Is All Publicity Good Publicity?

Some Insights from Cracker Barrel

GUEST POST from Pete Foley

The Cracker Barrel rebrand has certainly created a lot of media and social media attention. Everything happened so fast that I have had to rewrite this introduction twice in as many days. Originally written when the new logo was in place, it has subsequently been withdrawn and replaced with the original one.

It’s probably been a expensive, somewhat embarrassing and sleepless week for the Cracker Barrel management team. But also one that generated a great deal of ‘free’ publicity for them. You could argue that despite the cost of a major rebranding and de-branding, this episode was priceless from a marketing penetration perspective. There is no way they could have spent enough to generate the level of media and social media they have achieved, if not necessarily enjoyed.

But of course, it raises the perennial question ‘is all publicity good publicity?’ With brands, I’d argue not always. For certain, both good and bad publicity adds to ‘brand fluency’ and mental availability. But whether that is positively or negatively valanced, or triggers implicit or explicit approach or avoid responses is less straightforward. A case in point is of course Budweiser, who generated a lot of free media, but are still trying to drag themselves out of the Bud Light controversy.

Listening to the Customer: But when the dust settles, I suspect that Cracker Barrel will come out of this quite well. They enjoyed massive media and social media exposure, elevating the ‘mindshare’ of their brand. And to their credit, they’ve also, albeit a little reluctantly, listened to their customers. The quick change back to their legacy branding must ave been painful, but from a customer perspective, it screams ‘I hear you, and I value you’.

The Political Minefield. But there is some lingering complexity. Somehow the logo change became associated with politics. That is not exactly unusual these days, and when it happens, it inevitably triggers passion, polarization and outrage. I find it a quite depressing commentary on the current state of society that a restaurant logo can trigger ‘outrage. But like it or not, as change agents, these emotions, polarization and dubious political framing are a reality we all have to deal with. In this case, I personally suspect that any politically driven market effects will be short-lived. To my eye, any political position was unintentional, generated by social media rather than the company, and the connection between logo design and political affiliation is at best tenuous, and lacks the depth of meaning typically required for persistent outrage. The mobs should move on.

The Man on the Moon: But it does illustrate a broader problem for innovation derived from our current polarized society. If a logo simplification can somehow take on political overtones, pretty much any change or innovation can. Change nearly always comes with supporters and detractors, reflecting the somewhat contradictory nature of human behavior and cognition – we are change agents who also operate largely from habits. Our response to innovation is therefore inherently polarized, both as individuals and as a society, with elements of both behavioral inertia and change affinity. But with society deeply polarized and divided, it is perhaps inevitable that we will see connections between two different polarizations, whether they are logical or causal or not. We humans are pattern creators, evolved to see connections where they may or may not exist. This ability to see patterns using partial data protected us, and helped us see predators, food or even potential mates using limited information. Spotting a predator from a few glimpses through the trees obviously has huge advantages over waiting until it ambushes us. So we see animals in clouds, patterns in the stars, faces on the moon, and on some occasions, political intent where none probably exists.

My original intent with this article was to look at the design change for the logo from a fundamental visual science perspective. From that perspective, I thought it was quite flawed. But as the story quickly evolved, I couldn’t ignore the societal, social media and political element. Context really does matter. But if we step back from that, there are stillo some really interesting technical design insights we can glean.

1. Simplicity is deceptively complex. The current trend towards reducing complexity and even color in a brands visual language superficially makes sense. After all, the reduced amount of information and complexity should be easier for our brains to visually process. And low cognitive processing costs come with all sorts of benefits. But unfortunately it’s not quite that simple. With familiar objects, our brain doesn’t construct images from scratch, but instead takes the less intuitive, but more cognitively efficient route of unconsciously matching what we see to our existing memory. This allows us to recognize familiar objects with a minimum of cognitive effort, and without needing to process all of the visual details they contain. Our memory, as opposed to our vision, fills in much of the details. But this process means that dramatic simplification of a well established visual language or brand, if not done very carefully, can inhibit that matching process. So counterintuitively, if we remove the wrong visual cues, it can make a simplified visual language or brand more difficult to process than it’s original, and thus harder to find, at least for established customers. Put another way, the way our visual system operates, it automatically and very quickly (faster than we can consciously think) reduces images down to their visual essence. If we try to do that ourselves, we need to very clearly understand what the key visual elements are, and make sure we keep the right ones. Cracker Barrel has lost some basic shapes, and removed several visual elements completely, meaning it has likely not done a great job in that respect.

2. Managing the Distinctive-Simple Trade Off. Our brains have evolved to be very efficient, so as noted above, we only do the ‘heavy lifting’ of encoding complex designs into memory once. We then use a shortcut of matching what we see to what we already know, and so can recognize relatively complex but familiar objects with relatively little effort. This matching process means a familiar visual scene like the old Cracker Barrel logo is quickly processed as a ‘whole’, as opposed to a complex, detailed image. But unfortunately, this means the devil is in the details, and a dramatic simplification like Cracker Barrels can unintentionally remove many of the cues or signals that allowed us to unconsciously recognize it with minimal cognitive effort.

