Tag Archives: Artificial Intelligence

McKinsey is Wrong That 80% Companies Fail to Generate AI ROI

GUEST POST from Robyn Bolton

Sometimes, you see a headline and just have to shake your head. Sometimes, you see a bunch of headlines and need to scream into a pillow. This week’s headlines on AI ROI were the latter:

Companies are Pouring Billions Into A.I. It Has Yet to Pay Off – NYT
MIT report: 95% of generative AI pilots at companies are failing – Forbes
Nearly 8 in 10 companies report using gen AI – yet just as many report no significant bottom-line impact – McKinsey

AI has slipped into what Gartner calls the Trough of Disillusionment. But, for people working on pilots, it might as well be the Pit of Despair because executives are beginning to declare AI a fad and deny ever having fallen victim to its siren song.

Because they’re listening to the NYT, Forbes, and McKinsey.

And they’re wrong.

ROI Reality Check

In 20205, private investment in generative AI is expected to increase 94% to an estimated $62 billion. When you’re throwing that kind of money around, it’s natural to expect ROI ASAP.

But is it realistic?

Let’s assume Gen AI “started” (became sufficiently available to set buyer expectations and warrant allocating resources to) in late 2022/early 2023. That means that we’re expecting ROI within 2 years.

That’s not realistic. It’s delusional.

ERP systems “started” in the early 1990s, yet providers like SAP still recommend five-year ROI timeframes. Cloud Computing“started” in the early 2000s, and yet, in 2025, “48% of CEOs lack confidence in their ability to measure cloud ROI.” CRM systems’ claims of 1-3 years to ROI must be considered in the context of their 50-70% implementation failure rate.

That’s not to say we shouldn’t expect rapid results. We just need to set realistic expectations around results and timing.

Measure ROI by Speed and Magnitude of Learning

In the early days of any new technology or initiative, we don’t know what we don’t know. It takes time to experiment and learn our way to meaningful and sustainable financial ROI. And the learnings are coming fast and furious:

Trust, not tech, is your biggest challenge: MIT research across 9,000+ workers shows automation success depends more on whether your team feels valued and believes you’re invested in their growth than which AI platform you choose.

Workers who experience AI’s benefits first-hand are more likely to champion automation than those told, “trust us, you’ll love it.” Job satisfaction emerged as the second strongest indicator of technology acceptance, followed by feeling valued. If you don’t invest in earning your people’s trust, don’t invest in shiny new tech.

More users don’t lead to more impact: Companies assume that making AI available to everyone guarantees ROI. Yet of the 70% of Fortune 500 companies deploying Microsoft 365 Copilot and similar “horizontal” tools (enterprise-wide copilots and chatbots), none have seen any financial impact.

The opposite approach of deploying “vertical” function-specific tools doesn’t fare much better. In fact, less than 10% make it past the pilot stage, despite having higher potential for economic impact.

Better results require reinvention, not optimization: McKinsey found that call centers that gave agents access to passive AI tools for finding articles, summarizing tickets, and drafting emails resulted in only a 5-10% call time reduction. Centers using AI tools to automate tasks without agent initiation reduced call time by 20-40%.

Centers reinventing processes around AI agents? 60-90% reduction in call time, with 80% automatically resolved.

How to Climb Out of the Pit

Make no mistake, despite these learnings, we are in the pit of AI despair. 42% of companies are abandoning their AI initiatives. That’s up from 17% just a year ago.

But we can escape if we set the right expectations and measure ROI on learning speed and quality.

Because the real concern isn’t AI’s lack of ROI today. It’s whether you’re willing to invest in the learning process long enough to be successful tomorrow.

Image credit: Microsoft CoPilot

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This AI Creativity Trap is Gutting Your Growth

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This AI Creativity Trap is Gutting Your Growth

GUEST POST from Robyn Bolton

“We have to do more with less” has become an inescapable mantra, and goodness, are you trying. You’ve slashed projects and budgets, “right-sized” teams, and tried any technology that promised efficiency and a free trial. Now, all that’s left is to replace the people you still have with AI creativity tools. Welcome to the era of the AI Innovation Team.

It sounds like a great idea. Now, everyone can be an innovator with access to an LLM. Heck, even innovation firms are “outsourcing” their traditional work to AI, promising the same radical results with less time and for far less money.

It sounds almost too good to be true.

Because it is too good to be true.

AI is eliminating the very brain processes that produce breakthrough innovations.

This isn’t hyperbole, and it’s not just one study.

MIT researchers split 54 people into three groups (ChatGPT users, search engine users, and no online/AI tools using ChatGPT) and asked them to write a series of essays. Using EEG brain monitoring, they found that the brain connectivity in networks crucial for creativity and analogous thinking dropped by 55%.

Even worse? When people stopped using AI, their brains stayed stuck in this diminished state.

University of Arkansas researchers tested AI against 3,562 humans on a series of four challenges involving finding new uses for everyday objects, like a brick or paperclip. While AI scored slightly higher on standard tests, when researchers introduced a new context, constraint, or modification to the object, AI’s performance “collapsed.” Humans stayed strong.

Why? AI relies on pattern matching and is unable to transfer its “creativity” to unexpected scenarios. Humans use analogical reasoning so are able to flex quickly and adapt.

University of Strasbourg researchers analyzed 15,000 studies of COVID-19 infections and found that teams that relied heavily on AI experts produced research that got fewer citations and less media attention. However, papers that drew from diverse knowledge sources across multiple fields became widely cited and influential.

The lesson? Breakthroughs require cross-domain thinking, which is precisely what diverse human teams provide, and, according to the MIT study, AI is unable to produce.

How to optimize for efficiency AND impact (and beat your competition)

While this seems like bad news if you’ve already cut your innovation team, the silver lining is that your competition is probably making the same mistake.

Now that you know better, you can do better, and that creates a massive opportunity.

