Tag Archives: Innovation

Agile Innovation Management (Part Two)

How Agility Enables Innovation

Agile Innovation Management (Part Two)

GUEST POST from Diana Porumboiu

In the previous article on agile innovation we covered the main concepts around agile, business agility and its role as a driver for innovation. Now, let’s see how to actually leverage agility to innovate and how other companies have succeeded in this area.

Agility is an enabler for innovation. The pace of innovation, while not easy to achieve, has become the ultimate competitive advantage as we all need to adapt quickly to evolving environments, the digital age and increasing pressing needs.  

The reality is that agile thinking is changing the world whether we decide to adopt it or not.  

Those who succeed at this are ahead of the game. McKinsey research suggests that agility is a critical factor for organizational success. 

The Organizational Health Index (OHI) assesses various aspects of organizational health, including agility, and examines how these factors correlate with business success. An increased organizational health is linked with more resilient, adaptive, and high-performing organizations that can better navigate complexity, drive innovation, and achieve strategic goals. 

What’s more, agile organizations are best at balancing both speed and stability, and these are also the companies that rank highest in the organizational health index.  

McKinsey Ability
Source: McKinsey&Company

The research goes even deeper and identifies a series of management practices that differentiate the most from the least agile companies.  

As you can see, there’s more to business agility than meets the eye and a few sprints just won’t cut it.  

However, if we look at the agile principles, there are several ways in which they can enable innovation:

  • They bring an empirical process control approach, which emphasizes transparency, evaluation, and adaptation.  
  • They enable experimentation and learning as teams are encouraged to test hypotheses, validate assumptions, and learn from both successes and failures. This experimental mindset is essential for innovation.
  • They are about adaptive planning processes that allow teams to adjust their priorities, strategies, and product roadmaps based on emerging opportunities and threats.
  • They emphasize customer-centricity. By focusing on delivering value to customers through continuous delivery and customer feedback loops, you make sure your innovations meet real market demands and solve genuine problems.
  • They encourage cross-functional collaboration and self-organizing teams, bringing together diverse perspectives and expertise.  

To get a better idea of how this looks in practice, we’ll take the example of ING Bank.

ING Bank

ING is a global financial institution originally from the Netherlands and a good example to illustrate how agile can be introduced organization-wide, the right way.

ING wanted to become agile for the right reasons. The shift to agility wasn’t about working faster or growing more—it was about being flexible and adaptable. Even though things were going well financially in 2015, ING noticed that customer behavior was changing due to trends in other industries, not just in banking. So, they knew they had to change too.

ING Bank embraced several key principles of agility, drawing inspiration from the practices of tech companies to align with their objectives and operations: 

  • Cross-Functional Teams: ING structured its IT and commercial departments into agile squads, mirroring the approach seen at Tesla. This integration fosters cross-functionality and collaboration, with teams physically situated together within the same premises.
Agile at ING Bank - McKinsey
source: McKinsey & Company
  • Rapid Decision-Making and Experimentation: Without bottlenecks created by middle management, ING facilitates swift decision-making and continuous experimentation. This agile approach enables the organization to constantly refine and test customer offerings without bureaucratic delays.
  • Enhanced Collaboration and Transparency: Recognizing the importance of collaboration, ING implemented structural changes to break down silos. Clear delineation of roles, responsibilities, and governance structures fosters improved cooperation across teams and departments.
  • Accelerated Delivery: Instead of their usual annual product launches, ING adopted a more agile release cycle, rolling out software updates every two weeks. This agile delivery model allows the organization to respond promptly to market demands and customer feedback, ensuring rapid innovation and adaptation.  

The first step in achieving this agile transformation was to develop a clear strategy and vision. They started small and rolled out the new structures and way of working across the entire headquarters in eight to nine months.  

Last, but not least, they invested significant energy and leadership time in fostering a culture of ownership, empowerment, and customer-centricity, which are foundational elements of an agile culture.