And the process of minimizing visual complexity can also remove much of what made the brand both familiar and distinctive in parallel. And it’s the relatively low resolution elements of the design that make it distinctive. To get a feel for this, try squinting at the old and new brand. With the old design, squinting loses the details of the barrel, or the old man, But the rough shape of them, and of the logo, and their relative positions remain. That gives a rough approximation of what our visual system feeds into our brain when looking for a match with our memory. Do the same with the new logo, and it has little or no consistency or distinctivity. This means the new logo is unintentionally making it harder for customers to either find it (in memory or elsewhere) or recognize it.

As a side effect, oversimplification also risks looking ‘generic’, and falling into the noise created by a growing sea of increasingly simplified logos. Now, to be fair, historical context matters. If information is not encoded into memory, the matching process fails, and a visual memory needs to be built from scratch. So if we were a new brand, Cracker Barrels new brand visual language might lack distinctivity, but it would certainly carry ease of processing benefits for new customers, whereas the legacy label would likely be too complex, and would quite likely be broadly deselected. But because the old design already owns ‘mindspace’ with existing customers, the dramatic change risks and removal of basic visual cues asks repeat customers to ’think’ at a more conscious level, and so potentially challenges long established habits. A major risk for any established brand

3. Distinctivity Matters. All visual branding represents a trade off. We need signal to noise characteristics that stand out from the crowd, or we are unlikely to be noticed. But we also need to look like we belong to a category, or we risk being deselected. It’s a balancing act. Look too much like category archetypes, and lack distinctivity, and we fade into the background noise, and appear generic. But look too different, and we stand out, but in a potentially bad way, by asking potential customers to put in too much work to understand us. This will often lead a customer to quickly de-select us. It’s a trade off where controlled complexity can curate distinctive cues to stand out, while also incorporating enough category prototype cues to make it feel right. Combine this with sufficient simplicity to ease processing fluency, and we likely have a winning design, especially for new customers. But it’s a delicate balancing act between competing variables

4. People don’t like change. As mentioned earlier, we have a complex relationship with change. We like some, but not too much. Change asks their brains to work harder, so it needs to provide value. I’m skeptical the in this case, it added commensurate value to the customer. And change also breaks habits. So any major rebrand comes with risk for a well established brand. But it’s a balancing act, and we should remain locked into aging designs forever. As the context we operate in changes, we need to ‘move with the times’, and remain consistent in our relationship with our context, at least as much as we remain consistent with our history.

And of course, there is also a trade off between a visual language that resonates with existing customers and one designed to attract new ones, as ultimately, virtually every brand needs both trial and repeat. But for established brands evolutionary change is usually the way to achieve reach and trial without alienating existing customers. Coke are the masters of this. Look at how their brand has evolved over time, staying contemporary, but without creating the kind of ‘cognitive jolts’ the Cracker Barrel rebrand has created. If you look at an old Coke advertisement, you intuitively know both that it’s old, but also that it is Coke.

Brands and Politics. I generally advise brands to stay out of politics. With a few exceptions, entering this minefield risks alienating 50% of our customers. And any subsequent ‘course corrections’ risk alienating those that are left. For a vast majorities of companies, the cost-benefit equation simply doesn’t work!

But in this case, we are seeing consumers interpreting change through a political lens, even when that was not the intent. But just because it’s not there doesn’t mean it doesn’t matter, as Cracker barrel is discovered. So I’m changing my advice from ‘don’t be political’ to ‘try and anticipate if you’re initiative could be misunderstood as political’. It’s a subtle, but important difference.

And as a build, marketers often try to incorporate secondary messages into their communication. But in todays charged political climate, I think we need to be careful about being too ‘clever’ in this respect. Consumer’s sensitivity to socio-political cues is very high at present, as the Cracker Barrel example shows. So if they can see political content where none was intended, they are quite likely to spot any secondary or ‘implicit’ messaging. So for example, an advertisement that features a lot of flags and patriotic displays, or one that predominately features members of the LBGTQ community both run a risk of being perceived as ‘making a political statement’, whether it is intended to or not. There is absolutely nothing wrong with either patriotism or the LBGT community, and to be fair, as society becomes increasingly polarized, it’s increasingly hard to create content that doesn’t somehow offend someone. At least without becoming so ‘vanilla’ that the content is largely pointless, and doesn’t cut through the noise. But from a business perspective, in today’s socially and politically fractured world, any perceived political bias or message in either direction comes with business risks. Proceed with caution.

