Tag Archives: Artificial Intelligence

Top 10 Human-Centered Change & Innovation Articles of April 2023

Top 10 Human-Centered Change & Innovation Articles of April 2023Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are April’s ten most popular innovation posts:

  1. Rethinking Customer Journeys — by Geoffrey A. Moore
  2. What Have We Learned About Digital Transformation Thus Far? — by Geoffrey A. Moore
  3. Design Thinking Facilitator Guide — by Douglas Ferguson
  4. Building A Positive Team Culture — by David Burkus
  5. Questions Are More Powerful Than We Think — by Greg Satell
  6. 3 Examples of Why Innovation is a Leadership Problem — by Robyn Bolton
  7. How Has Innovation Changed Since the Pandemic? — by Robyn Bolton
  8. 5 Questions to Answer Before Spending $1 on Innovation — by Robyn Bolton
  9. Customers Care About the Destination Not the Journey — by Shep Hyken
  10. Get Ready for the Age of Acceleration — by Robert B. Tucker

BONUS – Here are five more strong articles published in March that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Unlocking the Power of Imagination

How Humans and AI Can Collaborate for Innovation and Creativity

Unlocking the Power of Imagination

GUEST POST from Teresa Spangler

Imagination is not only the uniquely human capacity to envision that which is not, and, therefore, the foundation of all invention and innovation. In its arguably most transformative and revelatory capacity, it is the power that enables us to empathize with humans whose experiences we have never shared. ~J.K. Rowling

Part I of a 3 Part Series: Simple Strategies

Imagination has always been crucial in human innovation, creativity, and problem-solving. It enables us to envision possibilities beyond the present and find solutions to problems that do not exist yet. 

However, with Artificial Intelligence (AI) advancements, humans must develop their imaginative skills to stay competitive. To achieve that, it is important to understand the eight subsections of imagination – Effectuative, Intellectual or Constructive, Imaginative Fantasy, Empathy, Strategic, Emotional, Dreams, and Memory Reconstruction. Each of these subsections plays a crucial role in the human imagination, and as AI becomes more sophisticated, it will challenge humans to improve their imaginative abilities in each of these areas.

Understanding the Eight Subsections of Imagination

Effectuative Imagination: Effectuative imagination is the ability to imagine the consequences of one’s actions. It enables individuals to plan, make decisions, and predict outcomes. For example, a CEO may use effectuative imagination to anticipate the impact of a new product launch on the company’s financials.

To improve effectuative imagination, humans can:

  • Think about potential consequences before making decisions
  • Analyze data and use it to predict possible outcomes
  • Consider alternative scenarios before deciding on a course of action

Intellectual or Constructive Imagination: Intellectual or constructive imagination is the ability to imagine abstract concepts and ideas. It allows individuals to envision new inventions, theories, and ways of thinking. For example, a scientist may use constructive imagination to develop a new theory that explains a complex natural phenomenon.

To improve intellectual or constructive imagination, humans can:

  • Challenge assumptions and think outside the box
  • Experiment with new ideas and concepts
  • Combine existing ideas and concepts to create new ones

Imaginative Fantasy:  Imaginative fantasy is the ability to create and imagine fantastical worlds and stories. It enables individuals to escape from reality and explore the limits of their imaginations. For example, a writer may use imaginative fantasy to create a new universe for their story.

To improve imaginative fantasy, humans can:

  • Engage in creative activities such as writing, drawing, or painting
  • Read books or watch movies that take them to new worlds
  • Use their imagination to create new stories or scenarios

Empathy:  Empathy is the ability to imagine and understand the emotions and experiences of others. It enables individuals to connect with others more deeply and be more compassionate. For example, a therapist may use empathy to understand their client’s emotions and experiences.

To improve empathy, humans can:

  • Practice active listening and ask questions to understand others better
  • Imagine themselves in other people’s shoes
  • Practice kindness and compassion towards others

 Strategic Imagination:  Strategic imagination is the ability to imagine and plan for the future. It enables individuals to anticipate potential challenges and opportunities and to develop effective strategies. For example, a business leader may use strategic imagination to create a long-term growth plan for their company.

To improve strategic imagination, humans can:

  • Develop a long-term vision for their personal or professional life
  • Identify potential challenges and opportunities and develop strategies to address them
  • Use data and analysis to inform their decisions

Emotional Imagination:  Emotional imagination is the ability to imagine and experience a wide range of emotions. It enables individuals to empathize with others and connect with their emotions. For example, an actor may use emotional imagination to convincingly portray a character’s emotions.

To improve emotional imagination, humans can:

  • Practice mindfulness and self-awareness to understand their own emotions better
  • Engage in creative activities such as writing or acting to explore different emotions
  • Practice empathy to understand other people’s emotions better

Dreams:  Dreams are a manifestation of our subconscious mind and imagination. They enable us to explore our deepest desires, fears, and aspirations. Dreams are also a way for our brains to process and consolidate memories. For example, a person may have a dream about an experience that helps them understand and process their emotions.

