Tag Archives: AI

Unlocking Trapped Value with AI

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Unlocking Trapped Value with AI

GUEST POST from Geoffrey A. Moore

Anyone who has used Chat GPT or any of its cousins will testify to its astonishing ability to provide valuable responses to virtually any query. This is hardly a threat—indeed, it is a boon. So, what are we worrying about?

Well, there is the issue of veracity, of course, and it is true, GPT-enabled assistants can indeed make mistakes. But, come on—humans don’t? We are not looking for gospel truth here. We want highly probable, highly informed answers to questions where we need guidance, and it is clear that GPT-enabled applications are outstanding at meeting this need, for at least three reasons. They are remarkably well-informed. They are available 24/7 on demand with no hold time. And they have infinite patience. So, let’s not kid ourselves. We are massively better off for their emergence on the scene.

What we should be worrying about, on the other hand, is their impact on jobs to be done, employment, and career development. A simple way to think about this is that for any of us to earn money, we have to release some form of trapped value. A bank clerk helps a customer get access to the trapped value in their savings account. A bus driver helps a passenger cope with their trapped value by transporting them to the location where they need to be. A lawyer helps a client get access to trapped value by constructing a contract that meets their needs while protecting against risk. A teacher helps a student access trapped value by helping her solve problems she couldn’t handle before. The principle applies to every job. All systems have points of trapped value, and all jobs are organized around releasing and capturing that value.

Now, let’s introduce generative AI. All of a sudden, a whole lot of trapped value that funded a whole lot of jobs can now be released for free (or virtually for free). Those jobs can be protected in the short term but not forever. In other words, the environment really has changed, and we must assess our new circumstances or fall behind. This is Darwinism at work. Evolution never stops. It can’t. As long as there is change, there will be dislocation, which in turn will stimulate innovation. That’s life.

But here’s the good news. The universe can never eliminate trapped value, it can only move it from place to place. That is, there are always emergent problems to solve, always new opportunities to capitalize on, because every system always traps value somewhere. What Darwinism requires is that we detect the new value traps and redirect our activity to engage with them.

Publicly funded agencies sometimes interpret this as a mandate for training programs, but we have to be careful here. Training works well for disseminating established skills that address known problems. It does not work well, however, where the problems are still being determined and the skills are as yet undeveloped. Novelty, in other words, demands creativity. It is simply not negotiable.

Getting back to the impact of generative AI, we should understand that it is an advisory technology. It is not automation. That is, it is not eliminating the need for human beings to make judgment calls. Rather, it is accelerating the preparation for so doing and framing the options in ways that make decision-making more straightforward. By solving for the old value traps, it is giving us the opportunity to up our game. It’s our job to step up to add net new value to the equation.

The best way to do this is to ferret out the emerging new value traps. Who is the customer now? What is the bottleneck that is holding them back? How could that bottleneck be broken open? What is the reward for so doing? These are the fundamental questions that drive any business model. We know how to do this. It’s just that we have been riding on the inertia of the past set of solutions for so long we may have atrophied in some of the muscles we need now. One thing we need not worry about is the universe running out of trapped value. If you are ever in doubt, just read the day’s headlines and be reassured. The world needs our help. Any tool that helps us do our part better is a blessing.

That’s what I think. What do you think?

Image Credit: Pexels

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Beyond the AI Customer Experience Hype

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Beyond the AI Customer Experience Hype

GUEST POST from Shep Hyken

I’ve been writing a lot about artificial intelligence (AI) and the digital customer experience (CX). Many of the executives I interview and the articles I refer to are all about how AI is revolutionizing, changing, helping and sometimes hurting CX. So we’ve heard from experts. How about if we heard from the customers?

That’s exactly what we did in our annual customer service and CX research sponsored by RingCentral. We asked more than 1,000 U.S. consumers about their experiences with AI and digital customer support, and here are the basic findings for 2024:

The Good

  1. Sixty-two percent of U.S. consumers expect that AI (and related technologies) will be the primary mode of customer service in the future. But how about today? As you will see in some of the findings below, not everyone feels AI is ready for primetime customer service and CX.
  2. Thirty-eight percent believe AI and related technologies will lead to more personalized customer experiences. Personalization has been a hot topic for marketing and CX leaders. AI is giving companies and brands far greater capabilities to use customer data to create a personalized experience. Customers enjoy doing business with companies that recognize them and use the information they have to create a better experience.
  3. Forty-nine percent think AI technologies have the potential to improve the overall customer experience. This is good news, however, the next group of findings shows that companies still have an uphill battle to get customers to adopt and embrace a CX fueled by AI.

The Bad

  1. Only 32% of customers have successfully resolved a customer service issue using AI or ChatGPT technologies. That number is low. One theory is that customers often don’t realize AI is what’s behind what they are doing. Some think AI is chatbots and automated voice response systems that interact with them like a human would or should.
  2. Fifty-six percent of customers admit to being scared of technologies like AI and ChatGPT. Some of these customers may have watched movies where computers take over the world or robots go rogue, none of which are grounded in reality. However, some customers simply don’t trust the technology because of past bad experiences.
  3. Sixty-three percent of customers are frustrated with self-service options using AI, ChatGPT and similar technologies. Frustration is different than being scared, but it has the same impact: customers would rather avoid technology and talk to a live human for support and service.

