Tag Archives: AI

7 Things Leaders Need to Know About Team AI Usage

GUEST POST from David Burkus

Leaders, we need to talk about intelligence.

By now you’ve–hopefully–started to take it as seriously as many leaders of industry have been. Either way you look at artificial intelligence, good or bad, it is here to stay. And so we need to start thinking of answers for several questions at the intersection of leadership and AI.

How can it be used effectively, not just to cut costs but to supercharge productivity? How can we use artificial intelligence to supplement our solid foundational leadership? Where should we NOT be using artificial intelligence?

It’s still early in the new world of artificial intelligence in the workplace. A lot of companies are delaying hiring, some are already cutting teams to embrace the optimistic promises AI will bring. But I don’t think we should be all in…yet.

I do know one thing to be true: Leaders using AI will quickly outpace leaders who don’t. And it’s important you get equipped, and in the right way.

Artificial intelligence will make good managers better, but not mediocre bosses better

They say a great actor can bring a C+ movie script up to a B+ or even an A if they are really good. But if a C+ actor is given a C+ script, then it’s going be a C+ movie. The same goes for artificial intelligence and leadership. You need to be a great leader before you start implementing artificial intelligence. AI will not bump up a mediocre manager and turn them into a great leader. It’s not some miracle machine. The truth is you need to have your foundations as a manager be solid first. AI is a good supplement for already successful managers.

Don’t use artificial intelligence to monitor

Often the first temptation of leaders experimenting with AI is to find a productivity AI tool out there, plug it into their IT systems, and start virtually looking over their team’s shoulders to monitor output. There are already dozens of stories…horror stories…of companies doing just that. And it’s not a good look, and deeply hurts morale.

If you need a technology tool to ensure your people are actually working when they say they are, you screwed up a long time ago—back during the hiring process.

And the current research on this isn’t in artificial intelligence’s favor. If AI is used to “collect and analyze data about workers,” then eight out of ten workers say AI use on them would definitely or probably make them feel inappropriately watched. In addition, about a one third of the public does not think AI would lead to equitable evaluations. A majority also agrees this would lead to the information collected about workers being misused (66%).

Artificial intelligence is good at turning anything and everything into a metric. Time is an easy metric. Number of sales calls is an easy metric. Messages on slack is an easy metric. How often you move your mouse is an easy, and terrifying, metric. But just because you have easy numbers to pull on your team doesn’t mean they are the right metrics to be pulling.

Leadership is really about people, not the metrics. How you solicit and give feedback is important. How you support and grow individual employees is important. Inspiring your team and being transparent is important. If you monitor your team endlessly, and your team knows that you’re outsourcing the process of harvesting that data with artificial intelligence, it creates distance between you and them.

And that ultimately works against you in the long run. People don’t like leaders who seem far from them and far from…reality.

Become fluent in artificial intelligence, or risk getting lost in translation

There’s some interesting data from Deloitte on AI that came out in Spring 2024. Organizations reporting “very high” Generative AI expertise expect to change their talent strategies even faster, with 32 percent already making changes. According to their findings, a lot of companies are redesigning work processes and changing workflows to integrate AI at different points.

You’re probably already experiencing this with Google, Microsoft and others integrating artificial intelligence into their core products like email and chats.

Another big focus is going to be on AI fluency. Deloitte found that 47 percent of respondents are dedicating time towards it. The leadership who gets educated on AI early, and keeps training consistently on as it develops, will be the best equipped to shepherd their teams going forward. It’s inevitable that career paths and job descriptions are going to evolve. It’s up to you to stay current.

You NEED to know what the technology is, how it’s being used, and how it’s helping those you’re serving. Be it clients, customers, the public–whomever. Saying you just typed some words into a text box and out came some more words….is not a good answer. Or a good look for you. You sound like you’re treating it like magic, when it’s actually just code.

