Category Archives: Technology

Why Business Transformations Fail

(and What Data Centers Can Teach Us About Getting Them Right)

Why Business Transformations Fail - Pexels

GUEST POST from Robyn Bolton

On May 6, Nvidia CEO Jensen Huang and ServiceNow CEO Bill McDermott joined CNBC’s “Power Lunch” to discuss the companies’ partnership.  But something that Huang said about large-scale cloud service providers (i.e., hyperscalers) at the end of the interview stopped me in my tracks:

It’s not a data center that stores information. It’s a factory that produces intelligence. And these intelligence tokens could be reformulated into music, images, words, avatars, recommendations of music, movies, or, you know, supply chain optimization techniques.

What struck me wasn’t the claim about what data centers and AI could create — we’ve seen evidence of that already. It was the re-framing of data centers from storage solutions to “intelligence factories.”

When leaders fail to lead, or even recognize that the business they’re in is different, even the best efforts at business transformation are doomed.

Because re-framing is how Disruption begins.

Data Centers Are No Longer in the Data Business

Repositioning your company to serve a new job requires rethinking, redesigning, and rebuilding everything.

Consider the old adage that railroads failed because they thought they were in the railroad business. By defining themselves by their offering (railroad transportation) rather than the Jobs to be Done they solve (move people and cargo from A to B), railroads struggled to adapt as automobiles became common and infrastructure investments shifted from railroads to highways.

Data centers have similarly defined themselves by their offering (data storage). However, Huang’s reframing signals a critical shift in thinking about the Jobs that data centers solve: “provide intelligence when I need it” and “create X using this intelligence.”

Intelligence Factories Require a New Business Model

This shift—from providing infrastructure for storing data to producing intelligence, strategic analysis, and creative output—will impact business models dramatically.

Current pricing models based on power consumption or physical space will fail to capture the full value created. Capabilities mustexpand beyond building infrastructure to include machine learning and AI partnerships.

But Intelligence Factories are Just the Beginning

While Intelligence Factories will require data centers to rethink their business models and may even introduce a new basis of competition (a requirement for Disruption), they’re only a stepping-stone to something far more disruptive: Dream Factories.

While the term “Dream Factory” was coined to describe movie studios during  Golden Era, the phrase is starting to be used to describe the next iteration of data centers and AI. Today’s AI is limited to existing data and machine learning capabilities, but we’re approaching the day when it can create wholly new music, images, words, avatars, recommendations, and optimization techniques.

This Is Happening to Your Business, Too

This progression will transform industries far beyond technology. Here’s what the evolution from data storage to Intelligence Factory to Dream Factory could look like for you:

  • Healthcare: From storing medical records to diagnosing conditions to creating novel treatments
  • Financial Services: From tracking transactions to predicting market movements to designing new financial instruments
  • Manufacturing: From inventory management to process optimization to inventing new materials
  • Retail: From cataloging products to personalizing recommendations to generating products that don’t yet exist

How to prepare for your Dream Factory Era

Ask yourself and your team these three questions:

  1. Is my company defining itself by what it produces today or by the evolving needs it serves?
  2. What is our industry’s version of the shift from data storage to dream factory?
  3. What happens to our competitive advantage if someone else creates our industry’s dream factory before we do?

If you’re serious about transformation, take a cue from the data centers: redefine what business you’re in—before someone else does.

After all, the key to success isn’t trying to stay a data center. It’s recognizing you’ve become an intelligence factory, and your long-term success depends on becoming a dream factory.

Image credit: Pexels

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Innovation or Not – SpinLaunch

Innovation or Not - SpinLaunch

GUEST POST from Art Inteligencia

In the fast-paced world of space exploration, innovation is a driving force that propels new companies and ideas into the spotlight. One such company is SpinLaunch, which is making waves with its novel approach to launching payloads into space. But what sets SpinLaunch apart, and how do we assess whether its approach is truly an innovation or not?

The Concept Behind SpinLaunch

SpinLaunch is taking a radically different approach to space launch by using a kinetic energy-based system rather than traditional rocketry. Their technique involves a high-speed rotating arm that builds up momentum and catapults a payload to the edge of space, drastically reducing the need for fuel and cutting down on costs. This approach is not only cost-effective but also environmentally friendly, addressing two significant pain points in the space industry.

Key Criteria for Innovation Assessment

  • Novelty: Is the concept fresh and previously unexplored?
  • Feasibility: Can the technology be realistically executed?
  • Impact: What benefits does the innovation provide to the industry and society?
  • Scalability: Can the idea grow and adapt to broader applications?

Case Study: Assessing SpinLaunch

Novelty

SpinLaunch undoubtedly introduces a novel approach to space launches. Traditional methods rely heavily on chemical propulsion. In contrast, SpinLaunch’s kinetic system stands out by leveraging physics in a way that hasn’t been commercially applied to space launches before.

Feasibility

The technical feasibility of SpinLaunch’s idea has been demonstrated through successful suborbital launches, proving that their kinetic system can indeed hurl payloads into space. However, the transition from suborbital to orbital flights will be the true test of feasibility. Critical engineering challenges remain, particularly related to the G-forces sustained by payloads during launch.

