Category Archives: Innovation

You Are Probably Not Prepared to Innovate

GUEST POST from Greg Satell

Becoming a successful executive is a fairly linear path. You start at the bottom and learn to solve basic problems in your field or industry. As you gain experience and improve your skills you are given more responsibility, begin to manage teams and work diligently to set up the practices and processes to help your team succeed.

The best executives make those around them better, by fostering a positive work environment, minimizing drama and providing strategy and direction that will enable the team meet its objectives. That’s how you deliver consistent results and continue to rise up through the ranks to the top of your profession.

At some point, however, you need to do more than just plan and execute strategy, you have to innovate. Every business model is disrupted eventually. Changes in technology, competitive landscape and customer needs make that inevitable and, unfortunately, executive experience doesn’t equip your for it. Here’s four things that will help you make the shift from operations to innovation.

1. Learn How To Be The Dumbest Guy In The Room

Good executives are often the smartest guys in the room. Through years of experience solving tough problems, they learn to be masters of their craft and are able to mentor those around them. A great operational manager is a great coach, guiding others around them to achieve more than they thought they could.

Unfortunately, innovation isn’t about what you know, but what you don’t. It requires you to explore, push boundaries and venture into uncharted areas in which there often are no true experts. You’re basically flying blind, which can be incredibly uncomfortable, especially to those who have had a strong track record of success in a structured environment.

That’s why the first step to making the shift from operations to innovation is to learn how to become the dumbest guy in the room instead of the smartest. Admit to yourself that you don’t know what you need to succeed and begin to explore. Actively seek out those who know and understand things that you don’t.

Being the smartest guy in the room helps you operate smoothly, but being the dumbest guy in the room helps you learn. The best way to start is by seeking out new rooms to spend time in.

2. Create A Bias For Action

Operations thrive on predictability. People need to know what to expect and what’s expected of them so that things can run smoothly. Every great operation needs to coordinate activities between a diverse set of stakeholders, including team members, partners and customers. That level of interoperability doesn’t just happen by itself.

Over the years, a variety of methods, such as Total Quality Management (TQM) and Six Sigma have arisen that use rigorous statistical methods to optimize for established metrics. The idea is to hone processes continuously in order to elevate them to paragons of efficiency.

When you seek to innovate, however, established metrics are often of little use, because you are trying to do something new and change the basis of competition. Again, you are venturing into the unknown, doing things you and your organization have not developed the knowledge and skills to do well. Instead of seeking excellence, you need to dare to be crap.

The key to making this work is not to abandon all sense of restraint and accountability, but to manage risk by reducing scale. In an operational setting you always want to look for the largest addressable market you can find, but when you are trying to do something truly new, you need to find a hair on fire use case — a customer who needs a problem solved so badly that they are willing to work through the inevitable glitches and snafus with you.

3. Solve The Monkey First

Every good operational project has a roadmap, whether that is an ordinary budget, a project plan or a defined strategy. The early stages of a plan are usually the easiest. You want to get everybody on board, build momentum and then begin to tackle tougher problems. When you are trying to do something new and different, however, you often want to do exactly the opposite.

Every significant innovation involves something that’s never been done before, so you can’t be sure how long it will take or even if the core objectives can be achieved at all. So it’s best to get started working on the toughest problems early, because until you resolve those unknowns, the whole project is unworkable.

At Google’s X division, the company’s “moonshot factory,” the mantra is #MonkeyFirst. The idea is that if you want to get a monkey to recite Shakespeare on a pedestal, you’d better start by training the monkey, not building the pedestal, because training the monkey is the hard part. Anyone can build a pedestal.

Operational executives like to build pedestals so that they can show early progress against a timeline. Unfortunately, when you are striking out into the unknown, building a pedestal gets you nowhere. Unless you can actually train the monkey, working on the pedestal is wasted effort. You have to learn how to train monkeys.

4. Move from Metrics To Mission

Good operational executives sweat the numbers. They work within existing frameworks and hone operations to improve performance against established metrics. Yet when you are trying to do something truly new, established metrics often tell you little. The goal isn’t to play the game better, but to change it entirely.

In fact, established businesses often get disrupted precisely because they are focusing on outdated metrics. For example, when digital cameras first came out, they performed poorly by traditional standards of quality. They did, however, perform much better in terms of convenience and, as the quality of the pictures improved, replaced the earlier technology.

In a similar vein, while traditional brokerages focused on service, Charles Schwab offered minimal service at a far lower price. At first, it didn’t seem like a threat to incumbents, but as technology improved, it was able to improve service and keep the low flat fees. The model ended up transforming the industry.

So it’s important to not get blinded by metrics and focus on your mission. True innovation never happens in a straight line or proceeds at a measured pace. That’s why there is a basic tradeoff between innovation and optimization and very few people can do both. The best executives, however, learn how to bridge that gap.

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

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SpaceX is a Masterclass in Innovation Simplification

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GUEST POST from Pete Foley

This capture from a recent SpaceX tweet is a stunning visual example of smart innovation and simplification.

While I’m not even close to being a rocket scientist, and so am far from familiar with all of the technical details, I’ve heard that breakthroughs incorporated into this include innovative sensor design that allows for streamlined feedback loops. But this goes beyond just impressive technical innovation. To innovate at this level requires organizational and cultural flexibility as well as technical brilliance. That latter flexibility is probably far more broadly transferable and adoptable than specific advances in rocket science, and hence more useful to the broader innovation community. So let’s dig a little deeper into that space.

Secret Sauce? Organizationally SpaceX is well known for less formal hierarchies, passion, ownership and engineers working on the production floor. This hands on approach creates a different, but important kind of feedback, while passion feeds intrinsic motivation, ownership and engagement, which is so critical to consistent innovation.

