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Innovative organizations understand that innovation has many enemies, some seen and others unseen, and its leaders are actively involved in keeping them at bay. What enemies?
As they grow and mature, established organizations develop many obstacles that impede innovation. The biggest obstacle to transformation is human nature; this is why innovation is as much about attitude and perspective as it is about process.
FantasySCOTUS is an online fantasy league in which contestants compete by predicting decisions made by the U.S. Supreme Court. Players are ranked as in any fantasy league, and the best performers can win prizes such as a “Golden Gavel” and even $10,000 in cash.
Our research suggests the reasons more ideas from open innovation aren’t being adopted are political and cultural, not technical. Multiple gatekeepers, skepticism regarding anything “not invented here,” and turf wars all hold back adoption. But it doesn’t have to be this way.
We talk about artificial intelligence (AI), robots, and machine learning as if they’re coming soon, or are just some tech pipe dream. They’re not. They’re here today. In fact, a special report from Bank of America, Merrill Lynch predicts the global market for AI and robots will be just under $153 billion by 2020, and some industries will experience up to a 30% productivity increase through the use of those technologies alone.
Plants are incredible organisms. They tend to be very simple, only requiring a little CO2, water, and oxygen in order to live, but they’re capable of tremendous diversity and adaptability. Dr. Joanne Chory is using that information to create new plant varieties that could pull incredible amounts of CO2 out of the atmosphere and dramatically reduce the effects of climate change.
The objective of this short article is not to explain the breadth and the depth of the domain of innovation. Instead, this is an attempt to simplify innovation and encourage you to better understand and practice it. Is your organization innovative? How many new products and processes you introduced in the past one year? How do you compare the numbers with other players in the industry and also with the globally best? This write up will enable you to understand and practice innovation by answering the following questions. When to innovate? Where to innovate? How to innovate?
If you want big ideas, you need to encourage them, yes, but also talk about them in ways that open up dialog, thinking and idea generation to a much larger dimension. While language, word choice and conversation may not seem to have all that much impact on idea generation and innovation, in reality these are the building blocks of a corporate culture. As a colleague of mine is fond of saying: we need to switch from "I'll believe it when I see it" to "I'll see it when I believe it".
There are three things that astound me about most organizations: The cro-magnon way performance reviews are done; the pitiful way brainstorm sessions are run and; the voo doo way decisions are made. What follows is an elaboration of the third -- 12 common phenomena that contribute to funky decision making.
I've been thinking, long and hard, about the correct analogy to describe a lot of corporate innovation efforts. I'm sorry to say that the best analogy I can come up with is a campfire. Let's map what's going on in corporate settings to the ingredients for a campfire, to understand what's underway, what's working and what's missing in corporate innovation.
Increasingly we are looking constantly for better value. We are increasingly restless and explorative. The big question for many companies that simply sell products is can they benefit from making changes in these platform models. How do they go about it to capitalize on this restlessness and constant need of new experiences? Is the stand-alone product model breaking down? Do the more traditional approaches to customers, those that are more supply sided, still serve their needs today? The answer is no, platforms are building very different connected experience for customers, they are voting with their digital clicks to move their business to these offerings. Are you building platform businesses? You should.
You’re sitting at home minding your own business when you get a call from your credit card’s fraud detection unit asking if you’ve just made a purchase at a department store in your city. It wasn’t you who bought expensive electronics using your credit card — in fact, it’s been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent?
Often, it appears, companies are seeing the need to showcase how innovative they are. And the solution seems to be to build a flashy new work environment, install bean bags, foosball tables, whiteboards, and cold-brew some coffee and voila, we’ve got ourselves a lab. I’m a bit of a skeptic about these labs.
Young people are no longer satisfied with the prospect of becoming anonymous cogs in a larger corporate machine. Disruptive new technologies have already led to profound cultural shifts in attitudes towards consumption, work, education, energy and health. Technology helps make the world more transparent, authentic and open.
The lack of understanding that the most crucial factor that defines the ultimate success or failure of any crowdsourcing campaign is the ability to properly identify, define and articulate the problem that the crowd will be asked to solve. I call it the “80:20 rule”: 80% of unsuccessful crowdsourcing campaigns I’m aware of failed because of the inability to properly formulate the question to be presented to the crowd; only 20% did so because of a poor match between the question and the crowd.