Turn your conversations and meetings into a database

Middle managers spend a lot of time, arguably too much time, sending progress reports up the chain to the C-Suite and marching orders down to the individual contributors at the bottom. And there’s a fair amount of investigating to find out where things really stand, and time can be spent having to meet multiple people to get all the correct and current information. This is a time slog.

Meanwhile, there are dozens of AI tools now that just take notes. Notes from meetings. Notes from calls. They take the transcript and pair it down to the key takeaways, action items, attendance –a full brief for your records.

So, instead of asking someone to take notes during a meeting or having all your notes in the chat only to evaporate once the zoom call ends, you have a searchable document that you can reference, build on, and keep track of. New hires can use the database to catch up, and senior leaders can get a quick read of the progress and where everything stands.

Use AI/Chat bots to offload small, clerical questions

Here’s a situation: You run a small team and maybe you have a few new hires. You’re going to get a bunch of clerical questions from them over their first 90 days. That’s normal. That’s how it’s supposed to be. Onboarding takes time. “Who’s the point person for this? What’s so and so’s email from HR? What’s the policy for remote days at the company?”

Here’s where artificial intelligence can be really useful. Depending on the sort of chat platform you use– Slack, Teams, whatever, you could make a simple chat bot that you upload a full archive of the company’s policies and your own team norms, clerical details– everything new hires will probably ask you about. So, when those quick questions, quick stop-and-chats happen, the chat-bot can take care of that.

This shouldn’t subtract your time with your new hires. This just subtracts the lower stakes conversations. Now, you have more time for the high-level conversations with them. More coaching. More mentorship. More progression towards team goals. It might sound simple but…that’s because it is.

Use AI as an audience for decisions before taking them public

Being in a leadership role requires making decisive decisions. You include feedback and perspectives from your team as much as possible. Do the research. Talk to people. But then comes the actual decision making. And that is often just you, alone, with your thoughts.

Instead of making your pros and cons list, one practical thing to try is inputting proposed decisions or actions in an AI tool and then asking for all the counterpoints and possible outcomes.

You could even scale this out to your whole team. Ideally, teams should be leveraging task-focused conflict in team discussions to spark new and better ideas. But conflict can be tricky. So, what if AI is always the devil’s advocate? As your team is generating or discussing ideas, you can be feeding those ideas into an AI tool and asking it for counterpoints or how competitors might respond.

Don’t let it make the decision for you but do let it help guide you to possible solutions.

Get the legal clearance before going too deep

One last disclaimer: check with your human resources or your senior leadership, your informational technology (IT) people—or honestly, all of them—to know the boundaries you can work within when using AI tools.

Many of the tools out there are free and still in beta mode or come with a small fee. And most of the larger AI companies are taking whatever data you input and using it to better refine their product. Your company may have rules on the books about data privacy. Certainly, if you work in legal, healthcare, or government services, you’re dealing with sensitive data that may be protected.

Get clear answers before using any AI tools. Until someone above you with authority gives you the OK, you should probably just play with the tools on your own time with your own personal projects.

Conclusion

Artificial intelligence is just getting started in the workplace. And it’s all playing out in real time. If you’re a manager starting to get your hands dirty with these new tools, acknowledge to your team that this is all a work in progress and the norms around AI are likely to evolve. Be sure to keep the playing field level with your team. Practice that transparency, onboard everyone to the tools you’re using and that they can use and see where this takes you. Remember, AI, at its best, is here to enhance our human capabilities, not replace them.

AI will never take the place of a great boss…. but it might be better than being managed by a bad one.

Image credit: David Burkus

Originally published at https://davidburkus.com on September 9, 2024.