And keep in mind we’ve evolved to respond more intensely to negatives than positives – Caution kept our ancestors alive. If we half see a coiled object in the grass that could be a garden hose or a snake, our instinct is to back off. If we mistake a garden hose for a snake to cost is small. But if we mistake a venomous snake for a garden hose, the cost could be high.

As I implied earlier, when consumers look at our content though specific and increasingly intense partisan lens, it’s really difficult for us to not be perceived as being either ‘for’ or ‘against’ them. And keep in mind, the cost of undoing even an unintended political statement is inevitably higher than the cost of making it. So it’s at very least worth trying to avoid being dragged into a political space whenever possible, especially as a negative. So be careful out there, and embrace some devils advocate thinking. Even if we are not trying to make a point, implicitly or explicitly, we need to step back and look at how those who see the world from deeply polarized position could interpret us. The ‘no such thing as bad publicity’ concept sits on very thin ice at this moment in time, where social media often seeks to punish more than communicate

Image credits: Wikimedia Commons

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How Neuromorphic Computing Will Unlock Human-Centered Innovation

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The Next Great Leap

How Neuromorphic Computing Will Unlock Human-Centered Innovation

GUEST POST from Art Inteligencia

I’ve long advocated that the most transformative innovation is not just about technology, but about our ability to apply it in a way that creates a more human-centered future. We’re on the cusp of just such a shift with neuromorphic computing.

So, what exactly is it? At its core, neuromorphic computing is a radical departure from the architecture that has defined modern computing since its inception: the von Neumann architecture. This traditional model separates the processor (the CPU) from the memory (RAM), forcing data to constantly shuttle back and forth between the two. This “von Neumann bottleneck” creates a massive energy and time inefficiency, especially for tasks that require real-time, parallel processing of vast amounts of data—like what our brains do effortlessly.

Neuromorphic computing, as the name suggests, is directly inspired by the human brain. Instead of a single, powerful processor, it uses a network of interconnected digital neurons and synapses. These components mimic their biological counterparts, allowing for processing and memory to be deeply integrated. Information isn’t moved sequentially; it’s processed in a massively parallel, event-driven manner.

Think of it like this: A traditional computer chip is like a meticulous librarian who has to walk to the main stacks for every single piece of information, one by one. A neuromorphic chip is more like a vast, decentralized community where every person is both a reader and a keeper of information, and they can all share and process knowledge simultaneously. This fundamental change in architecture allows neuromorphic systems to be exceptionally efficient at tasks like pattern recognition, sensor fusion, and real-time decision-making, consuming orders of magnitude less power than traditional systems.

It’s this leap in efficiency and adaptability that makes it so critical for human-centered innovation. It enables intelligent devices to operate for years on a small battery, allows autonomous systems to react instantly to their environment, and opens the door to new forms of human-machine interaction.

Case Study 1: Accelerating Autonomous Systems with Intel’s Loihi 2

In the world of autonomous vehicles and robotics, real-time decision-making is a matter of safety and efficiency. Traditional systems struggle with **sensor fusion**, the complex task of integrating data from various sensors like cameras, lidar, and radar to create a cohesive understanding of the environment. This process is energy-intensive and often suffers from latency.

The Intel Loihi 2 neuromorphic chip represents a significant leap forward. Researchers have demonstrated that by using spiking neural networks, Loihi 2 can handle sensor fusion with remarkable speed and energy efficiency. In a study focused on datasets for autonomous systems, the chip was shown to be over 100 times more energy-efficient than a conventional CPU and nearly 30 times more efficient than a GPU. This dramatic reduction in power consumption and increase in speed allows for quicker course corrections and improved collision avoidance, moving us closer to a future where robots and vehicles don’t just react to their surroundings, but intelligently adapt.

Case Study 2: Revolutionizing Medical Diagnostics with IBM’s TrueNorth

The field of medical imaging is a prime candidate for neuromorphic disruption. Diagnosing conditions from complex scans like MRIs requires the swift and accurate **segmentation** of anatomical structures. This is a task that demands high computational power and is often handled by GPUs in a clinical setting.

A pioneering case study on the IBM TrueNorth neurosynaptic system demonstrated its ability to perform spinal image segmentation with exceptional efficiency. A deep learning network implemented on the TrueNorth chip was able to delineate spinal vertebrae and disks more than 20 times faster than a GPU-accelerated network, all while consuming less than 0.1W of power. This breakthrough proves that neuromorphic hardware can perform complex medical image analysis with the speed needed for real-time surgical or diagnostic environments, paving the way for more accessible and instant diagnoses.

The Vanguard of Innovation: A Glimpse at the Leaders

The innovation in neuromorphic computing is being driven by a powerful confluence of established tech giants and nimble startups. Intel and IBM, as highlighted in the case studies, continue to lead with their research platforms, Loihi and TrueNorth, respectively. Their work provides the foundational hardware for the entire ecosystem.