Impact

SpinLaunch has the potential to revolutionize the space industry by making launches significantly cheaper and more frequent. The environmental benefits of reducing fuel consumption cannot be understated either. If successfully scaled, the impact would reach beyond cost — it could democratize access to space.

Scalability

Currently, SpinLaunch is focused on small to medium-sized payloads. For scalability, the company must expand its capabilities to accommodate larger satellites and potentially human passengers. Adapting the technology for broader applications will be essential.

Conclusion: Is SpinLaunch an Innovation?

SpinLaunch exhibits the hallmarks of a true innovation. By addressing cost, environmental impact, and frequency of launches, it provides substantial benefits to the space industry. However, the road to demonstrating full potential is fraught with engineering and market challenges. Yet, the novelty and promise of their approach cannot be ignored.

Here is a 40 minute documentary that dives deep into the engineering, problem solving and innovation approach:

Opportunities for Expansion

To strengthen the case for SpinLaunch as an innovation, future assessments could involve the impact on related industries such as satellite manufacturing. More real-world data from further launches will offer insights into long-term feasibility and environmental impact. Engaging with regulators and potential partners early will be crucial to addressing scalability challenges.

Revision & Expansion

The ongoing journey of SpinLaunch should be closely monitored. As the company progresses, it should aim to address:

  • Risk Management: How can the company mitigate potential risks associated with high G-force impacts on sensitive equipment?
  • Regulatory Hurdles: Navigating international laws and space treaties will be essential as SpinLaunch aims for global reach.
  • Commercial Partnerships: Collaborations with established aerospace companies could fast-track development and market entry.

The future of SpinLaunch lies in its ability to resolve these emerging challenges while maintaining its innovative edge, positioning the company as a potential leader in transforming space access.

So, what do you think? Innovation or not?

Image credit: SpinLaunch

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The Rise of Data Alchemy

How an entrepreneurial couple helped start a retail revolution

The Rise of Data Alchemy

GUEST POST from John Bessant

A gold ingot about the size of an older generation smartphone weighs 1kg, 2.2 lbs. To make it requires at least a million times that weight in ore, often a great deal more. That raw material doesn’t look particularly promising — it’s plain old boring rock, grey or brown with, if you’re lucky, some tiny tell-tale flecks of glistening yellow. But there is a lot of it about; gold comes from a variety of ores , often embedded in rocks like quartz which can be found anywhere on earth. Most of which is discarded in the extensive process of refining the metal, left in mountains of yellowing rock.

There may be great value in what finally comes out of all of this but getting to that shiny soft and heavy metal requires a lot of effort. The idea of lucky prospectors panning for gold and finding a pure clean nugget glinting away below the surface of the water is as far from reality as the presence of unicorns dipping their mythical heads to drink from the stream.

That doesn’t mean gold mining isn’t worth doing; that ingot is worth around $100,000 at today’s prices. But it does focus our attention on the importance or finding ways to mine and process the precious metal as effectively as possible. A kind of alchemy, transmuting base material into something of great value.

Which is what a couple of entrepreneurs started doing thirty years ago, developing tools and techniques for refining something similarly unprepossessing into a resource increasingly prized around the world. Knowledge.

Much like the raw ore which carries the high value of gold we have mountains of data available in various forms. The trick is to turn that unpromising source into high value knowledge of the kind which increasingly fuels economic growth and underpins effective decision-making in our organizations.

Edwina Dunn and Clive Humby were early prospectors. They met back in 1980 working at the London office of CACI, a company originally founded as the California Analysis Center, Inc. by a couple of RAND Corporation scientists who thought that using simulation and analytical techniques could prove commercially useful. Their instincts were good; from its origins in the 1960s the company had grown successfully and spread its work internationally.

Dunn and Humby were a good fit for such a knowledge-based organization; they worked on a variety of projects, drawing on his skills as a mathematician and her abilities in marketing. In particular she found herself working on the retail sector, trying to use data to help retail stores with location plans by developing improved understanding of their local customer bases. They were both successful; she rose from being a marketing assistant to become the youngest vice-president (at the age of 26) in the company with a team of 40 working for her.

But growing frustration led the couple to develop a plan to set up on their own; they formed a company taking its name from theirs — dunnhumby. The idea was that Clive would leave and Edwina would continue to support him from her senior position in CACI. A good plan in theory but one which soon crashed when unfortunate realities intervened. Within ten minutes of his submitting his resignation she was sacked by the company, leaving the couple sitting round a kitchen table in their home in Chiswick, west London, with some great ideas and a vision for how data science might change the world. But not a lot actually coming in to help them make ends meet.

Using her marketing skills and his knowledge of the key mathematical tools and how to use them they set about trying to promote their big idea. Their value proposition was around helping businesses unlock the hidden value in the data which they already collected and which could offer deep insights into their customer base. An early success came with signing the Booker cash and carry group as their first client, giving them a foothold in the retail world. But it also brought a major problem; their former employer began a lawsuit claiming that they were using ‘confidential knowledge’ which the couple had been party to when working for them.

