholders: customers, suppliers, and the firm — maximizing value solely for the owners is not enough.
A corporation can have the same idea, product, service or technology as its main competitor, but to win in the marketplace it must develop a new business model, customer experience, or service system that will put that new idea, product, or technology to use.
In January of 2018, Annette Zimmermann, vice president of research at Gartner, proclaimed: “By 2022, your personal device will know more about your emotional state than your own family.” Just two months later, a landmark study from the University of Ohio claimed that their algorithm was now better at detecting emotions than people are.
AI systems and devices will soon recognize, interpret, process, and simulate human emotions. A combination of facial analysis, voice pattern analysis, and deep learning can already decode human emotions for market research and political polling purposes. With companies like Affectiva, BeyondVerbal and Sensay providing plug-and-play sentiment analysis software, the affective computing market is estimated to grow to $41 billion by 2022, as firms like Amazon, Google, Facebook, and Apple race to decode their users’ emotions.
Emotional inputs will create a shift from data-driven IQ-heavy interactions to deep EQ-guided experiences, giving brands the opportunity to connect to customers on a much deeper, more personal level. But reading people’s emotions is a delicate business. Emotions are highly personal, and users will have concerns about fear privacy invasion and manipulation. Before companies dive in, leaders should consider questions like:
What are you offering? Does your value proposition naturally lend itself to the involvement of emotions? And can you credibly justify the inclusion of emotional clues for the betterment of the user experience?
What are your customers’ emotional intentions when interacting with your brand? What is the nature of the interaction?
Has the user given you explicit permission to analyze their emotions? Does the user stay in control of their data, and can they revoke their permission at any given time?
Is your system smart enough to accurately read and react to a user’s emotions?
What is the danger in any given situation if the system should fail — danger for the user, and/or danger for the brand?
Keeping those concerns in mind, business leaders should be aware of current applications for Emotional AI. These fall roughly into three categories:
Systems that use emotional analysis to adjust their response.
In this application, the AI service acknowledges emotions and factors them into its decision making process. However, the service’s output is completely emotion-free.
Conversational IVRs (interactive voice response) and chatbots promise to route customers to the right service flow faster and more accurately when factoring in emotions. For example, when the system detects a user to be angry, they are routed to a different escalation flow, or to a human.
AutoEmotive, Affectiva’s Automotive AI, and Ford are racing to get emotional car software market-ready to detect human emotions such as anger or lack of attention, and then take control over or stop the vehicle, preventing accidents or acts of road rage.
The security sector also dabbles in Emotion AI to detect stressed or angry people. The British government, for instance, monitors its citizens’ sentiments on certain topics over social media.
In this category, emotions play a part in the machine’s decision-making process. However, the machine still reacts like a machine — essentially, as a giant switchboard routing people in the right direction.
Systems that provide a targeted emotional analysis for learning purposes.
In 2009, Philips teamed up with a Dutch bank to develop the idea of a “rationalizer” bracelet to stop traders from making irrational decisions by monitoring their stress levels, which it measures by monitoring the wearer’s pulse. Making traders aware of their heightened emotional states made them pause and think before making impulse decisions.
Brain Power’s smart glasses help people with autism better understand emotions and social cues. The wearer of this Google Glass type device sees and hears special feedback geared to the situation — for example coaching on facial expressions of emotions, when to look at people, and even feedback on the user’s own emotional state.
These targeted emotional analysis systems acknowledge and interpret emotions. The insights are communicated to the user for learning purposes. On a personal level, these targeted applications will act like a Fitbit for the heart and mind, aiding in mindfulness, self-awareness, and ultimately self-improvement, while maintaining a machine-person relationship that keeps the user in charge.
Targeted emotional learning systems are also being tested for group settings, such as by analyzing the emotions of students for teachers, or workers for managers. Scaling to group settings can have an Orwellian feeling: Concerns about privacy, creativity, and individuality have these experiments playing on the edge of ethical acceptance. More importantly, adequate psychological training for the people in power is required to interpret the emotional results, and to make adequate adjustments.
Systems that mimic and ultimately replace human-to- human interactions.
When smart speakers entered the American living room in 2014, we started to get used to hearing computers refer to themselves as “I.” Call it a human error or an evolutionary shortcut, but when machines talk, people assume relationships.
There are now products and services that use conversational UIs and the concept of “computers as social actors” to try to alleviate mental-health concerns. These applications aim to coach users through crises using techniques from behavioral therapy. Ellie helps treat soldiers with PTSD. Karim helps Syrian refugees overcome trauma. Digital assistants are even tasked with helping alleviate loneliness among the elderly.
Casual applications like Microsoft’s XiaoIce, Google Assistant, or Amazon’s Alexa use social and emotional cues for a less altruistic purpose — their aim is to secure users’ loyalty by acting like new AI BFFs. Futurist Richard van Hooijdonk quips: “If a marketer can get you to cry, he can get you to buy.”