However, the field is also teeming with promising newcomers. Companies like BrainChip are pioneering ultra-low-power AI for edge applications, enabling sensors to operate for years on a single charge. SynSense is at the forefront of event-based vision, creating cameras that only process changes in a scene, dramatically reducing data and power requirements. Prophesee is another leader in this space, with partnerships with major companies like Sony and Bosch for their event-based machine vision sensors. The Dutch startup Innatera is focused on ultra-low-power processors for advanced cognitive applications, while MemComputing is taking a unique physics-based approach to solve complex optimization problems. This dynamic landscape ensures a constant flow of new ideas and applications, pushing the boundaries of what’s possible.

In the end, neuromorphic computing is not just about building better computers; it’s about building a better future. By learning from the ultimate example of efficiency—the human brain—we are creating a new generation of technology that will not only perform more efficiently but will empower us to solve some of our most complex human challenges, from healthcare to transportation, in ways we’ve only just begun to imagine.

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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Seeing the Invisible

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Seeing the Invisible

GUEST POST from Mike Shipulski

It’s relatively straightforward to tell the difference between activities that are done well and those that are done poorly. Usually sub-par activities generate visual signals to warn us of their misbehavior. A bill isn’t paid, a legal document isn’t signed or the wrong parts are put in the box. Though the specifics vary with context, the problem child causes the work product to fall off the plate and make a mess on the floor.

We have tools to diagnose the fundamental behind the symptom. We can get to root cause. We know why the plate was dropped. We know how to define the corrective action and implement the control mechanism so it doesn’t happen again. We patch up the process and we’re up and running in no time. This works well when there’s a well-defined in place, when process is asked to do what it did last time, when the inputs are the same as last time and when the outputs are measured like they were last time.

However, this linear thinking works terribly when the context changes. When the old processes are asked to do new work, the work hits the floor like last time, but the reason it hits the floor is fundamentally different. This time, it’s not that an activity was done poorly. Rather, this time there’s something missing altogether. And this time our linear-thinker toolbox won’t cut it. Sure, we’ll try with all our Six Sigma might, but we won’t get to root cause. Six Sigma, lean and best practices can fix what’s broken, but none of them can see what isn’t there.

When the context changes radically, the work changes radically. New-to-company activities are required to get the new work done. New-to-industry tools are needed to create new value. And, sometimes, new-to-world thinking is the only thing that will do. The trick isn’t to define the new activity, choose the right new tool or come up with the new thinking. The trick is to recognize there’s something missing, to recognize there’s something not there, to recognize there’s a need for something new. Whether it’s an activity, a tool or new thinking, we’ve got to learn to see what’s not there.

Now the difficult part – how to recognize there’s something missing. You may think the challenging part is to figure out what’s needed to fill the void, but it isn’t. You can’t fill a hole until you see it as a hole. And once everyone agrees there’s a hole, it’s pretty easy to buy the shovels, truck in some dirt and get after it. But if don’t expect holes, you won’t see them. Sure, you’ll break your ankle, but you won’t see the hole for what it is.

If the work is new, look for what’s missing. If the problem is new, watch out for holes. If the customer is new, there will be holes. If the solution is new, there will be more holes.

When the work is new, you will twist your ankle. And when you do, grab the shovels and start to put in place what isn’t there.

Image credit: Pixabay

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Bringing a Hospitality Mentality to Customer Experience

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Bringing a Hospitality Mentality to Customer Experience

GUEST POST from Shep Hyken

Want to know the secret to creating an amazing customer experience? It’s simpler than you might think. I recently interviewed Michael Cecchi-Azzolina on my podcast, Amazing Business Radio, and his answer was refreshingly straightforward: “Be kind. Just be nice.”

Cecchi is the owner of Cecchi’s restaurant in New York City and author of Your Table is Ready: Tales of a New York City Maître D’. With nearly 40 years in the hospitality industry, he’s learned that kindness trumps everything else.

It’s Called “Hospitality” for a Reason

Cecchi noticed something interesting. Customers weren’t just thanking him for good service—they were specifically thanking him for his “hospitality.” This shift represents something important. People don’t just want service. They want to feel welcomed, valued and cared for.

Cecchi said, “This is new. I’ve been doing this for almost forty years, and I’ve only been hearing this the past year and a half or so.” The trend in what customers want and expect—for all industries, not just hospitality—is an experience that includes employees who are friendly, knowledgeable and helpful. That’s hospitality.

The Food or Service Question

Years ago, Cecchi interviewed for a job with legendary restaurateur Danny Meyer, who asked him a question that would stick with him for decades: “What’s more important, food or service?” After years of working with world-class chefs, Cecchi’s answer is clear: “It always came down to the service.”

His point is, you could have the best product in the world, but if your service is poor, customers won’t come back. As Cecchi put it, “If you have a surly waiter, a maître d’ who’s rude, a bartender who doesn’t acknowledge you … chances are you’re not coming back.”