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

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

McKinsey is Wrong That 80% Companies Fail to Generate AI ROI — by Robyn Bolton
Back to Basics for Leaders and Managers — by Robyn Bolton
Growth is Not the Answer — by Mike Shipulski
The Most Challenging Obstacles to Achieving Artificial General Intelligence — by Art Inteligencia
Charlie Kirk and Innovation — by Art Inteligencia
You Just Got Starbucked — by Braden Kelley
Metaphysics Philosophy — by Geoffrey Moore
Invention Through Co-Creation — by Janet Sernack
Sometimes Ancient Wisdom Needs to be Left Behind — by Greg Satell
The Crisis Innovation Trap — by Braden Kelley and Art Inteligencia

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

This AI Creativity Trap is Gutting Your Growth — by Robyn Bolton
How Neuromorphic Computing Will Unlock Human-Centered Innovation — by Art Inteligencia
Why Context Engineering is the Next Frontier in AI — by Braden Kelley and Art Inteligencia
The Nordic Way of Leadership in Business — by Stefan Lindegaard
Three Strategies for Overcoming Change Resistance — by Greg Satell

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 Marketing Guide for Humanity’s Next Chapter

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How AI Changes Your Customers

Exclusive Interview with Mark Schaefer

Mark W Schaefer

LAST UPDATED: October 1, 2025 at 12:00PM

The rise of artificial intelligence isn’t just an upgrade to our technology; it’s a fundamental shift in what it means to be human and what it takes to lead a successful business. We’ve entered a new epoch defined by “synthetic humanity,” a term coined by Mark Schaefer to describe AI interactions that are indistinguishable from real human connection. This blurring of lines creates an enormous opportunity, which Mark Schaefer refers to as a “seam” — a moment of disruption wide open for innovators. But as algorithms become more skilled at simulating empathy and insight, what must leaders do to maintain authenticity and relevancy? In this exclusive conversation, Mark Shaefer breaks down why synthetic humanity is the most crucial concept for leaders to grasp today, how to use AI as a partner rather than a replacement, and the vital role of human creativity in a world of supercharged innovation.

The Internet, Smartphones, Social Media, and Now AI, Have All Shifted Customer Expectations

Mark Schaefer is a globally-acclaimed author, keynote speaker, and marketing consultant. He is a faculty member of Rutgers University and one of the top business bloggers and podcasters in the world. How AI Changes Your Customers: The Marketing Guide to Humanity’s Next Chapter is his twelfth book, exploring what companies should consider when it comes to artificial intelligence (AI) and their customers.

Below is the text of my interview with Mark and a preview of the kinds of insights you’ll find in How AI Changes Your Customers presented in a Q&A format:

1. I came across the term ‘synthetic humanity’ fairly early on in the book. Why is this concept so important, and what are the most important aspects for leaders to consider?

“Synthetic humanity” is my term for describing the emerging wave of AI interactions that appear, sound, and even feel human — yet are not human at all. This is not science fiction. Already, chatbots can hold natural conversations, generate art, or simulate empathy in ways that blur the line between authentic and artificial.

For leaders, this matters because customers don’t care whether an experience is powered by code or carbon; they care about how it feels. If synthetic humanity can deliver faster, easier, and more personalized service, people will embrace it. The more machines convincingly mimic us, the more vital it becomes to emphasize distinctly human qualities like compassion, vulnerability, creativity, and trust.

Leaders must navigate two urgent questions: Where do we lean into automation for efficiency? And where do we intentionally preserve human touch for meaning? Synthetic humanity can scale interactions, but it cannot scale authenticity. The most successful brands will be those that strike this balance — leveraging AI’s strengths while showcasing the irreplaceable heartbeat of humanity.

2. We discuss disruption quite a bit here on this blog. Can you share a bit more with our innovators about ‘seams’ and the opportunities they create with AI or otherwise?

Throughout history, disruptions to the status quo, such as pandemics, wars, or economic recessions, can either sink a business or elevate it to new heights. Every disruption creates a seam — a moment where the fabric of culture, business, or belief rips just wide enough for an innovator to crawl through and create something new.

We might be living in the ultimate seam.