As Bart Schlatmann from ING points out, agility is a means to an end, not the end goal itself; it is the pathway to achieving innovation.

Drawing from these examples and research from other organizations, we can summarize the five tenets of agile organizations:

  1. Purpose-Driven Mindset: Shift from a focus on capturing value to co-creating value with stakeholders, embodying a shared vision across the organization.
  2. Empowered Network of Teams: Transition from top-down direction to self-organizing teams with clear responsibility and authority, fostering engagement, innovative thinking, and collaboration.
  3. Rapid Learning Cycles: Embrace uncertainty and continuous improvement through iterative decision-making and experimentation, prioritizing quick adaptation over rigid planning.
  4. Innovation Culture: Cultivate ownership, empowerment, and customer-centricity, enabling employees to drive organizational success.
  5. Integrated Technology Enablement: View technology as integral to unlocking value and enabling responsiveness to business and stakeholder needs, leveraging advanced tools for seamless integration and rapid innovation.

Actionable Steps to Drive Innovation through  Business Agility

We can’t wrap things up without going through some of the key steps that should not be missed in an agile transformation journey.  

Constancy of purpose  

You might have heard of Edwards Deming and even used his PDCA cycle in your continuous improvement work. He is well known for his legacy in the field of quality management, particularly for his contributions to the improvement of production processes in Japan after World War II. To some degree, his work is also seen as one of the main inspirations for the agile movement.

Among his work, we can also find the “14 Points for Management,” where Deming outlines how essential it is to have a clear and unwavering commitment to a long-term vision or mission. 

He called it constancy of purpose. You can also call it your North Star. Regardless of the words you choose, it’s important to set your goals and align all activities, processes, and resources towards achieving them. How to do this?

  • Communicate the Purpose: Regularly communicate the organization’s purpose, mission and goals as well as how agility contributes to achieving them.
  • Define Goals: Clearly define objectives and goals that align with the organization’s purpose. These goals should support the overall mission and vision.
  • Empower Teams: Trust by default and enable teams to make decisions, take ownership of their ideas and work. Provide them with the autonomy and resources they need to innovate and deliver value.
  • Measure Progress: Measure progress towards your goals, but also establish metrics that can measure your ability to be responsive. Regularly review and assess how agile practices are contributing to the overall mission.
  • Adapt and Iterate: Embrace continuous improvement processes that align with your internal structures and needs. Encourage teams to experiment, learn, and iterate on their approaches.

Agile leadership

Adopt the ABC of leadership which drives innovation and makes the shift from “vertical ideology of control” to “horizontal ideology of enablement”.

Linda Hill, renowned professor at Harvard Business School, specializing in leadership and innovation makes a great point about the roles a leader should take if they want to drive innovation and agility.  

Over time leadership evolved from a purely strategic role, to providing a vision that guides people in the same direction. More recently, research showed that a visionary leader is not enough. You need leaders that can also shape the culture and capabilities needed for people to co-create the future. This requires a different approach to leadership.

Research has identified that in order to lead an organization that innovates at scale with speed, you need leaders that fill in three different functions:  

  • the Architect – to build the culture and capabilities necessary to collaborate, experiment and work.
  • the Bridger – to create the bridge between the outside and the inside of the organization by bringing together skills and tools to innovate at speed.
  • the Catalyst – to accelerate co-creation through the entire ecosystem.  

Here is Hill’s short summary on the ABC of leadership:

Another top voice is Steve Denning who has been an advocate of agile and agile management for years. He makes some great points about the agile mindset which requires a new way of running organizations.

For an organization to be truly agile, the so called industrial-era management needs to be replaced with digital-age management which is strongly driven by an agile mindset.  

The traditional management style makes it hard for agile to work because the old command-and-control approach goes against the agile principles. The top-down approach is riddled with bureaucracy which obstructs visibility to the customer and the realities at the lower levels of the organization.  