Learning from Failure – An Innovation Superpower? But perhaps most important of all is the innovation culture. Within SpaceX there is a very clear willingness to experiment and learn from failure. Not lip service, or the sometimes half-hearted embrace of failure often found in large, bureaucratic organizations, where rewards and career progression often doesn’t reflect the mantra of learning by failing. This is an authentic willingness to publicly treat productive failure of individual launches as a learning success for the program, and to reward productive failure and appropriate risk taking. Of course, it’s not always easy to walk the talk of celebrating failure, especially in spacecraft design, where failures are often spectacular, public, and visual gold for the media. And no doubt this is compounded by Musk’s controversial public profile, where media and social media are often only too keen to highlight failures. But the visual of Raptor 3 is for me a compelling advertisement for authentically embedding learning by failure deeply into the DNA of an innovative organization.

Stretch Goals: Musk is famous for, and sometimes ridiculed for setting ambitious stretch goals, and for not always achieving them. But in a culture where failure is tolerated, or if done right, celebrated, missing a stretch goal is not a problem, especially if it propelled innovation along at a pace that goes beyond conventional expectation.

Challenging Legacy and ‘Givens’: Culturally, this kind of radical simplification requires the systematic challenge of givens that were part of previous iterations. You cannot make these kind of innovation leaps unless you are both willing and able to discard legacy technical and organizational structures.

At risk of kicking Boeing while it is down, it is hard not to contrast SpaceX with Boeing, whose space (and commercial aviation) program is very publicly floundering, and facing the potentially humiliating prospect of needing rescue from the more agile SpaceX program.

Innovation Plaque: But in the spirit of learning from failure, if we look a bit deeper, perhaps it should not be a surprise that Boeing are struggling to keep up. They have a long, storied, and successful history as a leader in aerospace. But history and leadership can be a blessing and a curse, as I know from P&G. It brings experience, but also bureaucracy, rigid systems, and deeply rooted culture that may or may not be optimum for managing change. Deep institutional knowledge can be a similar mixed blessing. It of course allows easy access to in-domain experience, and is key to not repeating past mistakes, or making naïve errors. But is also comes with an inherent bias towards traditional solutions, and technologies. Perhaps even more important is the organizationally remembered pain of past failures, especially if a ‘learn by failure’ culture isn’t fully embraced. Failure is good at telling us what didn’t work, and plays an important role in putting processes in place that help us to avoid repeating errors. But over time these ‘defensive’ processes can build up like plaque in an artery, making it difficult to push cutting edge technologies or radical changes through the system.

Balance is everything. Nobody wants to be the Space Cowboy. Space exploration is expensive, and risks the lives of some extraordinarily brave people. Getting the balance between risk taking and the right kind of failure is even more critical than in most other contexts. But SpaceX are doing it right, certainly until now. Whatever the technical details, the impact on speed, efficiency and $$ behind the simplification of Raptor 3 is stunning. I suspect that ultimately reliability and efficiency will also likely helped by increased simplicity. But it’s a delicate line. The aforementioned ‘plaque’ does slow the process, but done right, it can also prevent unnecessary failure. It’s important to be lean, but not ‘slice the salami’ too thin. Great innovation teams mix diverse experience, backgrounds and personalities for this reason. We need the cynic as well as the gung-ho risk taker. For SpaceX, so far, so good, but it’s important that they don’t become over confident.

The Elon Musk Factor: For anyone who hasn’t noticed. Musk has become a somewhat controversial figure of late. But even if you dislike him, you can still learn from him, and as innovators, I don’t think we can afford not to. He is the most effective innovator, or at least innovation leader for at least a generation. The teams he puts together are brilliant at challenging ‘givens’, and breaking out of legacy constraints and the ‘ghosts of evolution’. We see it across the SpaceX design, not just the engine, but also the launch systems, recycling of parts, etc. We also see an analogous innovation strategy in the way Tesla cars so dramatically challenged so many givens in the auto industry, or the ‘Boring company in my hometown of Las Vegas.

Ghosts of Evolution I’d mentioned the challenges of legacy designs and legacy constraints. I think this is central to SpaceX’s success, and so I think it’s worth going a little deeper on this topic. Every technology, and every living thing on our planet comes with its own ghosts. They are why humans have a literal blind-spot in our vision, why our bodies pleasure centers are co-located with our effluent outlets, and why the close proximity of our air and liquid/solid intakes lead to thousands of choking deaths every year. Nature is largely stuck with incrementally building on top of past designs, often leading to the types of inefficiency described above. Another example is the Pronghorn antelope that lives in my adopted American West. It can achieve speeds of close to 90 mph. This is impressive, but vastly over-designed and inefficient for it’s current environment. But it is a legacy design, evolved at a time when it was predated upon by long extinct North American Cheetah. It cannot simply undo that capability now that it’s no longer useful. So far, it’s survived this disadvantage, but it is vulnerable to both competition and changing environment simply because it is over-designed.

Bio-Inspiration: I’ve long believed we can learn a great deal from nature and bio-inspired design, but sometimes learning what not to do is as useful as ‘stealing’ usable insights. It’s OK to love nature, but also acknowledge that evolution has far more failures than successes. There are far, far more extinct species than living ones. And virtually every one was either too specialized, or lacked the ability to pivot and adapt in the face of changing context.