Google CEO Sundar Pinchai calls AI the most significant innovation in human history — more important than fire, medicine, or the internet. The power of AI seems absolute and threatening. For many, it’s terrifying.

Through my new book, I’m trying to get people to view disruption through a different lens: not fear, but immense possibility.

3. Given that AI has access to all of our accumulated wisdom, does it actually create unique insights and ideas, or will innovation always be left to the humans?

AI is extraordinary at remixing existing content. It can scan millions of data points, connect patterns we might miss, and surface possibilities at lightning speed. That feels like insight, and sometimes it is. However, there is a crucial distinction: AI doesn’t truly care. It lacks context, longing, and lived experience.

Innovation often begins with a problem that aches to be solved or a vision that comes from deep within human culture. AI can suggest ten thousand options, but only a person can say, “This one matters because it touches our values, our customers, our future.”

So the real power is in the partnership. AI accelerates discovery, clears away routine work, and even provokes us with new connections. Humans bring the spark of meaning, the intuition, and the courage to act on something that has never been tried before. Innovation is not being replaced. It is being supercharged. In my earlier book “Audacious: How Humans Win in an AI Marketing World,” I note that the bots are here, but we still own crazy!

This is a time for humans to transcend “competent.” Bots can be competent and ignorable.

4. Do you have any tips for us mere mortals on how to productively use AI without developing creative and intellectual atrophy?

Yes, and it starts with how you frame the role of AI in your life. If you treat it as a replacement, you risk letting your creative muscles go slack. If you treat it as a partner, you can actually get stronger.

Here are a few practical approaches. First, use AI to stretch your perspective, not to finish your work for you. Ask it to give you ten angles on a problem, then choose one and make it your own. Second, set boundaries. Write your first draft by hand or sketch ideas before you ever touch a prompt. Let AI react to your thinking, not define it. Third, use the tool to challenge yourself. Feed it your work and ask, “What am I missing? Where are my blind spots?”

Most importantly, keep doing hard things. Struggle is where growth happens. AI can smooth the path, but sometimes you need the climb. Treat the technology as a coach, not a crutch, and you will come out sharper, faster, and even more creative on the other side.

5. I’ve heard a little bit about AI literacy. What are some of the critical aspects that we should all be aware of or try to learn more about?

There are a few critical aspects everyone should know. First, bias. AI models are trained on human data, which means they inherit our blind spots and prejudices. If you don’t recognize this, you may mistake bias for truth. Second, limits. AI is confident even when it is wrong. Knowing how to fact-check and verify is essential. Third, prompting. The quality of your input shapes the quality of the output, so learning how to ask better questions is a new core skill.

Finally, ethics. Just because AI can do something does not mean it should. We all need to be asking: How does this affect privacy, autonomy, and trust?

AI literacy isn’t about becoming a coder. It is about being a thoughtful user, a skeptic when needed, and a leader who understands both the promise and the peril of these tools.

6. What do companies and sole proprietors worried about falling below the fold of the new AI-powered search results need to change online to stay relevant and successful?

I have many practical ideas about this in the book. In short, the old game of chasing clicks and keywords is fading. AI-powered search doesn’t just list links, it delivers answers. That means the winners will be those whose content and presence are woven deeply enough into the digital fabric that the algorithms can’t ignore them.

This requires a shift in focus. Instead of creating content that only ranks, create content that is referenced, cited, and trusted across the web. Build authority by being the source others turn to. Make your ideas so distinct and valuable that they become part of the training data itself. We are entering a golden age for PR!

It also means doubling down on brand signals that AI can’t manufacture. Human stories, original research, strong communities, and unique perspectives will travel farther than generic blog posts. And remember, AI models reward freshness and relevance, so showing up consistently matters.

The book also covers what I call “overrides.” If you create a meaningful, loyal relationship with customers and word of mouth recommendations, that will override the AI recommendations. We consider AI recommendations. We ACT on human recommendations.