Some of the most successful and innovative organizations, like Apple, Google, and Microsoft understood this early on and shifted their focus to delivering customer value first, one of the agile principles. This required a change in mindset but also in the corporate culture, which is no easy undertaking.

To make this transition, Denning talks about five major shifts that companies need to make:

  • From profit-focused to customer-focused goals.
  • From direct reporting to self-organizing teams where management’s role is not to check on employees, but to enable them to do their work by removing obstacles.
  • From bureaucracy, rules, and reports to work coordinated by Agile methods and customer feedback.
  • Prioritize transparency and continuous improvement over predictability.
  • Encourage horizontal communication rather than top-down directives.  

While they are straightforward and make sense for most of us, these changes are maybe the hardest to make, especially for established organizations that are not used to challenging the status quo.  

These big undertakings are what make agile possible at scale. But even if you’re not there yet, you can still apply the agile principles at a smaller scale to enable innovation.  

Minimize complexity  

Complexity is the enemy of agility. People in companies both large and small try to come up with the perfect solution, that often doesn’t exist in the first place, and only end up having solved the wrong problem.

On the other hand, if you were to simply move ahead quickly with something that creates real value and solves at least some of the problems, you’ll see which of your assumptions and concerns are real, and which aren’t. You’ll also see which problems you can work around, and which ones you simply must address directly.

This obviously eliminates a lot of uncertainty and reduces the complexity associated with solving the problem, which again helps you focus your innovation efforts on what matters – creating real value.

The bigger and more complex the problem, the more important it is to take an agile and modular approach. 

Thus, the bigger and more complex the problem, the more important it is to take this agile and modular approach that focuses on the speed of making tangible progress. 

Conclusion

As we explained in our complete guide to innovation management, there is no single perfect way of managing innovation. Different companies have different approaches for innovation management.  

However, the common thread of successful organizations are structures and processes that mitigate the somehow chaotic nature of innovation management.  

In these two articles we explored agile as a method to enable innovation and improve its management for sustained success. We don’t believe in quick fixes or miracle solutions. That’s why we made the case of agile as a mindset that should permeate every aspect of the organization.


Article originally published in full format on viima.com/blog

Image credit: Unsplash, McKinsey

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Will Innovation Management Leverage AI in the Future?

Will Innovation Management Leverage AI in the Future?

GUEST POST from Jesse Nieminen

What role can AI play in innovation management, and how can we unlock its true potential?

Unless you’ve been living under a rock, you’ve probably heard a thing or two about AI in the last year. The launch of ChatGPT has supercharged the hype around AI, and now we’re seeing dramatic progress at a pace unlike anything that’s come before.

For those of us into innovation, it’s an exciting time.

Much has been said about the topic at large so I won’t go over the details here. At HYPE, what we’re most excited about is what AI can do for innovation management specifically. We’ve had AI capabilities for years, and have been looking into the topic at large for quite some time.

Here, I share HYPE’s current thinking and answer some key questions:

  • What can AI do for innovation management?
  • What are some common use cases?
  • How can you operationalize AI’s use in innovation management?

The Current State of Innovation Management

Before we answer those questions, let’s review how most organizations carry out innovation management.

We’re all familiar with the innovation funnel.

Hype Innovation Image 1

To oversimplify, you gather ideas, review them, and then select the best ones to move forward to the pilot stage and eventual implementation. After each phase, poor ideas get weeded out.

It’s systematic, it’s conceptually simple, and investment is tiered so that you don’t spend too much time or money before an idea has shown its potential. What’s not to love?

Well, there are a few key challenges: the process is slow, linear, and is usually biased due to the evaluation criteria selected for the gates or decision points (if you use a Phase-Gate model).

Each of these challenges can be mitigated with smart adaptations of the process, but the funnel has another fundamental limitation: It’s generally built for a world where innovation requires significant capital expenditures and vast amounts of proprietary information.