The discussion around addictive technology is starting to examine the intentions behind voice assistants. What does it mean for users if personal assistants are hooked up to advertisers? In a leaked Facebook memo, for example, the social media company boasted to advertisers that it could detect, and subsequently target, teens’ feelings of “worthlessness” and “insecurity,” among other emotions.
Judith Masthoff of the University of Aberdeen says, “I would like people to have their own guardian angel that could support them emotionally throughout the day.” But in order to get to that ideal, a series of (collectively agreed upon) experiments will need to guide designers and brands toward the appropriate level of intimacy, and a series of failures will determine the rules for maintaining trust, privacy, and emotional boundaries.
The biggest hurdle to finding the right balance might not be achieving more effective forms of emotional AI, but finding emotionally intelligent humans to build them.
Productive: “Achieving or producing a significant amount of result.” Enough: “As much or as many as required.”
As a time management coach, I’m keenly aware that you could answer the question “Am I productive enough?” using a variety of methods. I’m also familiar with the fact that individuals fall on a productivity spectrum. One person’s maximum productivity for a certain role in a particular environment could look vastly different from another person’s. These variations result from a combination of intrinsic ability, experience level, overall capacity, and desire.
For the purposes of this discussion, I’m narrowing the definition of “productive enough” to whether you are meeting the requirements of your job when operating at your personal peak performance. This reasoning process is outlined in the flowchart below, and we’ll walk through it step-by-step by answering a series of questions. At the end of this you should have a clearer sense of whether you can wrap up for the day knowing you were productive enough or whether you have room for improvement.
Question 1: Am I meeting expectations?
If “enough” is defined as “as much or as many as required,” then the initial essential question is whether you meet the requirements of your job. For people who have a well-defined job scope, answering this question may be easy: Did you meet the project milestones? Did you reply to customers within the specified times? Did you hit your sales targets? If you have a less clear job scope, this question may be a little harder to answer, but the answer should be evident by whether your manager has noted you have areas that need improvement.
If the answer is yes in regard to your key job responsibilities, then you’re productive enough. You could do more, but you don’t have to do more to meet expectations. If the answer is no, proceed to question two.
Question 2:Are these expectations my own, and not required by others?
Having high expectations of yourself can be a positive quality. But if you find yourself getting extremely stressed or working longer hours than you would prefer in order to meet expectations that aren’t significant to anyone else, your positive quality may have turned negative.
In these situations, you need to seriously ask yourself: Are these expectations my own, and not required — or potentially even noticed — by others? If the answer is yes, most likely you are productive enough. Instead of beating yourself up about what you’re not doing, it’s time to lower your expectations of yourself to a manageable level, aligned with everyone else’s. If the answer is no, if other people really do care about these expectations, then proceed to the next question.
Question 3: Am I owning my time management and using productivity resources?
Once you’ve clarified that you’re not meeting expectations that truly are important to fulfilling your job function, you need to evaluate whether you are owning your time management and using productivity resources.
Let’s dive a bit deeper into the two parts of this question.
Part one is: “Am I owning my time management”? From my perspective as a time management coach, this is asking whether you are proactive in how you allocate your time and effort. That includes clarifying priorities, planning your time, setting boundaries, and being focused when you are working. (Hint: If you obsessively check email, social media, or your phone and have little to no focused work time, you’re probably not meeting expectations in this area.) This is the strategic portion of your relationship with time.
Part two is: “Am I using productivity resources?” From my perspective, this entails utilizing the tools available to help you achieve efficiency. That could include having a written to-do list instead of keeping everything in your head, using tools like SaneBox or other email filtering systems, delegating more, or learning how to use your existing tools more efficiently. This is the tactical portion of your time management.
If you can confidently answer yes to both of the above, then within your current skill set, I would say you’re likely productive enough — you are doing the best you can within the circumstances. If you answer no to one or more of the above, then you’re likely not productive enough, meaning you are not producing the most you can within the circumstances.
How to Become Productive Enough
If you come to the end of the flowchart and recognize that you likely aren’t productive enough, then it’s time to evaluate your results and determine next steps.
One potential next step involves negotiating expectations. If you feel that you are owning your time management and using your productivity resources (so in a personal sense you’re productive enough), but you still worry you’re not meeting expectations, have a discussion with your manager. Lay out your different projects and deadlines as well as your work estimates and time capacity. Then see if you can get adjustments to your responsibilities. If your manager wants to consider a simple system for overall resource planning, tools such as float.com can help.
Another potential next step involves honing your time-management skills. If you’re not planning, prioritizing, and focusing at certain times throughout the day, and your job requires any type of proactive work, I’m 98.2% positive you’re leaving productivity on the table. It’s your responsibility to get the help you need to improve these skills.
The same is true for productivity resources. If you’re not utilizing any tools — even paper ones — that can help you stay organized, you’re very likely missing out and wasting time. I would work on improving in these areas before asking for significant adjustments to expectations.
If you’ve been wondering whether you’re productive enough, this is one way to answer that question from a time management point of view. I hope the answer frees you to breathe a little easier or to get motivated to do what you can to improve your situation.
Source: HBR
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