7. ‘Weaponizing kindness’ was a terrifying headline I stumbled across in your book. What do organizations need to consider when using AI to interact with customers and what traps are out in front of them?

That phrase is unsettling for a reason. AI can mimic empathy so well that it risks crossing into manipulation. Imagine a chatbot that remembers your child’s name, mirrors your mood, or expresses concern in just the right tone. Done responsibly, that feels like service. Done carelessly, it feels like exploitation.

Organizations need to recognize that kindness delivered at scale is powerful, but if it is hollow or purely transactional, customers will sense it. The first trap is confusing simulation with sincerity. Just because an AI can sound caring does not mean it actually cares. The second trap is overreach. Using personal data to create hyper-tailored interactions can quickly slip from helpful to creepy.

The safeguard is transparency and choice. Be clear about when a customer is interacting with AI. Use technology to enhance human care, not replace it. Always provide people with a way to connect with a real person.

Kindness is a sacred trust in business. Weaponize it, and you erode the very loyalty and love you are trying to build. Use it authentically, and you create relationships no machine can ever replicate.

8. What changing customer expectations (thanks to AI) might companies easily overlook and pay a heavy price for?

One of the biggest shifts is speed. Customers already expect instant answers, but AI raises the bar even higher. If your competitor offers a seamless, AI-powered interaction that solves a problem in seconds, your slower, clunkier process will feel intolerable.

Another overlooked expectation is personalization. People are starting to experience products, services, and recommendations that feel almost eerily tailored to them. That sets a new standard. Companies still delivering one-size-fits-all communication will look outdated. Don’t confuse “personalization” with “personal.”

Perhaps the most subtle change is trust. As customers realize machines can fake warmth and empathy, they will value genuine human touch even more. If every interaction feels synthetic, you risk losing trust, especially if you’re not transparent about it.

The price of ignoring these shifts is steep: irrelevance. Customers rarely complain about unmet expectations anymore; they simply leave. The opportunity is to stay alert, listen closely, and respond quickly as AI reshapes what “good enough” looks like. The companies that thrive will be those that not only keep pace with AI, but also double down on the irreplaceable humanity customers still crave.

9. What unintended consequences of AI do you think companies might face and may not be preparing for? (overcoming AI slander and falsehoods might be one – agree or disagree? Others?)

I agree. In fact, I predict in the book that we cannot foresee AI’s biggest impact yet, as it will likely be an unintended consequence of the technology’s use in an unexpected way.

Where could that occur? Maybe reputational risk at scale. AI systems will generate falsehoods with the same confidence they generate facts, and those errors can stick. A single hallucination about your company, repeated enough times, becomes “truth” in the digital bloodstream. Most companies are not prepared for the speed and reach of misinformation of this kind.

Another consequence is customer dependency. If people hand over more of their decisions to AI, they may lose patience for complexity or nuance in your offerings. That can push companies toward oversimplification, even when a richer human experience would build deeper loyalty.

There is also the cultural risk. Employees might over-rely on AI, quietly eroding skills, judgment, and creativity. A workforce that outsources too much thinking can become brittle in ways that only show up during a crisis.

The real challenge is that these consequences don’t announce themselves. They creep in. Which means leaders must actively audit how AI is being used, question where it might distort reality or weaken capability, and set up safeguards now. The companies that prepare will navigate disruption. The ones that ignore it will be blindsided.

10. Can companies make TOO MUCH use of AI? If so, what would the impacts look like?

Yes, and we will start seeing this more often. It is a pattern that has repeated through history — over-indexing on tech and then bringing the people back in!

When companies lean too heavily on AI, they risk draining the very humanity that makes them memorable. On the surface, it might seem like efficiency: faster service, lower costs, and greater scale. But underneath, the impacts can be corrosive. You might be messing with your brand!