But, regardless of your industry, that just isn’t the case anymore. Now most information is freely available, and technology has come a long way, in many cases because of AI. For example, pharmaceutical companies use AI to accelerate drug discovery while infrastructure and manufacturing companies use advanced simulation techniques, digital twins (virtual replicas of physical objects or systems), and rapid prototyping.

It’s now possible to innovate, test, and validate ideas faster than ever with minimal investment. With the right guidance, these tasks don’t have to be limited to innovation experts like you anymore. That can be an intimidating thought, but it’s also an empowering one. Soon, thanks to AI, you’ll be able to scale your expertise and make an impact significantly bigger than before.

For more than 20 years, we’ve been helping our customers succeed in this era of systematic innovation management. Today, countless organizations manage trends at scale, collect insights and ideas from a wide and diverse audience, and then manage that funnel highly effectively.

Yet, despite, or maybe because of this, more and more seemingly well-run organizations are struggling to keep up and adapt to the future.

What gives?

Some say that innovation is decelerating. Research reveals that as technology gets more complex, coming up with the next big scientific breakthrough is likely to require more and more investment, which makes intuitive sense. This type of research is actually about invention, not innovation per se.

Innovation is using those inventions to drive measurable value. The economic impact of these inventions has always come and gone in waves, as highlighted in ARK Investment’s research, illustrated below.

Throughout history, significant inventions have created platforms that enable dramatic progress through their practical application or, in other words, through innovation. ARK firmly believes that we’re on the precipice of another such wave and one that is likely to be bigger than any that has come before. AI is probably the most important of these platforms, but it’s not the only one.

Mckinsey Hype Innovation Image 2

Whether that will be the case remains to be seen, but regardless, the economic impact of innovation typically derives from the creative combination of existing “building blocks,” be they technologies, processes, or experiences.

Famously, the more such building blocks, or types of innovation, you combine to solve a specific pain point or challenge holistically, the more successful you’re likely to be. Thanks to more and more information and technology becoming free or highly affordable worldwide, change has accelerated rapidly in most industries.

That’s why, despite the evident deceleration of scientific progress in many industries, companies have to fight harder to stay relevant and change dramatically more quickly, as evidenced by the average tenure of S&P500 companies dropping like a stone.

Hype Innovation 3

In most industries, sustainable competitive advantages are a thing of the past. Now, it’s all about strategically planning for, as well as adapting to, change. This is what’s known as transient advantage, and it’s already a reality for most organizations.

How Innovation Management Needs to Change

In this landscape, the traditional innovation funnel isn’t cutting it anymore. Organizations can’t just focus on research and then turn that into new products and expect to do well.

To be clear, that doesn’t mean that the funnel no longer works, just that managing it well is no longer enough. It’s now table stakes. With that approach, innovating better than the next company is getting harder and more expensive.

When we look at our most successful customers and the most successful companies in the world in general, they have several things in common:

  • They have significantly faster cycle times than the competition at every step of the innovation process, i.e., they simply move faster.
  • For them, innovation is not a team, department, or process. It’s an activity the entire organization undertakes.
  • As such, they innovate everything, not just their products but also processes, experiences, business models, and more.

When you put these together, the pace of innovation leaves the competition in the dust.

How can you then maximize the pace of innovation at your organization? In a nutshell, it comes down to having:

  • A well-structured and streamlined set of processes for different kinds of innovation;
  • Appropriate tools, techniques, capabilities, and structures to support each of these processes;
  • A strategy and culture that values innovation;
  • A network of partners to accelerate learning and progress.

With these components in place, you’ll empower most people in the organization to deliver innovation, not just come up with ideas, and that makes all the difference in the world.

Hype Innovation 4

What Role Does AI Play in Innovation Management?

In the last couple of years, we’ve seen massive advancements not just in the quality of AI models and tools, but especially in the affordability and ease of their application. What used to be feasible for just a handful of the biggest and wealthiest companies out there is now quickly commoditizing. Generative AI, which has attracted most of the buzz, is merely the tip of the iceberg.