Use AI for what it does well:

Data analysis and synthesis
Rapid testing and iteration to refine an advanced prototype
Process optimization

Use humans for what we do well:

Make meaningful connections across unrelated domains
Recognize when discoveries from one field apply to another
Generate the “aha moments” that redefine industries

Three Questions to Ask This Week

Where did your most recent breakthroughs come from? How many came from connecting insights across different domains? If most of your innovations require analogical leaps, cutting creative teams could kill your pipeline.
How are teams currently using AI tools? Are they using AI for data synthesis and rapid iteration? Good. Are they replacing human ideation entirely? Problem.
How can you see it to believe it? Run a simple experiment: Give two teams an hour to solve a breakthrough challenge. Have one solve it with AI assistance and one without. Which solution is more surprising and potentially breakthrough?

The Hidden Competitive Advantage

As AI commoditizes pattern recognition, human analogical thinking and creativity become a competitive advantage.

The companies that figure out the right balance will eat everyone else’s lunch.

Image credit: Gemini

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Why Context Engineering is the Next Frontier in AI

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Why Context Engineering is the Next Frontier in AI

by Braden Kelley and Art Inteligencia

Observing the rapid evolution of artificial intelligence, one thing has become abundantly clear: while raw processing power and sophisticated algorithms are crucial, the true key to unlocking AI’s transformative potential lies in its ability to understand and leverage context. We’ve seen remarkable advancements in generative AI and machine learning, but these technologies often stumble when faced with the nuances of real-world situations. This is why I believe context engineering – the discipline of explicitly designing and managing the contextual information available to AI systems – is not just an optimization, but the next fundamental frontier in AI innovation.

Think about human intelligence. Our ability to understand language, make decisions, and solve problems is deeply rooted in our understanding of context. A single word can have multiple meanings depending on the sentence it’s used in. A request can be interpreted differently based on the relationship between the people involved or the situation at hand. For AI to truly augment human capabilities and integrate seamlessly into our lives, it needs a similar level of contextual awareness. Current AI models often operate on relatively narrow inputs, lacking the broader understanding of user intent, environmental factors, and historical interactions that humans take for granted. Context engineering aims to bridge this gap, moving AI from being a powerful but often brittle tool to a truly intelligent and adaptable partner.

In the realm of artificial intelligence, context engineering is the strategic and human-centered practice of providing an AI system with the relevant background information it needs to understand a query or situation accurately. It goes beyond simple prompt design by actively building and managing the comprehensive context that surrounds an interaction. This includes integrating historical data, user profiles, real-time environmental factors, and external knowledge sources, allowing the AI to move from a narrow, transactional understanding to a more holistic, human-like awareness. By engineering this context, we enable AI to produce more accurate, personalized, and genuinely useful responses, bridging the gap between a machine’s logic and the nuanced complexity of human communication and problem-solving.

The field of context engineering encompasses a range of techniques and strategies focused on providing AI systems with relevant and actionable context. This includes:

Prompt Engineering: Crafting detailed and context-rich prompts that guide AI models towards desired outputs.
Memory Management: Implementing mechanisms for AI to remember past interactions and use that history to inform current responses.
External Knowledge Integration: Connecting AI systems to external databases, APIs, and real-time data streams to provide up-to-date and relevant information.
User Profiling and Personalization: Leveraging data about individual users to tailor AI responses to their specific needs and preferences.
Situational Awareness: Incorporating real-world contextual cues, such as location, time of day, and user activity, to make AI more responsive to the current situation.

A Human-Centered Blueprint for Implementation

Implementing context engineering is not a one-time technical fix; it is a continuous, human-centered practice that must be embedded into your innovation lifecycle. To move beyond a static, one-size-fits-all model and create truly intelligent, context-aware AI, consider this blueprint for action:

Step 1: Start with the Human Context. Before you even think about data streams or algorithms, you must first deeply understand the human being you are serving. Conduct ethnographic research, user interviews, and journey mapping to identify what context is truly relevant to your users. What are their goals? What unspoken needs do they have? What external factors influence their decisions? The most valuable context often isn’t in a database—it’s in the real-world experiences and emotional states of your users.
Step 2: Map the Contextual Landscape. Once you understand the human context, you can begin to identify and integrate the necessary data. This involves creating a “contextual map” that connects the human need to the available data sources. For a customer service AI, this map would link a customer’s inquiry to their purchase history, recent support tickets, and even their browsing behavior on your website. For a medical AI, the map would link a patient’s symptoms to their genetic data, environmental exposure, and family medical history. This mapping process ensures that the AI’s inputs are directly tied to what matters most to the user.
Step 3: Build a Dynamic Feedback Loop. The context of a situation is constantly changing. A great context-aware AI is not a static system but a learning one. Implement a continuous feedback loop where human users can correct the AI’s understanding, provide additional information, and refine its responses. This “human-in-the-loop” approach is vital for ethical and accurate AI. It allows the system to learn from its mistakes and adapt to new, unforeseen contexts, ensuring its relevance and reliability over time.
Step 4: Prioritize Privacy and Ethical Guardrails. The more context you provide to an AI, the more critical it becomes to manage that information responsibly. From the outset, you must design for privacy, collecting only the data you absolutely need and ensuring it is stored and used in a secure and transparent manner. Establish clear ethical guardrails for how the AI uses and interprets contextual information, particularly for sensitive data. This is not just a regulatory requirement; it is a fundamental aspect of building trust with your users and ensuring that your AI serves humanity, rather than exploiting it.

By following these best practices, you can move beyond simple, reactive AI to a proactive, human-centered intelligence that understands the world not just as a collection of data points, but as a rich tapestry of interconnected context. This is the work that will define the next generation of AI and, in doing so, will fundamentally change how technology serves humanity.