As innovators, we have unique option of creating totally new 2.0 designs, and challenging the often unarticulated givens that are held within a category. And we have the option of changing our culture and organizational structures too. But often we fail do so because we are individually or organizationally blind to legacy elements that are implicitly part of our assumptions for a category or a company. The fish doesn’t see the water, or at least not until it’s dangling from a hook. By then it’s too late. Whatever you think of Musk, he’s taught us it is possible to create innovation cultures that challenge legacy designs extremely effectively. It’s a lesson worth learning

Image credits: Twitter (via SpaceX)

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Racing Towards Electrical Vehicle Innovation

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Racing Towards Electrical Vehicle Innovation

GUEST POST from Art Inteligencia

Since the inception of automotive racing over a century ago, the roar of engines and the telltale scent of burning rubber have been synonymous with the thrill and excitement of motorsport. Yet, in 2014, the landscape began to transform dramatically with the launch of Formula E, an all-electric street racing series that has not only revolutionized the sport but also acted as a catalyst for innovation in the electric vehicle (EV) industry. This pivotal shift has paved the way for a convergence of sustainability, cutting-edge technology, and exhilarating competition on the asphalt. Let’s take a closer look at the evolution of Formula E from its inception to today, and delve into the remarkable advancements across Gen1, Gen2, Gen3, and the anticipated Gen4 cars.

The Genesis – Gen1: Sparking a New Era (2014-2017)

When Formula E made its debut in 2014, skepticism was rife. Could electric cars truly capture the imagination of racing enthusiasts? However, the Gen1 cars quickly silenced doubters with their impressive capabilities. These vehicles boasted a maximum power output of 200 kW (equivalent to about 268 horsepower), accelerating from 0 to 100 km/h in approximately three seconds. Despite their limitations—such as the need for mid-race car swaps due to battery constraints—the Gen1 cars showcased the immense potential of electric propulsion.

Here is a video of the inaugural race:

The Gen1 era highlighted the importance of efficient energy management, as teams and drivers grappled with balancing speed and battery life. Every race turned into a strategic battle of conservation versus performance, laying the groundwork for the monumental shifts that would follow.

Gen2: Revolutionizing Range and Power (2018-2022)

The arrival of Gen2 vehicles brought with it a surge of advancements that propelled Formula E into a thrilling new chapter. With an enlarged battery capacity, these cars could now complete entire races without the need for a mid-race swap. The power output increased to a maximum of 250 kW (around 335 horsepower), delivering improved acceleration and peak speeds.

In addition to increased power and range, Gen2 cars introduced the iconic Halo safety device—a crucial step in enhancing driver safety. The cars also introduced “Attack Mode,” which allowed drivers to momentarily access an extra boost of power, adding another layer of strategic depth to the races.

With a sleeker, more aggressive design, the Gen2 cars began to bridge the gap between traditional motorsport and futuristic innovation. Fans started to see Formula E as more than just an experiment; it was now a viable and exciting racing series in its own right.

Gen3: The Dawn of Efficiency and Sustainability (2023-Present)

The current era, marked by the introduction of Gen3 cars, represents a quantum leap in efficiency, technology, and sustainability. Gen3 cars boast an even greater power output—over 350 kW (roughly 470 horsepower)—and feature regenerative braking systems that can recover almost half of the energy consumed during a race. This innovation not only prolongs battery life but also significantly reduces the environmental impact of the races.

Moreover, Gen3 cars are designed with sustainability at their core. The car’s carbon footprint has been minimized with the use of sustainable and recyclable materials, aligning with Formula E’s mission to create a greener planet. The additional power has also made the races faster and more competitive, increasingly captivating audiences around the world.

Here is a video highlighting some of the new developments in the Gen3 car:

The Gen3 era underscores the sport’s commitment to a future where high performance and environmental responsibility coexist harmoniously. Formula E’s push towards using more sustainable materials and reducing emissions has set a new benchmark not just in racing but across the entire automotive industry.

Looking Ahead – Gen4: The Future Beckons

Anticipation is already building for the next leap forward with Gen4 cars, expected to hit the tracks in the not-so-distant future. While official specifications remain under wraps, the trajectory of innovation hints at even lighter, more powerful (boost from 350kw to 600kw), and more efficient vehicles (increase from 600kw to 700kw max regen). We can expect further advancements in battery technology, potentially doubling the range and enabling more aggressive and continuous racing.

Potential improvements in AI and autonomous driving technologies could further redefine the strategic and technical landscape of Formula E. The integration with smart city ecosystems, dynamic in-race adjustments, and real-time energy management are all buzzing as possible features of the Gen4 evolution.

Conclusion

The journey from Gen1 to Gen3 has shown how Formula E is not just a racing series but a transformative force, accelerating the adoption of electric vehicle technology and fostering a new age of sustainable racing. Each generation of cars has pushed the boundaries of what’s possible, marrying performance with efficiency and environmental stewardship.

As we race towards the Gen4 era, Formula E continues to encourage global automakers to innovate, experiment, and excel. In doing so, it not only redefines the landscape of motorsport but also paves the way for a greener, faster, and more electrifying future for all.

The evolution of Formula E demonstrates that the future of racing—and perhaps the automotive world at large—is electric. Hold on tight, because the checkered flag heralds not the end of the race but the beginning of an electrifying new journey.

Image credit: FIA Formula E, Wikimedia Commons – Nico Müller (SUI, ABT Cupra Formula E Team)

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Innovation or Not – Oklahoma State Football Helmets Seek to Revolutionize NIL

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GUEST POST from Art Inteligencia

In the rapidly changing landscape of collegiate athletics, the Name, Image, and Likeness (NIL) revolution is creating both challenges and opportunities. Oklahoma State University (OSU) is taking a bold step to embrace this shift by introducing a unique, possibly groundbreaking concept – integrating NIL into their football helmets.