As I studied the significance of these findings as a whole, the overarching theme of why AI has not caught on as a viable and reliable customer support option is inconsistency. Included in the annual study is a finding that 70% of customers choose talking to a live customer service agent on the phone as their primary channel for customer service.

Why? It’s easier, and customers know what to expect when they talk and interact with live agents. What they don’t want to experience is a self-service solution powered by AI that takes them through a series of prompts that eventually lead to a dead end, where they end up having to call the company anyway.

There’s good reason for the fear and frustration. As more customers are exposed to AI and start to understand it, their inconsistent experiences from one company to the next are creating a confidence problem. The latest technology, which is very cost-effective for even small businesses, has not been purchased and implemented by a majority of businesses.

As of the beginning of this year, just 27% of customers think self-service or automated customer support using AI-powered technology can deliver as good of a customer experience as a live agent. That number will eventually go up, although not as quickly as it needs to. Once companies recognize that bad service equates to lost business, they will make the investment to do it right. It’s not an option if they want their customers to say, “I’ll be back!”

This article was originally published on Forbes.com

Image Credits: Unsplash

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Deeply Seeking the Future of AI

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Deeply Seeking the Future of AI

GUEST POST from Robert B. Tucker

Earlier this week, news that a Chinese startup called DeepSeek created a workable AI competitor to ChatGPT sent shock waves through Silicon Valley and Washington. In what is being called a “Sputnik Moment,” DeepSeek’s breakthrough has turned the $100 billion Stargate AI initiative that President Trump announced last week into an “Interstellar Graveyard,” noted a post on Fancaiju, a business blog on WeChat.

DeepSeek shocked the world with its new AI offering, which it developed for $5 million, contrasting sharply with the billions that OpenAI, Microsoft, and Google spent developing theirs. Result: a $1 trillion bloodbath on Wall Street and an assumption-assaulting moment heard worldwide.

While DeepSeek’s claims have yet to be verified, gone in a nanosecond is the assumption that the US is ahead of China in the AI arms race. Gone, too, is the assumption that only big tech firms with deep pockets can dominate the AI space.

The larger point here is that we now live in a world where making assumptions or going along unthinkingly with conventional wisdom can lead us into the jaws of disruption. In the Age of Acceleration, we can choose to challenge our assumptions, or somebody else will do it for us. (If they do, it won’t be pretty.)

As an innovation coach and futurist, I teach managers the techniques of identifying and “assaulting” individual, organizational, and big-picture assumptions, often called paradigms. I explain that assumptions are what everybody believes to be true … until it isn’t, as DeepSeek appears to have demonstrated. And I show how innovation begins where assumptions end.

Challenging assumptions is essential, whether personal (“I’m too old/young to do that”), organizational (“that’s not how we do things around here”), or national (“America is way out ahead in AI”).

One technique is to ask open-ended questions and invite new thinking. For example: “what’s a better way to do X?” is such a challenging question because it opens the mind to the realization that the way we do X currently might be obsolete.

In researching a book about navigating a world of hyper-change, I realized we will all need to build muscle mass in this area. The world is moving too fast to harbor erroneous assumptions. We are now living through an “inflection point” that is unlike anything in history. Over the coming decade, there will be more social, technological, and geopolitical change than over the past 100 years. And we are not ready for this world of constant overdrive.

The forces that will characterize this period are becoming clearer: the rise of authoritarianism both in the United States and globally; the pervasiveness of social media and the diminishment of freedom and human consciousness; China vs. the USA race for world dominion; and, most importantly, the burgeoning AI Revolution.

We now live in a world of exponential change, a clear departure in the human experience. Something that happens in a lab in Wuhan explodes into a global pandemic. And something that happens in a startup lab in China wipes out a trillion dollars in market value overnight. In such a world, assumptions are like barnacles on the side of a boat.

DeepSeek not only vaporizes assumptions we may have made about AI but also the future of humanity. One such assumption is that AI will lead us into the promised land. Silicon Valley promulgates this assumption with passionate intensity. It is techno-determinism writ large: If we can invent it, it must be a good thing for society.

But all too often, while technology brings about clear and immediate benefits – it has unintended consequences that become evident over time.

In 2011, 23 percent of teenagers in the United States and Western countries owned a smartphone, meaning they had only limited access to social media; most had to use the family computer to access the internet. But by 2016, 79 percent of teens owned a smartphone, as did 28 percent of children ages eight to 12. Teens indicated they spent an average of almost seven hours a day on screens. Researchers found that one out of every four young people admitted that they were “almost constantly” online. This meant that they spent far less time playing with, talking to, touching, or even making eye contact with their friends and families, according to researchers. Instead, they withdrew from “embodied social behaviors” essential for successful human development.

As researchers such as Jonathan Haidt and others have observed, signs of a mental health crisis began to emerge. Rates of mental illness among the young shot up dramatically in many Western countries between 2010 and 2015. Major depression among teens went up 145 percent among girls, and 161 percent among boys. A similar rise in anxiety disorders occurred.

Will AI have similar benefits and unintended consequences? Champions of AI see positive progress and productivity.

“Every person will have an AI assistant, coach, mentor, trainer, advisor, and therapist who is infinitely patient, infinitely compassionate, infinitely knowledgeable, and infinitely helpful,” observes Marc Andreessen in a widely read essay on why AI will save the world. “Your AI assistant will be present through all of life’s opportunities and challenges, maximizing every person’s outcomes.”