Customers may feel manipulated or devalued if a machine drives every interaction. Even perfect personalization can feel hollow if it lacks genuine care. Second, trust erodes when people sense that a brand hides behind automation rather than showing up with real human accountability. Third, within the company, over-reliance on AI can weaken employee judgment and creativity, resulting in a workforce that follows prompts rather than breaking new ground.

The real danger is commoditization. If every company automates everything, then no company stands out. The winners will be those who know when to say, “This moment deserves a person.” AI should be an amplifier, not a replacement. Too much of it and you don’t just lose connection, you lose your soul.

Conclusion

Thank you for the great conversation Mark!

I hope everyone has enjoyed this peek into the mind of the man behind the inspiring new title How AI Changes Your Customers: The Marketing Guide to Humanity’s Next Chapter!

Image credits: BusinessesGrow.com (Mark W Schaefer)

Content Authenticity Statement: If it wasn’t clear above, the short section in italics was written by Google’s Gemini with edits from Braden Kelley, and the rest of this article is from the minds of Mark Schaefer and Braden Kelley.

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You Need to Know What Your Customers Think of AI

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You Need to Know What Your Customers Think of AI

GUEST POST from Shep Hyken

Ten years ago, only the most technologically advanced companies used AI — although it barely resembled what companies use today when communicating with customers — and it was very, very expensive. But not anymore. Today, any company can implement an AI strategy using ChatGPT-type technologies, often creating experiences that give customers what they want. But not always, which is why the information below is important.

The 2025 Findings

My annual customer service and customer experience (CX) research study surveys more than 1,000 U.S. consumers weighted to the population’s demographics of age, gender, ethnicity and geography. It included an entire group of questions focused on how customers react to and accept (or don’t accept) AI options to ask questions, resolve problems and communicate with a company or brand. Consider the following findings:

AI Success: Half of U.S. customers (50%) said they have successfully resolved a customer service issue using AI or ChatGPT-type technologies without needing human assistance. In 2024, only three out of 10 customers (32%) did so. That’s great news, but it’s important to point out that age makes a difference. Six out of 10 Gen-Z customers (61%) successfully used AI support versus just 32% of Boomers.
AI Is Far From Perfect: Half of U.S. customers (51%) said they received incorrect information from an AI self-service bot. Even with incredible improvement in AI’s capabilities, it still serves up wrong information. That destroys trust, not only in the company but also in the technology as a whole. A few bad answers and customers will be reluctant, at least in the near term, to choose self-service over the traditional mode of communication, the phone.
Still, Customers Believe: Four out of 10 customers (42%) believe AI and ChatGPT can handle complex customer service inquiries as effectively as humans. Even with the mistakes, customers believe AI solutions work. However, 86% of customers think companies using AI should always provide an option to speak or text with a real person.
The Phone Still Rules: It’s still too early to throw away phone support. My prediction is that it will be years, if ever, that human-to-human interactions completely disappear, which was proven when we asked, “When you have a problem or issue with a company, which solution do you prefer to use: phone or digital self-service?” The answer is that 68% of customers will still choose the phone over digital self-service. That number is highly influenced by the 82% of Baby Boomers who choose to call a company over any other type of digital support.
The Future Looks Strong For AI Customer Support: Six out of 10 customers (63%) expect AI-fueled technologies to become the primary mode of customer support. We asked the same question in 2021, and only 21% of customers felt this way.

The Strategy Behind Using AI For CX

Age Matters: As you can see from some of the above findings, there is a big generational gap between younger and older customers. Gen-Z customers are more comfortable, have had more success, and want more digital/AI interactions compared to older customers. Know your customer demographics and provide the appropriate support and communication options based on their age. Recognize you may need to provide different support options if your customer base is “everyone.”
Trust Is a Factor: Seven out of 10 customers (70%) have concerns about privacy and security when interacting with AI. Once again, age makes a difference. Trust and confidence with AI consistently decrease with age.