Language and common definitions are critical to any interaction. When each party has their own definitions of innovation and rather narrow definitions at that, little can be accomplished and many opportunities are left by the wayside. Stopping to create a shared definition, expanding the range of opportunities and options, means we can explore more together.
Curiosity and empathy are the foundation for innovation. You have to get curious about what’s happening, and about people. This means that to innovate you have to ask more interesting questions, observe and learn more. You have to develop a big appetite for knowledge because you want to expand your perspective by having many mental models to use when taking on challenges.
What, then, would constitute a “theory of innovation”? What value, then, could a theory of “innovation” offer in this setting where creativity is an essential element? We must begin with some definitions, which will also provide the foundation for our theory.
We are in urgent need to build a clear innovation architecture as we do need this new business innovation capability. We are entering a different innovation era and if we don’t pay specific attention to the architecture of innovation we will not have a bright future, we will increasingly fall behind those that do get the need for serious investment in building innovation capability and capacity.
The most common goal, from a corporate point of view, is to reduce the cost of human labor and thus increase the profits of the business. But that shouldn’t be the only perspective. I’ve yet to encounter business leaders who think about automation from the perspective of the person who should benefit the most from it: the customer.
You don't have to drain your savings account or quit your day job to get a concept off the ground. You don't need co-founders, investors, or even to write a business plan. From the advent of rapid prototyping and crowdfunding to the widespread affordability of digital tools and technologies, the opportunities available to creative people today are fundamentally different -- and enormously exciting.
Why would a company ever outsource anything? In part, it may be because teams of talented operators have already demonstrated excellence in a specialized task or function, and it’s easier or cheaper to tap those teams than to create new teams of your own.
Collaboration is way of working with others with shared understanding to achieve mutual goals. Sometimes collaboration goes outside of our comfort zone to accomplish goals. It is critical to understand it to be more effective at it.
Even if you aren't interested in innovation, but merely want to remain competitive and keep pace with your industry and your competitors, understanding trends and predicting the potential direction of your industry and customer base is vital. It's the minimum to simply keep up with your market. Good innovators will be looking for clues to find the big shifts that they can take advantage of.
The political rhetoric of economic nationalism has grown heated in recent years. The clarion call often sounds for more restrictive trade or immigration policies that supporters believe will promote growth and employment at home. Studies have shown that a number of countries have embraced this mind-set to varying degrees, adopting policies that favor domestic industry and companies.
Internally, company executives have coined a phrase that gets to the heart of these goals: “Break the glass.” That means not just relying on internal thinking and ideas, but going out to “actually sit next to customers, go to their homes,” Harris says. “Not just consumers, but bartenders and other stakeholders in the industry.”
Innovation is geographically uneven. The world's 40 richest mega-regions — expansive conurbations such as the Boston–New York–Washington DC corridor, Greater London, or the passage that runs from Shanghai to Beijing — account for more than 85% of the world's patents, and 83% of the most-cited scientists. And yet, only 18% of the world's population lives in them.
Energy experts believe that blockchain technology can solve a maze of red tape and data management problems. In the longer term, Jesse Morris envisions a world in which homes and buildings are equipped with software that automatically sells and buys power to and from the grid on the basis of real-time price signals.
The real success of true AI will be in connecting disparate High Performance Computing systems and frameworks together. Figuring out how to connect them altogether is incredibly complex and presents as a true challenge, but when this issue is solved, we’ll surely be entering the golden age of research computing.
A recent McKinsey report found that while 84% of corporate executives think innovation is key to achieving growth objectives, only 6% are satisfied with the innovation performance of their firm. That’s quite a mismatch. It’s hard to imagine that a success rate that low would be tolerated in any other business function.
Sales and marketing were once disciplines ruled by emotions. But somewhere along the way, we recognized that they were based on definable pipelines and applied technology to manage those pipelines. Today you can put a corporate dashboard in place to manage them and tweak the settings to try to boost your results. What if we applied the same thinking to innovation?
An audacious Chinese entrepreneur wants to test your body for everything. But are computers really smart enough to make sense of all that data?
Fresh combinations of technologies, processes, materials, people, trends, concepts, and other factors are what creates innovation. It’s interesting that large organizations with lots of intellectual property don’t systematically examine what they can combine to innovate. It’s a big missing, and, an opportunity to improve.
Innovation is not a solo activity. While the rare lone genius may be able to invent something on their own (although still always inspired by others), nobody can innovate by themselves. Innovation, by its very nature, requires collaboration.