Perhaps AI will lead us to the promised land, but only if we challenge our assumptions, and consciously shape the future.

This article originally appeared in Forbes
Image credit: Gemini

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Innovation or Not – AI Birdwatching

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Innovation or Not - AI Birdwatching

GUEST POST from Art Inteligencia

Welcome to another installment of the “Innovation or Not” series, where we dissect intriguing products and services to determine whether they truly represent ingenuity, or if they’re just another notch on the belt of incremental progress. Today, we’re venturing into the realm of birdwatching — a niche hobby that surprisingly intersects with cutting-edge artificial intelligence and automated insights into our avian neighbors.

Introducing FeatherSnap

The product up for review is the FeatherSnap bird feeder camera. At its core, FeatherSnap is a bird feeder equipped with a camera that not only captures images of our feathery friends but also uses AI to identify species and offer insights to the user. The idea itself blends the tranquility of birdwatching with the technological advancements of AI and machine learning. It also has a smart design to integrate the food storage into the structure itself to save space, and has solar panels to power the onboard technology. But the question remains: is this a pleasant convenience or a groundbreaking innovation?

The Tech Behind FeatherSnap

FeatherSnap integrates a high-quality camera with AI capabilities to recognize and catalog bird species visiting your garden or backyard. It allows the user to receive real-time alerts on their smartphone, providing information about the birds that stop by for a snack. This records data such as the species, time of day, and frequency of visits, creating a rich, personalized avian database over time.

“AI birdwatching may be niche, but it bridges a gap between nature enthusiasts and technology, making the act of observation more engaging and informed.”

Innovation Analysis

When assessing FeatherSnap through the lens of innovation, we explore several key criteria:

  • Originality: AI-augmented birdwatching is a fresh take on a traditional hobby, significantly enhancing the user experience.
  • Technology Application: The application of AI in identifying bird species represents an advancement in both hobbyist technology and AI’s practical capabilities.
  • Value Creation: FeatherSnap adds substantial value to the birdwatching experience by providing educational insights and personalized interaction with nature.
  • Market Impact: While its potential market may seem limited to bird enthusiasts, the push towards automated, intelligent environmental engagement could have broader applications.

Final Verdict: Innovation or Not?

So, is FeatherSnap an innovation or not? Taking all factors into consideration, I would argue that FeatherSnap qualifies as an innovation. Despite its niche market, it presents a clever integration of AI with everyday life that could inspire further applications across different domains. The product encourages a deeper interaction with nature and presents a template for utilizing technology to enrich leisure activities.

In the broader context of our tech-driven world, FeatherSnap’s introduction to the market both exemplifies ingenuity in leisure tech and challenges developers to think creatively about AI’s scope and potential in nature-based contexts.

As we reflect on this product, it reminds us that innovation isn’t always about life-changing inventions but also about elevating the simple joys of life with smart adaptations.

I encourage you to share your thoughts and opinions on FeatherSnap and whether you consider it groundbreaking or just another incremental product in the tech landscape. Until next time, keep questioning and exploring the ever-changing facets of innovation around you.

Image credit: FeatherSnap

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Will AI Threaten Your Job?

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Take This Quiz To Get Future-Ready

Will AI Threaten Your Job?

GUEST POST from Robert B. Tucker

Will artificial intelligence threaten your job? Not to alarm you, but it probably already does. The assessment below will tell you if you have the mindset and skillset that makes you future-proof.

The current “white collar” recession is at least partially attributable to productivity gains from AI. Since 2022, Silicon Valley firms have eliminated 552,000 jobs. Numerous firms from Accenture (19,000 jobs eliminated in 2024 alone) to Nike are quietly shedding jobs. Already, companies are finding they can get by with fewer workers.

In years past, blue collar jobs were threatened by recession and automation. But the future economy infused with a growing array of AI tools, is showing that white collar office jobs are vulnerable. More importantly, the organization of the future will demand different skillsets from those who work in offices. There is a shift underway.

LinkedIn researcher Aneesh Raman and Maria Flynn, president of Jobs for the Future, recently examined what they call “a huge shift in the skills our economy values most.” They parsed which skills any given job actually requires. From this, they then identified over 500 jobs likely to be affected by generative AI technologies. For example, they discovered that 96 percent of a software engineer’s current skills — mainly proficiency in programming languages — can eventually be replicated by AI. Skills associated with jobs like legal associates and finance officers will also be highly exposed.

“Technical and data skills that have been highly sought after for decades appear to be among the most exposed to advances in artificial intelligence,” note Raman and Flynn. “But other skills, particularly the people skills that we have long undervalued as soft, will very likely remain the most durable. That is a hopeful sign that AI could usher in a world of work that is anchored more, not less, around human ability.”

Yet while large organizations the world over are shedding jobs, they are desperately in need of people with what I call innovation skills (I-Skills) — the all too rare talent who can think ahead of the curve, conceptualize new products and services that grow revenue, delight customers, slash costs, motivate teams, and achieve outsized results.

Below are 14 questions that will help you determine if you’re AI vulnerable.

Print out this assessment and rate yourself on your I-Skills. If you strongly agree with the statement, give yourself a 10. If you strongly disagree, give yourself a 1 or 2. Remember to consider not only your self-perception, but also the perception of your coworkers, customers and your boss.