The Future of AI

As AI continues to evolve, especially in the customer service and experience world, companies and brands must find a balance between technology and the human touch. While customers are becoming more comfortable and finding success with AI, we can’t become so enamored with it that we abandon what many of our customers expect. The future of AI isn’t a choice between technology and humans. It’s about creating a blended experience that plays to the technology’s strengths and still gives customers the choice.

Furthermore, if every business had a 100% digital experience, what would be a competitive differentiator? Unless you are the only company that sells a specific product, everything becomes a commodity. Again, I emphasize that there must be a balance. I’ll close with something I’ve written before, but bears repeating:

The greatest technology in the world can’t replace the ultimate relationship-building tool between a customer and a business: the human touch.

This article was originally published on Forbes.com.

Image Credits: Google 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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Top 10 Human-Centered Change & Innovation Articles of July 2025

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

Three Executive Decisions for Strategic Foresight Success or Failure — by Robyn Bolton
3 Secret Saboteurs of Strategic Foresight — by Robyn Bolton
Five Unsung Scientific Discoveries Driving Future Innovation — by Art Inteligencia
Unblocking Change — by Mike Shipulski
Why Elastocalorics Will Redefine Our World — by Art Inteligencia
People Will Be Competent and Hardworking – If We Let Them — by Greg Satell
The Unsung Heroes of Culture — by Braden Kelley and Art Inteligencia
Making it Safe to Innovate — by Janet Sernack
Strategic Foresight Won’t Save Your Company — by Robyn Bolton
Your Work Isn’t Transformative — by Mike Shipulski

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

Making People Matter in AI Era — by Janet Sernack
The Trust Network Knows — by Mike Shipulski
Fueling Competitive Advantage Through Continuous Experience Improvement — by Braden Kelley and Art Inteligencia
Overcoming the Fear of Innovation Failure — by Stefan Lindegaard
How Customer Advisory Boards Fuel World-Class Experiences — by Braden Kelley and Art Inteligencia

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

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Why Innovators Can’t Ignore the Quantum Revolution

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Why Innovators Can't Ignore the Quantum Revolution

GUEST POST from Art Inteligencia

In the world of innovation, we are always looking for the next big thing—the technology that will fundamentally change how we solve problems, create value, and shape the future. For the past several decades, that technology has been the classical computer, with its exponential increase in processing power. But a new paradigm is on the horizon, one that promises to unlock capabilities previously thought impossible: quantum computing. While it may seem like a distant, esoteric concept, innovators and business leaders who ignore quantum computing are doing so at their own peril. This isn’t just about faster computers; it’s about a complete re-imagining of what is computationally possible.

The core difference is simple but profound. A classical computer is like a single light switch—it can be either ON or OFF (1 or 0). A quantum computer, however, uses qubits that can be ON, OFF, or in a state of superposition, meaning it’s both ON and OFF at the same time. This ability, combined with entanglement, allows quantum computers to perform calculations in parallel and tackle problems that are intractable for even the most powerful supercomputers. The shift is not incremental; it is a fundamental leap in computational power, moving from a deterministic, linear process to a probabilistic, multi-dimensional one.

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Quantum as an Innovation Engine: Solving the Unsolvable

For innovators, quantum computing is not a threat to be feared, but a tool to be mastered. It provides a new lens through which to view and solve the world’s most complex challenges. The problems that are “hard” for classical computers—like simulating complex molecules, optimizing global supply chains, or cracking certain types of encryption—are the very problems where quantum computers are expected to excel. By leveraging this technology, innovators can create new products, services, and business models that were simply impossible before.