My annual customer service and experience research backs this up. Every year, my survey finds that rudeness and apathy are the top reasons customers leave businesses. Sure, the product is important, but kindness — the opposite of rudeness and apathy — is what keeps them coming back.

We Don’t Sell Products—We Sell Experiences

One of my favorite quotes from our conversation was when Cecchi said, “We don’t sell food. We sell an experience. The experience begins when our front door opens. If the lights are perfect and the music is right and you’re getting this wonderful smile from the person at the door … you’re winning.” This is true for every business. You aren’t selling insurance, software or consulting services. You’re selling an experience wrapped around those things.

What does this look like in your business? What’s your equivalent of perfect lighting and the right music? It might be as simple as answering the phone with a smile in your voice or remembering a customer’s name.

The Broadway Principle

Cecchi’s first job out of high school was working at Playwrights Horizons. They had no money to pay him, but he wanted the experience. His boss knew Cecchi needed money to live, and it would be a short time before Cecchi would have to move on, so the boss got him a job at the restaurant across the street.

Cecchi compared restaurant service to Broadway theater: “This is a theater. We’ve got a script. We’ve got a set … those actors who were crushing it, they might have had a breakup that day or someone died in the family. You must put that aside.”

I call this the Broadway Principle. Legendary actor Richard Burton used to tell himself before performances (paraphrased): “Tonight, I want to be so good that I cheat the audience that was here last night.”

What if everyone, no matter their business or industry, approached customer interactions with that level of commitment?

Hiring for Heart, Not Just Experience

Cecchi’s hiring philosophy is not focused on the experience that employees have in the restaurant industry. Although that helps, he’s looking for people who genuinely love interacting with others. “I don’t hire people because of their resume,” he explained. “It takes a really special person to understand what real hospitality is.”

In 2011, I interviewed Jim Bush, former SVP of Worldwide Customer Experience at American Express. His hiring philosophy was similar. I’ll never forget his advice about hiring. Bush explained that given the choice between someone with 10 years of experience in a contact center or someone who worked at a restaurant, he’d hire the restaurant worker every time because they understood how to take care of people. In other words, they understand the hospitality mentality.

It’s All About Emotion

At its core, business is emotional. As Cecchi put it, “Restaurants are an emotional experience. People come in because they’re on a date, or celebrating a birthday or an anniversary.” Again, this isn’t just true for restaurants. Whether you’re buying a car, choosing a healthcare provider or selecting a software vendor, emotions drive decisions.

Cecchi shared a story that perfectly captures the power of hospitality: “I had six women at one table who’d been in the restaurant about 12 times. I jokingly said, ‘Thank God there are no other restaurants in New York City.’ And one of them looked at me and said, ‘Michael, there’s no restaurant in New York City that treats us the way you do here.’”

That story summarizes what we should all aim for—to be the one business that treats customers like no one else does. And it starts with something as simple as being kind, the core of the hospitality mentality.

Image Credits: Unsplash, Shep Hyken

This article originally appeared on Forbes.com

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Is Your Innovation Strategy on Track?

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Is Your Innovation Strategy on Track?

GUEST POST from Stefan Lindegaard

A solid innovation strategy is key to setting your organization up for long-term success. But how do you know if you’re on the right path? Here are a few signs that your innovation strategy is sound – and some KPI/metrics tips to guide you along the way.

1. Alignment with Corporate Strategy

A strong innovation strategy doesn’t stand alone—it’s integrated with the overall corporate strategy. While innovation teams often lean more visionary, the core business balances daily execution with future growth. Finding the “sweet spot” between these perspectives helps shape an innovation strategy that is bold yet achievable.

KPI/metrics: Strategic alignment score. Are innovation initiatives aligned with overall business goals and timelines? Does the strategy push far enough to create the future, but close enough to today’s realities?

2. Clarity on Innovation Type

It’s critical to know what type of innovation your organization is pursuing. Incremental innovation? Breakthrough or radical? Or perhaps you’re aiming for “in-between” innovation – meaningful advancement without the high stakes of disruptive change.

KPI/metrics: Track innovation project distribution across types (incremental, in-between, breakthrough). Are you focusing on the sweet spot for your capabilities?

3. Understanding of Ecosystem Dynamics

In-between innovation, where companies push beyond small improvements but not into complete market disruption, often benefits from ecosystem collaboration. This means tapping into external assets and building alliances that complement internal capabilities.

KPI/metrics: Number and quality of ecosystem partnerships. How many productive partnerships are helping you access needed assets or knowledge?

Six Innovation Models by BCG

4. Balance Between Vision and Reality

The innovation team may lean toward bold, future-shaping ideas, while the core business focuses on today’s realities. A sound strategy balances both perspectives – pushing boundaries while staying feasible within current business structures.