  1. I approach my work with an opportunity mindset and show initiative and solve problems with a “can-do” attitude.
  2. I embrace and use new technology (AI) and am often among the first in my organization to try out new tools.
  3. I volunteer to lead new initiatives. I regularly get involved in projects having to do with building the future of my organization.
  4. I align myself with the strategic goals of my organization’s senior leadership.
  5. I engage deeply with people in my company and work to improve my communication, collaboration, and innovation skills.
  6. I have a genuine passion for serving the end user (internal or external customer).
  7. I look for ways to take on the customer’s problem.
  8. I often take calculated risks.
  9. I collaborate effectively in cross-functional teams.
  10. I see through barriers and hurdles to achieving my goals.
  11. I welcome feedback and use it to grow.
  12. I am idea-oriented and constantly gather new ideas to build more creative outcomes.
  13. I work to build a network of people who I create value for and receive value from.
  14. I sell my ideas effectively and work hard at enrolling and converting others to my vision.

Here’s how to score yourself on your I-Skills.

As you completed the survey, did you consider your coworkers’ perceptions of you, or did you answer the questions based on your self-perception? Bear in mind that it’s not only about how you perceive your I-Skills. It’s about what you do and what you’re recognized for by others on your team. If you’re going to add unique value to your organization, you must take all your great ideas, skills, and abilities and turn them into concrete actions and initiatives that make a positive difference.

If you scored yourself 100 or higher, congratulations. You’ve developed quite a few of the I-Skills already. If you scored yourself in the 75 to 99 range, you’re still ahead of most of your peers, but you’ve got some skill building to do.

If you scored below 74, take heart. These are new skills for the vast majority of people, ones they haven’t had to use to be successful in the past.

Summary: AI doesn’t have to threaten your job. Not as long as you work to build up your I-Skills. The time to get started on this is right now.

This article originally appeared in Forbes
Image credit: Pexels

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Artificial Innovation

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Artificial Innovation

by Braden Kelley

Recently several people have asked me whether or not artificial intelligence (AI) has a role to play innovation. One of the ways I’ve answered this question is by speaking about how artificial intelligence can be used to help test/disprove assumptions. Innovation always makes assumptions and often the success or failure of any innovation effort is determined by how well the team identifies the critical assumptions to test, those that if incorrectly assumed to be true could later derail the pursuit of innovation or waste limited innovation investment dollars.

But I thought it could be interesting to use AI to answer this question in more detail, leveraging my Eight I’s of Infinite Innovation framework to highlight how artificial intelligence could be used at each step of the continuous innovation journey.

Below you will find a detailed explanation of the Eight I’s of Infinite Innovation framework along with clearly called out contextual responses generated by Microsoft CoPilot detailing how AI could be used productively during that specific phase of the continuous innovation journey from prompts generated by me after uploading a PDF version of the original Eight I’s of Infinite Innovation article (see the link at the bottom).

Eight I's of Infinite Innovation

Creating a Continuous Innovation Capability

To achieve sustainable success at innovation, you must work to embed a repeatable process and way of thinking within your organization, and this is why it is important to have a simple common language and guiding framework of infinite innovation that all employees can easily grasp. If innovation becomes too complex, or seems too difficult then people will stop pursuing it, or supporting it.

Some organizations try to achieve this simplicity, or to make the pursuit of innovation seem more attainable, by viewing innovation as a project-driven activity. But, a project approach to innovation will prevent it from ever becoming a way of life in your organization. Instead you must work to position innovation as something infinite, a pillar of the organization, something with its own quest for excellence – a professional practice to be committed to.

So, if we take a lot of the best practices of innovation excellence and mix them together with a few new ingredients, the result is a simple framework organizations can use to guide their pursuit of continuous innovation – the Eight I’s of Infinite Innovation. This framework anchors what is a very collaborative process. Here is the framework and some of the many points organizations must consider during each stage of the continuous process:

1. Inspiration

  • Employees are constantly navigating an ever changing world both in their home context, and as they travel the world for business or pleasure, or even across various web pages in the browser of their PC, tablet, or smartphone.
  • What do they see as they move through the world that inspires them and possibly the innovation efforts of the company?
  • What do they see technology making possible soon that wasn’t possible before?
  • The first time through we are looking for inspiration around what to do, the second time through we are looking to be inspired around how to do it.
  • What inspiration do we find in the ideas that are selected for their implementation, illumination and/or installation?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can help employees find inspiration by analyzing vast amounts of data from various sources, such as social media, news articles, and industry reports. By identifying emerging trends and patterns, AI can provide insights into what is possible and inspire new ideas for innovation. Additionally, AI-powered tools can help employees visualize potential solutions and explore creative possibilities.

2. Investigation

  • What can we learn from the various pieces of inspiration that employees come across?
  • How do the isolated elements of inspiration collect and connect? Or do they?
  • What customer insights are hidden in these pieces of inspiration?
  • What jobs-to-be-done are most underserved and are worth digging deeper on?
  • Which unmet customer needs that we see are worth trying to address?
  • Which are the most promising opportunities, and which might be the most profitable?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can assist in the investigation phase by processing and analyzing large datasets to uncover hidden insights and customer needs. Machine learning algorithms can identify patterns and correlations that may not be immediately apparent to humans, helping organizations understand which opportunities are most promising and worth pursuing. AI can also automate the process of gathering and organizing information, making it easier for employees to focus on deeper analysis.