Case Study 1: Improving Customer Service with Context-Aware AI Assistants

The Challenge: Generic and Frustrating Customer Service Chatbots

Many companies have implemented AI-powered chatbots to handle customer inquiries. However, these chatbots often struggle with complex or nuanced issues, leading to frustrating experiences for customers who have to repeat information or are given irrelevant answers. The lack of contextual awareness is a major limitation.

Context Engineering in Action:

A telecommunications company sought to improve its customer service chatbot by implementing robust context engineering. They integrated the chatbot with their CRM system, allowing it to access the customer’s purchase history, past interactions, and current account status. They also implemented memory management so the chatbot could retain information shared earlier in the conversation. Furthermore, they used prompt engineering to guide the chatbot to ask clarifying questions and to tailor its responses based on the specific product or service the customer was inquiring about. For example, if a customer asked about a billing issue, the chatbot could access their latest bill and provide specific details, rather than generic troubleshooting steps. It could also remember if the customer had contacted support recently for a related issue and take that into account.

The Impact:

The context-aware chatbot significantly improved customer satisfaction scores and reduced the number of inquiries that had to be escalated to human agents. Customers felt more understood and received more relevant and efficient support. The company also saw a decrease in customer churn. This case study highlights how context engineering can transform a basic AI tool into a valuable and helpful resource by enabling it to understand the customer’s individual situation and history.

Key Insight: By providing AI customer service assistants with access to relevant customer data and interaction history, companies can significantly enhance the quality and efficiency of support, leading to increased customer satisfaction and loyalty.

Case Study 2: Enhancing Medical Diagnosis with Contextual Patient Information

The Challenge: Over-reliance on Isolated Symptoms in AI Diagnostic Tools

AI is increasingly being used to assist medical professionals in diagnosing diseases. However, early AI diagnostic tools often focused primarily on analyzing individual symptoms in isolation, potentially missing crucial contextual information such as the patient’s medical history, lifestyle, environmental factors, and even subtle cues from their recent health records.

Context Engineering in Action:

A research hospital in the Pacific Northwest developed an AI-powered diagnostic tool for a specific type of rare disease. Recognizing the importance of context, they engineered the AI to integrate a wide range of patient data beyond just the presenting symptoms. This included the patient’s complete medical history (past illnesses, medications, allergies), family medical history, lifestyle information (diet, exercise, smoking habits), recent lab results, and even notes from previous doctor’s visits. The AI was also connected to relevant medical literature to understand the broader context of the disease and potential co-morbidities. By providing the AI with this rich contextual information, the researchers aimed to improve the accuracy and speed of diagnosis, especially in complex cases where isolated symptoms might be misleading.

The Impact:

The context-aware AI diagnostic tool demonstrated a significantly higher accuracy rate in identifying the rare disease compared to traditional methods and earlier AI models that lacked comprehensive contextual input. It was also able to flag potential risks and complications that might have been overlooked otherwise. This case study underscores the critical role of context engineering in high-stakes applications like medical diagnosis, where a holistic understanding of the patient’s situation can lead to more timely and effective treatments.

Key Insight: Context engineering, by enabling a holistic view of a patient’s health and history, is crucial for improving the accuracy and reliability of AI in critical fields like medical diagnosis.

The Future of AI is Contextual

The future of AI is not about building bigger models; it’s about building smarter ones. And a smarter AI is one that can understand and leverage the richness of context, just as humans do. From a human-centered perspective, context engineering is the practice that makes AI more useful, more reliable, and more deeply integrated into our lives in a way that truly helps us. By moving beyond simple prompts and isolated data points, we can create AI systems that are not just powerful tools, but truly intelligent and invaluable partners. The work of bridging the gap between isolated data and meaningful context is where the next great wave of AI innovation will emerge, and it is a task that will demand our full attention.

Image credit: Pexels

Content Authenticity Statement: The topic area and the key elements to focus on were decisions made by Braden Kelley, with help from Google Gemini to shape the article and create the illustrative case studies.

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Why Explainable AI is the Key to Our Future

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The Unseen Imperative

Why Explainable AI is the Key to Our Future

GUEST POST from Art Inteligencia

We’re in the midst of an AI revolution, a tidal wave of innovation that promises to redefine industries and transform our lives. We’ve seen algorithms drive cars, diagnose diseases, and manage our finances. But as these “black box” systems become more powerful and more pervasive, a critical question arises: can we truly trust them? The answer, for many, is a hesitant ‘maybe,’ and that hesitation is a massive brake on progress. The key to unlocking AI’s true, transformative potential isn’t just more data or faster chips. It’s Explainable AI (XAI).

XAI is not a futuristic buzzword; it’s the indispensable framework for today’s AI-driven world. It’s the set of tools and methodologies that peel back the layers of a complex algorithm, making its decisions understandable to humans. Without XAI, our reliance on AI is little more than a leap of faith. We must transition from trusting AI because it’s effective, to trusting it because we understand why and how it’s effective. This is the fundamental shift from a blind tool to an accountable partner.

This is more than a technical problem; it’s a strategic business imperative. XAI provides the foundation for the four pillars of responsible AI that will differentiate the market leaders of tomorrow:

Transparency: Moving beyond “what” the AI decided to “how” it arrived at that decision. This sheds light on the model’s logic and reasoning.
Fairness & Bias Detection: Actively identifying and mitigating hidden biases in the data or algorithm itself. This ensures that AI systems make equitable decisions that don’t discriminate against specific groups.
Accountability: Empowering humans to understand and take responsibility for AI-driven outcomes. When things go wrong, we can trace the decision back to its source and correct it.
Trust: Earning the confidence of users, stakeholders, and regulators. Trust is the currency of the future, and XAI is the engine that generates it.

For any organization aiming to deploy AI in high-stakes fields like healthcare, finance, or justice, XAI isn’t a nice-to-have—it’s a non-negotiable requirement. The competitive advantage will go to the companies that don’t just build powerful AI, but build trustworthy AI.

