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Anchoring and Framing
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ZOPA and BATNA
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In this course, you’ll learn how compelling blogs, videos, podcasts, and other media can reach customers and drive sales. You’ll also learn steps for creating an effective content marketing plan, and some important ways to measure its impact and success.
Content marketing is a essential digital marketing strategy for companies looking to provide relevant and useful information to support your community and attract new customers.
Get started on your content marketing journey today.
Sustainable Innovation in Times of Disruption: Choices for a Better Society
There are opportunities for progress all around us. The key is to innovate on these opportunities sustainably.
To help identify most effective path forward, you'll need to gain a global perspective to these challenges in an open discussion. How can Japan and the world take action to create a more sustainable, innovative world? Where do you fit in?
It's time to find out.
Social Media & Digital Communications: Impact on Global Public Opinion
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What do companies need to be aware of when utilizing social and digital media? How can these mediums be used to better communicate strategically with the world?
Discover what top media and communications experts have to say.
Blockchain is one of the most captivating technologies out there. Learn what it is and how to make use of its opportunities in this short online course.
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Country Analysis Framework
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The SECI model illustrates how knowledge is created and shared. Learn how to put it to use for best practices, and how the Japanese concept of “ba” fits in to broaden your perspective.
Johari Window Model
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CAGE Distance Framework
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Groupthink refers to group pressure and the perception of consensus which together lead to ill-formed decisions—or even unnecessary risks. Learn to identify the warning signs of groupthink and apply countermeasures in this online course.
Deductive and Inductive Reasoning
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Critical Thinking: Hypothesis-Driven Thinking
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Critical Thinking: Structured Reasoning
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Critical Thinking: Problem-Solving
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How to Dream
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Logical thinking is at the heart of confident, persuasive decisions. This course will equip you with a five-point approach to more becoming a more logical thinker. Learn to classify ideas and distinguish fact from opinion.
Investing & Diversity: The Changing Faces of Venture Capitalists
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Organizational Behavior and Leadership
Ever wonder what makes a great leader? Whether your role requires leadership or not, understanding organizational behavior is useful for your career. This course from GLOBIS Unlimited can set you on your way.
Leadership vs. Management
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Strategy: Creating Value Inside Your Company
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Strategy: Understanding the External Environment
To plan strategy on any level, you need to understand your company's external environment. In fact, your level of understanding can impact hiring, budgeting, marketing, or nearly any other part of the business world. Want to learn how to do all that? This course from GLOBIS Unlimited is the perfect first step!
Using Japanese Values to Thrive in Global Business
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Turnaround Leadership: The Differences Between Japan and the West
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Evernote Founder: How Tech Startups Can Break through in Japan
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Marketing: Reaching Your Target
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The Principles of Negotiation
With the proper skills and attitude, anyone can become a successful negotiator. But first, you'll need to learn the basics to prepare for, assess, and respond to offers for the best results. GLOBIS Unlimited can help.
Negotiation: Creating Value
Want to create more shared value between yourself and your negotiation opponent? Discover how cognitive bias affects the judgment of others. Try this course from GLOBIS Unlimited to master the value of negotiation.
Finding Your Life Purpose with Ikigai
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Maslow’s Hierarchy of Needs
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An Investor's Lesson to Entrepreneurs
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Managerial accounting is a powerful way to measure progress, identify problems, and meet your goals. Check out this course to learn how data-backed decisions can help you run your business.
Finance Basics: 1
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Basic Accounting: Financial Analysis
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What drives you to be good at your job?
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Try this short GLOBIS Unlimited course to identify which of the eight career anchors is yours!
Digital Marketing Psychology to Transform Your Business
How does digital marketing really differ from traditional marketing? How is social media changing things really? And what's going on in Asia?
Having the pyramid structure in your communication toolkit can not only help you approach a problem, but convince others that your solution is valid. Break away from linear thinking and test your logical thinking with this course from GLOBIS Unlimited!
Leadership with Passion through Kokorozashi
The key ingredient to success? Passion.
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AI First Companies – Implementation and Impact
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Technovate in the Era of Industry 4.0
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Product Life Cycle
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Logical thinking is the most valuable asset any business professional can have. That's why logic trees are such a valuable tool—they can help you identify a problem, break it down, and build it back up to a solution.
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Companies need to digitalize their processes to stay competitive in today’s market. For that, many rely on AI startups, which offer everything from machine learning (ML) to decision trees and promise everything from 100% accuracy to immediate results.
But not every startup is successful in what it offers or promises.
Early signs of a failing AI startup go beyond technical issues. Much of the problem actually comes from unrealistic expectations—both on the client side and from within the startup itself. Identifying success factors within a startup comes down to a simple question: Did the startup deliver what the client asked for? That can be harder to achieve than it sounds. Our research team spoke to several experts who lamented that there is still a kind of “magic potion” attitude toward AI. Many clients ask for a ML solution to boost sales before there even is something to sell.