Key Areas Where Quantum Will Drive Innovation

Revolutionizing Material Science: Simulating how atoms and molecules interact is a notoriously difficult task for classical computers. Quantum computers can model these interactions with unprecedented accuracy, accelerating the discovery of new materials, catalysts, and life-saving drugs in fields from energy storage to pharmaceuticals.
Optimizing Complex Systems: From optimizing financial portfolios to routing delivery trucks in a complex network, optimization problems become exponentially more difficult as the number of variables increases. Quantum algorithms can solve these problems much faster, leading to incredible efficiencies and cost savings.
Fueling the Next Wave of AI: Quantum machine learning (QML) can process vast, complex datasets in ways that are impossible for classical AI. This could lead to more accurate predictive models, better image recognition, and new forms of artificial intelligence that can find patterns in data that humans and classical machines would miss.
Securing Our Digital Future: While quantum computing poses a threat to current encryption methods, it also offers a solution. Quantum cryptography promises to create uncrackable communication channels, leading to a new era of secure data transmission.

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Case Study 1: Accelerating Drug Discovery for a New Tomorrow

A major pharmaceutical company was struggling to develop a new drug for a rare disease. The traditional method involved months of painstaking laboratory experiments and classical computer simulations to model the interactions of a new molecule with its target protein. The sheer number of variables and possible molecular configurations made the process a slow and expensive trial-and-error loop, often with no clear path forward.

They partnered with a quantum computing research firm to apply quantum simulation algorithms. The quantum computer was able to model the complex quantum mechanical properties of the molecules with a level of precision and speed that was previously unattainable. Instead of months, the simulations were run in days. This allowed the human research team to rapidly narrow down the most promising molecular candidates, saving years of R&D time and millions of dollars. The quantum computer didn’t invent the drug, but it acted as a powerful co-pilot, guiding the human innovators to the most probable solutions and dramatically accelerating the path to a breakthrough.

This case study demonstrates how quantum computing can transform the bottleneck of complex simulation into a rapid discovery cycle, augmenting the human innovator’s ability to find life-saving solutions.

Case Study 2: Optimizing Global Logistics for a Sustainable Future

A global shipping and logistics company faced the monumental task of optimizing its entire network of ships, trucks, and warehouses. Factors like fuel costs, weather patterns, traffic, and delivery windows created a mind-bogglingly complex optimization problem. The company’s classical optimization software could only provide a suboptimal solution, leading to wasted fuel, delayed deliveries, and significant carbon emissions.

Recognizing the limitations of their current technology, they began to explore quantum optimization. By using a quantum annealer, a type of quantum computer designed for optimization problems, they were able to model the entire network simultaneously. The quantum algorithm found a more efficient route and scheduling solution that reduced fuel consumption by 15% and cut delivery times by an average of 10%. This innovation not only provided a significant competitive advantage but also had a profound positive impact on the company’s environmental footprint. It was an innovation that leveraged quantum computing to solve a business problem that was previously too complex for existing technology.

This example shows that quantum’s power to solve previously intractable optimization problems can lead to both significant cost savings and sustainable, planet-friendly outcomes.

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The Innovator’s Call to Action

The quantum revolution is not a distant sci-fi fantasy; it is a reality in its nascent stages. For innovators, the key is not to become a quantum physicist overnight, but to understand the potential of the technology and to start experimenting now. Here are the steps you must take to prepare for this new era:

Educate and Evangelize: Start a dialogue about quantum computing and its potential applications in your industry. Find internal champions who can explore this new frontier and evangelize its possibilities.
Find Your Partners: You don’t have to build your own quantum computer. Partner with academic institutions, research labs, or quantum-as-a-service providers to start running pilot projects on a cloud-based quantum machine.
Identify the Right Problems: Look for the “intractable” problems in your business—the optimization challenges, the material science hurdles, the data analysis bottlenecks—and see if they are a fit for quantum computing. These are the problems where a quantum solution will deliver a true breakthrough.

The greatest innovations are born from a willingness to embrace new tools and new ways of thinking. Quantum computing is the most powerful new tool we have ever seen. For the innovator of tomorrow, understanding and leveraging this technology will be the key to staying ahead. The quantum leap is upon us—are you ready to take it?

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

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