Case Study 1: Empowering Doctors with Transparent Diagnostics

Consider a team of data scientists who develop a highly accurate deep learning model to detect early-stage cancer from medical scans. The model’s accuracy is impressive, but it operates as a “black box.” Doctors are understandably hesitant to stake a patient’s life on a recommendation they can’t understand. The company then integrates an XAI framework. Now, when the model flags a potential malignancy, it doesn’t just give a diagnosis. It provides a visual heat map highlighting the specific regions of the scan that led to its conclusion, along with a confidence score. It also presents a list of similar, previously diagnosed cases from its training data, providing concrete evidence to support its claim. This explainable output transforms the AI from an un-auditable oracle into a valuable, trusted second opinion. The doctors, now empowered with understanding, can use their expertise to validate the AI’s findings, leading to faster, more confident diagnoses and, most importantly, better patient outcomes.

Case Study 2: Proving Fairness in Financial Services

A major financial institution implements an AI-powered system to automate its loan approval process. The system is incredibly efficient, but its lack of transparency triggers concerns from regulators and consumer advocacy groups. Are its decisions fair, or is the algorithm subtly discriminating against certain demographic groups? Without XAI, the bank would be in a difficult position to defend its practices. By implementing an XAI framework, the company can now generate a clear, human-readable report for every single loan decision. If an application is denied, the report lists the specific, justifiable factors that contributed to the outcome—e.g., “debt-to-income ratio is outside of policy guidelines” or “credit history shows a high number of recent inquiries.” Crucially, it can also definitively prove that the decision was not based on protected characteristics like race or gender. This transparency not only helps the bank comply with fair lending laws but also builds critical trust with its customers, turning a potential liability into a significant source of competitive advantage.

The Architects of Trust: XAI Market Leaders and Startups to Watch

In the rapidly evolving world of Explainable AI (XAI), the market is being defined by a mix of established technology giants and innovative, agile startups. Major players like Google, Microsoft, and IBM are leading the way, integrating XAI tools directly into their cloud and AI platforms like Azure Machine Learning and IBM Watson. These companies are setting the industry standard by making explainability a core feature of their enterprise-level solutions. They are often joined by other large firms such as FICO and SAS Institute, which have long histories in data analytics and are now applying their expertise to ensure transparency in high-stakes areas like credit scoring and risk management. Meanwhile, a number of dynamic startups are pushing the boundaries of XAI. Companies like H2O.ai and Fiddler AI are gaining significant traction with platforms dedicated to providing model monitoring, bias detection, and interpretability for machine learning models. Another startup to watch is Arthur AI, which focuses on providing a centralized platform for AI performance monitoring to ensure that models remain fair and accurate over time. These emerging innovators are crucial for democratizing XAI, making sophisticated tools accessible to a wider range of organizations and ensuring that the future of AI is built on a foundation of trust and accountability.

The Road Ahead: A Call to Action

The future of AI is not about building more powerful black boxes. It’s about building smarter, more transparent, and more trustworthy partners. This is not a task for data scientists alone; it’s a strategic imperative for every business leader, every product manager, and every innovator. The companies that bake XAI into their processes from the ground up will be the ones that successfully navigate the coming waves of regulation and consumer skepticism. They will be the ones that win the trust of their customers and employees. They will be the ones that truly unlock the full, transformative power of AI. Are you ready to lead that charge?

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credit: Gemini

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Boring AI is the Key to Better Customer Service

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Boring AI is the Key to Better Customer Service

GUEST POST from Shep Hyken

Boring can be a good thing. When something works the way it’s supposed to, it shouldn’t be a surprise. There shouldn’t be friction or drama if a customer has a problem or wants a question answered. It should just be easy. And when it comes to customer service, “easy” and “boring” are good. The experience should just happen the way the customer wants it to happen. You might call that boring. I call that excellent.

That was the beginning of a conversation I had with Damon Covey, general manager of unified communications and collaboration for GoTo, on Amazing Business Radio. GoTo is one of the leading cloud communications companies, providing software and solutions to companies of all sizes and helping them implement AI systems that work, without the complexity and stress that can come from new technology. Covey’s goal for our conversation was to demystify AI, cutting through the noise and complexities of flashy AI and taking it down to a practical level. Boring was the word he liked to use, emphasizing it should be easy, simple and uncomplicated.

In our discussion, Covey said that large companies used to make six- and seven-figure investments to implement AI. Today, AI technology is far superior and, at the same time, much less expensive, so even the smallest companies can afford it. They can get advanced technology for hundreds of dollars, not hundreds of thousands of dollars. Covey said, “For example, a small bike shop or an automotive dealership can now provide the same advanced customer service options as large corporations.” With that in mind, here are the main takeaways from our conversation:

Conversational AI

Until recently (within the past two or three years), a basic chatbot had to follow pre-set rules. Conversational AI provides a much broader opportunity, allowing a computer to interact with people in a natural, human-like manner. Today, AI can understand and respond to customers’ questions and issues with much more flexibility. It has the capability to recognize different languages and understand fumbled phrases, much like a human would. By using conversational AI, businesses can provide 24/7 service, allowing them to respond to customer queries and schedule appointments even when the customer contacts them outside of regular business hours.

Treat AI Like a Team Member

If you hire a new employee, you train them. Treat your AI solutions the same way. Covey said that, similar to training an employee, you need to set specific parameters and provide the AI with the necessary information to ensure it stays within the scope of your business requirements. He emphasized the importance of making sure the AI only draws from the information provided by your business, such as your website, FAQ pages, product manuals, etc., rather than pulling from a source outside of your company, to maintain accuracy and relevance. Covey said that AI should be continuously optimized and trained over time to improve its performance, much like you would train and coach a human employee to expand their capabilities.