In an interview Edwina Dunn explained that this ‘…..was rubbish, because we invented the knowledge, or certainly Clive had…”. But it put them under severe pressure; if they fought they risked losing their home and everything they had built up. “It was incredibly stressful. There was a big moment where we looked at each other and knew we could lose everything. They could break us. But we came to terms with the fact that if we lost everything, including the house and what we’d saved so far, we’d start again. That was a moment where you realize you’re quite strong — and you have to be in order to survive.”

Fortunately they eventually won their battle in the High Court and were able to concentrate on developing the business, bringing their vision of helping firms use data effectively to life.. One of the key advantages which they had was an understanding of how valuable data could be at a time when organizations didn’t. Indeed for many it was seen as a cost rather than an asset; once collected for whatever purpose it was too expensive to store, still less analyze, because computer power was still expensive.

dunnhumby’s alternative strategy was based on using data analytics to create a deep understanding of customers at a differentiated level so that clients could target sales promotions and engineer deals much more accurately. Over their first couple of years they worked from home, keeping costs as low as possible and paying themselves very little as they refined the plan and gradually grew the business.

A key challenge for them, analogous to gold mining, was getting access to suitable raw material. They needed datasets just as gold miners needed deposits of ore. Their big breakthrough came when they were invited to a meeting with the Tesco supermarket chain to talk about their ideas. Grant Harrison, a Tesco manager responsible for the rollout of a new loyalty card for the chain had seen Clive Humby at a conference and was interested in the ideas he was putting forward.

(Time for a quick detour into the wonderful world of customer loyalty programs)

Rewarding customers for their loyalty to a shop or a brand is not a new idea. Indeed it has been around at least since the eighteenth century when a US merchant began giving small copper tokens which could be redeemed for purchases at his store. In the mid-19th century the UK Co-operative Wholesale Society (the Co-op) began rewarding its customers with tokens which could be saved up and redeemed for cash or goods. The Great Atlantic and Pacific Tea Company began putting coupons in its packets of tea which could be redeemed for gifts in a catalogue; by 1915 customers could choose from over 60 luxury items on offer. The idea soon spread with an increasing number of retailers offering rewards for loyalty to shoppers in the form of tokens, stamps and points which could be collected. Frequent flyers were rewarded for traveling with the same airline, drivers could receive loyalty points to get discounts on fuel and shoppers could collect stamps to be redeemed for an ever increasing range of goods and services. New businesses emerged acting as the brokers, supplying the stamps or tokens and operating the schemes on behalf of major clients.

Tesco Clubcard

Data as a By-Product

The idea underpinning this long-standing business was essentially about getting close to and keeping customers; a by-product was the information that some of these schemes could reveal about customer identity and behavior. Something which the team at Tesco saw as a possibility when it was planning the launch of its ‘Clubcard’ as a points-based loyalty program in the early 1990s. Early trials of the idea suggested that in addition to the usual benefits of keeping customers loyal to the brand the Clubcard might also give them access to useful customer insights which could help future planning.

Harrison’s early work suggested that it might be possible to ‘mine’ the data about transactions made using the card but talks with major IT services companies suggested the costs and timescale would be significant. Faced with estimates suggesting a development time of three years and a cost running into tens of millions of pounds he thought it worth exploring what outsiders like dunnhumby might be able to offer.

Their initial offer proposed a 10-week development project costing around £250k and he decided to take the risk of seeing what they could come up with. dunnhumby developed a version of the Clubcard which was trialed in nine stores over a three month period; they presented their results at the end of that time and caught the attention of increasingly senior management. Eventually they were invited to present to the Tesco board; their report was met by a long and awkward silence.

It was finally broken by Lord MacLaurin, the chairman, who memorably captured the huge implications of what the couple had presented. “What scares me about this is that you know more about my customers after three months than I know after 30 years.”

What began as a short-term consulting project was transformative for both sides. dunnhumby’s work showed in detail patterns in what customers were buying, who they were broken down by various categories and identifying where further ‘data mining’ might be useful. Clubcard became the world’s first mass customization loyalty program in the world, offering a much finer degree of insight into particular groups of customers than anything that had previously been available.

That project became a long-term partnership from which both sides learned and were able to grow. Tesco’s success helped it overtake Sainsbury’s to become the top UK supermarket within a year of the launch of Clubcard. They not only benefited from their own use of the data analytics approach; in partnership with dunnhumby they signed similar deals with other supermarkets around the world. So successful was the Clubcard for Tesco that dunnhumby was soon approached to do the same for Kroger, the US chain competing with the giant Walmart.

In 2002 Tesco bought a 53% stake in the business and in 2010 bought the remainder. By that time the business was making profits of £46 million on a £248 million turnover. It employed 1,300 people across 30 offices worldwide and had other clients including Cadbury, Vodafone, Shell and Unilever. What had started as a kitchen table office and a real risk of bankruptcy for the two entrepreneurs had paid off to the tune of an estimated £93million.