The Concept

OSU’s idea is straightforward yet revolutionary: use the football helmet as a platform for NIL branding. Instead of traditional school logos or player numbers, the helmets will display personal brand logos and endorsements. This turns every game into a live advertisement for players, directly tying their on-field performance to their marketability.

Key Elements of the Concept

Player-Centric Branding: Helmets will feature personalized logos or endorsements chosen by players, subject to NIL agreements.
Dynamic Advertising: The design can change weekly or according to the duration of individual endorsement deals.
Visibility and Impact: Enhances the visibility of players’ personal brands during high-visibility game broadcasts.

Potential Benefits

This innovative approach could have several major advantages:

For Players

Increased earning potential through personalized brand endorsements.
Enhanced marketability by combining athletic performance with brand visibility.
Empowerment in controlling their personal brand narrative.

For Schools

Attracting top talent by offering a unique platform for NIL opportunities.
Strengthening alumni and fan base connection through support of player-driven initiatives.
Potential new revenue streams through partnerships with brands aligned with athletes.

Challenges and Considerations

However, this initiative is not without its challenges. Key concerns include:

Ensuring fair and equitable opportunities for all players, regardless of their profile or position on the team.
Navigating NCAA regulations and maintaining compliance with NIL guidelines.
Managing potential conflicts between school sponsorship agreements and individual player deals.
Addressing potential aesthetic criticisms from traditionalists who prefer team-centric designs.

Integrating QR Codes for Enhanced Engagement

OSU is not stopping at logo-based branding; they are keen on leveraging technology to amplify the impact of their NIL initiative. The next phase of this bold experiment involves integrating QR codes onto the helmets and distributing them at local bars and restaurants.

Details of the QR Code Initiative

Helmet QR Codes: Each player’s helmet will sport a unique QR code that fans can scan with their smartphones. This will redirect them to the player’s personalized NIL content, including social media profiles, merchandise, and sponsorship deals.
Local Business Partnerships: QR codes will also be placed on tables at bars and restaurants around Stillwater, Oklahoma. This aims to create a seamless connection between the local business community and the athletic program.

Benefits of QR Code Integration

Increased Fan Interaction: Fans can engage more deeply with their favorite players by easily accessing content and offers through QR scans.
Boosting Local Economy: Encouraging local fans and visitors to frequent businesses supporting OSU athletics helps keep revenue within the community.
Augmented Revenue Streams: Creates additional opportunities for NIL deals, as businesses directly benefit from increased foot traffic and fan engagement.

Conclusion

OSU’s innovative approach to integrating NIL into football helmets represents a bold step into the future of collegiate athletics. It exemplifies the evolving dynamics of sports marketing, where athletes are increasingly seen as individual brands. While there are challenges to address, this initiative underscores the importance of embracing change and fostering creativity in an ever-competitive landscape.

Whether this will be a fleeting experiment or a long-lasting transformation remains to be seen. For now, OSU is at the forefront of redefining how college athletes can capitalize on their fame and pave the way for a more equitable sharing of revenues generated by their incredible talents and efforts.

Innovation or not, the journey of NIL in sports has only just begun, and Oklahoma State’s helmets might just be the catalyst for the revolution we’ve been waiting for.

Innovation or not?

Image credit: Oklahoma State University Athletics via ArizonaSports.com

This photo provided by Oklahoma State Athletics shows a QR code on an Oklahoma State NCAA college football helmet, Thursday, Aug. 15, 2024, at Boone Pickens Stadium in Stillwater, Okla. (Bruce Waterfield/OSU Athletics via AP)

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The Coming Leadership Confidence Crisis

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Executive Trust in Their Teams is Plummeting – Here is How to Rebuild It

The Coming Leadership Confidence Crisis

GUEST POST from Robyn Bolton

“Trust no one. Suspect everyone.” Great advice if you’re an MI6 agent trying to uncover a spy at the height of the Cold War. Not great advice if you’re a senior executive responsible for leading a team to deliver record results. So, when a report titled “Leadership Confidence Falls to Three-Year Low” was published, I hoped it was clickbait. So I clicked.

Things only got worse.

While two-thirds of CEOs believe that their teams role model the right culture and behaviors, work together effectively as a team, and effectively embrace change, everyone else disagrees. Only about half the C-suite believes their teams work together well, are role models, and embrace change. The lower in the organization you go, the lower those percentages get.

Why confidence is at an all-time low

In a word – change. Neither humans nor financial markets like change, and that’s all we’ve experienced for the past four years. “From the conflicts in Ukraine and the Middle East and their destabilizing effects on the world, to inflation, rising interest rates, and the launch of ChatGPT igniting massive interest in generative AI, the leadership landscape has been far from quiet. What’s more, nearly half of the world’s population is set to head to the polls for what many are calling a ‘super election year.’”

None of this is the executive team’s fault, but the relentless nature of depressing and destabilizing news wears everyone down. As a result, people have less patience and empathy and are quicker to anger, judge, and blame others. Senior execs are people, too. And they’re taking their exhaustion out on the people they spend the most time with – their teams.

What you can do about it

If you have the power to stop the wars, improve the financial markets, quell GenAI fears, and ensure that democracy reigns, please use that power now. (Also, what have you been waiting for?)

If you do not have such powers, there is still something you can do: Build trust.