To improve dreams and imagination, humans can:

  • Keep a dream journal to record and reflect on their dreams
  • Practice lucid dreaming to control and direct their dreams
  • Engage in creative activities such as writing or drawing to explore dream worlds and scenarios

Memory Reconstruction:  Memory reconstruction is remembering past events and experiences and using our imagination to fill in gaps and create a coherent narrative. It enables individuals to sense their past and create a personal identity. For example, a person may reconstruct childhood memories to understand how past experiences have shaped who they are today.

To improve memory reconstruction and imagination, humans can:

  • Reflect on past experiences and try to understand how they have influenced their life
  • Engage in creative activities such as writing or drawing to explore memories and create new narratives
  • Practice mindfulness to become more aware of their thoughts and feelings

How AI May Challenge Us to Become More Imaginative

As AI becomes more sophisticated, it will challenge humans to improve their imaginative abilities in each of the eight subsections of imagination. Working alongside AI can spark us to new levels of thinking. Consider AI as your partner in growing your own imagination, your creativity and your abilities to be more inventive.

Examples:

  • Effectuative Imagination: AI can analyze data and provide insights humans can use to make better decisions.
  • Intellectual or Constructive Imagination: AI can identify patterns and connections between data sets that humans may not have noticed, leading to new ideas and theories.
  • Imaginative Fantasy: AI can generate creative ideas and scenarios that humans may not have thought of independently. However, imaginative people can raise the bar on creativity by using these AI-generated fantasies.
  • Empathy: AI can analyze social media data and sentiment analysis to understand how people are feeling and to predict future trends.
  • Strategic Imagination: AI can analyze market data and economic indicators to identify potential challenges and opportunities for businesses and organizations.
  • Emotional Imagination: AI can analyze facial expressions and tone of voice to understand people’s emotions and to provide personalized recommendations and support.
  • Dreams: AI can use generative models to create realistic dream scenarios and to help individuals explore and process their subconscious thoughts and emotions.
  • Memory Reconstruction: AI can analyze large data sets and identify patterns and connections humans may not have noticed, leading to new insights and narratives.

Working Alongside AI to Unlock Human Imagination

AI is not a threat to human imagination but rather a tool that can help us unlock our full imaginative potential. By collaborating with AI, humans can gain new insights and perspectives that inform their imaginative processes. Here are some strategies for working alongside AI to unlock human imagination:

  • Use AI-powered tools to analyze data and identify patterns and connections that can inform imaginative processes.
  • Collaborate with AI-powered systems to generate new ideas and scenarios.
  • Use virtual and augmented reality tools to explore new environments and possibilities.
  • Leverage AI-powered chatbots and virtual assistants to provide personalized support and recommendations.
  • Participate in cross-functional teams that include both humans and AI-powered systems.

How Companies Can Support Employee Imagination

Companies have a critical role in supporting their employees’ imaginative skills. By doing so employees will be come more engaged and productive.  Here are a few strategies that companies can use to support employee imagination:

  • Encourage creativity by providing opportunities for employees to explore new ideas and experiment with new approaches.
  • Foster diversity and inclusion by promoting diverse perspectives and experiences and creating a safe and inclusive work environment.
  • Invest in technology and tools that support imaginative work, such as AI-powered systems, virtual and augmented reality tools, and collaborative platforms.
  • Provide learning and development opportunities focusing on imaginative skills, such as creativity, emotional intelligence, and strategic thinking.
  • Encourage collaboration by creating cross-functional teams and promoting a culture of collaboration.

Imagination is essential for the future of innovation and human productivity. The eight subsections of imagination provide a framework for understanding and developing imaginative abilities. As AI becomes more sophisticated, it will challenge humans to improve their imaginative skills in each of these areas. Humans can unlock new ideas and solutions that drive innovation and progress by developing imaginative skills and working alongside AI.

Moreover, individuals can develop their imaginative skills by embracing change, fostering curiosity, developing a growth mindset, practicing mindfulness, collaborating with AI, exploring new forms of art and creativity, practicing divergent thinking, and fostering the eight subsections of imagination.

Companies have a crucial role in supporting their employees’ imaginative skills. They can encourage creativity, foster diversity and inclusion, invest in technology and tools supporting imaginative work, provide learning and development opportunities focusing on imaginative skills, and encourage collaboration.

In the age of AI, humans must strive to maintain their unique skills and abilities. Imagination is a human trait that AI cannot replicate. Developing our imaginative skills and working alongside AI can unlock new opportunities and create a better future for ourselves and the world.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Top 10 Human-Centered Change & Innovation Articles of March 2023

Top 10 Human-Centered Change & Innovation Articles of March 2023Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are March’s ten most popular innovation posts:

  1. Taking Care of Yourself is Not Impossible — by Mike Shipulski
  2. Rise of the Prompt Engineer — by Art Inteligencia
  3. A Guide to Effective Brainstorming — by Diana Porumboiu
  4. What Disruptive Innovation Really Is — by Geoffrey A. Moore
  5. The 6 Building Blocks of Great Teams — by David Burkus
  6. Take Charge of Your Mind to Reclaim Your Potential — by Janet Sernack
  7. Ten Reasons You Must Deliver Amazing Customer Experiences — by Shep Hyken
  8. Deciding You Have Enough Opens Up New Frontiers — by Mike Shipulski
  9. The AI Apocalypse is Here – 3 Reasons You Should Celebrate! — by Robyn Bolton
  10. Artificial Intelligence is Forcing Us to Answer Some Very Human Questions — by Greg Satell

BONUS – Here are five more strong articles published in February that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Artificial Intelligence is Forcing Us to Answer Some Very Human Questions

Artificial Intelligence is Forcing Us to Answer Some Very Human Questions

GUEST POST from Greg Satell

Chris Dixon, who invested early in companies ranging from Warby Parker to Kickstarter, once wrote that the next big thing always starts out looking like a toy. That’s certainly true of artificial intelligence, which started out playing games like chess, go and playing humans on the game show Jeopardy!

Yet today, AI has become so pervasive we often don’t even recognize it anymore. Besides enabling us to speak to our phones and get answers back, intelligent algorithms are often working in the background, providing things like predictive maintenance for machinery and automating basic software tasks.

As the technology becomes more powerful, it’s also forcing us to ask some uncomfortable questions that were once more in the realm of science fiction or late-night dorm room discussions. When machines start doing things traditionally considered to be uniquely human, we need to reevaluate what it means to be human and what is to be a machine.

What Is Original and Creative?

There is an old literary concept called the Infinite Monkey Theorem. The basic idea is that if you had an infinite amount of monkeys pecking away an infinite amount of keyboards, they would, in time, produce the complete works of Shakespeare or Tolstoy or any other literary masterpiece.

Today, our technology is powerful enough to simulate infinite monkeys and produce something that looks a whole lot like original work. Music scholar and composer David Cope has been able to create algorithms that produce original works of music which are so good that even experts can’t tell the difference. Companies like Narrative Science are able to produce coherent documents from raw data this way.

So there’s an interesting philosophical discussion to be had about what what qualifies as true creation and what’s merely curation. If an algorithm produces War and Peace randomly, does it retain the same meaning? Or is the intent of the author a crucial component of what creativity is about? Reasonable people can disagree.

However, as AI technology becomes more common and pervasive, some very practical issues are arising. For example, Amazon’s Audible unit has created a new captions feature for audio books. Publishers sued, saying it’s a violation of copyright, but Amazon claims that because the captions are created with artificial intelligence, it is essentially a new work.

When machines can create does that qualify as an original, creative intent? Under what circumstances can a work be considered new and original? We are going to have to decide.

Bias And Transparency

We generally accept that humans have biases. In fact, Wikipedia lists over 100 documented biases that affect our judgments. Marketers and salespeople try to exploit these biases to influence our decisions. At the same time, professional training is supposed to mitigate them. To make good decisions, we need to conquer our tendency for bias.

Yet however much we strive to minimize bias, we cannot eliminate it, which is why transparency is so crucial for any system to work. When a CEO is hired to run a corporation, for example, he or she can’t just make decisions willy nilly, but is held accountable to a board of directors who represent shareholders. Records are kept and audited to ensure transparency.

Machines also have biases which are just as pervasive and difficult to root out. Amazon had to scrap an AI system that analyzed resumes because it was biased against female candidates. Google’s algorithm designed to detect hate speech was found to be racially biased. If two of the most sophisticated firms on the planet are unable to eliminate bias, what hope is there for the rest of us?

So, we need to start asking the same questions of machine-based decisions as we do of human ones. What information was used to make a decision? On what basis was a judgment made? How much oversight should be required and by whom? We all worry about who and what are influencing our children, we need to ask the same questions about our algorithms.

The Problem of Moral Agency

For centuries, philosophers have debated the issue of what constitutes a moral agent, meaning to what extent someone is able to make and be held responsible for moral judgments. For example, we generally do not consider those who are insane to be moral agents. Minors under the age of eighteen are also not fully held responsible for their actions.

Yet sometimes the issue of moral agency isn’t so clear. Consider a moral dilemma known as the trolley problem. Imagine you see a trolley barreling down the tracks that is about to run over five people. The only way to save them is to pull a lever to switch the trolley to a different set of tracks, but if you do one person standing there will be killed. What should you do?

For the most part, the trolley problem has been a subject for freshman philosophy classes and avant-garde cocktail parties, without any real bearing on actual decisions. However, with the rise of technologies like self-driving cars, decisions such as whether to protect the life of a passenger or a pedestrian will need to be explicitly encoded into the systems we create.

On a more basic level, we need to ask who is responsible for a decision an algorithm makes, especially since AI systems are increasingly capable of making judgments humans can’t understand. Who is culpable for an algorithmically driven decision gone bad? By what standard should they be evaluated?

Working Towards Human-Machine Coevolution

Before the industrial revolution, most people earned their living through physical labor. Much like today, tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.