KPI/metrics: Time-to-market for innovation projects. Are projects moving efficiently from concept to market, indicating a practical balance between vision and execution?

5. Talent and Skills Alignment

A clear innovation strategy should inform talent requirements. Are the right skills and roles in place to support the type of innovation you’re aiming for?

KPI/metrics: Skills gap analysis for innovation-related roles. Does your team have the capabilities needed to bring your strategy to life?

6. Adaptability and Resilience

Innovation doesn’t follow a straight line. A sound strategy allows for flexibility and quick pivots based on market feedback, technology shifts, and emerging opportunities.

KPI/metrics: Percentage of innovation projects adapted or redirected based on feedback. How adaptable is your team in responding to change?

Your innovation strategy should guide you in defining what’s possible, aligning with your corporate strategy, and fostering a collaborative yet grounded approach. The right KPIs help you measure progress and ensure alignment with your strategic vision.

I hope this shorter post can help spur some reflection and raise some guiding questions for your efforts and initiatives.

Image Credit: Pexels

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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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Three Strategies for Overcoming Change Resistance

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Three Strategies for Overcoming Change Resistance

GUEST POST from Greg Satell

Max Planck’s work in physics changed the way we were able to see the universe. Still, even he complained that “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”

For most transformational efforts we need to pursue, we simply don’t have that kind of time. To drive significant change we have to overcome staunch resistance. Unfortunately, most change management strategies assume that opposition can be overcome through communication efforts that are designed to persuade.

This assumes that resistance always has a rational basis and clearly that’s not true. We all develop emotional attachments to ideas. When we feel those are threatened, it offends our dignity, identity and sense of self. If we are going to overcome our most fervent opponents we don’t need a better argument, we need a strategy. Here are three approaches that work:

Strategy 1: Designate An Internal Red Team

Resistance is never monolithic. While some people have irrational attachments based on their sense of identity and dignity, others are merely skeptical. One key difference between these two groups is that the irrational resistors rarely voice their opposition, but try to quietly sabotage change. The rational skeptics, on the other hand, are much more eager to engage.

While these are different groups, they often interact with each other behind the scenes. In many cases, it is the active, irrational opposition that is fueling the skeptics’ doubts. One useful strategy for dealing with this dynamic is to co-opt the opposition by setting up an internal red team to channel skepticism in a constructive way.

Red-teaming is a process in which an adversarial team is set up to poke holes in an operational or strategic plan. For example, red teams are used in airports and computer systems to see if they can find weaknesses in security. The military uses red teams to test battle plans. Perhaps most famously, a red team was used to help determine whether the conclusions that led to the raid on Osama bin Laden’s hideout were valid or if there was some other explanation.

Recruiting skeptics to be an internal red team provides two benefits. First, they can alert you to actual problems with your ideas, which you can then fix. Second, they not only voice their own objections, but also bring those of the irrational opposition out into the open (remember, irrational resisters rarely speak out.)

What’s key here is to make the distinction between rational skeptics and the irrational saboteurs. Engage with skeptics, leave the saboteurs to themselves.

Strategy 2: Don’t Engage And Quietly Gain Traction

Have you ever had this happen?: You’re in a meeting where things are moving slowly towards a consensus. Issues are discussed, objections raised and solutions devised. Toward the end of the meeting, just as things are shifting gears to next steps, somebody who had hardly said a word the whole time all of a sudden throws a hissy fit in the middle of the conference room and completely discredits themself.

There’s a reason why this happens. Remember saboteurs are not acting rationally. They have emotional attachments that they often can’t articulate, which is why they rarely give voice to their objections, but rather look for more discreet opportunities to derail the process. When they see things moving forward, they panic.

This doesn’t happen just in conference rooms. Those who are trying to sabotage change prefer to lurk in the background and hope they can quietly derail it. But when they see genuine progress being made, they will likely lash out, overreach and inadvertently further your cause.

This behavior is incredibly consistent. In fact, whenever I’m speaking to a group of transformation and change professionals and I describe this phenomenon to them, I always get people coming up to me afterwards. “I didn’t know that was a normal thing, I thought it was just something crazy that happened in our case!”

It’s important to resist the urge to respond to every attack. You don’t need to waste precious time and energy engaging with those who want to derail your initiative, which is more likely to frustrate and exhaust you than anything else. It’s much better to focus on empowering those who support change. Non-engagement can be a viable way to deal with opposition.

Strategy 3: Design A Dilemma Action

I once had a six-month assignment to restructure the sales and marketing operations of a troubled media company and the Sales Director was a real stumbling block. She never overtly objected, but would rather nod her head and then quietly sabotage progress. For example, she promised to hand over the clients she worked directly with to her staff, but never seemed to get around to it.

It was obvious that she intended to slow-walk everything until the six months were over and then return everything back to the way it was. As a longtime senior employee, she had considerable political capital within the organization and, because she was never directly insubordinate, creating a direct confrontation with her would be risky and unwise.