3. Ideation

  • We don’t want to just get lots of ideas, we want to get lots of good ideas
  • Insights and inspiration from first two stages increase relevance and depth of the ideas
  • We must give people a way of sharing their ideas in a way that feels safe for them
  • How can we best integrate online and offline ideation methods?
  • How well have we communicated the kinds of innovation we seek?
  • Have we trained our employees in a variety of creativity methods?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can enhance the ideation process by generating a wide range of ideas based on input from employees and external sources. Natural language processing (NLP) algorithms can analyze and categorize ideas, making it easier to identify the most relevant and promising ones. AI-powered collaboration tools can also facilitate brainstorming sessions, allowing employees to share and build on each other’s ideas in real-time, regardless of their physical location.

4. Iteration

  • No idea emerges fully formed, so we must give people a tool that allows them to contribute ideas in a way that others can build on them and help uncover the potential fatal flaws of ideas so that they can be overcome
  • We must prototype ideas and conduct experiments to validate assumptions and test potential stumbling blocks or unknowns to get learnings that we can use to make the idea and its prototype stronger
  • Are we instrumenting for learning as we conduct each experiment?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can support the iteration phase by providing tools for rapid prototyping and experimentation. Machine learning models can simulate different scenarios and predict potential outcomes, helping teams identify and address potential flaws in their ideas. AI can also automate the process of collecting and analyzing feedback from experiments, enabling continuous improvement and refinement of prototypes.

Eight I's of Infinite Innovation

5. Identification

  • In what ways do we make it difficult for customers to unlock the potential value from this potentially innovative solution?
  • What are the biggest potential barriers to adoption?
  • What changes do we need to make from a financing, marketing, design, or sales perspective to make it easier for customers to access the value of this new solution?
  • Which ideas are we best positioned to develop and bring to market?
  • What resources do we lack to realize the promise of each idea?
  • Based on all of the experiments, data, and markets, which ideas should we select?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can help organizations identify the most viable ideas by analyzing data from experiments, market research, and customer feedback. Predictive analytics can assess the potential success of different ideas and prioritize those with the highest likelihood of success. AI can also identify potential barriers to adoption and suggest strategies to overcome them, ensuring that innovative solutions are accessible and valuable to customers.

You’ll see in the framework that things loop back through inspiration again before proceeding to implementation. There are two main reasons why. First, if employees aren’t inspired by the ideas that you’ve selected to commercialize and some of the potential implementation issues you’ve identified, then you either have selected the wrong ideas or you’ve got the wrong employees. Second, at this intersection you might want to loop back through the first five stages though an implementation lens before actually starting to implement your ideas OR you may unlock a lot of inspiration and input from a wider internal audience to bring into the implementation stage.

6. Implementation

  • What are the most effective and efficient ways to make, market, and sell this new solution?
  • How long will it take us to develop the solution?
  • Do we have access to the resources we will need to produce the solution?
  • Are we strong in the channels of distribution that are most suitable for delivering this solution?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can streamline the implementation process by optimizing production, marketing, and sales strategies. AI-powered project management tools can help teams plan and execute tasks more efficiently, while machine learning algorithms can optimize supply chain and distribution processes. AI can also personalize marketing campaigns and sales approaches, ensuring that new solutions reach the right customers at the right time.

7. Illumination

  • Is the need for the solution obvious to potential customers?
  • Are we launching a new solution into an existing product or service category or are we creating a new category?
  • Does this new solution fit under our existing brand umbrella and represent something that potential customers will trust us to sell to them?
  • How much value translation do we need to do for potential customers to help them understand how this new solution fits into their lives and is a must-have?
  • Do we need to merely explain this potential innovation to customers because it anchors to something that they already understand, or do we need to educate them on the value that it will add to their lives?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can enhance the illumination phase by helping organizations communicate the value of their innovations to potential customers. NLP algorithms can generate compelling marketing content and product descriptions, while sentiment analysis can gauge customer reactions and adjust messaging accordingly. AI can also identify key influencers and target them with personalized messages to amplify the reach of new solutions.

8. Installation

  • How do we best make this new solution an accepted part of everyday life for a large number of people?
  • How do we remove access barriers to make it easy as possible for people to adopt this new solution, and even tell their friends about it?
  • How do we instrument for learning during the installation process to feedback new customer learnings back into the process for potential updates to the solution?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can facilitate the installation of new solutions by removing barriers to adoption and ensuring a seamless customer experience. AI-powered customer support tools can provide instant assistance and troubleshooting, while machine learning algorithms can personalize onboarding processes to meet individual customer needs. AI can also monitor usage patterns and gather feedback, enabling continuous improvement and updates to the solution.

Conclusion

The Eight I’s of Infinite Innovation framework is designed to be a continuous learning process, one without end as the outputs of one round become inputs for the next round. It’s also a relatively new guiding framework for organizations to use, so if you have thoughts on how to make it even better, please let me know in the comments. The framework is also ideally suited to power a wave of new organizational transformations that are coming as an increasing number of organizations (including Hallmark) begin to move from a product-centered organizational structure to a customer needs-centered organizational structure. The power of this new approach is that it focuses the organization on delivering the solutions that customers need as their needs continue to change, instead of focusing only on how to make a particular product (or set of products) better.