In just a few years, AI is likely to play a transformative role in the products and services most organizations provide.

For innovation managers too, AI will have dramatic and widely applicable benefits by speeding up and improving the way you work and innovate.

Let’s dive a bit deeper.

AI as an Accelerator

At HYPE, because we believe that using AI as a tool is something every organization that wants to innovate needs to do, we’ve been focusing on applying it to innovation management for some time. For example, we’ve identified and built a plethora of use cases where AI can be helpful, and it’s not just about generative AI. Other types of models and approaches still have their place as well.

There are too many use cases to cover here in detail, but we generally view AI’s use as falling into three buckets:

  • Augmenting: AI can augment human creativity, uncover new perspectives, kickstart work, help alleviate some of the inevitable biases, and make top-notch coaching available for everyone.
  • Assisting: AI-powered tools can assist innovators in research and ideation, summarize large amounts of information quickly, provide feedback, and help find, analyze, and make the most of vast quantities of structured or unstructured information.
  • Automating: AI can automate both routine and challenging work, to improve the speed and efficiency at which you can operate and save time so that you can focus on the value-added tasks at the heart of innovation.

In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.

While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.

In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.

While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.

Putting AI Into Practice

So, what’s the key to success with AI?

At HYPE, we think the key is understanding that AI is not just one “big thing.” It’s a versatile and powerful enabling technology that has become considerably cheaper and will likely continue on the same trajectory.

There are significant opportunities for using AI to deliver more value for customers, but organizations need the right data and talent to maximize the opportunities and to enable AI to support how their business operates, not least in the field of innovation management. It’s essential to find the right ways to apply AI to specific business needs; just asking everybody to use ChatGPT won’t cut it.

The anecdotal evidence we’re hearing highlights that learning to use a plethora of different AI tools and operationalizing these across an organization can often become challenging, time-consuming, and expensive.

To overcome these issues, there’s a real benefit in finding ways to operationalize AI as a part of the tools and processes you already use. And that’s where we believe The HYPE Suite with its built-in AI capabilities can make a big difference for our customers.

Final Thoughts

At the start of this article, we asked “Is AI the future of innovation management?”

In short, we think the answer is yes. But the question misses the real point.

Almost everyone is already using AI in at least some way, and over time, it will be everywhere. As an enabling technology, it’s a bit like computers or the Internet: Sure, you can innovate without them, but if everyone else uses them and you don’t, you’ll be slower and end up with a worse outcome.

The real question is how well you use and operationalize AI to support your innovation ambitions, whatever they may be. Using AI in combination with the right tools and processes, you can innovate better and faster than the competition.

At HYPE, we have many AI features in our development roadmap that will complement the software solutions we already have in place. Please reach out to us if you’d like to get an early sneak peek into what’s coming up!

Originally published at https://www.hypeinnovation.com.

Image credits: Pixabay, Hype, McKinsey

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3 Steps to Building a Psychologically Safe Environment

or The No-Cost, No-Hug Secret to Smarter Teams

3 Steps to Building a Psychologically Safe Environment

GUEST POST from Robyn Bolton

Welcome to the exciting conclusion of “Everything You Ever Wanted to Know About Psychological Safety but Were Afraid to Ask.”

Our generous expert, Alla Weinberg, CEO and Culture designer at Spoke & Wheel, has been patiently leading us beyond and through the buzzy frothiness that we (I) usually associate with Psychological Safety and into the deeply powerful and absolutely essential core elements.

In Part 1, we learned that psychological safety is more neuroscience than psychology (and required to be your smartest self).

In Part 2, we learned the first step to creating safety (and why corporate mandates are antithetical to the goal). 

Today, we’re going where we need but don’t want to go – how to create a psychologically safe environment so everyone can thrive.


If Step 1 in creating Psychological Safety is verbalizing your emotions and understanding others’ emotions, I’m hoping Step 2 is easier.