At the heart of their original business was a simple belief — that buyer behavior wasn’t random but something which could be analyzed and the resulting understanding used to develop far more effective strategies for reaching and satisfying customer needs. With millions of customers the task of data mining was difficult but the rewards in terms of deep and tailored insights about segments and even individual purchasers would outweigh the costs in developing the necessary analytical technology. In a world increasingly driven by mass customization the potential for getting close to the individual customer and communicating with them, responding to their needs, anticipating their preferences and engaging their long-term loyalty offered a real strategic advantage to whoever could realize it.

These days we take the power of such analytics for granted; the spectacular rise of many of the big players on the global business scene like Google, Meta, Amazon and Alibaba owes a huge amount to such customer data science. Its tentacles reach beyond commercial transactions to the densely-populated world of social media and down into murky waters of electoral influencing and opinion manipulation. And with the rapid rise of machine learning the potential for deeply customized interactions individualized from a population of billions becomes a distinct possibility.

Dunnhumby offers an entrepreneurial success story demonstrating how a vision — in this case seeing the potential value in something others discounted or threw away — can become a reality. Dunn and Humby can claim to being pioneers in the world of data science and to have worked some alchemical magic, turning waste into gold. But it’s not a story of getting lucky; instead it reminds us of some key lessons about successful innovation management.

· First it involves much more than a big idea; it’s recognizing and shaping opportunity from the context around that idea. And it’s about both vision — seeing what’s possible — and what Angela Duckworth calls ‘grit’. Being able to put in the hard work to bring the idea to life and coping with the setbacks and unexpected challenges which the journey throws up. Perseverance and resilience are qualities which the couple clearly had in spades, not least when they were sitting round the kitchen table with no income, no clients and the threat of a lawsuit putting their self-belief to the test.

· Their success wasn’t built on a magic single idea which turned out to be just what Tesco needed — right idea, right place, right time. It was more along the lines of Pasteur’s famous dictum ‘ chance favors the prepared mind’. In their case they were able to respond quickly and effectively to the Tesco challenge because of the deep knowledge they’d already acquired developing and honing the tools of their trade.

Nor was it something which emerged overnight. It is a story of scaling a great idea through careful strategic development. At the core is a commitment to the knowledge base , the core competence which enabled them to enter and pioneer the field of customer data science (CDS). They hired smart people and built close relationships with universities who helped them identify the talent needed to contribute to the growing workload. When they started with Tesco they employed 30 staff and this number doubled each year over the next five. They created an academic partnerships program, developing research links with world leading institutions which has enabled them to stay abreast of the science shaping the future of their industry.

· Scaling innovation is a multi-player game and in many interviews the couple have repeatedly drawn attention to their commitment to developing partnerships as a way of growing. Their early and close relationship with Tesco was a deep and long-term relationship; one indicator was the level of trust which developed between them to the point where dunnhumby had access to all of Tesco’s cost information. They were able to see the profit margin of every product sold in the stores and with that kind of data it became possible to develop some of the elements of the Clubcard approach which gave it such a competitive edge.

They developed similar close links with other players like Kroger as they grew the business. Building a value network in this fashion enabled them to leverage resources, open up market access, and develop enhanced solutions and services. As Edwina Dunn explained in an interview looking back on how they successfully scaled their idea ‘….my best decision… was to do joint ventures with companies. Where they win, we win, and they make sure you never lose’.

Conclusion

There’s a famous old Yorkshire expression; ‘where there’s muck, there’s brass’ — which , roughly translated suggests that sometimes there’s real value in what might otherwise be seen as worthless. The challenge, whether we are talking about recovering precious metals from discarded scrap, re-using waste heat in sustainable energy schemes or seeing and exploiting the value in discarded data, is the same. How to turn what might be alchemy to robust and widely used science. A case well made by Edwina Dunn and Clive Humby thirty years ago and as relevant now as ever.


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Six Revolutionary AI CX and Customer Service Strategies

Six Revolutionary AI CX and Customer Service Strategies

GUEST POST from Shep Hyken

Artificial Intelligence (AI) is reshaping customer service and customer experience faster than we could ever imagine. But some are getting it wrong. While everyone’s racing to implement AI, many are missing the most important part – keeping the human element alive. Smart companies have found the balance between the human touch and the digital experience.

One of my favorite AI and marketing experts is Ford Saeks, who recently released his latest book, AI Mindshift: Unleash the Power of AI, Avoid the Pitfalls, and Keep the Human Experience. The book is filled with practical strategies and tactics to help organizations leverage AI while maintaining the personal touch. The book isn’t about which specific AI tools to use. Many of those will be obsolete in a very short time. It’s about how to think about AI, hence the title, AI Mindshift. With that in mind, here are some of my top takeaways from the book:

  1. The Human-AI Balance Is Essential: This is the book’s central theme. Don’t fall into the trap of thinking AI can replace your customer service team. Instead, let AI handle the routine questions and problems while keeping your people focused on what they do best – building relationships and handling more complicated issues. This creates efficiency without sacrificing the personal touch customers value.
  2. Speed Matters: Your customers want answers now, not later. AI can deliver immediate first responses through chatbots, but here’s the key – make sure your customers can seamlessly transition to a human agent when needed. I refer to this as Time to Happiness – how quickly you can move a customer from frustrated to satisfied. The faster, the better.
  3. Feedback Is Your Friend: Create processes to continuously gather both customer and employee feedback about AI interactions. Consistently use this data to refine and improve your AI systems. If customers are frustrated with certain AI responses, fix them quickly. Otherwise, your faulty systems may frustrate your customers and drive them to the competition.