Today, we’re going through a similar shift, but now machines are taking over cognitive tasks. Just as the industrial revolution devalued certain skills and increased the value of others, the age of thinking machines is catalyzing a shift from cognitive skills to social skills. The future will be driven by humans collaborating with other humans to design work for machines that creates value for other humans.

Technology is, as Marshal McLuhan pointed out long ago, an extension of man. We are constantly coevolving with our creations. Value never really disappears, it just shifts to another place. So, when we use technology to automate a particular task, humans must find a way to create value elsewhere, which creates an opportunity to create new technologies.

This is how humans and machines coevolve. The dilemma that confronts us now is that when machines replace tasks that were once thought of as innately human, we must redefine ourselves and that raises thorny questions about our relationship to the moral universe. When men become gods, the only thing that remains to conquer is ourselves.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

The Coming Innovation Slowdown

The Coming Innovation Slowdown

GUEST POST from Greg Satell

Take a moment to think about what the world must have looked like to J.P. Morgan a century ago, in 1919. He was not only an immensely powerful financier with access to the great industrialists of the day, but also an early adopter of new technologies. One of the first electric generators was installed at his home.

The disruptive technologies of the day, electricity and internal combustion, were already almost 40 years old, but had little measurable economic impact. Life largely went on as it always had. That would quickly change over the next decade when those technologies would drive a 50-year boom in productivity unlike anything the world had ever seen before.

It is very likely that we are at a similar point now. Despite significant advances in technology, productivity growth has been depressed for most of the last 50 years. Over the next ten years, however, we’re likely to see that change as nascent technologies hit their stride and create completely new industries. Here’s what you’ll need to know to compete in the new era.

1. Value Will Shift from Bits to Atoms

Over the past few decades, innovation has become almost synonymous with digital technology. Every 18 months or so, semiconductor manufacturers would bring out a new generation of processors that were twice as powerful as what came before. These, in turn, would allow entrepreneurs to imagine completely new possibilities.

However, while the digital revolution has given us snazzy new gadgets, the impact has been muted. Sure, we have hundreds of TV channels and we’re able to talk to our machines and get coherent answers back, but even at this late stage, information and communication technologies make up only about 6% of GDP in advanced countries.

At first, that sounds improbable. How could so much change produce so little effect? But think about going to a typical household in 1960, before the digital revolution took hold. You would likely see a TV, a phone, household appliances and a car in the garage. Now think of a typical household in 1910, with no electricity or running water. Even simple chores like cooking and cleaning took hours of backbreaking labor.

The truth is that much of our economy is still based on what we eat, wear and live in, which is why it’s important that the nascent technologies of today, such as synthetic biology and materials science, are rooted in the physical world. Over the next generation, we can expect innovation to shift from bits back to atoms.

2. Innovation Will Slow Down

We’ve come to take it for granted that things always accelerate because that’s what has happened for the past 30 years or so. So we’ve learned to deliberate less, to rapidly prototype and iterate and to “move fast and break things” because, during the digital revolution, that’s what you needed to do to compete effectively.

Yet microchips are a very old technology that we’ve come to understand very, very well. When a new generation of chips came off the line, they were faster and better, but worked the same way as earlier versions. That won’t be true with new computing architectures such as quantum and neuromorphic computing. We’ll have to learn how to use them first.

In other cases, such as genomics and artificial intelligence, there are serious ethical issues to consider. Under what conditions is it okay to permanently alter the germ line of a species. Who is accountable for the decisions and algorithm makes? On what basis should those decisions be made? To what extent do they need to be explainable and auditable?

Innovation is a process of discovery, engineering and transformation. At the moment, we find ourselves at the end of one transformational phase and about to enter a new one. It will take a decade or so to understand these new technologies enough to begin to accelerate again. We need to do so carefully. As we have seen over the past few years, when you move fast and break things, you run the risk of breaking something important.

3. Ecosystems Will Drive Technology

Let’s return to J.P. Morgan in 1919 and ask ourselves why electricity and internal combustion had so little impact up to that point. Automobiles and electric lights had been around a long time, but adoption takes time. It takes a while to build roads, to string wires and to train technicians to service new inventions reliably.

As economist Paul David pointed out in his classic paper, The Dynamo and the Computer, it takes time for people to learn how to use new technologies. Habits and routines need to change to take full advantage of new technologies. For example, in factories, the biggest benefit electricity provided was through enabling changes in workflow.

The biggest impacts come from secondary and tertiary technologies, such as home appliances in the case of electricity. Automobiles did more than provide transportation, but enables a shift from corner stores to supermarkets and, eventually, shopping malls. Refrigerated railroad cars revolutionized food distribution. Supply chains were transformed. Radios, and later TV, reshaped entertainment.

Nobody, not even someone like J.P. Morgan could have predicted all that in 1919, because it’s ecosystems, not inventions, that drive transformation and ecosystems are non-linear. We can’t simply extrapolate out from the present and get a clear future of what the future is going to look like.

4. You Need to Start Now

The changes that will take place over the next decade or so are likely to be just as transformative—and possibly even more so—than those that happened in the 1920s and 30s. We are on the brink of a new era of innovation that will see the creation of entirely new industries and business models.