Researchers found that leaders of high-performing organizations are 8x more likely to feel that their teams practice and role model high levels of trust in all their interactions across the organization. But the teams won’t practice and role model trust if you don’t set the example through:

Inclusive, transparent, and vulnerable communication – Most of us grew up in cultures where information is power, so it is hard to build a habit of sharing information with everyone on the team, especially if it isn’t good news. But if you want your people to work together as a team, you can’t create cliques or pick and choose the information you share. There is no trust where there are Haves and Have Nots.
Lead by listening and collaborating – In case you haven’t noticed, command and control styles of management don’t work anymore. The people on your teams are experienced adults with good ideas. Treat them like adults, value their experience, and listen to their ideas. You’ll be pleasantly surprised by what you hear and earn.
Be consistent – If one of the causes of the problem is change and you want to be part of the solution, do the opposite – be consistent. Yes, things can change, but who you are, the values you role model, and how you treat people shouldn’t. When things change (and they will), remember that decisions made with data should only be unmade with data. Then, communicate those changes broadly, transparently, and honestly (see #1)

What will you do about it?

Rebuilding trust within your team isn’t a quick fix; it’s an ongoing process that requires commitment and consistency. By being transparent, authentic, and reliable, fostering open communication, and empowering your team, you can create a high-trust environment that drives success.

What steps are you taking to (re)build trust within your teams? Share your thoughts and let’s navigate this journey together. Remember, trust is the glue that holds your team together and propels your organization forward.

Image credit: Pixabay

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Five Things Most Managers Don’t Know About Innovation

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Five Things Most Managers Don't Know About Innovation

GUEST POST from Greg Satell

Every business knows it needs to innovate. What isn’t so clear is how to go about it. There is no shortage of pundits, blogs and conferences that preach the gospel of agility, disruptive innovation, open innovation, lean startups or whatever else is currently in vogue. It can all be overwhelming.

The reality is that there is no one ‘true’ path to innovation. In researching my book, Mapping Innovation, I found that organizations of all shapes and sizes can be great innovators. Some are lean and nimble, while others are large and bureaucratic. Some have visionary leaders, others don’t. No one model prevails.

However, there are common principles that we can apply. While there is no “right way” to innovate, there are plenty of wrong ways. So perhaps the best way forward is to avoid the pitfalls that can undermine innovative efforts in your organization and kill promising new solutions. Here are five things every business should know about innovation.

1. Every Square-Peg Business Eventually Meets Its Round-Hole World

IBM is many peoples’ definition of a dinosaur. Not too long ago, it announced its 22nd consecutive quarter of declining revenues. Nevertheless, it seems to be turning a corner. What’s going on? How can a century-old technology company survive against the onslaught of the 21st century phenoms like Google, Amazon, Apple and Facebook?

The truth is that this is nothing new for IBM. Today, its business of providing installed solutions for large enterprises is collapsing due to the rise of the cloud. In the 90s it was near bankruptcy. In the 50s, its tabulating machine business was surpassed by digital technology. Each time eulogies are paraded around for Big Blue it seems to come back even stronger.

What IBM seems to understand better than just about anybody else is that every square-peg business eventually meets its round-hole world. Changes in technology, customer preferences and competitive environment eventually render every business model irrelevant. That’s just reality and there really is no changing it.

IBM’s secret weapon is its research division, which explores pathbreaking technologies long before they have a clear path to profitability. So when one business dies they have something to replace it with. Despite those 22 quarters of declining revenues it has a bright future with things like Watson, quantum computing and neuromorphic chips.

It’s better to prepare than adapt.

2. Innovation Isn’t About Ideas, It’s About Solving Problems

Probably the biggest misconception about innovation is that it’s about ideas. So there is tons of useless advice about brainstorming methods, standing meetings and word games, such as replacing “can’t” with “can if.” If these things help you work more productively, great, but they will not make you an innovator.

In my work, I speak to top executives, amazingly successful entrepreneurs and world class scientists. Some of these have discovered or created things that truly changed the world. Yet not once did anyone tell me that a brainstorming session or “productivity hack” set them on the road to success. They were simply trying to solve a problem that was meaningful to them.

What I do hear a lot from mid-level and junior executives is that they are not given “permission” to innovate and that nobody wants to hear about their ideas. That’s right. Nobody wants to hear about your ideas. People are busy with their own ideas.

So stop trying to come up with some earth shattering idea. Go out and find a good problem and start figuring out how to solve it. Nobody needs an idea, but everybody has a problem they need solved.

3. You Don’t Hire Or Buy Innovation, You Empower It

One of the questions I always get asked when I advise organizations is how to recruit and retain more innovative people. I know the type they have in mind. Someone fashionably dressed, probably with some tasteful piercings and some well placed ink, that spouts off a never-ending stream of ideas.

Yet that’s exactly what you don’t want. That’s exactly the type of unproductive hotshot that can stop innovation in its tracks. They talk over other people, which discourages new ideas from being voiced and their constant interruptions kill collaboration.

The way you create innovation is by empowering an innovative culture. That means creating a safe space for ideas, fostering networks inside and outside the organization, promoting collaboration and instilling a passion for solving problems. That’s how you promote creativity.

So if you feel that your people are not innovating, ask yourself what you’re doing to get in their way.

4. If Something Is Truly New And Different, You Need a “Hair On Fire” Use Case

As a general operational rule, you should seek out the largest addressable market you can find. Larger markets not only have more money, they are more stable and usually more diverse. Identifying even a small niche in a big market can make for a very profitable business.

Unfortunately, what thrives in operations can often fail for innovation. When you have an idea that’s truly new and different, you don’t want to start with a large addressable market. You want to find a hair-on-fire use case — somebody that needs a problem solved so badly that they either already have a budget for it or have scotched-taped together some half solution.

The reason you want to find a hair-on-fire use case is that when something is truly new and different, it is untested and poorly understood. But someone who needs a problem solved really badly will be willing to work with you to find flaws, fix them and improve your offer. From there you can begin to scale up and hunt larger game.