I think we often over complicate the work of innovation, because we believe it cannot be simple and straightforward. After all, how can an activity that can disrupt an industry, create compelling new products or services and reap significant riches be simple? To drive all of this change, certainly innovation must be difficult and complex, right?
Culturally diverse views bring valuable insights to local problems and issues that are pertinent to solving international challenges. In today’s constantly changing business environment, we need to pay more attention to local voices in order to orchestrate innovation around the world. Listening and learning from culturally diverse perspectives nurtures an open and creative mind.
While we - human beings - like to think of ourselves as rational creatures, the truth is we are anything but. We are prone to over 100 cognitive biases that may subconsciously shape our perceptions, beliefs and decisions. As one might imagination, innovation and entrepreneurship is not immune from said biases.
In a survey by McKinsey, 94% of senior executives said that it’s the people and corporate culture that drive innovation. Hierarchical structures where the decision-maker is difficult to reach and the decision-making process is not transparent do not foster innovation. That’s why employees need a degree of autonomy to execute actions and set their innovation goals.
There is this need to have a new cycle of innovative design. We need a really radical way forward on innovation, a highly adaptive solution, where all these solution parts are available on demand, constantly adjusting and adaptable to the situation you have to resolve. We build a process that relates to the problem on hand in its structure, offering the suggested process needed in frames, tools, process designs.
Business innovation has to do with new value, not necessarily new things — and comes in many flavors. The authors presented an “innovation radar” so companies can consider 12 different areas in which they might innovate — ranging from method of value capture to operating processes to platforms. “When a company identifies and pursues neglected innovation dimensions, it can change the basis of competition and leave other firms at a distinct disadvantage,” the authors concluded.
Artifical Intelligence (AI) is now being used to transform businesses, meaning companies are better positioned to provide consumers with the experience that they both demand and expect. But despite the respite the rise of the machine presents staff, conversations regarding automation are often littered with fear over the potential loss of jobs. This shouldn‘t be the case, instead, businesses and staff need to recognise the introduction of AI as the start of a new era where staff can ‘work smart‘ and become increasingly effective.
AI has the potential to provide many benefits to our economy and society. However, startups and established firms that are just beginning to use AI need access to data in order to train their AI systems. Difficulty in accessing the necessary data can create a barrier to entry, potentially reducing competition and innovation.
BabyX, the virtual creation of Mark Sagar and his researchers, looks impossibly real. If it sounds odd to encounter a virtual child that can read words from a book, it’s much more disorienting to feel a sense of fatherly pride after she nails a bunch in a row and lights up with what appears to be authentic joy. BabyX and I seemed to be having a moment, learning from each other while trading expressions and subtle cues so familiar to the human experience.
Collaboration is a hot buzzword in the business world. And with good reason. Working with people who have different perspectives or areas of expertise can result in better ideas and outcomes.
We need to recognize that innovation is one of the hardest things to align to strategy. It’s inherently messy, fairly unpredictable and its team-orientated approach sometimes cuts across borders, challenges different established positions and seemingly conflicting priorities.
Innovation happens at work when a few things are true, two of them are organizational support and rewarding bravery; both critical for a culture of innovation. As a leader or manager you have to remember that people don’t leave their jobs, they leave their managers.The reason is simple: It’s critical for people to feel like they’re making progress. Enthusiasm is oxygen, and people’s spirit dies if you kill it.
AI will eliminate some forms of this digital labour—software, for instance, has got better at transcribing audio. Yet AI will also create demand for other types of digital work. The technology may use a lot of computing power and fancy mathematics, but it also relies on data distilled by humans.
But what if everyone in your company had to account for their time, and what if everyone had a specific time allocation for innovation? That might differ depending on the individual, their experience and interest in innovation of course, but what if at the end of each year you could look across your team and see how much time an individual spent building innovation skills and contributing to innovation projects. Wouldn't that be valuable as a manager?
We discovered that experts with a broad external network were more innovative only when they devoted enough time and attention to those sources. For instance, people who interacted with eight different types of partners were only more innovative if they allocated half of their time outside the organization. Those who spent more time cultivating external relationships reported higher innovation outcomes, in terms of either patent quantity or quality. When people created a broad external network but did not spend adequate time learning how to use the information gained, the costs of being more distant from the organization and engaging in external networking outweighed the benefits of identifying novel information.