Yet the technologies that will drive the 21st century are still mostly in the discovery and engineering phases, so they’re easy to miss. Once the transformation begins in earnest, however, it will likely be too late to adapt. In areas like genomics, materials science, quantum computing and artificial intelligence, if you get a few years behind, you may never catch up.

So the time to start exploring these new technologies is now and there are ample opportunities to do so. The Manufacturing USA Institutes are driving advancement in areas as diverse as bio-fabrication, additive manufacturing and composite materials. IBM has created its Q Network to help companies get up to speed on quantum computing and the Internet of Things Consortium is doing the same thing in that space.

Make no mistake, if you don’t explore, you won’t discover. If you don’t discover you won’t invent. And if you don’t invent, you will be disrupted eventually, it’s just a matter of time. It’s always better to prepare than to adapt and the time to start doing that is now.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Rise of the Prompt Engineer

Rise of the Prompt Engineer

GUEST POST from Art Inteligencia

The world of tech is ever-evolving, and the rise of the prompt engineer is just the latest development. Prompt engineers are software developers who specialize in building natural language processing (NLP) systems, like voice assistants and chatbots, to enable users to interact with computer systems using spoken or written language. This burgeoning field is quickly becoming essential for businesses of all sizes, from startups to large enterprises, to remain competitive.

Five Skills to Look for When Hiring a Prompt Engineer

But with the rapid growth of the prompt engineer field, it can be difficult to hire the right candidate. To ensure you’re getting the best engineer for your project, there are a few key skills you should look for:

1. Technical Knowledge: A competent prompt engineer should have a deep understanding of the underlying technologies used to create NLP systems, such as machine learning, natural language processing, and speech recognition. They should also have experience developing complex algorithms and working with big data.

2. Problem-Solving: Prompt engineering is a highly creative field, so the ideal candidate should have the ability to think outside the box and come up with innovative solutions to problems.

3. Communication: A prompt engineer should be able to effectively communicate their ideas to both technical and non-technical audiences in both written and verbal formats.

4. Flexibility: With the ever-changing landscape of the tech world, prompt engineers should be comfortable working in an environment of constant change and innovation.

5. Time Management: Prompt engineers are often involved in multiple projects at once, so they should be able to manage their own time efficiently.

These are just a few of the skills to look for when hiring a prompt engineer. The right candidate will be able to combine these skills to create effective and user-friendly natural language processing systems that will help your business stay ahead of the competition.

But what if you want or need to build your own artificial intelligence queries without the assistance of a professional prompt engineer?

Four Secrets of Writing a Good AI Prompt

As AI technology continues to advance, it is important to understand how to write a good prompt for AI to ensure that it produces accurate and meaningful results. Here are some of the secrets to writing a good prompt for AI.

1. Start with a clear goal: Before you begin writing a prompt for AI, it is important to have a clear goal in mind. What are you trying to accomplish with the AI? What kind of outcome do you hope to achieve? Knowing the answers to these questions will help you write a prompt that is focused and effective.

2. Keep it simple: AI prompts should be as straightforward and simple as possible. Avoid using jargon or complicated language that could confuse the AI. Also, try to keep the prompt as short as possible so that it is easier for the AI to understand.

3. Be specific: To get the most accurate results from your AI, you should provide a specific prompt that clearly outlines what you are asking. You should also provide any relevant information, such as the data or information that the AI needs to work with.

4. Test your prompt: Before you use your AI prompt in a real-world situation, it is important to test it to make sure that it produces the results that you are expecting. This will help you identify any issues with the prompt or the AI itself and make the necessary adjustments.

By following these tips, you can ensure that your AI prompt is effective and produces the results that you are looking for. Writing a good prompt for AI is a skill that takes practice, but by following these secrets you can improve your results.

So, whether you look to write your own AI prompts or feel the need to hire a professional prompt engineer, now you are equipped to be successful either way!

Image credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

AI is a Powerful New Tool for Entrepreneurs

AI is a Powerful New Tool for Entrepreneurs

by Braden Kelley

In today’s digital, always connected world, Google too often stands as a gatekeeper between entrepreneurs and small businesses and financial success. Ranking well in the search engines requires time and expertise that many entrepreneurs and small business owners don’t have, because their focus must be on fine tuning the value proposition and operations of their business.

The day after Google was invented, the search engine marketing firm was probably created to make money off of hard working entrepreneurs and small businesses owners trying to make the most of their investment in a web site through search engine optimization (SEO), keyword advertising, and social media strategies.

According to IBISWorld the market size of the SEO & Internet Marketing Consulting industry is $75.0 Billion. Yes, that’s billion with a ‘b’.

Creating content for web sites is an even bigger market. According to Technavio the global content marketing size is estimated to INCREASE by $584.0 Billion between 2022 and 2027. This is the growth number. The market itself is MUCH larger.

The introduction of ChatGPT threatens to upend these markets, to the detriment of this group of businesses, but to the benefit to the nearly 200,000 dentists in the United States, more than 100,000 plumbers, million and a half real estate agents, and numerous other categories of small businesses.