  1. Practice “Ethical AI” in Customer Service: Saeks emphasizes two big areas: transparency about when customers interact with AI versus humans and making sure your AI technology protects your customers’ privacy and data.
  2. Proactive Support: If you want to impress your customers, identify issues or problems before the customer finds them. Then, tell them you did. AI can help identify these issues.
  3. Think Big, but Start Small: Begin AI implementation with specific, manageable customer service tasks rather than trying to overhaul everything at once. For example, start with AI handling basic FAQs, then gradually expand to more complex customer interactions as you learn what works. Remember the old saying, “Rome wasn’t built in a day.”

The bottom line is this: AI isn’t about replacing your customer service team. It’s about making them more amazing at what they do. Saeks’ book reminds us that the future of customer service and CX isn’t about choosing between AI and humans. It’s about combining both to create experiences that get your customers to say, “I’ll be back!”

Image Credit: Pexels, Shep Hyken

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Contemporary Science versus Natural Language

Contemporary Science versus Natural Language

GUEST POST from Geoffrey A. Moore

Item 1. The fastest human-created spacecraft goes 165,000 mph. Pretty amazing. But for it to travel one light year would take roughly 3000 years—basically, the length of recorded human history. The closest star system that hosts an earth-like planet (Alpha Centauri) is 4.4 light years away. Thus, it would take today’s fastest vehicle 14,000 years to make a one-way trip. On our earth, 14,000 years ago humanity’s most sophisticated technology was a stone axe. Thus, while we love to talk about space travel outside the solar system, as well as aliens in UFOs coming to Earth, neither is remotely possible, not now, not ever.

Item 2. There are 30 trillion cells in the average human body. There are 100 trillion atoms in a typical human cell. That means there are three thousand trillion trillion atoms, give or take, in you or me. Atoms are so small that it is not clear any words we have would apply to how they actually operate. Particle and wave are two of the ones we end up using the most. Neither of them, however, can coherently explain something as simple as the double-slit experiment.

Item 3. The metabolic reactions that support all life are mind-bogglingly fast. Take mitochondria for example. They are the organelles that produce the bulk of our ATP, the energy molecule that drives virtually all life’s chemical reactions. Of the 30 trillion cells in your body, on average each one uses around 10 million molecules of ATP per second and can recycle all its ATP in less than a minute. There is simply no way to imagine something happening a million times per second simultaneously in thirty million different places inside your own body.

Item 4. Craig Venter has been quoted as saying, “If you don’t like bacteria, you’re on the wrong planet. This is the planet of the bacteria.” In one-fifth of a teaspoon of seawater, there are a million bacteria (and perhaps 10 million viruses). The human microbiome, which has staked out territory all over our body, in our gut, mouth, skin, and elsewhere, harbors upwards of three thousand kinds of bacteria, comprising some 3 million distinct genes, which they swap with each other wherever they congregate. How in the world are we supposed to keep track of that?

Okay, okay. So what’s your point?

The point is that contemporary science engages with reality across a myriad of orders of magnitude, from the extremely small to the extremely large, somewhere between sixty and one hundred all told. Math can manage this brilliantly. Natural languages cannot. All of which means: philosophers beware!

Philosophers love analogies, and well they should. They make the abstract concrete. They enable us to transport a strategy from a domain where it has been proven effective and test its applicability in a completely different one. Such acts of imagination are the foundation of discovery, the springboard to disruptive innovation. But to work properly they have to be credible. That means they must stand up to the kind of pressure testing that determines the limits to which they can be applied, the boundaries beyond which they must not stretch. This is where the orders of magnitude principle comes in.

It is not credible that there could be a cause that is a million million times smaller than its effect. Yes, it is theoretically conceivable that via a cascading set of emergent relationships, one could build a chain from such an A to such a B, but the amount of coordination that would be required to lever something up a million million times is just ridiculously improbable. So, when philosophers refer to the uncertainty principles embedded in quantum mechanics, and then infer or imply that such uncertainty permeates human affairs, or when they trace consciousness down to quantum fluctuations in messenger RNA, when, in short, they are correlating things that are more than a trillion, trillion times different in size and scope, then they are misusing both the mathematics of science and the resources of natural language. We simply have to stay closer to home.

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

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Innovation or Not – Kawasaki Corleo

Innovation or Not - Kawasaki Corleo

GUEST POST from Art Inteligencia

Alright, let’s dive deep into the fascinating case of the Kawasaki Corleo, a hydrogen-powered four-legged robot, and dissect it through the lens of human-centered change and innovation. As our founder Braden Kelley would tell you, it’s not simply a matter of “yes” or “no.” Innovation is a complex beast, and we must approach it with nuance.