This is similar to the dilemma Stuart Russel presents in his book Human Compatible: Artificial Intelligence and the Problem of Control: a customer keeps changing his mind on pizza toppings, driving a robot waiter crazy. How do you deliver what a client asked for when the client doesn’t know what to ask for?
Short answer: by ensuring you’re growing your AI startup the right way. To that end, here are some crucial dos and don’ts.
Things You Should DO
DO focus on value.
Clients want to see the greater value of the solution they purchased, whether they’re working with an AI startup or a traditional one. After all, companies want to boost sales and reduce costs—that hasn’t changed for decades.
Every AI startup should start with research, even before meeting with a client. That way, they’ll be prepared to explain both the problem and how their solution will solve it. Next, they need to think about cost—not just implementation, but also maintenance. AI projects aren’t cheap, nor are they normally paid for upfront.
Research and practical cost consideration will help you position your startup to provide value to clients.
DO understand the value of specialists.
When people think of AI, they imagine the machines. But there is a remarkable number of hands involved in the creation of an AI model—highly specialized hands. AI professionals are rarely interchangeable, so someone working in industry sector A may find it’s difficult to get hired by sector B. Sometimes, even moving from the purchasing department to the sales department of the same company can be an issue.
Accenture PLC research by Paul R. Daughterty and H. James Wilson predicts that the future will have three new job types: explainers, trainers, and sustainers. Specialists who communicate the best use of AI will fill the explainer role. Therefore, it’s to the benefit of AI startups today to understand and leverage that potential for tomorrow.
DO make plans for scaling up.
There’s one big, often overlooked thing that will make or break an AI startup: scalability.
Having worked in a multinational organization, I’ve suffered first-hand the long purchasing cycles that often make it impossible to work with small startups. Big clients lack flexibility, and cloud operations can be more complex and costly than traditional approaches. VCs, too, want scalable products and cheap implementation.
Making an AI startup scalable is, in itself, a huge project, but a good place to start is with company culture. Never underestimate the importance of keeping coders motivated. The partner of one consulting firm told me that he makes a point to alternate “stimulating” and “profitable” projects to keep coders on their toes. Without happy coders, there’s simply no way to even start thinking of scaling up.
Startups lacking scalability will find themselves stuck in a whirlpool of small consulting projects that never quite push them into the big leagues.
Things You Should NOT DO
DON’T forget the human element within AI.
Every company that uses AI still has humans working for it, and those humans will need to work in harmony with the machines. AI startups and their clients therefore need to identify what level of human-machine interaction their solution requires. Will the machine simply assist the humans, like a grammar corrector? Or will it augment the abilities of a skilled professional, like a skin cancer screening system that narrows down potential prognoses? Will there still be manual elements to the solution, or can we attempt full automation?
AI is about interpretability, but that interpretability will change significantly depending on the goal and level of human interaction. A 90% interpretability rate is outstanding, but reaching 99% is exponentially more complex and still human driven.
Don’t forget to factor in the human element, or your whole operation will fall to pieces.
DON’T discount the importance of forecasting.
AI startups should plan for scenarios both general and extreme. Like playing chess, you don’t just need to move—you need to move in anticipation of your opponent’s next several moves. You’ll need contingencies for data drift, black swan events (like COVID-19), and attempted application of the AI outside its valid domain. Failing to do this could lead to major setbacks, exhaust your resources, and ultimately ruin the project.
A great forecaster performs frequent updates and introduces new, competing models. The same should apply to AI projects. If nothing else, be sure you can answer these questions: How often does the model get updated? How often is new data integrated to make the solution more accurate over time?
DON’T confuse desktop pilots with reality.
The “magic potion” of an AI solution that works everywhere, every time is a fairy tale. Here in reality, AI models can require as many iterations as any other product. Data collected during and after implementation will lead to scaling up, customizations, and a final product far removed from anything seen during testing. Not to mention, AI inevitably encounters scenarios in which it doesn’t perform as expected. Data evolves, so higher accuracy in a model actually makes it harder to find data sets to identify parameters.
ML master Andrew Ng warns that desktop app testing—a way of testing software functionality, security, etc. prior to deployment—solves only very specific problems. The solutions, therefore, are not transferable to other companies or projects. Many startups discover too late that successful pilot projects fail as a solid business model.
So make sure your expectations are aligned with reality, not fairy tales.
Despite all the bells and whistles, AI businesses are still businesses. That means they can fail just as much as any other business—and for many of the same reasons. Savvy AI startup founders know to offer great ML solutions without forgetting to learn about business. Understand the skills you need and the market you serve, and you can define the best value propositions for your customers.