Many of these content marketing businesses create a number of different types of content for the tens of millions of small businesses in the United States, from blog articles to tweets to Facebook pages and everything in-between. The content marketing agencies that small businesses hire recent college graduates or offshore resources in places like the Philippines, India, Pakistan, Ecuador, Romania, and lots of other locations around the world and bill their work to their clients at a much higher rate.

Outsourcing content creation has been a great way for small businesses to leverage external resources so they can focus on the business, but now may be the time to bring some of this content creation work back in house. Particularly where the content is pretty straightforward and informational for an average visitor to the web site.

With ChatGPT you can ask it to “write me an article on how to brush your teeth” or “write me ten tweets on teethbrushing” or “write me a facebook post on the most common reasons a toilet won’t flush.”

I asked it to do the last one for me and here is what it came up with:

Continue reading the rest of this article on CustomerThink (including the ChatGPT results)

Image credits: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Our Fear of China is Overblown

Our Fear of China is Overblown

GUEST POST from Greg Satell

The rise of China over the last 40 years has been one of history’s great economic miracles. According to the World Bank, since it began opening up its economy in 1979, China’s GDP has grown from a paltry $178 billion to a massive $13.6 trillion. At the same time, research by McKinsey shows that its middle class is expanding rapidly.

What’s more, it seems like the Asian giant is just getting started. China has become increasingly dominant in scientific research and has embarked on two major initiatives: Made in China 2025, which aims to make it the leading power in 10 emerging industries, and a massive Belt and Road infrastructure initiative that seeks to shore up its power throughout Asia.

Many predict that China will dominate the 21st century in much the same way that America dominated the 20th. Yet I’m not so sure. First, American dominance was due to an unusual confluence of forces unlikely to be repeated. Second, China has weaknesses—and we have strengths—that aren’t immediately obvious. We need to be clear headed about China’s rise.

The Making of an American Century

America wasn’t always a technological superpower. In fact, at the turn of the 20th century, much like China at the beginning of this century, the United States was largely a backwater. Still mostly an agrarian nation, the US lacked the industrial base and intellectual heft of Europe. Bright young students would often need to go overseas for advanced degrees. With no central bank, financial panics were common.

Yet all that changed quickly. Industrialists like Thomas Edison and Henry Ford put the United States at the forefront of the two most important technologies of the time, electricity and internal combustion. Great fortunes produced by a rising economy endowed great educational institutions. In 1913 the Federal Reserve Act was passed, finally bringing financial stability to a growing nation. By the 1920s, much like China today, America had emerged as a major world power.

Immigration also played a role. Throughout the early 1900s immigrants coming to America provided enormous entrepreneurial energy as well as cheap labor. With the rise of fascism in the 1930s, our openness to new people and new ideas attracted many of the world’s greatest scientists to our shores and created a massive brain drain in Europe.

At the end of World War II, the United States was the only major power left with its industrial base still intact. We seized the moment wisely, using the Marshall Plan to rebuild our allies and creating scientific institutions, such as the National Science Foundation (NSF) and the National Institutes of Health (NIH) that fueled our technological and economic dominance for the rest of the century.

There are many parallels between the 1920s and the historical moment of today, but there are also many important differences. It was a number of forces, including our geography, two massive world wars, our openness as a culture and a number of wise policy choices that led to America’s dominance. Some of these factors can be replicated, but others cannot.

MITI and the Rise of Japan

Long before China loomed as a supposed threat to American prosperity and dominance, Japan was considered to be a chief economic rival. Throughout the 1970s and 80s, Japanese firms came to lead in many key industries, such as automobiles, electronics and semiconductors. The United States, by comparison, seemed feckless and unable to compete.

Key to Japan’s rise was a long-term industrial policy. The Ministry of International Trade and Industry (MITI) directed investment and funded research that fueled an economic miracle. Compared to America’s haphazard policies, Japan’s deliberate and thoughtful strategy seemed like a decidedly more rational and wiser model.

Yet before long things began to unravel. While Japan continued to perform well in many of the industries and technologies that the MITI focused on, it completely missed out on new technologies, such as minicomputers and workstations in the 1980s and personal computers in the 1990s. As MITI continued to support failing industries, growth slowed and debt piled up, leading to a lost decade of economic malaise.

At the same time, innovative government policy in the US also helped turn the tide. For example, in 1987 a non-profit consortium made up of government labs, research universities and private sector companies, called SEMATECH, was created to regain competitiveness in the semiconductor industry. America soon retook the lead, which continues even today.

China 2025 and the Belt and Road Initiative

While the parallels with America in the 1920s underline China’s potential, Japan’s experience in the 1970s and 80s highlight its peril. Much like Japan, it is centralizing decision-making around a relatively small number of bureaucrats and focusing on a relatively small number of industries and technologies.

Much like Japan back then, China seems wise and rational. Certainly, the technologies it is targeting, such as artificial intelligence, electric cars and robotics would be on anybody’s list of critical technologies for the future. The problem is that the future always surprises us. What seems clear and obvious today may look ridiculous and naive a decade from now.