5. You Need To Seek Out A Grand Challenge

Most of the problems we deal with are relatively small. We cater to changing customer tastes, respond to competitive threats and fix things that are broken. Sometimes we go a bit further afield and enter a new market or develop a new capability. These are the bread and butter of a good business. That’s how you win in the marketplace.

Yet every business is ultimately disrupted. When that happens, normal operating practice will only make you better and better at things people care less and less about. You can’t build the future by looking to the past. You build the future by creating something that’s new and important, that solves problems that are currently unsolvable.

That’s why every organization needs to seek out grand challenges. These are long, sustainable efforts that solve a fundamental problem in your industry or field that change the realm of what’s considered possible. They are not “bet the company” initiatives and shouldn’t present a material risk to the business if they fail, but have a transformational impact if they succeed.

As I noted above, there is no one “true” path to innovation. Everybody needs to find their own way. Still, there are common principles and by applying them, every business can up their innovation game.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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Bringing Emotional Energy and Creative Thinking to AI

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Bringing Emotional Energy and Creative Thinking to AI

GUEST POST from Janet Sernack

The impact of disruption, hyper-connectivity, and uncertainty, coupled with the pace of change, is causing many people to feel fearful and anxious. They become defensive and reactive and ‘go under’ emotionally and ‘go inwards’ cognitively by ruminating about their past and what bad things may happen in the future. Dwelling on past mistakes, failures, and poor performance also causes them to disengage emotionally, take flight and move away, avoid taking action, fight, or freeze and become inert, paralyzed, and immobilized. The outcome is resistance to the possibilities and creative changes using Generative AI might bring. Because they lack the vital creative and emotional energy to generate creative thinking in partnership with AI, they will resist innovation-led change and stay ‘stuck’ in their habitual, safe and conventional roles, capabilities and identities.

Emotional energy is the catalyst that fuels the creative process. Understanding and harnessing this energy inspires and motivates individuals to explore and embrace creative thinking strategies in partnership with AI.

When a person’s emotional energy has contracted, it results in constrained, negative, pessimistic, and even catastrophic thinking habits.

Where there is no space, doorway, or threshold to take on anything new, novel, or different or to imagine what might be possible in an uncertain future to evolve, advance, or transform their personal or professional lives.

Emotional energy catalyzes people’s hope, positivity and optimism to approach their worlds differently.

When people are constrained from becoming hopeful, positive, and optimistic, they cannot apply foresight to explore future possibilities and opportunities at the accelerating pace that Generative AI tools offer in unleashing the human ingenuity and generating creative thinking required to solve challenges and increasingly complex problems.

Augmenting human creativity

Generative AI, as highlighted in a recent Harvard Business Review article, How Generative AI can Augment Human Creativity, has the potential to assist humans in creating innovative solutions. Its role is not to replace humans but to augment their creativity, helping them generate and identify novel ideas and improve the quality of raw ideas.

To empower individuals to make intelligent decisions and solve complex problems, it is crucial to notice, disrupt, dispute and deviate from their unresourceful default patterns or habitual ways of doing things.

Because emotional energy is the catalyst that fuels the creative process, it is crucial to help people find ways to re-ignite their emotional energy.

Empowering, enabling, and equipping them to embody and take on new, more resourceful emotional states and traits that allow them to break free from the constraints by identifying and letting go of old, irrelevant roles, capabilities, and identities. To take on new ones to facilitate positive changes, solve challenges, and deliver highly valued innovative solutions in partnership with Generative AI to generate creative thinking.

Generating the power of questions in problem-solving

I applied and implemented three key strategies to partner with Generative AI during the six-month coaching partnership. I used creative thinking strategies to develop a comprehensive life-coaching plan for a coaching client that serendipitously co-created a range of transformational outcomes.

Identify the key challenges, strengths, and systemic nature of the core problem and set a goal for change.

Encouraged to experiment with coaching in partnership with Generative AI, I created a comprehensive summary of what my client and I agreed her core problem was. We defined a goal for effecting positive and constructive change and outlined evidence of achieving a successful outcome. I incorporated these elements into a descriptive paragraph and uploaded it into the Generative AI platform.

Develop a range of catalytic questions.

I focused on designing four key catalytic questions, to evoke and provoke creative thinking strategies. I requested the platform to design and develop a life-coaching plan to achieve our goal and solve her unique problem:

Integration involves showing that two things which appear to be different are actually the same:

What might be some key existing transformational coaching elements that can be integrated into the new life-coaching plan I am trying to create to solve this problem?
Splitting involves seeing how two things that look the same might actually be different and can be divided into useful parts, like an assembly line:
What might be some key components of transformational coaching plans that can be combined to connect with a life-coaching plan to help solve this problem?
Figure-ground reversal involves realizing that what is crucial is in the background and not in the foreground, like the invention of Slack.
What might be some of the missing parts in the transformational and life coaching processes that might be included to help solve this problem?
Distal thinking involves imagining things different from the present, like the Tesla electric car.
What could be possible without boundaries, rules or limitations in harnessing the emotional energy required to partner with my client in our coaching relationship?
How might I create value for my client? What key constraints in her whole system relate to life coaching, and how might I leverage these to solve the problem differently?

It took less than a minute and consisted of a comprehensive, step-by-step, detailed plan that would have taken me at least half a day to consider and construct.

I was delighted to have an evidence-based example of successfully augmenting human creativity, partnering with Generative AI to generate creative thinking to advance my coaching partnership.

Partner with applying a transformational process.