By leveraging AI at each stage of the innovation process, organizations can enhance their ability to generate, develop, and implement successful innovations.

So, as you move from the project approach that is preventing innovation from ever becoming a way of life in your organization, consider using the Eight I’s of Infinite Innovation to influence your organization’s mindset and to anchor your common language of innovation. The framework is great for guiding conversations, making your innovation outputs that much stronger, and will contribute to your quest for innovation excellence – it is even more powerful when you combine it with my Value Innovation Framework (found here). The two are like chocolate and peanut butter. They’re powerful tools when used separately, but even more powerful when used together.

Click to access this framework as a FREE scalable 11″x17″ PDF download

Click to download the PDF version of this article

People who upgrade to the Bronze Version of the Change Planning Toolkit™ will get access to my Innovation Planning Canvas™ which combines the Value Innovation Framework together with the Eight I’s of Infinite Innovation, allowing you to track the progress of each potential innovation on the three value innovation measures as you evolve any individual idea through this eight step process.

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The State of Customer Experience and the Contact Center

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The State of Customer Experience and the Contact Center

GUEST POST from Shep Hyken

Oh, what a difference a year makes. A few months ago I traveled to Las Vegas to attend the Customer Contact Week (CCW), the largest conference and trade show in the contact center industry. For the past several years, the big discussion has centered on artificial intelligence (AI), and that continues, but Customer Experience (CX) is also moving into the spotlight. AI and natural language models can give customers an almost human-like experience when they have a question or complaint. However, no surprise, some companies do it better than others.

First, all the hype around AI is not new. AI has been in our lives for decades, just at a much simpler level. How do you think Outlook and other email companies recognize that an email is spam and belongs in the junk/spam folder? Of course, it’s not 100% perfect, and neither are today’s best AI programs.

Many of us use Siri and Alexa. That’s AI. And as simple as that is, it’s obviously more sophisticated when you apply it to customer support and CX.

Let’s go back 10 years ago when I attended the IBM Watson conference in Las Vegas. The big hype then was around AI. There were some incredible cases of AI changing customer service, sales and marketing, not to mention automated processes. One of the demonstrations during the general session showcased AI’s stunning capability. Here’s what I saw:

A customer called the contact center. While the customer service agent listened to the customer, the computer (fueled by AI) listened to the conversation and fed the agent answers without the agent typing the questions. In addition, the computer informed the agent how long the customer had been doing business with the company, how often they made purchases, what products they had bought and more. The computer also compared this customer to others who had the same questions and suggested the agent answer those questions. Even though the customer didn’t yet know to ask them, at some point in the future, they would surely be calling back to do so.

That demonstration was a preview of what we have today. One big difference is that implementing that type of solution back then could have cost hundreds of thousands of dollars, if not more than a million. Today, that technology is affordable to almost any company, costing a fraction of what it cost back then (as in just a few thousand dollars).

Voice Technology Gets Better

Less than two years ago, ChatGPT was introduced to the world. Similar technologies have been developed. The capability continues to improve at an incredibly rapid pace. The response from an AI-fueled chatbot is lightning fast. Now, the technology is moving to voice. Rather than type a question for the chatbot, you talk, and it responds in a human-like voice. While voice technology has existed for years, it’s never been this good. Google introduced voice technology that seemed almost human-like. The operative word here is almost. As good as it was, people could still sense they weren’t talking to a human. Today, the best systems are human-like, not almost human-like. Think Alexa and Siri on steroids.

Foreign Accents Are Disappearing

We’ve all experienced calling customer support, and an offshore customer service agent with a heavy accent answers the call. Sometimes, it’s nearly impossible to understand the agent. New technologies are neutralizing accents. A year ago, the software sounded a little “digital.” Today, it sounds almost perfect.

Why Customers Struggle with AI and Other Self-Service Solutions

As far as these technologies have come, customers still struggle to accept them. Our customer service research (sponsored by RingCentral) found that 63% of customers are frustrated by self-service options, such as ChatGPT and similar technologies. Furthermore, 56% of customers admit to being scared of these technologies. Even though 32% of the customers surveyed said they had successfully resolved a customer service issue using AI or ChatGPT-type technologies, it’s not their top preference as 70% still choose the phone as their first level of support. Inconsistency is part of the problem. Some companies still use old technology. The result is that the customer experience varies from company to company. In other words, customers don’t know whether the next time they experience an AI solution if it will be good or not. Inconsistency destroys trust and confidence.

Companies Are Investing in Creating a Better CX

I’ve never been more excited about customer service, CX and the contact center. The main reason is that almost everything about this conference was focused on creating a better experience for the customer. The above examples are just the tip of the iceberg. Companies and brands know what customers want and expect. They know the only way to keep customers is to give them a product that works with an experience they can count on. Price is no longer a barrier as the cost of some of these technologies has dropped to a level that even small companies can afford.

Customer Service Goes Beyond Technology: We Still Need People!

This article focused on the digital experience rather than the traditional human experience. But to nail it for customers, a company can’t invest in just tech. It must also invest in its employees. Even the best technology doesn’t always get the customer what they need, which means the customer will be transferred to a live agent. That agent must be properly trained to deliver the experience that gets customers to say, “I’ll be back.”