Step two is relational intelligence.

There are three intelligences: emotional, relational, and systems

Relational intelligence is about understanding how to connect with different people, being aware when disconnection happens, and then acknowledging and repairing it. That last part is the most important because, without repair, there’s no safety.

Are you saying that saying, “I’m sorry” is essential to building psychological safety?  Because I would much rather ignore the issues and move on.  Or, better yet, pretend it never happened.

Nice try.  But you know as well as I do that people are messy, and when we come together, there’s tension and conflict, and someone will get hurt or make mistakes. It’s normal.  It’s okay as long as you know how to recover, repair, and heal.

The issue isn’t the conflict but how we handle it and whether we can repair it. I have a diagram of a relationship, which is a circle of connection, disconnection, and repair. We go around this circle just like breathing is inhaling and exhaling.  Relating, connecting, disconnecting, and repairing is what a relationship is.

OK, step 2 is relational intelligence which requires repairing relationships, so how do I do that?  Bonus points if I don’t have to admit to being wrong.

Not only do you have to admit that, but you also need to take responsibility for your impact, not just your intentions. Intentions are great, but without action, they don’t mean much.

When apologizing, we tend to try to explain ourselves.  For example, we say, “I didn’t say anything in that meeting, and I’m sorry, but that wasn’t my intention, and I wanted to, but I had my own issue.” Instead, we should say, “I didn’t say anything in that meeting, and I’m sorry.”

When you apologize, don’t say “but.” To repair a relationship, you must take responsibility for your actions and their impact. Saying “but” negates all of that.

(head now on the desk because this is a lot to take in): I’m afraid to ask what Step 3 is, but I will practice verbalizing my feelings and ask anyway.  What’s Step 3?

You’re doing great.  This is a lot, and it’s ok that you feel overwhelmed.

Step 3 is systems intelligence, which focuses on the relationships within an organization that gives rise to its culture. Systems thinking is about understanding how structures, policies, processes, and relationships interact to create a greater whole,

Systems thinking!  We’re getting back to left-brained stuff now.  I’m feeling better.

Yes, and since connection is core to psychological safety, systems thinking tells us that we must fundamentally rethink how people work together by centering connection.

How do we do that?

We must reinvent, innovate, and rethink how we work together.

Lack of safety leads to power struggles, walls, and departmental rivalries, creating divisions and “othering.”

Hierarchy doesn’t align with connection, but shared leadership does. Hierarchy erodes trust because you need manager approvals, beg for budgets, or are told to prove your worth to get a seat at the table.

Silos are another problem because they lead to turf wars and people making decisions to protect themselves or their team rather than do what’s best for the greater good. 

Look, I love challenging the status quo, but you’re suggesting that we burn it all to the ground and start over.

(Laughing) I don’t lead with that.  When I work with organizations, I start with meetings.

Most meetings focus on work topics like status, decisions, and updates. But where are the meetings where we discuss emotions, share personal stories, and express hurt feelings? Everything shifts when we center connection.

Isn’t that called therapy?

Organizations value information, right?  Emotions are information.

Emotions reside in our bodies, but in many organizations, the focus is on the intellect.  It’s as if the head is the only important part, and the body is merely a vessel to transport the head from meeting to meeting.

And that brings us full circle to why psychological safety is mostly neuroscience.  Our body houses our nervous system, where we feel safety or the lack thereof. So, when people talk about bringing their whole selves to work, I mean our entire body, not just the intellect. Our bodies contain wisdom and information that we often overlook and undervalue, yet this is where the crucial information resides to create psychological safety.

We don’t think of emotions as information.  We think of them as signs of weakness, and you can’t be weak and successful.

It’s a lot of fear because how we’ve worked for the last 50 years gave us an illusion of certainty.  Acknowledging that there is no certainty and that we’re in entirely uncharted territory is scary, and there’s a fear that everything will fall apart. We think the business won’t survive if we do it the other way.