It took less than a minute and consisted of a comprehensive, step-by-step, detailed plan that would have taken me at least half a day of using my pause-power to construct. I was delighted to have an evidence-based example of successfully augmenting human creativity to experiment with when partnering with Generative AI to generate creative thinking in my coaching partnership:

Generating and identifying a range of novel ideas towards improving her well-being.
Exploring and improving the range and quality of the initial raw ideas by applying pause power to incubate, illuminate, and generate creative thinking.
Identifying and developing a range of options for my client to choose from, allowing her to let go of what was depleting her emotional energy and retain her hopefulness, positivity, and optimism.
Identifying and developing a range of options for my client to choose from, to take on to manifest the desired future state of well-being and re-energize her emotional energy.

What was the outcome?

By co-creating a safe and collective holding space with my client, we supported her in re-energizing emotionally and applying future-oriented creative thinking strategies. We partnered with Generative AI to innovate my coaching approach and maximize our intelligence.

The outcome was personally transformative and sustained by:

Ensuring she re-ignited and identified strategies and new habits to sustain her emotional energy and make the necessary changes and future choices.
Applying circuit breakers and divergent thinking strategies to disrupt and dispute unresourceful beliefs, biases and behavior patterns.
Creating a safe space allowed her to deviate from her feelings, thoughts, and mindset to identify what new roles, capabilities, and identities to take on in the future and how they could benefit her and add value to the quality of her life.
Assisting in creating various ideas and options to refine when making significant lifestyle change choices.

It was a powerful learning experience for both my client and myself, reinforcing and validating that “Generative AI’s greatest potential is not replacing humans; it is to assist humans in their individual and collective efforts to create hitherto unimaginable solutions. It can truly democratize innovation.”

Please find out more about our work at ImagineNation™.

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

Image Credit: Pixabay

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Delivering Customer Value is the Key to Success

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Delivering Customer Value is the Key to Success

GUEST POST from Mike Shipulski

Whatever your initiative, start with customer value. Whatever your project, base it on customer value. And whatever your new technology, you guessed it, customer value should be front and center.

Whenever the discussion turns to customer value, expect confusion, disagreement, and, likely, anger. To help things move forward, here’s an operational definition I’ve found helpful:

When they buy it for more than your cost to make it, you have customer value.

And when there’s no way to pull out of the death spiral of disagreement, use this operational definition to avoid (or stop) bad projects:

When no one will buy it, you don’t have customer value and it’s a bad project.

As two words, customer and value don’t seem all that special. But, when you put them together, they become words to live by. But, also, when you do put them together, things get complicated. Here’s why.

To provide customer value, you’ve got to know (and name) the customer. When you asked “Who is the customer?” the wheels fall off. Here are some wrong answers to that tricky question. The Board of Directors is the customer. The shareholders are the customers. The distributor is the customer. The OEM that integrates your product is the customer. And the people that use the product are the customer. Here’s an operational definition that will set you free:

When someone buys it, they are the customer.

When the discussions get sticky, hold onto that definition. Others will try to bait you into thinking differently, but don’t bite. It will be difficult to stand your ground. And if you feel the group is headed in the wrong direction, try to set things right with this operational definition:

When you’ve found the person who opens their wallet, you’ve found the customer.

Now, let’s talk about value. Isn’t value subjective? Yes, it is. And the only opinion that matters is the customer’s. And here’s an operational definition to help you create customer value:

When you solve an important customer problem, they find it valuable.

And there you have it. Putting it all together, here’s the recipe for customer value:

Understand who will buy it.
Understand their work and identify their biggest problem.
Solve their problem and embed it in your offering.
Sell it for more than it costs you to make it.

Image credit: Unsplash

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Adjacent Innovation is the Key to Growth and Risk

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Adjacent Innovation is the Key to Growth and Risk

GUEST POST from Robyn Bolton

It’s not easy leading innovation. Especially these days. You need to do more with less. Take risks while guaranteeing results. Keep up with competition through incremental innovation and redefine the industry with radical and disruptive innovation. It’s maddening. Until you find the Goldilocks Zone of adjacent innovation.

Adjacent Innovation: From Middle Child to Just Right

As HBS Professor Regina E. Herzlinger and her co-authors point out in a recent HBR article, the US is in the midst of an innovation crisis. The cost of lost productivity, estimated at over $10 trillion between 2006 and 2018, is a stark reminder of the economic consequences of a lack of innovation. This figure, equivalent to $95,000 per US worker, should serve as a wake-up call to the importance of innovation in driving economic growth.

The authors identify the root cause of this loss as the ‘polarized approach companies take to innovation.’ While companies focus on incremental innovation, the safe and reliable oldest child of the innovation family, the Venture Captialists chase after radical, transformative innovations, the wild, charismatic, free-spirited youngest child. Meanwhile, adjacent innovation – new offerings and business models for existing customers or new customers for existing offerings and business models – is, like the middle child, too often overlooked.

It’s time to rediscover it. In fact, it’s also time to embrace and pursue it as the most promising path back to growth. While incremental innovation is safe and reliable, it’s also the equivalent of cold porridge. Radical or transformative innovation is sexy, but, like hot porridge, it’s more likely to scorch than sustain you. Adjacent innovation, however, is just right – daring enough to change the game and leapfrog the competition and safe enough to merit investment and generate short-term growth.

Proof in the Porridge: 4x the returns in HALF the time

Last year, I worked with an industrial goods company. Their products aren’t sexy, and their brands are far from household names, but they make the things that make America run and keep workers (and the public) safe. The pandemic’s supply chain disruptions battered their business, and their backlog ballooned from weeks to months and even years. Yet amidst these challenges, they continued to look ahead, and what they saw was a $6M revenue cliff that had to be filled in three years and a product and innovation pipeline covered in dust and cobwebs.