An established organization maintains stability because they’re specialized on optimizing their existing business model to deliver more of what already works. While this is great and keeps the wheels turning it’s also why they’re set up to fail because their experience and success is a straight jacket that’s hard to shake out off; put simply: Your internal culture is the biggest enemy to innovation.
A senior team member kicks things off with a fairly interesting idea, but comfortably within the ballpark of what your company does. People begin jumping in, pinging off of that idea, proposing innovations not too different from those your company has already tried. Then it happens: Someone utters a truly original, off-the-wall idea. The room suddenly gets quiet. Everyone waits to see how the boss will react.
The business case for diversity is clear. Diversity can boost innovation and employee engagement, and companies with greater gender and racial diversity financially outperform their peers. Yet progress within organizations has been slow – there is still a lack of women and minorities in leadership positions, and certain industries like tech and finance are lacking diversity at all levels.
The best lead gen tools will always be people; having the opportunity to spend one on one time with targets, listening to their goals and frustrations is a dream for every sales person. But it’s not realistic. Luckily, organizations like LeadCrunch are enhancing AI-fueled lead-gen platforms that enable organizations to understand customers on an individual basis so that they can eventually connect with them, person-person throughout the sales cycle. Providing a holistic B2B demand generation solution, it uses an effective combination of AI technology and human verification to engage the best targets for your customer base.
The play instinct is something that everyone is born with. From a young age we want to explore, create, and have fun. However, some people become very good at honing this skill, at playing and fiddling so creatively that they develop revolutionary technologies. Sometimes these discoveries can come later in life, but many times it is younger individuals, who have held onto their playful spirit, who innovate in extraordinary ways and in the process, play a role in inventing the future.
Technology non-believers need to understand that innovation is rarely disruptive; the media and pundits have taken that word and made it meaningless. What is certain is businesses who were not born with a digital first perspective are currently being forced to make the transition to a digital business. Making this transition requires a new type of leadership; an exponential mindset.
There are a lot of myths out there about artificial intelligence (AI). The most common confusion may be about AI and repetitive tasks. Automation is just computer programming, not AI.
The question is how do retailers and other enterprises with decades (and even centuries) of history defend themselves against expanding tech companies? The standard answer is to embrace the latest technology and orient themselves towards what younger customers want today. Instead corporations have to learn from the competition while leveraging the one advantage they still have: scale.
The fact is, going it alone, we believe, is simply not the way to go at all. Collaboration is the essential new secret sauce for startups and industry leaders alike. For true disruption to take hold, old and new must work together, playing to each other’s strengths.
Every business misses the future and gets disrupted by an outsider. This happens because the incumbents are stuck in their ways, doing the same thing over and over again and never zoom out to take a look at the macro view.
There are enormous benefits from Big Data analytics, but also massive potential for exposure that could result in anything from embarrassment to outright discrimination. Here's what to look out for — and how to protect yourself and your employees.
Researchers at DeepMind have published a paper illustrating how they are teaching artificially intelligent computer agents to traverse alien environments. While the results are slightly goofy, they represent a major step forward on the path to autonomous AI movement.
We cannot reject reason when we innovate, in fact we must rely on insight, intelligence, research and reason when we innovate. That's because the only way to encourage people to commit to new ideas is to demonstrate new insights, new needs or new experiences, which are all based on research, insights into unsolved problems or challenges or new technologies. This all appeals to reason - why would I choose an uncertain unknown over a predictable certainty? Only if the unknown is promising, compelling and valuable.
Artificial intelligences don’t make decisions in the same way that humans do. Even the best image recognition algorithms can be tricked into seeing a robin or cheetah in images that are just white noise, for example. It’s a big problem.
Often it is within the strategies that should be outlined, lies the potential new spaces to play for innovation’s design. Yet how often do we fail to connect the innovation’s we design and execute specifically aligned to the strategic need?
The theory of disruptive innovation applies in some circumstances but not others. In mastering the theory, we master the practice. In mastering the practice, we learn how to connect strategy with tactics. The risk of indulging in dogma around theory exists, always. The risk of being rudderless—a death sentence in the Digital Age for firms—is far greater, however.
Let’s face it – most people just don’t like change. We like things being the way that they are – even the word “upset” has a negative connection – to turn over the “set” to break with the established order. The only problem is – there are so many things we have the ability to just SOLVE, only we would be willing to “upset” things.