The Corleo: A Spark in the Hydrogen Horizon

At first glance, the Corleo is undeniably captivating. A four-legged robot, powered by hydrogen, designed to navigate challenging terrains. That’s a headline grabber. But does it translate to meaningful innovation? To answer that, we must move beyond the “wow” factor and examine its potential impact on people and the world.

Innovation: More Than Just Novelty

Innovation, in my view, isn’t just about creating something new. It’s about creating valuable new. It’s about solving real problems, addressing unmet needs, and improving lives. True innovation is human-centered; it’s about making a positive difference.

Let’s break down the Corleo through this framework:

  1. Novelty: Yes, the Corleo is novel. A hydrogen-powered, four-legged robot is a significant technological leap. The integration of hydrogen fuel cells into a quadruped platform is a clear differentiator. Kawasaki’s expertise in robotics and hydrogen technology is evident.
  2. Value: This is where the real questions arise. What value does the Corleo bring? Is it merely a technological demonstration, or does it offer tangible benefits?

Potential Value Propositions: Navigating the Uncharted

Kawasaki envisions the Corleo as a tool for infrastructure inspection, disaster response, and remote operations. These are areas where traditional robots or human intervention might be difficult or dangerous.

  • Infrastructure Inspection: Imagine the Corleo inspecting pipelines in remote areas, or bridges in hazardous environments. This could significantly reduce human risk and improve efficiency.
  • Disaster Response: In the aftermath of earthquakes or floods, the Corleo could navigate debris-filled areas, locate survivors, and deliver supplies.
  • Remote Operations: In industries like mining or offshore oil and gas, the Corleo could perform tasks in remote or challenging locations, minimizing human exposure to risk.

The Hydrogen Advantage: Sustainability and Endurance

The use of hydrogen is a critical differentiator. It offers several potential advantages:

  • Longer Endurance: Hydrogen fuel cells can provide significantly longer operating times than battery-powered robots, enabling extended missions in remote areas.
  • Faster Refueling: Hydrogen refueling is much faster than battery recharging, minimizing downtime.
  • Sustainability: Hydrogen, when produced from renewable sources, offers a clean and sustainable energy solution.

The Human-Centered Lens: Addressing Real Needs

To truly assess the Corleo’s innovation potential, we must consider its impact on people.

  • Worker Safety: By performing hazardous tasks, the Corleo can reduce the risk of injury or death for human workers.
  • Improved Efficiency: The Corleo can automate tasks, freeing up human workers for more complex and creative work.
  • Enhanced Disaster Response: By providing faster and more effective disaster response, the Corleo can save lives and reduce suffering.
  • Environmental Impact: The use of hydrogen, when sourced properly, can contribute to a cleaner and more sustainable future.

The Challenges and Considerations

However, the Corleo is not without its challenges.

  • Cost: Hydrogen fuel cells and the necessary infrastructure can be expensive, potentially limiting widespread adoption.
  • Infrastructure: Building a robust hydrogen refueling infrastructure is crucial for the Corleo’s practicality.
  • Complexity: Integrating hydrogen fuel cells into a quadruped robot is a complex engineering challenge, requiring significant expertise.
  • Social Acceptance: Any new technology, especially robots, can face social resistance. Addressing concerns about job displacement and ethical implications is essential.

Is It Innovation? A Conditional Yes

In conclusion, the Kawasaki Corleo has the potential to be a significant innovation. Its novelty, potential value propositions, and hydrogen advantage are undeniable. However, true innovation requires more than just technological prowess.

The Corleo’s success will depend on:

  • Demonstrating tangible value: Kawasaki must prove that the Corleo can effectively address real-world problems and deliver significant benefits.
  • Addressing the challenges: Overcoming the cost, infrastructure, and complexity challenges is crucial for widespread adoption.
  • Adopting a human-centered approach: Focusing on worker safety, efficiency, and environmental sustainability will be key to gaining social acceptance.

As a thought leader in human-centered change and innovation, I believe the Corleo is a promising step in the right direction. It represents a bold attempt to leverage cutting-edge technology to solve real-world problems. But the journey from novelty to true innovation is a long and challenging one. Kawasaki must demonstrate that the Corleo is not just a technological marvel, but a valuable tool that improves lives and makes the world a better place. Only then can we definitively declare it a true innovation.

The Corleo is a spark in the hydrogen horizon. Let’s see if Kawasaki can fan that spark into a flame of transformative innovation.

Image credit: Kawasaki Heavy Industries
Guest assistant writer: Open AI called in sick today, so Google Gemini is filling in

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Catalyst Cap Accelerates Innovation and Creativity

Unlocking Potential through Neuro-Selective Stimulation

GUEST POST from Art Inteligencia

The time for neuro-selective stimulation has arrived!

In the landscape of human-centered innovation, one of the most intriguing concepts revolves around our ability to unlock latent potential in ourselves and others. Imagine a technology capable of selectively stimulating parts of the brain to enhance creativity, focus, empathy, or even physical dexterity. Enter the Catalyst Cap, an earth-shattering innovation that challenges our imagination and inspires conversations about the possibilities of neuro-enhancement.