Productivity: Automating Processes

Covey talked about automating processes. Anything you do more than three times can be a candidate for AI automation. For example, AI can integrate with a business’ telecommunications system to automate the process of taking notes during calls. It can then summarize the call, put the information into the customer’s record and create a list of next steps, if appropriate. This is a simple function that helps employees be more productive. Instead of an employee typing notes and summarizing the call, AI can handle the task so the employee can move on to helping the next customer.

Augmenting the Business

AI can help businesses do things they don’t normally do, such as remain open for certain functions (like customer support) after hours. It can act as an after-hours receptionist, answering phone calls, setting appointments or providing basic information to customers after business hours. That turns a business that’s typically open during traditional hours to a 24/7 operation.

It is Easier Than You Think

At the end of the interview, Covey dropped a nugget of wisdom that is the perfect way to close this article. For many, especially smaller organizations, deciding what technology to use and how to best use AI can be a daunting decision. It shouldn’t be. Covey says, “Start with the problem you want to solve, and solve for that problem.” He added that you should start using the technology for small problems. Once you understand how it works, the more complicated issues will be easier to solve for.

And that brings us back to where we started. AI doesn’t need to be complicated or flashy. It should be boring—in a good way. Start small, focus on one problem at a time and let AI do what it’s supposed to do: make customer service easier and more efficient. When done right, your customers won’t be amazed by the AI—they’ll just be amazed by how easy it is to do business with you.

Image Credit: Unsplash

This article was originally published on Forbes.com

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Top 10 Human-Centered Change & Innovation Articles of June 2025

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Top 10 Human-Centered Change & Innovation Articles of June 2025 Drum 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 June’s ten most popular innovation posts:

Why Business Transformations Fail — by Robyn Bolton
Three Ways Strategic Idleness Accelerates Innovation and Growth — by Robyn Bolton
Overcoming the Fear of Innovation Failure — by Stefan Lindegaard
Making People Matter in AI Era — by Janet Sernack
Yes the Comfort Zone Can Be Your Best Friend — by Stefan Lindegaard
Your Digital Transformation Starting Point — by Braden Kelley
Learn More About the Problem Before Trying to Solve It — by Mike Shipulski
Putting Human Agency at the Center of Decision-Making — by Greg Satell
Innovation or Not – SpinLaunch — by Art Inteligencia
Team Motivation Does Not Have to be Hard — by David Burkus

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

You Already Have Too Many Ideas — by Mike Shipulski
Portfolio Management and Category Power — by Geoffrey A. Moore
Your Legends Define Your Culture — by Shep Hyken
Should We Stop Asking Employees to Innovate? — by Stefan Lindegaard
Benchmarking Innovation Performance — by Noel Sobelman

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

Have something to contribute?

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

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

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The Most Powerful Question

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The Most Powerful Question

GUEST POST from Mike Shipulski

Artificial intelligence, 3D printing, robotics, autonomous cars – what do they have in common? In a word – learning.

Creativity, innovation and continuous improvement – what do they have in common? In a word – learning.

And what about lifelong personal development? Yup – learning.

Learning results when a system behaves differently than your mental model. And there four ways make a system behave differently. First, give new inputs to an existing system. Second, exercise an existing system in a new way (for example, slow it down or speed it up.) Third, modify elements of the existing system. And fourth, create a new system. Simply put, if you want a system to behave differently, you’ve got to change something. But if you want to learn, the system must respond differently than you predict.

If a new system performs exactly like you expect, it isn’t a new system. You’re not trying hard enough.

When your prediction is different than how the system actually behaves, that is called error. Your mental model was wrong and now, based on the new test results, it’s less wrong. From a learning perspective, that’s progress. But when companies want predictable results delivered on a predictable timeline, error is the last thing they want. Think about how crazy that is. A company wants predictable progress but rejects the very thing that generates the learning. Without error there can be no learning.

If you don’t predict the results before you run the test, there can be no learning.

It’s exciting to create a new system and put it through its paces. But it’s not real progress – it’s just activity. The valuable part, the progress part, comes only when you have the discipline to write down what you think will happen before you run the test. It’s not glamorous, but without prediction there can be no error.

If there is no trial, there can be no error. And without error, there can be no learning.

Let’s face it, companies don’t make it easy for people to try new things. People don’t try new things because they are afraid to be judged negatively if it “doesn’t work.” But what does it mean when something doesn’t work? It means the response of the new system is different than predicted. And you know what that’s called, right? It’s called learning.

When people are afraid to try new things, they are afraid to learn.

We have a language problem that we must all work to change. When you hear, “That didn’t work.”, say “Wow, that’s great learning.” When teams are told projects must be “on time, on spec and on budget”, ask the question, “Doesn’t that mean we don’t want them to learn?”

But, the whole dynamic can change with this one simple question – “What did you learn?” At every meeting, ask “What did you learn?” At every design review, ask “What did you learn?” At every lunch, ask “What did you learn?” Any time you interact with someone you care about, find a way to ask, “What did you learn?”

And by asking this simple question, the learning will take care of itself.

Image credit: Pixabay

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Making People Matter in AI Era

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Making People Matter in AI Era

GUEST POST from Janet Sernack

People matter more than ever as we witness one of the most significant technological advancements reshaping humanity. Regardless of size, every industry and organization can adopt AI to enhance operations, innovate, stay competitive, and grow by partnering AI with people. Our research highlights three workplace trends and four global, strategic, and systemic human crises that affect the successful execution of all organizational transformation initiatives, posing potential barriers to implementing AI strategies. This makes the importance of people mattering in the age of AI greater than ever.