So rather than create a conflict, I designed a dilemma. I arranged with the CEO of a media buying agency for one of the salespeople to meet with a senior buyer and take over the account. The Sales Director had two choices. She could either let the meeting go ahead and lose her grip on the department or try to derail the meeting. She chose the latter and was fired for cause. Once she was gone, her mismanagement became obvious and sales shot up.

Dilemma actions have been around for at least a century. One early example was Alice Paul’s Silent Sentinels who picketed the Wilson White House with his own quotes in 1917. More recently, the tactic has been the subject of increasing academic interest. What’s becoming clear is that these actions share clear design principles that can be replicated in almost any context.

Key to the success of a dilemma action is that it is seen as a constructive act rooted in a shared value. In the case of the Sales Director, she had agreed to give up her accounts and setting up the meeting was aligned with that agreement. That’s what created the dilemma. She had to choose between violating the shared value or giving up her resistance.

How Change Really Happens

One of the biggest misconceptions about change is that it is an exercise in persuasion. Yet anyone who has ever been married or had kids knows how hard it can be to convince even a single person of something they don’t want to be convinced about. Seeking to persuade hundreds or thousands to change what they think or how they act is a tall order indeed.

The truth is that radical, transformational change is achieved when not when those who oppose it are convinced, but when they discredit themselves. It was the brutality of Bull Connor’s tactics in Birmingham that paved the way for the Civil Rights Act in 1964. It was Russia’s poisoning of Viktor Yushchenko in 2004 that set Ukraine on a different path. The passage of Proposition 8 in California created such controversy that it actually furthered the cause of same-sex marriage.

We find the same dynamic in our work with organizational transformations. Whenever you set out to make a significant impact, there will always be people who will hate the idea and seek to undermine it in ways that are dishonest, underhanded and deceptive. Once you are able to internalize that you are ready to move forward.

Through sound strategies, you can learn to leverage opposition to further your change initiative. You can co-opt those who are rationally skeptical to find flaws in your idea that can be fixed. For those who are adamantly and irrationally opposed to an initiative, there are proven strategies that help lead them to discredit themselves.

The status quo always has inertia on its side and never yields its power gracefully. The difference between successful revolutionaries and mere dreamers is that those who succeed anticipate resistance and build a plan to overcome it.

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

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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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The Future is Rotary

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Human-Centered Innovation in Rotating Detonation Engines

The Future is Rotary - Human-Centered Innovation in Rotating Detonation Engine

GUEST POST from Art Inteligencia

For decades, the pursuit of more efficient and sustainable propulsion systems has driven innovation in aerospace and beyond. Among the most promising advancements on the horizon is the Rotating Detonation Engine (RDE). This technology, which harnesses supersonic combustion waves traveling in a circular channel, offers the potential for significant leaps in fuel efficiency and reduced emissions compared to traditional combustion methods. However, the true impact of RDEs will not solely be defined by their technical prowess, but by a human-centered approach to their development and integration.

A Paradigm Shift for a Better Future

Human-centered change innovation focuses on understanding and addressing the needs and aspirations of people affected by technological advancements. In the context of RDEs, this means considering not only the engineers and scientists developing the technology but also the pilots, passengers, communities living near airports, and the planet as a whole. The potential benefits are immense:

Enhanced Fuel Efficiency: RDEs promise a significant reduction in fuel consumption, leading to lower operating costs and a smaller carbon footprint for air travel and other applications.
Reduced Emissions: More efficient combustion can translate to lower emissions of harmful pollutants, contributing to cleaner air and a healthier environment.
Increased Performance: The unique properties of detonation combustion could lead to more powerful and lighter engines, opening up new possibilities for aircraft design and space travel.
Economic Growth: The development and adoption of RDE technology will create new jobs in research, manufacturing, and maintenance, fostering economic growth.

Navigating the Winds of Change: Key Areas for Innovation

Realizing the full potential of RDEs requires a concerted effort across various domains, guided by a human-centered perspective:

Materials Science: Developing materials that can withstand the extreme temperatures and pressures of detonation combustion is crucial. This requires innovative research and collaboration between material scientists and engineers.
Engine Design and Control Systems: Creating robust and reliable RDE designs, along with sophisticated control systems to manage the complex detonation process, is essential for safe and efficient operation. Human factors engineering will play a vital role in designing intuitive and user-friendly control interfaces.
Manufacturing Processes: Scaling up the production of RDE components will require innovative manufacturing techniques that are both cost-effective and environmentally sustainable.
Infrastructure Development: The widespread adoption of RDEs may necessitate changes in fuel production, storage, and delivery infrastructure. Planning for these changes with community needs and environmental impact in mind is critical.
Education and Training: A new generation of engineers, technicians, and pilots will need to be trained in the principles and operation of RDE technology. Educational programs must adapt to incorporate this emerging field.
Regulatory Frameworks: Governments and regulatory bodies will need to develop new standards and certifications to ensure the safe and responsible deployment of RDE-powered systems. Engaging stakeholders in the development of these frameworks is crucial.