The default state of every new idea is no, and every single innovator has to figure out a way to turn that no into a yes. It’s not easy because people are resistant to technology because they’re afraid of losing what they’ve built, what sustains them. This is “the messy part about innovation“, what no one talks about because they opted for an easier path. But the truth is change is messy, and no one is immune to it.
In our experience, managers tend to focus their innovation efforts on processes that are either large in scale (new products and business models ) or swift in development (hackathons, rapid prototyping, or emerging platforms). There’s nothing wrong with this, per se, as both approaches can pay huge dividends. But there’s also another type of innovation that is more gradual and smaller in scale. We call it slow innovation.
Google is one of the most innovative companies in the world, because it actively pursues a culture of innovation. Google for years has worked to ensure that innovative ideas are continually flowing in order to position the company for success well into the future. What is Google doing, and how can we learn from their innovative culture?
Ideas are the seeds of innovation, and all people are creative. So, the answer is yes. But it comes with a caveat: Most every organization wants innovation but rejects creativity. Traditionally managed businesses with a short-term, profit maximization mindset are hard-wired to reject innovation.
We need to start treating innovation like other business disciplines — as a set of tools that are designed to accomplish specific objectives. Just as we wouldn’t rely on a single marketing tactic or a single source of financing for the entire life of an organization, we need to build up a portfolio of innovation strategies designed for specific tasks.
Mike Curtis did reveal that introducing a deep neural net to the search-ranking system boosted Airbnb's recent conversion rate by 1 percent. "One percent may not sound like a lot, but as you can imagine, a 1 percent increase in conversion rate compounds over time," he explained. Curtis doesn't just see Airbnb's use of cutting-edge technology in terms of ROI, but also as a hopeful sign regarding innovation's impact on the future.
To achieve better outcomes and to drive sustained growth we need different management practices. We require scalable participation (ecosystems) to relate to and generate new knowledge flows. We need to be increasingly responsive, adaptive and fluid in any design of structures and solutions.
What ends up happening in a brainstorming session is that a small number of people end up dominating the discussions and suggesting a lot of ideas. The truth is, even the quietest people often have ideas that are as good as, or often better, than the loudest people. You just need to find a way to let them share the ideas.
Universities, businesses and even cities and regions are talking about innovation and the need to create accelerators or innovation enablers. I'm glad that everyone is excited about innovation, and that they want to provide the means to help it flourish and help it move more quickly. But the thing is, like most late arrivals, they've got the wrong end of the stick as the Brits like to say.
Bill Janeway described the way AI could allow an elite group of firms to innovate at speeds the rest of the world could only dream about. When I listened to his interview with Kenneth Cukier, it made me wonder whether economic ideas could be applied to the world of big data.
Technology is approaching the man-machine and man-animal boundaries. And with this, society may be leaping into humanity-defining innovation without the equivalent of a constitutional convention to decide who should have the authority to decide whether, when, and how these innovations are released into society. What are the ethical ramifications? What checks and balances might be important?
Artificial Intelligence and Machine Learning are taking the Automobile industry(Car industry in particular) by storm while all the major automobile players are utilizing their resources and technology to come up with the best. The beauty of devices with artificial intelligence is that it tries to learn from sensor inputs like real sounds and images.
If governments are leading investors in blockchain applications it may tell you that there is indeed something different happening here. They see need and opportunity, and their uses can consolidate and prove that they hype is not hype.
“Innovation is an unpredictable process, but one with predictable features,” says Reeves. “It’s not just a matter of luck. It’s possible to have a strategy of innovation.”
Mark Zuckerberg, the man who created Facebook and dropped out of Harvard University, recently gave some surprising revelations about what it takes to have your ideas succeed when he gave the commencement address to the class of 2017.
We need to bring innovation and its process up to date. With cognitive computing, artificial intelligence, cloud-based solutions, purposefully designed apps and specific tools and frameworks, we do need to begin to stretch our imaginations further and flex our technology and app solutions more towards providing a better, more connected innovation process. I want to see a new innovation era happen.
Artificial intelligence is changing the world and doing it at breakneck speed. The promise is that intelligent machines will be able to do every task better and more cheaply than humans. And that raises an interesting question: when will artificial intelligence exceed human performance? More specifically, when will a machine do your job better than you?
So, can you be creative on purpose without waiting for lightining to strike?
Yes, you can be creative on command but in my experience two things have to be true for it to work:
1) A well defined problem;
2) Deep immersion and then distancing yourself from the challenge.