Three Key Global Trends

According to Udemy’s 2024 Global Learning and Skills Trends Report, three key trends are core to the future of work, stating that organizations and their leaders must:

Understand how to navigate the skills landscape and why it is essential to assess, identify, develop, and validate the skills their teams possess, lack, and require to remain innovative and competitive.
Adapt to the rise of AI, focusing on how generative AI and automation disrupt our work processes and their role in supporting a shift to a skills-based approach.
Develop strong leaders who can guide their teams through change and foster resilience within them.

Five Key Global Crises

1. Organizational engagement is in crisis.

Recently, Gallup reported that Global employee engagement fell by two percentage points in 2024, only the second time it has fallen in the past 12 years. Managers (particularly young managers and female managers) experienced the sharpest decline. Employee engagement significantly influences economic output; Gallup estimates that a two-point drop in engagement costs the world $438 billion in lost productivity in 2024.

2. People are burning out, causing a crisis in well-being.

In 2019, the World Health Organization included burnout in its International Classification of Diseases, describing “Burn-out is a syndrome conceptualized as resulting from chronic workplace stress that has not been successfully managed. Three dimensions characterize it:

Feelings of energy depletion or exhaustion;
Increased mental distance from one’s job, or feelings of negativism or cynicism related to one’s job; and
Reduced professional efficacy.

Burn-out refers specifically to phenomena in the occupational context and should not be applied to describe experiences in other areas of life.”

They estimate that globally, an estimated 12 billion working days are lost every year to depression and anxiety, costing US$ 1 trillion per year in lost productivity.

Burnout is more than just an employee problem; it’s an organizational issue that requires a comprehensive solution. People’s mental and emotional health and well-being are still not prioritized or managed effectively. Well-being in the workplace is a complex systemic issue that must be addressed. Making people matter in the age of AI involves empowering, enabling, and equipping them to focus on developing their self-regulation and self-management skills, shifting them from languishing in a constant state of emotional overwhelm and cognitive overload that leads to burnout.

3. The attention economy is putting people into crisis.

According to Johann Hari, in his best-selling book, “Stolen Focus,” people’s focus and attention have been stolen; our ability to pay attention is collapsing, and we must intentionally reclaim it. His book describes the wide range of consequences that losing focus and attention has on our lives. These issues are further impacted by the pervasive and addictive technology we are compelled to use in our virtual world, exacerbated by the legacy of the global pandemic and the ongoing necessity for many people to work virtually from home. He reveals how our dwindling attention spans predate the internet and how its decline is accelerating at an alarming rate. He suggests that to regain your ability to focus, you should stop multitasking and practice paying attention. Yet, in the Thesaurus, there are 286 synonyms, antonyms, and words related to paying attention, such as listen and give heed.

4. Organizational performance is in crisis.

Research at BetterUp Labs analyzed behavioral data from 410,000 employees (2019-2025), linking real-world performance with organizational outcomes and psychological drivers. It reveals that performance isn’t just about efficiency, it’s about shifting fluidity between three performance modes – basic: the legacy from the industrial age, collaborative: the imperative of knowledge work, and adaptive: the core requirement to perform effectively in the face of technological disruption, by being agile, creative, and connected. The right human fuel powers these: motivation, optimism and agency, which our research has found to be in short supply and BetterUp states is running dry.

Data scientists at BetterUp uncovered that performance has declined by 2-6% across industries since 2019. In business terms, half of today’s workforce would land in a lower performance tier, across all three modes, by 2019 standards.

GenAI relies on activating all three performance gears, and the rise of AI-powered agents is reshaping the way teams work together. Research reveals that companies that invest in adaptive performance see up to 37% higher innovation.

5. Innovation is in crisis.

According to the Boston Consulting Group’s “Most Innovative Companies 2024 Report,” Innovation Systems Need a Reboot:

“Companies have never placed a higher priority on innovation—yet they have never been as unready to deliver on their innovation aspirations”

Their annual survey of global innovators finds that the pandemic, a shifting macroeconomic climate, and rising geopolitical tensions have all taken a toll on the innovation discipline. With high uncertainty, leaders shifted from medium-term advantage and value creation to short-term agility. In that environment, the systems guiding innovation activities and channeling innovation investments suffered, leaving organizations less equipped for the race to come. In particular, as measured by BCG’s proprietary innovation maturity score, innovation readiness is down across the elements of the innovation system that align with the corporate value creation agenda.

You can overcome these crises by transforming them into opportunities through a continuous learning platform that empowers, enables, and equips people to innovate today, making people matter in the age of AI. This will help develop new ways of shaping tomorrow while serving natural, social, and human capital, as well as humanity.

Current constraints of AI mean developing crucial human skills

While AI can perform many tasks, it cannot yet understand and respond to human emotions, build meaningful relationships, exhibit curiosity, or solve problems creatively.

This is why making people matter in the age of AI is crucial, as their human skills are essential.

Some of the most critical human skills are illustrated below.

Some of the Most Critical Human Skills

These essential human skills are challenging to learn and require time, repetition, and practice to develop; however, they are fundamental for creating practical solutions to address the three trends and four crises mentioned above.

Making people matter in the age of AI involves:

Providing individuals with the ‘chance to’ self-regulate their reactive responses by fostering self and systemic awareness and agility to flow with change and disruption in an increasingly uncertain, volatile, ambiguous, and complex world.
Inspiring and motivating people to ‘want to’ self-manage and develop their authentic presence and learning processes to be visionary and purposeful in adapting, innovating, and growing through disruption.
Teaching people ‘how to’ develop the states, traits, mindsets, behaviors, and skills that foster discomfort resilience, adaptive and creative thinking, problem-solving, purpose and vision, conflict negotiation, and innovation.

Human Skills Matter More Than Ever

The human element is critical to shaping the future of work, collaboration, and growth. The most effective AI outcomes will likely come from human-AI partnership, not from automation alone. Making people matter in the age of AI is crucial as part of the adoption journey, and partnering them with AI can turn their fears into curiosity, re-engage them purposefully and meaningfully, and enable them to contribute more to a team or organization. This, in turn, allows them to improve their well-being, maintain attention, innovate, and enhance their performance. Still, it cannot do this for them.