From Day 1, we agreed to focus on adjacent innovation. For four weeks, we brainstormed, interviewed customers, and analyzed their existing offerings and capabilities, ultimately developing three concepts – two new products for existing customers and one existing product repositioned to serve a new customer. After eight more weeks of work, we had gathered enough data to reject one of the concepts and double down on the other two. Three months later, the teams had developed business cases to support piloting two of the concepts.

It took six months to go from a blank piece of paper to pilot approval.

It took just another 12 months to record nearly $25M in new revenue.

Those results are more than “just right.”

Be Goldilocks. Pursue Adjacent Innovation

Every organization can pursue adjacent innovation. In fact, most of the companies we consider amongst the world’s “Most Innovative” have that reputation because of adjacent innovation.

How will you become your organization’s Innovation Goldilocks and use adjacent innovation to create “just right” growth?

Image credit: Pixabay

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How to Avoid AI Project Failures

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How To Avoid AI Project Failures

GUEST POST from Greg Satell

A survey a few years ago by Deloitte of “aggressive adopters” of cognitive technologies found that 76% believe that they will “substantially transform” their companies within the next three years. There probably hasn’t been this much excitement about a new technology since the dotcom boom years in the late 1990s.

The possibilities would seem to justify the hype. AI isn’t just one technology, but a wide array of tools, including a number of different algorithmic approaches, an abundance of new data sources and advancement in hardware. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further.

Still, there remains a large gap between aspiration and reality. Gartner estimated that 85% of big data projects fail. There have also been embarrassing snafus, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or Microsoft’s Tay chatbot went berserk on Twitter. Here’s how to transform the potential of AI into real results.

Make Your Purpose Clear

AI does not exist in a vacuum, but in the context of your business model, processes and culture. Just as you wouldn’t hire a human employee without an understanding of how he or she would fit into your organization, you need to think clearly about how an artificial intelligence application will drive actual business results.

“The first question you have to ask is what business outcome you are trying to drive,” Roman Stanek, CEO at GoodData, told me. “All too often, projects start by trying to implement a particular technical approach and not surprisingly, front-line managers and employees don’t find it useful. There’s no real adoption and no ROI.”

While change always has to be driven from the top, implementation is always driven lower down. So it’s important to communicate a sense of purpose clearly. If front-line managers and employees believe that artificial intelligence will help them do their jobs better, they will be much more enthusiastic and effective in making the project successful.

“Those who are able to focus on business outcomes are finding that AI is driving bottom-line results at a rate few had anticipated,” Josh Sutton, CEO of Agorai.ai, told me. He pointed to a McKinsey study from a few years ago that pegs the potential economic value of cognitive tools at between $3.5 trillion and $5.8 trillion as just one indication of the possible impact.

Choose The Tasks You Automate Wisely

While many worry that cognitive technologies will take human jobs, David Autor, an economist at MIT, sees the the primary shift as one of between routine and nonroutine work. In other words, artificial intelligence is quickly automating routine cognitive processes much like industrial era machines automated physical labor.

To understand how this can work, just go to an Apple store. Clearly, Apple is a company that clearly understands how to automate processes, but the first thing you see when you walk into an Apple store you see is a number employees waiting to help you. That’s because it has chosen to automate background tasks, not customer interactions.

However, AI can greatly expand the effectiveness of human employees. For example, one study cited by a White House report during the Obama Administration found that while machines had a 7.5 percent error rate in reading radiology images and humans had a 3.5% error rate, when humans combined their work with machines the error rate dropped to 0.5%.

Perhaps most importantly, this approach can actually improve morale. Factory workers actively collaborate with robots they program themselves to do low-level tasks. In some cases, soldiers build such strong ties with robots that do dangerous jobs that they hold funerals for them when they “die.”

Data Is Not Just An Asset, It Can Also Be A Liability

For a long time more data was considered better. Firms would scoop up as much of it as they could and then feed it into sophisticated algorithms to create predictive models with a high degree of accuracy. Yet it’s become clear that’s not a great approach.

As Cathy O’Neil explains in Weapons of Math Destruction, we often don’t understand the data we feed into our systems and data bias is becoming a massive problem. A related problem is that of over-fitting. It may sound impressive to have a model that is 99% accurate, but if it is not robust to changing conditions, you might be better off with one that is 70% accurate and simpler.

Finally, with the implementation of GDPR in Europe and the likelihood that similar legislation will be adopted elsewhere, data is becoming a liability as well as an asset. So you should think through which data sources you are using and create models that humans can understand and verify. “Black boxes” serve no one.

Shift Humans To Higher Value Tasks

One often overlooked fact about automation is that once you automate a task, it becomes largely commoditized and value shifts somewhere else. So if you are merely looking to use cognitive technologies to replace human labor and cut costs, you are most probably on the wrong track.

One surprising example of this principle comes from the highly technical field of materials science. A year ago, I was speaking to Jim Warren of the Materials Genome Initiative about the exciting possibility of applying machine learning algorithms to materials research. More recently, he told me that this approach has increasingly become a focus of materials research.

That’s an extraordinary shift in one year. So should we be expecting to see a lot of materials scientists at the unemployment office? Hardly. In fact, because much of the grunt work of research is being outsourced to algorithms, the scientists themselves are able to collaborate more effectively. As George Crabtree, Director of the Joint Center for Energy Storage Research, which has been a pioneer in automating materials research put it to me, “We used to advance at the speed of publication. Now we advance at the speed of the next coffee break.”

And that is the key to understanding how to implement cognitive technologies effectively. Robots are not taking our jobs, but rather taking over tasks. That means that we will increasingly see a shift in value from cognitive skills to social skills. The future of artificial intelligence, it seems, is all too human.

— Article courtesy of the Digital Tonto blog and previously appeared on Harvard Business Review
— Image credits: Pexels

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