Companies and Startups to Watch

The landscape of RDE development is dynamic, with several established aerospace companies and innovative startups making significant strides. Keep an eye on organizations like GE Aerospace and Rolls-Royce which have publicly acknowledged their research into detonation technologies. Emerging startups such as Venus Aerospace are focusing on leveraging RDEs for high-speed flight, while others like Purdue University’s research labs often spin out promising technologies. These entities are pushing the boundaries of RDE technology and demonstrating potential pathways for its future application, always with an eye on the practical and societal implications of their work.

Case Studies in Human-Centered RDE Application

Case Study 1: Sustainable Air Travel

Imagine a future where short-haul flights are powered by RDEs running on sustainable aviation fuels (SAFs). The increased fuel efficiency of RDEs could significantly reduce the amount of SAF required per flight, making sustainable travel more economically viable and environmentally friendly. This benefits passengers through potentially lower ticket prices in the long run and contributes to the well-being of communities near airports by reducing noise and air pollution. Aircraft manufacturers would need to prioritize designs that minimize noise impact and ensure passenger comfort within the new performance parameters of RDE-powered aircraft. This human-centered approach ensures that the technological advancement directly addresses the need for sustainable and accessible air travel.

Case Study 2: Enhanced Emergency Response

Consider the application of compact, high-power RDEs in heavy-lift drones for disaster relief. Their potential for increased payload capacity and range could enable faster and more efficient delivery of critical supplies to disaster-stricken areas. For first responders and affected populations, this translates to quicker access to necessities like medical equipment, food, and shelter. Developing user-friendly drone control systems and ensuring the safe operation of these powerful machines in complex, real-world scenarios are key human-centered considerations. The focus here is on leveraging RDE technology to improve the speed and effectiveness of humanitarian aid, directly impacting the lives and safety of vulnerable individuals.

A Future Forged Together

The future of rotating detonation engines is not just about technological advancement; it’s about creating a future where propulsion is more efficient, sustainable, and ultimately benefits humanity. By embracing a human-centered approach to innovation, we can navigate the challenges and unlock the transformative potential of RDEs, ushering in a new era of cleaner, more powerful, and more responsible propulsion.

Image credit: Gemini

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Prioritization Drives Productivity

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Prioritization Drives Productivity

GUEST POST from Mike Shipulski

If you haven’t noticed, the pace and complexity of our work is ever-increasing. There’s more to do and there are more interactions among the players and the tasks. And though there’s more need for thinking and planning, there’s less time to do it. And the answer from company leadership – more productivity.

With the traditional view of productivity, it’s do more with less. That works for a while and then it doesn’t. And when you can no longer do more, the only remaining way to improve productivity is to do less.

If you try to do all five things and get four done poorly, wouldn’t it be more productive if you tried to do only three things and did them well? None of the three would have to touched up or redone. And none of the three would occupy your emotional bandwidth because they were done well and they’re not coming back to bite you. And because you focused on three things, you spent only three things worth of energy. Your life force is conserved and when you get home you still have gas in the tank.

If you get three things done each day, you’ll accomplish more than anyone else in the company. Don’t think so? Three things per day is fifteen things per week. And if you work fifty weeks per year, three things per day is one hundred and fifty things per year. (I hope you don’t work fifty weeks per year, I chose this number because it makes the math cleaner.)

It’s not easy to get three things done per day. With meetings, email, texts and the various collaboration platforms, you have almost zero uninterrupted time. And with zero uninterrupted time, you get about zero things done. And if I have to choose between getting three things done or zero things done, I choose three. It’s difficult to allocate the time to get three things done, but it’s possible.

Three things may not seem like enough things, but three is enough. Here’s why. You don’t do just any three things, you do three important things. You choose what you want to get done and you get them done. The key is to decide which three things you’ll get done and which three hundred you won’t. To do this, take some time at the end of the day to define tomorrow’s three things. That way, first thing, you’ll get after the right three things. It’s productivity through prioritization. You’ve got to do fewer things to get more done.

And you can still deliver on large projects with the three-things-per-day method. For large projects, most, if not all, of the day’s three things should be directly related to the project. Remember the math – you can do fifteen things per week on a large project. And it works for long projects, too. Do one thing per week on the long project and you will accomplish fifty things over the course of the year. When was the last time you completed fifty things on a project?

And if you think three things is too few, that’s fine. If you want to do more than three things, you can. Just make sure you know which three you’ll complete before moving on to the fourth. But, remember, you want to leave work with some gas still in the tank so you can do three things when you get home.

Image credit: Pexels

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