Image Credits: Pexels, Shep Hyken

This article originally appeared on Forbes.com

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Top 10 Human-Centered Change & Innovation Articles of November 2024

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Top 10 Human-Centered Change & Innovation Articles of November 2024Drum 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 November’s ten most popular innovation posts:

  1. A Shared Language for Radical Change — by Greg Satell
  2. Leadership Best Quacktices from Oregon’s Dan Lanning — by Braden Kelley
  3. Navigating Uncertainty Requires a Map — by John Bessant
  4. The Most Successful Innovation Approach is … — by Howard Tiersky
  5. Don’t Listen to These Three Change Consultant Recommendations — by Greg Satell
  6. What We Can Learn from MrBeast’s Onboarding — by Robyn Bolton
  7. Does Diversity Increase Team Performance? — by David Burkus
  8. Customer Experience Audit 101 — by Braden Kelley and Art Inteligencia
  9. Daily Practices of Great Managers — by David Burkus
  10. An Innovation Leadership Fable – Wisdom from the Waters — by Robyn Bolton

BONUS – Here are five more strong articles published in October 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!

SPECIAL BONUS: While supplies last, you can get the hardcover version of my first bestselling book Stoking Your Innovation Bonfire for 51% OFF until Amazon runs out of stock or changes the price. This deal won’t last long, so grab your copy while it lasts!

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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 four years:

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Oprah’s AI Special Reveals Where Humanity is Headed

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Oprah's AI Special Reveals Where Humanity is Headed

GUEST POST from Robert B. Tucker

You know that artificial intelligence has gone mainstream when Oprah Winfrey weighs in with a special on the topic. Her primetime event, “AI and the Future of Us: An Oprah Winfrey Special,” will air Thursday on ABC, and promises to explore the profound impact of AI and help viewers “navigate the rapidly evolving AI landscape.”

As a futurist, I welcome focus on the broader impacts of the approaching “singularity” moment, when machines will become as smart as humans. (The futurist Ray Kurzweil predicts this will occur in 2029.)

Winfrey’s special will feature interviews with some of the most important and powerful people in AI, and fortunately, some of its toughest critics. Sam Altman, CEO of Open AI, explains how AI works in layman’s terms, and his vision for how AI might benefit humankind. Altman will also discuss “the immense personal responsibility that the executives of AI companies must bear” but this is where I’m hoping Oprah takes him to task. At present the technology is largely unregulated, and AI company leaders must weigh the profit motive against safety concerns.

Microsoft Co-Founder and Chair of the Gates Foundation Bill Gates will lay out the AI revolution coming in science, health, and education. And Gates will warn of the impact AI may have on the workforce and job creation.

McKinsey estimates that 30 percent of the world’s workforce will lose their job to AI within 7 years. And 400 to 800 million people will lose their jobs due to AI by 2030, which means in the worst-case scenario a third of the world’s workforce will lose their livelihoods.

Winfrey will also focus on the impact of AI on humanity, as humans and machines begin to merge. She’ll interview novelist and essayist Marilynne Robinson who has previously expressed deep ethical and philosophical concerns about artificial intelligence.

Robinson is wary of the dehumanizing potential of AI, suggesting that it risks undermining the complexity and dignity of human consciousness. She warns against the uncritical embrace of AI, emphasizing that it could exacerbate societal inequalities, compromise individual autonomy, and erode moral and spiritual values.

Robinson often points out that the fascination with AI reveals a kind of technological hubris—a misplaced confidence that machines can replicate or even surpass human wisdom. Her work suggests a need for humility and a broader, more thoughtful engagement with AI, and prioritizing humanistic values over mere efficiency.

Tristan Harris and Aza Raskin, co-founders of the Center for Humane Technology, will add to Robinson’s cautionary message. One of the main risks they have discussed in prior interviews is how AI systems, driven by large language models and recommendation algorithms, can manipulate human behavior on a massive scale. This includes influencing public opinion, spreading misinformation, and even nudging people toward specific actions without their awareness. As AI systems become more advanced, they could exert even greater control over our choices, reducing human agency and potentially undermining democratic processes.

I’m also looking forward to hearing from FBI Director Christopher Wray, whose testimony before Congress reveals the terrifying ways criminals and foreign adversaries are using AI. Wray has highlighted several extreme ways in which criminals and foreign adversaries are exploiting AI, emphasizing the growing threat to national security, public safety, and economic stability.

One of the most alarming uses of AI involves the creation of hyper-realistic deepfakes—manipulated audio, video, or images that can convincingly portray individuals saying or doing things they never did. These are being used to spread disinformation, manipulate public opinion, and even blackmail individuals. Foreign adversaries use these technologies to sow discord, interfere in elections, and damage reputations, making it increasingly difficult for the public to discern what is real.

AI is being leveraged by criminals and state actors to enhance cyberattacks, making them more sophisticated and harder to detect. This includes AI-driven phishing schemes that craft highly personalized and convincing messages, automated hacking tools that exploit vulnerabilities at scale, and AI bots that can conduct surveillance, data exfiltration, and launch ransomware attacks autonomously. These tools enable attackers to breach systems more effectively and efficiently, posing a severe threat to critical infrastructure, businesses, and government agencies.