What is the Catalyst Cap?

The Catalyst Cap is a wearable device designed to look like a stylish hat or cap, embedded with advanced neuro-stimulation technology. Through targeted impulses, it interacts with specific neural pathways to amplify or suppress certain cognitive or emotional traits on demand. While entirely safe, the concept pushes boundaries, urging us to explore what human enhancement looks like when designed ethically and inclusively.

Breaking Barriers in Human Potential

Traditional methods of personal development often require time-intensive practice, significant effort, or long-term interventions. The Catalyst Cap, with its instantaneous effects, offers a paradigm shift. Imagine needing razor-sharp focus for an important presentation—the Catalyst Cap activates your prefrontal cortex, allowing you to stay in the zone. Or consider an artist seeking an inspiration boost—the cap stimulates neural areas tied to imagination, unlocking a flood of creativity. The possibilities are endless.

The Ethical Considerations

No innovation exists in a vacuum. For an invention as transformative as the Catalyst Cap, ethics were paramount in its development. We asked ourselves many important questions. How do we ensure equitable access? What safeguards should be in place to prevent misuse? Can enhancing certain traits unintentionally diminish others? These are vital questions that reflect the human-centered values underpinning innovation.

The mere existence of the Catalyst Cap opens up important societal questions: Will the ability to boost empathy in leaders reduce conflict worldwide? Could enhancing focus in students democratize education outcomes? This innovation compels us to think critically about who we become as a society now that such advancements are possible.

Imagining Adoption and Impact

The Catalyst Cap, as transformative as it is, will likely follow a phased adoption curve. Early adopters will likely include competitive professionals, creatives, and educators eager to test its potential. However, mass-market integration will require public trust, clinical trials, and regulatory approval. Its impact on industries such as healthcare, education, and entertainment could be profound, reshaping how we view self-improvement.

Beyond individual users, organizations could deploy the Catalyst Cap to enhance team dynamics, foster innovation, and tackle challenges more effectively. Picture a world where collaboration and problem-solving are not hindered by cognitive limitations but enhanced by technological augmentation.

Conclusion: Inspiring Real Innovations

While entirely fictional and created in honor of this incredibly important day, the Catalyst Cap represents more than just an imaginative flight of fancy — it serves as a symbol of possibility. By exploring fake innovations like this, we engage our minds in thinking creatively about the future and challenge ourselves to consider the implications of what we create. What might the real-world equivalent of the Catalyst Cap look like? How can we ensure that future technologies prioritize the human experience?

Human-centered innovation is not just about inventing—it’s about inspiring. Let the Catalyst Cap spark your imagination and propel you toward creating what’s next.

April Fools!

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The Role Platforms Play in Business Networks

The Role Platforms Play in Business Networks

GUEST POST from Geoffrey A. Moore

A decade and a half ago, my colleague at TCG Advisors, Philip Lay, led a body of work with SAP around the topic of business network transformation. It was spurred by the unfolding transition from client-server architecture to a cloud-first, mobile-first world, and it explored the implications for managing both high-volume transactions as well as high-complexity relationships. Our hypothesis was that high-volume networks would be dominated by a small number of very powerful concentrators whereas the high-complexity networks would be orchestrated by a small number of very influential orchestrators.

The concentrator model has played out pretty much as expected, although the astounding success of Amazon in dominating retail is in itself a story for the ages. The key has been how IT platforms anchored in cloud and mobile, now supplemented with AI, have enabled transactional enterprises in multiple sectors of the economy to scale to levels previously unimaginable. And these same platforms, when opened to third parties, have proved equally valuable to the long tail of small entrepreneurial businesses, garnering them access to a mass-market distribution channel for their offerings, something well beyond their reach in the prior era.

The impact on the orchestrator model, by contrast, is harder to see, in part because so much of it plays out behind closed doors “in the room where it happens.” Enterprises like JP Morgan Chase, Accenture, Salesforce, Cisco, and SAP clearly extend their influence well beyond their borders. Their ability to orchestrate their value chains, however, has historically been grounded primarily in a network of personal relationships maintained through trustworthiness, experience, and intelligence, not technology. So, where does an IT platform fit into that kind of ecosystem?

Here it helps to bring in a distinction between core and context. Core is what differentiates your business; context is everything else you do. Unless you are yourself a major platform provider, the platform per se is always context, never core. So, all the talk about what is your platform strategy is frankly a bit overblown. Nonetheless, in both the business models under discussion, platforms can impinge upon the core, and that is where your attention does need to be focused.

In the case of the high-volume transaction model, where commoditization is an everyday fact of life, many vendors have sought to differentiate the customer experience, both during the buying process and over the useful life of the offer. This calls for deep engagement with the digital resources available, including accessing and managing multiple sources of data, applying sophisticated analytics, and programming real-time interactions. That said, such data-driven personalization is a tactic that has been pursued for well over a decade now, and the opportunities to differentiate have diminished considerably. The best of those remaining are in industries dominated by an oligopoly of Old Guard enterprises that are so encumbered with legacy systems that they cannot field a credible digital game. If you are playing elsewhere, you will likely fare better if you get back to innovating on the offering itself.