I respect that fear. It’s okay to be afraid. But if we acknowledge that all of this comes from fear, we will be open to new ideas or thoughts. For organizations that want to innovate, they must change how they work. You can’t keep doing the same thing and expect different results. You need to innovate your approach to work.

Thank you so much for all of this.  You’ve shared so much.  Some of it was hard to hear, but I think that’s also a sign that it’s important to hear.  Any last words of advice?

Give yourself and others permission to be human beings again.  Not robots or cogs, not human resources, but to be human beings. That includes our bodies, our emotions, our messiness, and our relationships with each other.


If you would like to learn more about Alla and her work, please visit her firm’s website, www.spokeandwheel.coand definitely download a FREE digital copy of her book, A Culture of Safety: Building a Work Environment Where People Can Think, Collaborate, and Innovate

Image Credit: Pexels

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Are You Testing Your Intuitions?

Are You Testing Your Intuitions?

GUEST POST from Dennis Stauffer

Do you trust your intuitions? When you have a hunch, do you go with it or hold back? There’s been a long-running debate about which is the better strategy.

Some have claimed that top executives are at their best when they “go with their gut” or “follow their instincts.” They can give examples of when that’s turned out well for them. But what we don’t know is how often other intuitions may have turned out badly.

Trusting your intuitions can sometimes keep you safe. Some research has found that firefighters are well-served by their intuitions, because it helps them avoid danger. Women who are uneasy walking alone at night are advised to follow their intuitions.

That makes sense when you’re crossing a dark parking lot or at the scene of a fire. Being cautious when there might be no threat is better than being careless when there might be one. But that doesn’t mean those intuitions are accurate.

Innovators also have intuitions—and need to. Hunches about the value of an idea, or a sense of how customers will react. For an innovator, asking whether you should trust your intuitions is the wrong question. What needs to be asked instead is: How can I test my intuitions? What can I do to find out whether those feelings are reliable?

That’s one reasons innovators have a bias for action. Because acting on their ideas—in ways that will test them—is how they find out whether those ideas will work. That’s not only a more prudent approach than just following hunches; it’s excellent practice at evaluating the merits of your ideas. So over time, you become better at forming those hunches. Because you know how well it worked in the past, and maybe where you might have biases.

If you want to enhance your intuitions—and your innovativeness—don’t trust them or distrust them.

Test them.

View this post on video here if you prefer:

Image Credit: misterinnovation.com

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The Emotions of an Innovator

The Emotions of an Innovator

GUEST POST from Dennis Stauffer

Your emotional state has a lot to do with how innovative you are, especially when those emotions are negative. How willing are you to act in the face of uncertainty and take those risks? How comfortable are you with new ideas and interpretations that may conflict with those you have? Can you overcome your biases to gain a clear-eyed understanding of the challenges you face? The fears and prejudices we all have can undermine our ability to find solutions.

Take a few moments to recall some of the negative emotions you’ve experienced in your life.

Things like:

  • Frustration
  • Disappointment
  • Jealously
  • Resentment
  • Annoyance
  • Anger     …and that’s just the short list.

One thing they all have in common is that they make you feel bad. They undermine your happiness. They can also hamper your ability to innovate.

Now ask yourself: What prompted those emotions? I suspect you think of something that happened or that someone did that upset you, but there are deeper reasons for these emotions. They form when something isn’t what you expect or hope for. Someone isn’t doing what you want, or that you think they should. You think something needs to be corrected. You already have some outcome you’d prefer, an expectation that isn’t being met.

That’s your mindset—your beliefs about how things should be—beliefs that generate those expectations. You may think someone is doing something wrong. Perhaps they’re being mean or rude. But that means you have an idea in your head of what’s right—how you think they should behave. Or, something may not have turned out the way you hoped. Maybe you didn’t get the promotion you wanted. But that means you think you should have been given something you didn’t receive.