Making people matter in the age of AI by investing in continuous learning tools that develop their human skills will empower them to adapt, learn, grow, and take initiative. External support from a coach or mentor can enhance support, alleviate stress, boost performance, and improve work-life balance and satisfaction.

Human problems require human solutions.

Our human skills are irreplaceable in making real-world decisions and solving complex problems. AI cannot align fragmented and dysfunctional teams, repair broken processes, or address outdated governance. These are human problems requiring human solutions. That’s where human curiosity and inspiration define what AI can never achieve. It is not yet possible to connect people, through AI, to what wants to emerge in the future.

Making people matter in the age of AI can ignite our human inspiration, empowering, engaging, and enabling individuals to unleash their potential at the intersection of human possibility and technological innovation. We can then harness people’s collective intelligence and technological expertise to create, adapt, grow, and innovate in ways that enhance people’s lives, which are deeply appreciated and cherished.

This is an excerpt from our upcoming book, “Anyone Can Learn to Innovate,” scheduled for publication in late 2025.

Please find out more about our work at ImagineNation™.

Please find out about our collective learning products and tools, including The Coach for Innovators, Leaders, and Teams Certified Program, presented by Janet Sernack . It is a collaborative, intimate, and profoundly personalized innovation coaching and learning program supported by a global group of peers over nine weeks. It can be customized as a bespoke corporate learning program.

It is a blended and transformational change and learning program that will give you a deep understanding of the language, principles, and applications of an ecosystem-focused, human-centric approach and emergent structure (Theory U) to innovation. It will also upskill people and teams and develop their future fitness within your unique innovation context. Please find out more about our products and tools.

Image Credit: Unsplash

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Nothing and Everything Has Changed in Customer Service

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Nothing and Everything Has Changed in Customer Service

GUEST POST from Shep Hyken

With all the talk of AI, ChatGPT and more, I’m often asked when interviewed, “What’s changed in customer service?”

My answer is accurate: Nothing!

For thousands of years – actually about 3,775 years – when customers have had a problem or question, they have contacted the company they are doing business with and hoped that it would be resolved to their satisfaction. That’s the way it’s been and will continue to be for thousands of years to come.

But there’s also another answer to the same question about what’s changed: Everything!

By everything, I’m referring to the latest methods of responding to customers’ questions and handling their problems and complaints. I mentioned that for 3,775 years, customers have been contacting companies when they have problems or questions. About 10 years ago, I wrote a Forbes.com article when I learned that tucked away in the British Museum is an ancient complaint that dates back to 1750 B.C.

Nanni, the customer, bought copper ore from a supplier, Ea-Nasir. Unhappy with his purchase, Nanni sent a letter in the form of a stone tablet with the engraved complaint. Loosely translated, the “letter” opens with these words, “What do you take me for that you treat somebody like me with such contempt?” The rest of the letter was a demand that he receive what he thought was right.

Ancient Customer Service Shep Hyken

Customers still complain, and companies – at least the good ones – respond and properly take care of their customers. But how they do so has radically changed.

What may have started as an engraved complaint on a stone tablet eventually turned into handwritten letters, then phone calls, emails, chat, and more modern-day ways of communicating. AI has become the topic of the day, and the strides made in automation and self-service have come a long way.

While many companies are still improving and trying to keep up with the technology, customers who take advantage of the new ways to get questions answered and complaints resolved are very happy with the companies that have kept up with the latest ways to manage the customer experience.

At its core, customer service hasn’t changed. Customers still want to be heard, understood and valued. Sometimes, they even want a little empathy. However, what has changed is the way we deliver that experience. The tools may have evolved from stone tablets to AI chatbots, but the goal remains the same: take care of the customer.

Companies that embrace new technologies while staying true to the timeless principles of great service – listening, responding quickly, and meeting or exceeding expectations – are the ones that will keep their customers coming back. The best companies know that while everything seems to change, the most important thing never changes: a relentless focus on the customer!

Image Credit: Pixabay

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Diverge and Disrupt Your Way to Success

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Diverge and Disrupt Your Way to Success

GUEST POST from Janet Sernack

I have earned my stripes as a rebellious maverick and serial misfit, who, until today, seldom feels content with complying with the status quo, especially when confronted by illogical, rules-bound, conventional, and conforming behaviors. My constant and disruptive search for new horizons has enabled me to make many professional changes and reinventions – from graphic to fashion designer, retail executive, design management consultant, culture and change management consultant, corporate trainer, group facilitator, executive, leadership and team coach, start-up entrepreneur, innovation coach, and award-winning blogger and author who has thrived by being different and disruptive. We need to reframe disruption to increase the possibilities for game-changing inventions and innovations to succeed in an uncertain and unstable future.

Through real-life experiences and by teaching, training, mentoring, and coaching others to learn, adapt, and grow by conquering high peaks and engaging in stimulating adventures, I have come to understand that being open to continuous disruption and constant reinvention is essential for survival and success in our chaotic and uncertain world.

This sense of restlessness continues to spark disruptive and creative changes in my life; as a result, it has taught me several key distinctions —being braver, daring, courageous, responsible, and accountable — throughout my forty-year professional career, which has spanned a period of being different and disruptive.

Being different and disruptive has allowed me to reach new inflection points, absorb new information, build new relationships, establish new systems and modalities, and elevate my confidence, capacity, and competence as an innovator through consulting, training, and coaching in innovation.

How does this link to being innovative?

This relates to innovation because when people impose barriers and roadblocks to innovation, they unconsciously inhibit and resist efforts to learn new ways of enacting constructive and creative change while being different and disruptive.