To understand the problem, consider quantum computing, which China is investing heavily in. However, the technology is far from monolithic. In fact, there are a wide variety of approaches being championed by different firms, such as IBM, Microsoft, Google, Intel and others. Clearly, some of these firms are going to be right and some will be wrong.

The American firms that get it wrong will fail, but others will surely succeed. In China, however, the ones that get it wrong will likely be government bureaucrats who will have the power to prop up state supported firms indefinitely. Debt will pile up and competitiveness will decrease, much like it did in Japan in the 1990s.

This is, of course, speculation. However, there are indications that it is already happening. A recent bike sharing bubble has ignited concerns that similar over-investment is happening in artificial intelligence. Many investors have also become concerned that China’s slowing economy will be unable to support its massive debt load.

The Path Forward

The rise of China presents a generational challenge. Clearly, we cannot ignore a rising power, yet we shouldn’t overreact either. While many have tried to cast China as a bad actor, engaging in intellectual theft, currency manipulation and other unfair trade policies, others point out that it is wisely investing for the long-term while the US manages by the quarter.

Interestingly, as Fareed Zakaria recently pointed out, the same accusations made about China’s unfair trade policies today were leveled at Japan 40 years ago. In retrospect, however, our fears about Japan seem almost quaint. Not only were we not crushed by Japan’s rise, we are clearly better for it, incorporating Japanese ideas like lean manufacturing and combining them with our own innovations.

I suspect, or at least I hope, that we will benefit from China’s rise much as we did from Japan’s. We will learn from its innovations and be inspired to develop more of our own. If a Chinese scientist invents a cure for cancer, American lives will be saved. If an American scientist invents a better solar panel, fewer Chinese will be choking on smog.

Perhaps most of all, we need to remember that what made the 20th Century the American Century was our ability to rise to the challenges that history presented. Whether it was rebuilding Europe in the 40s and 50s, or Sputnik in the 50s and 60s or Japan in the 70s and 80s, competition always brought out the best in us. Then, as now, our destiny was our own to determine.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Top 10 Human-Centered Change & Innovation Articles of January 2023

Top 10 Human-Centered Change & Innovation Articles of January 2023Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are January’s ten most popular innovation posts:

  1. Top 40 Innovation Bloggers of 2022 — Curated by Braden Kelley
  2. Back to Basics: The Innovation Alphabet — by Robyn Bolton
  3. 99.7% of Innovation Processes Miss These 3 Essential Steps — by Robyn Bolton
  4. Top 100 Innovation and Transformation Articles of 2022 — Curated by Braden Kelley
  5. Ten Ways to Make Time for Innovation — by Nick Jain
  6. Agility is the 2023 Success Factor — by Soren Kaplan
  7. Five Questions All Leaders Should Always Be Asking — by David Burkus
  8. 23 Ways in 2023 to Create Amazing Experiences — by Shep Hyken
  9. Startups Must Be Where Their Customers Are — by Steve Blank
  10. Will CHATgpt make us more or less innovative? — by Pete Foley

BONUS – Here are five more strong articles published in December that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last three years:

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Will CHATgpt make us more or less innovative?

Will CHATgpt make us more or less innovative?

GUEST POST from Pete Foley

The rapid emergence of increasingly sophisticated ‘AI ‘ programs such as CHATgpt will profoundly impact our world in many ways. That will inevitably include Innovation, especially the front end. But will it ultimately help or hurt us? Better access to information should be a huge benefit, and my intuition was to dive in and take full advantage. I still think it has enormous upside, but I also think it needs to be treated with care. At this point at least, it’s still a tool, not an oracle. It’s an excellent source for tapping existing information, but it’s (not yet) a source of new ideas. As with any tool, those who understand deeply how it works, its benefits and its limitations, will get the most from it. And those who use it wrongly could end up doing more harm than good. So below I’ve mapped out a few pros and cons that I see. It’s new, and like everybody else, I’m on a learning curve, so would welcome any and all thoughts on these pros and cons:

What is Innovation?

First a bit of a sidebar. To understand how to use a tool, I at least need to have a reasonably clear of what goals I want it to help me achieve. Obviously ‘what is innovation’ is a somewhat debatable topic, but my working model is that the front end of innovation typically involves taking existing knowledge or technology, and combining it in new, useful ways, or in new contexts, to create something that is new, useful and ideally understandable and accessible. This requires deep knowledge, curiosity and the ability to reframe problems to find new uses of existing assets. A recent illustrative example is Oculus Rift, an innovation that helped to make virtual reality accessible by combining fairly mundane components including a mobile phone screen and a tracking sensor and ski glasses into something new. But innovation comes in many forms, and can also involve serendipity and keen observation, as in Alexander Fleming’s original discovery of penicillin. But even this requires deep domain knowledge to spot the opportunity and reframing undesirable mold into a (very) useful pharmaceutical. So, my start-point is which parts of this can CHATgpt help with?