Clearly humanity is entering uncharted waters. The next decade, as we cross the Singularity moment when machines will be smarter than humans, will be fraught with opportunities, threats and surprises. “AI and the Future of Us: An Oprah Winfrey Special” provides a serious, entertaining and meaningful way for us to be part of one of the most important global conversations of the 21st century.

This article originally appeared in Forbes
Image credit: Pixabay

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AI Requires Conversational Intelligence

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AI Requires Conversational Intelligence

GUEST POST from Greg Satell

Historically, building technology had been about capabilities and features. Engineers and product designers would come up with new things that they thought people wanted, figure out how to make them work and ship “new and improved” products. The result was often things that were maddeningly difficult to use.

That began to change when Don Norman published his classic, The Design of Everyday Things and introduced concepts like dominant design, affordances and natural mapping into industrial design. The book is largely seen as pioneering the user-centered design movement. Today, UX has become a thriving field.

Yet artificial intelligence poses new challenges. We speak or type into an interface and expect machines to respond appropriately. Often they do not. With the popularity of smart speakers like Amazon Alexa and Google Home, we have a dire need for clear principles for human-AI interactions. A few years ago, two researchers at IBM embarked on a journey to do just that.

The Science Of Conversations

Bob Moore first came across conversation analysis as an undergraduate in the late 1980s, became intensely interested and later earned a PhD based on his work in the field. The central problems are well known to anybody who has ever watched Seinfeld or Curb Your Enthusiasm, our conversations are riddled with complex, unwritten rules that aren’t always obvious.

For example, every conversation has an unstated goal, whether it is just to pass the time, exchange information or to inspire an emotion. Yet our conversations are also shaped by context. For example, the unwritten rules would be different for a conversation between a pair of friends, a boss and subordinate, in a courtroom setting or in a doctor’s office.

“What conversation analysis basically tries to reveal are the unwritten rules people follow, bend and break when engaging in conversations,” Moore told me and he soon found that the tech industry was beginning to ask similar questions. So he took a position at Xerox PARC and then Yahoo! before landing at IBM in 2012.

As the company was working to integrate its Watson system with applications from other industries, he began to work with Raphael Arar, an award-winning visual designer and user experience expert. The two began to see that their interests were strangely intertwined and formed a partnership to design better conversations for machines.

Establishing The Rules Of Engagement

Typically, we use natural language interfaces, both voice and text, like a search box. We announce our intention to seek information by saying, “Hey Siri,” or “Hey Alexa,” followed by a simple query, like “where is the nearest Starbucks.” This can be useful, especially when driving or walking down the street,” but is also fairly limited, especially for more complex tasks.

What’s far more interesting — and potentially far more useful — is being able to use natural language interfaces in conjunction with other interfaces, like a screen. That’s where the marriage of conversational analysis and user experience becomes important, because it will help us build conventions for more complex human-computer interactions.

“We wanted to come up with a clear set of principles for how the various aspects of the interface would relate to each other,” Arar told me. “What happens in the conversation when someone clicks on a button to initiate an action?” What makes this so complex is that different conversations will necessarily have different contexts.

For example, when we search for a restaurant on our phone, should the screen bring up a map, information about pricing, pictures of food, user ratings or some combination? How should the rules change when we are looking for a doctor, a plumber or a travel destination?

Deriving Meaning Through Preserving Context

Another aspect of conversations is that they are highly dependent on context, which can shift and evolve over time. For example, if we ask someone for a restaurant nearby, it would be natural for them to ask a question to narrow down the options, such as “what kind of food are you looking for?” If we answer, “Mexican,” we would expect that person to know we are still interested in restaurants, not, say, the Mexican economy or culture.

Another issue is that when we follow a particular logical chain, we often find some disqualifying factor. For instance, a doctor might be looking for a clinical trial for her patient, find one that looks promising but then see that that particular study is closed. Typically, she would have to retrace her steps to go back to find other options.

“A true conversational interface allows us to preserve context across the multiple turns in the interaction,” Moore says. “If we’re successful, the machine will be able to adapt to the user’s level of competence, serving the expert efficiently but also walking the novice through the system, explaining itself as needed.”

And that’s the true potential of the ability to initiate more natural conversations with computers. Much like working with humans, the better we are able to communicate, the more value we can get out of our relationships.

Making The Interface Disappear

In the early days of web usability, there was a constant tension between user experience and design. Media designers were striving to be original. User experience engineers, on the other hand, were trying to build conventions. Putting a search box in the upper right hand corner of a web page might not be creative, but that’s where users look to find it.

Yet eventually a productive partnership formed and today most websites seem fairly intuitive. We mostly know where things are supposed to be and can navigate things easily. The challenge now is to build that same type of experience for artificial intelligence, so that our relationships with the technology become more natural and more useful.

“Much like we started to do with user experience for conventional websites two decades ago, we want the user interface to disappear,” Arar says. Because when we aren’t wrestling with the interface and constantly having to repeat ourselves or figuring out how to rephrase our questions, we can make our interactions much more efficient and productive.

As Moore put it to me, “Much of the value of systems today is locked in the data and, as we add exabytes to that every year, the potential is truly enormous. However, our ability to derive value from that data is limited by the effectiveness of the user interface. The more we can make the interface become intelligent and largely disappear, the more value we will be able unlock.”

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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