The crucial first step in managing this is to accept responsibility for recognizing and disrupting your internal structures, mental models, mindsets, and habitual behaviors.
The next step involves leveraging your cognitive dissonance to create cracks, positive openings, doorways, and thresholds, thus making space for profound changes that enable you to challenge accepted norms.
Finally, safely exit your comfort zone, unlearn, learn, and relearn variations in how you feel, think, and act to remain agile, adaptive, and innovative during uncertain and unstable times.

These three elements help you stand out and be disruptive, maximizing differences and diversity by fostering inquisitiveness and curiosity, and developing self-regulation strategies to manage your unconscious automatic reactions or reactive behaviors when faced with change imperatives, including digital transformation, cultural change programs, and innovation initiatives.

Being brave and different

Some of you come from learning environments that label students who challenge teachers or their learning processes as different, disruptive, and rebellious. These students are often punished, threatened, or ignored until they comply with the accepted norms and conform. This diminishes the possibilities and opportunities of maximizing diversity, difference, and disruption as catalysts for change and creativity in the classroom.

As a result, some individuals develop “negative anchors” due to being labelled as different or disruptive and learn how to act or speak to avoid their teacher’s displeasure and disapproval. This leads many to either rebel or adopt more compliant behaviors that keep them out of trouble. Those who choose to rebel miss the chance to benefit from the diversity and inclusion offered in the classroom and traditional education processes.

Only exceptional teachers and educators are curious and question why some individuals think or behave differently. Often labelled as “troublemakers,” these individuals tend to be alienated from the more compliant students, leading many “disruptive” students to fall by the wayside, unable to progress and achieve their full potential. Many of these “deviants” seek alternative ways of becoming socialized and educated. In contrast, others experience exclusion and social and intellectual alienation rather than maximizing the possibilities of being different and disruptive to the world.

Finding the courage to rebel.

Alternatively, many found the courage and resilience to persist in our rebellion and challenge the status quo. By being different, disruptive, and diverging from the norm, many of us changed our game and, ultimately, the world! People achieved this by thinking thoughts no one else considered and taking actions no one else pursued, flipping conventions on their heads and making the ordinary unexpected through difference and disruption.

The outdated labels and negative associations tied to being different and disruptive have become ingrained in the organizational mindset through schools and educational institutions. These continue to create paralyzing, fear-driven responses to embracing change and adopting innovation. This often hinders organizations from fully embracing people’s collective intelligence, developing the skills and maximizing the possibilities and creativity that disruption, diversity, inclusion, and difference present:

Diversity, inclusion, difference, and disruption are essential tools for thinking differently in ways that change the business landscape!
Disruptive, deviant and diverse teams that differ significantly and challenge the status quo can think the unthinkable, surprising the world with new inventions and unexpected solutions through their disruptive, collaborative, and creative thinking strategies, which are crucial for innovation success.

Being the disruptive change

Choosing the self-disruption path forces you to climb steep foothills of new information, relationships, and systems to take the first steps toward becoming the change you wish to see in the world.

Reframing Disruption

For many, even the word ” disruption ” is perceived as unfavorable and intimidating. When we were confronted at school by disruptive students, we would duck for cover to avoid the teacher’s wrath. Similarly, in group and team projects where one person opposes, argues, dominates the conversation, and doesn’t pay attention to or listen to anyone else’s opinions, we tend to stay silent and disengage from the discussion.

Many situations and problems require changes, upgrades, or removal of systems or processes, which disrupt the norm. The global pandemic significantly disrupted the traditional 9:00 am to 5:00 pm office workday, leading to the advantages of more flexible work environments where people have adapted to numerous challenges and forged a new working world.

This prompts us to reconsider how we might reframe disruption from its typical definition.

Original Definition of Disruption (Oxford Dictionary): “Disturbance or problems which interrupt an event, activity, or process.”“Radical change to an existing industry or market due to technological innovation” Reframing Disruption“An opening, doorway and threshold for intentionally disturbing or interrupting an event, activity, or process positively, constructively to effect radical changes that contribute towards the common good (people, profit and planet) differently.

Yet complacent, inwardly focused, conventional business methods result only in continuous or incremental disturbances or changes. In contrast, being different and safely disruptive to activate profound interruptions to business as usual is required to transform the business game.

Disruption without a positive, constructive, value-adding intent and relevant context makes people fearful and anxious. Many individuals have blind spots regarding how their fear-driven learning or survival anxieties negatively affect their effectiveness and productivity. They may even attempt to mask their fears and learning shortcomings by pretending to know things they don’t.

It starts with disrupting yourself.

Personal or self-disruption opens pathways for self-discovery, self-transformation, and innovation in a volatile and chaotic world where disruptive change is constant and inevitable.

This involves becoming emotionally energized and mentally stimulated by engaging in a journey of continuous discovery that maximizes the value and benefits of being different and disruptive. It includes a commitment to ongoing learning and a willingness to identify and take smart risks, reframe, and embrace constraints as catalysts for creative thinking. This approach involves failing fast to learn by doing, generating ground-breaking ideas, and taking unexpected and surprising right turns that lead to new ways forward. Particularly as we explore what AI can do and what it should do, we need to ensure that our courageous and rebellious traits support its development and applications to help build a brighter future for all.

Being different and disruptive shifts the needle, increasing the possibilities for game-changing reinventions and innovations. Co-creative relationships with AI can support us in restructuring and reimagining how we approach customers, markets, communities, and the world in unprecedented ways.

This is an excerpt from our upcoming book, Anyone Can Learn to Innovate, which is due for publication in late 2025.

Please find out more about our work at ImagineNation™.

It is a blended and transformational change and learning program that will give you a deep understanding of the language, principles, and applications of an ecosystem-focused, human-centric approach and emergent structure (Theory U) to innovation. It will also up-skill people and teams and develop their future fitness within your unique innovation context. Please find out more about our products and tools.

Image Credit: Pexels

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