Honkela believes peace - not weapon - technologies should be prioritised and says applications utilising neural networks, big data and digital humanities will be fully at our disposal in some 20 years. Trailblazing advancements, he says, are already under way. At present, most artificial intelligence technologies are focused on business and marketing applications. We're using them while browsing through social media, calling service lines and typing text messages with text prediction. So what would a Peace Machine entail?
The most successful organizations are those that can prep for the future and push the envelope creatively to find the next innovative idea. But what if how we have been thinking about innovation is all wrong? Open innovation is a newer idea that is spreading across industries and changing how companies work together and plan for the future.
Researchers at the University of California, Berkeley, developed an “intrinsic curiosity model” to make their learning algorithm work even when there isn’t a strong feedback signal. The trick may help address a shortcoming of today’s most powerful machine-learning techniques, and it could point to ways of making machines better at solving real-world problems.
Walmart recently launched a trial using a blockchain to fight food poisoning and waste by tracking food from supplier to shelf. Say a salmonella outbreak hits Sioux City, Iowa. Walmart’s supply chain blockchain would let it irrefutably trace the tainted product back to a spinach farm in Salinas, California.
Tech companies think biology may solve a looming data storage problem. Computer architects at Microsoft Research say the company has formalized a goal of having an operational storage system based on DNA working inside a data center toward the end of this decade.
There is a significant transformation of the value lying within this extended system of collaborators and organization design. It is the ability to leverage, through technology the broader ecosystem of partners and internal collaborators by a more holistic view of sharing building ‘greater’ value together
To be considered truly innovative, a product or service must be “meaningfully unique” or be protected as intellectual property. “Meaningfully unique” is essential for small businesses. Large companies like Proctor & Gamble and AT&T can successfully release a new product because they have name recognition and an existing customer base. Small businesses have to rely on free press and word-of-mouth, which is why unique concepts are make-or-break for startups and small businesses.
Major technology companies and new startups are at war over having the most valuable artificial intelligence and at the core of this war is having unique high quality visual data. This battle will be won by owning the connected camera.
Everyone’s talking about artificial intelligence (“AI”). Most of the talk is wrong, misleading and often intended to frighten us about a future that’s unlikely to occur. AI will not steal our babies, hold us hostage for Bitcoins or start nuclear wars. But it will fundamentally change the labor market through the intelligent automation of many routine tasks individuals and companies perform all the time.
Can we be bolder and more determined to differentiate ourselves or do we stay tucked into the pack, like a long distant runner, waiting for something (someone) to break away, hopefully, able to equally ‘kick in’ and stay in touch, still hopeful we will be in a position to win but mostly remain a fast follower only?
Big data is moving to a new stage of maturity — one that promises even greater business impact and industry disruption over the course of the coming decade. As big data initiatives mature, organizations are now combining the agility of big data processes with the scale of artificial intelligence (AI) capabilities to accelerate the delivery of business value.
Machine learning is different. Instead of pointing a limited set of “dumb” algorithms at data, machine learning algorithms are capable of ingesting huge amounts of data and spotting patterns related to particular desirable or undesirable outcomes.
I’ve been touting concept blends in innovation for some time. My reason is simple, it’s a fast path to new and different ideas. From the Printing Press to the iPhone, big new market-creating innovation happens when concepts from two different domains are combined. These Mash-Ups are not intuitive for most people to do and maybe that’s why some people try it and fail. Take heart, smart people can do concept blends with careful mental scaffolding.
Many companies feel that innovation programs, innovation labs, and other innovation initiatives are great in good times, but are superfluous in tough times. The number of times I've heard that firms are axing or "re-purposing" their innovation personnel to work on "core product" or "core services" when times are tough, are legion.
Artificial intelligence has shown promise in helping doctors predict which patients may be susceptible to chronic diseases like Alzheimer’s. But despite the rapid advances, the healthcare industry is still in the early days of rolling out AI-powered treatments and drugs, Morten Sogaard, Pfizer’s vice president and head of genome sciences and technologies, said.
“Our intelligence resides not in individual brains but in the collective mind.” This “division of cognitive labor is fundamental to the way cognition evolved and the way it works today.”
Innovation appears to be a random, inherently unpredictable process. But what if we could understand the causal factors that contribute variability to the innovation process and learn how to control them? Would that bring predictability to innovation? The answer is yes—we have proven it can be done, but it requires new thinking.