In the case of managing context in a high-complexity relationship model, it is friction that is the everyday fact of life worth worrying about. Most of it lies in the domain of transaction processing, the “paperwork” that tags along with every complex sale. Anything vendors can do to simplify transactional processes will pay off not only in higher customer satisfaction but also in faster order processing, better retention, and improved cross-sell and up-sell. It is not core, it does not differentiate, but it does make everyone breathe easier, including your own workforce. Here, given the remarkable recent advances in data management, machine learning, and generative AI, there is enormous opportunity to change the game, and very little downside risk for so doing. The challenge is to prioritize this effort, especially in established enterprises where the inertia of budget entitlement keeps resources trapped in the coffers of the prior era’s winning teams.

The key takeaway from all this is that for most of us platforms are not strategic so much as they are operational. That is, the risk is less that you might choose an unsuitable platform and more that you may insufficiently invest in exploiting whatever one you do choose. So, the sooner you get this issue off the board’s agenda and into your OKRs, the better.

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

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FORO: The Fear of Reaching Out

FORO: The Fear of Reaching Out

GUEST POST from Shep Hyken

We’ve all experienced long hold times, repeating our “story” because we get transferred from one customer service rep to another, etc. It’s an ugly customer service experience that gives many customers FORO, or the Fear of Reaching Out.

FORO is the result of an experience marred with time-wasting friction that makes customers dislike having to reach out to customer support. Our annual customer service and Customer Experience (CX) research (sponsored by RingCentral) finds that 43% of customers would rather clean a toilet than call customer support. The support experience can be so bad that 60% of customers admit to hanging up on a support agent, 34% admit to yelling at an agent, and 21% admit to cussing at an agent. So, it does make sense that customers would have FORO due to poor experiences they have had with some companies and brands (not all) in the past.

Because some companies get it right and others don’t, the inconsistency makes the customer wonder what the next time will be like. Their past frustration, anxiety or memory of a bad experience creates the unwillingness to call.

I had a chance to interview Gaurav Passi on Amazing Business Radio, and he introduced me to the concept of FORO. Passi is the co-founder and CEO of Zingly.AI, a platform that empowers customers to collaborate with a company, either through AI or with direct human-to-human interactions, to have their questions answered and problems resolved. Below are some of Passi’s most intriguing points, followed by my commentary:

  1. The next 15-20 years will be about the end customer experience. Many consulting companies and business experts recognize that customer experience is more important than a company’s product. Most customers can buy the same product—or at least similar products—from many different sources. What differentiates the companies and brands that sell these products is the experience. Passi agrees and adds that the way companies deliver support is changing. The future of CX is a blend of AI, digital and human/live support. That prompted me to ask Passi a question that concerns many people, especially customer support agents, “Do you see AI replacing live agents in that time frame?” He answered, “I don’t see a world where humans are completely taken out.”
  2. Customers don’t want to talk to a human being — until they do. Passi says that customers often don’t want to talk to a human. They just want an answer as quickly and efficiently as possible. If they can’t get it, then they want to talk to a human … as quickly and efficiently as possible. Even with many customers desiring this self-service approach, Passi cautions that companies should not make the mistake of 100% deflection to digital self-service. He asks, “Even if you achieve 100% deflection, what will happen to your customer satisfaction (CSAT) scores?” Passi shared an example of a client who had chosen to deflect 100% of customer support to digital self-service and had an outage. Because of the outage, the employees ended up talking to customers, human-to-human. Amazingly — or not — CSAT went up. Why? Passi says, “Because there was a human touch when needed.”
  3. Customer patience is at an all-time low. This is a primary symptom of FORO. Customers don’t have the time or patience to go online to a company’s website, find the customer support number, wait on hold, get authenticated, etc. They want, as Passi calls it, a “One shot, one kill experience.” Using the company’s self-service options, often fueled by AI, you ask a question, and an answer comes back. It’s as simple as that. The customer appreciates not having to get on the phone, wait on hold, etc., etc.
  4. AI is not the final answer! While AI is revolutionizing customer service and support by enabling businesses to scale their operations efficiently, maintaining the human touch with customers to foster genuine relationships is still important. But the human touch doesn’t have to kick in until it’s needed. And in the perfect world, the platform will recognize customers’ reactions when they aren’t getting the answers they need. Passi is proud of what he refers to as “the most magical component we’ve created in the past three years,” which is a technology that understands when the customer is not getting the right answer and seamlessly passes them to a human agent to take over.

If you’ve been following my work, you know I’m focused on helping my clients create amazing customer experiences. As Passi and I wrapped up our interview, he mentioned that amazing is what Zingly is about. He shared that his mission, like mine, is to help his customers create amaZINGLY great experiences for their customers. With an increasing demand for customers to have more control over how, when and on which devices they communicate with businesses, the combination of AI and human expertise, paired with transparency and collaboration with customers, can create a more personalized, effective and amaZINGLY great customer experience.

Image Credit: Pixabay

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Unlocking Trapped Value with AI

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?

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