Another sidebar is that innovation is of course far more than simply discovery or a Eureka moment. Turning an idea into a viable product or service usually requires considerable work, with the development of penicillin being a case in point. I’ve no doubt that CHATgpt and its inevitable ‘progeny’ will be of considerable help in that part of the process too.   But for starters I’ve focused on what it brings to the discovery phase, and the generation of big, game changing ideas.

First the Pros:

1. Staying Current: We all have to strike a balance between keeping up with developments in our own fields, and trying to come up with new ideas. The sheer volume of new information, especially in developing fields, means that keeping pace with even our own area of expertise has become challenging. But spend too much time just keeping up, and we become followers, not innovators, so we have to carve out time to also stretch existing knowledge. But if we don’t get the balance right, and fail to stay current, we risk get leapfrogged by those who more diligently track the latest discoveries. Simultaneous invention has been pervasive at least since the development of calculus, as one discovery often signposts and lays the path for the next. So fail to stay on top of our field, and we potentially miss a relatively easy step to the next big idea. CHATgpt can become an extremely efficient tool for tracking advances without getting buried in them.

2. Pushing Outside of our Comfort Zone: Breakthrough innovation almost by definition requires us to step beyond the boundaries of our existing knowledge. Whether we are Dyson stealing filtration technology from a sawmill for his unique ‘filterless’ vacuum cleaner, physicians combining stem cell innovation with tech to create rejection resistant artificial organs, or the Oculus tech mentioned above, innovation almost always requires tapping resources from outside of the established field. If we don’t do this, then we not only tend towards incremental ideas, but also tend to stay in lock step with other experts in our field. This becomes increasingly the case as an area matures, low hanging fruit is exhausted, and domain knowledge becomes somewhat commoditized. CHATgpt simply allows us to explore beyond our field far more efficiently than we’ve ever been able to before. And as it or related tech evolves, it will inevitably enable ever more sophisticated search. From my experience it already enables some degree of analogous search if you are thoughtful about how to frame questions, thus allowing us to more effectively expand searches for existing solutions to problems that lie beyond the obvious. That is potentially really exciting.

Some Possible Cons:

1. Going Down the Rabbit Hole: CHATgpt is crack cocaine for the curious. Mea culpa, this has probably been the most time consuming blog I’ve ever written. Answers inevitably lead to more questions, and it’s almost impossible to resist playing well beyond the specific goals I initially have. It’s fascinating, it’s fun, you learn a lot of stuff you didn’t know, but I at least struggle with discipline and focus when using it. Hopefully that will wear off, and I will find a balance that uses it efficiently.

2. The Illusion of Understanding: This is a bit more subtle, but a topic inevitably enhances our understanding of it. The act of asking questions is as much a part of learning as reading answers, and often requires deep mechanistic understanding. CHATgpa helps us probe faster, and its explanations may help us to understand concepts more quickly. But it also risks the illusion of understanding. When the heavy loading of searching is shifted away from us, we get quick answers, but may also miss out on the deeper mechanistic understanding we’d have gleaned if we’d been forced to work a bit harder. And that deeper understanding can be critical when we are trying to integrate superficially different domains as part of the innovation process. For example, knowing that we can use a patient’s stem cells to minimize rejection of an artificial organ is quite different from understanding how the immune system differentiates between its own and other stem cells. The risk is that sophisticated search engines will do more heavy lifting, allow us to move faster, but also result in a more superficial understanding, which reduces our ability to spot roadblocks early, or solve problems as we move to the back end of innovation, and reduce an idea to practice.

3. Eureka Moment: That’s the ‘conscious’ watch out, but there is also an unconscious one. It’s no secret that quite often our biggest ideas come when we are not actually trying. Archimedes had his Eureka moment in the bath, and many of my better ideas come when I least expect them, perhaps in the shower, when I first wake up, or am out having dinner. The neuroscience of creativity helps explain this, in that the restructuring of problems that leads to new insight and the integration of ideas works mostly unconsciously, and when we are not consciously focused on a problem. It’s analogous to the ‘tip of the tongue’ effect, where the harder we try to remember something, the harder it gets, but then comes to us later when we are not trying. But the key for the Eureka moment is that we need sufficiently deep knowledge for those integrations to occur. If CHATgpt increases the illusion of understanding, we could see less of those Eureka moments, and the ‘obvious in hindsight ideas’ they create.

Conclusion

I think that ultimately innovation will be accelerated by CHATgpt and what follows, perhaps quite dramatically. But I also think that we as innovators need to try and peel back the layers and understand as much as we can about these tools, as there is potential for us to trip up. We need to constantly reinvent the way we interact with them, leverage them as sophisticated innovation tools, but avoid them becoming oracles. We also need to ensure that we, and future generations use them to extend our thinking skill set, but not become a proxy for it. The calculator has in some ways made us all mathematical geniuses, but in other ways has reduced large swathes of the population’s ability to do basic math. We need to be careful that CHATgpt doesn’t do the same for our need for cognition, and deep mechanistic and/or critical thinking.

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.