Change those expectations and your emotional response changes. What’s happening in your head has just as much or more impact on the emotions you feel, as whatever is happening around you—and that’s empowering. When you blame your emotions on what others do, you hand them control over your emotional state. They determine how you feel.

When you realize that your beliefs and expectations—your mindset—primes you to feel those emotions, you gain control over how you feel. Instead of anger, you can substitute curiosity about why someone would behave that way. Instead of annoyance at someone’s missteps, you can choose to be amused. Instead of disappointment, you can shift to resolve to learn from your setbacks. Instead of embarrassment, you can choose to feel humility. Instead of feeling the urge to punish someone, you can choose to feel compassion and understanding.

External events may not have changed. Those are things you don’t control. What changes is your mindset—something you can control. When you realize that you create your own emotions and take steps to create fewer negative ones, you increase your own happiness—regardless of what life throws at you. Skilled innovators have a mindset that minimizes their negative emotions. Because instead of focusing on what needs to be corrected—to restore the status quo—they focus on what can be improved. That enhances their capacity to enhance. Enhance a product or service, enhance their community and the larger world, and enhance their own lives.

Here is a video of this post if you prefer:

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How to Use Your Nervous System to Feel Psychologically Safe

or “Why Mandating a Return to the Office Destroys Safety”

How to Use Your Nervous System to Feel Psychologically Safe

GUEST POST from Robyn Bolton

In last week’s episode, we learned that psychological safety is more neuroscience than psychology and the huge role our nervous system plays in our experience of safety. 

This week, we’re going deeper into our nervous system and how we can use our understanding of it to influence our psychology.


I’m sensing I can’t think my way to safety.  So, can I fix my nervous system to feel safe and smart?

This is where I go beyond Dr. Amy Edmondson’s definition of psychological safety to incorporate neuroscience and how our nervous system works.

Our nervous system has three states:

  1. Immobilization or the freeze response, as you felt, is often accompanied by a sense of overwhelm
  2. Fight-and-flight when you try to either end the conversation or become more aggressive, resistant, and push back on exploring other alternatives.
  3. Rest-and-Digest when you feel safe, social, and connected to the people around you

This third state sets humans and mammals apart from other living things.  Communicating and connecting serve as a survival mechanism and represent a safe state for our nervous system.  When we communicate and connect, our tribe looks out for us and keeps us safe from threats like lions or unfriendly tribes.

So, the answer is to foster more profound connections among human beings, which requires going well beyond our work roles and activities.

Does it require hugging?  I knew it would require hugging.

Don’t worry, hugging isn’t mandatory.

We, as individuals, have a strong desire to connect and communicate, but it doesn’t necessarily require physical proximity. Being physically together doesn’t guarantee anything.

But what about the push to return to the office? There’s even research to support executives’ claims that physical proximity is essential to culture, innovation, and connection.

Not only does physical proximity not guarantee anything, but being forced to return to the office causes more harm than good. 

From a safety perspective, our nervous system doesn’t want to feel trapped. Being forced back to the office activates our flight-or-fight response and erodes safety. Because of how our nervous system perceives choices, the more choices people have, the safer they feel.

Even though I’m tempted to ask questions about building psychological safety at the team or company level, I want to stay on the individual level for a moment. We talked about how I wasn’t consciously unsafe during a phone call. How can I tell when I feel unsafe if I’m not conscious of it?

There’s physical science behind what happens when you feel unsafe. Your heart rate increases, you might hold your breath, and your body may tense up.  Your thoughts might blank out, and your peripheral vision may narrow as your body prepares for fight or flight.  Your body doesn’t differentiate; it treats any threat as a threatening event.

On the other hand, feeling safe doesn’t mean you lack emotions or feel calm. Feeling calm and internally relaxed signifies safety, but it’s more than that.  When your nervous system is regulated, your emotions align with the situation. They’re not an extreme overreaction or underreaction. There’s congruence. If your emotional response matches the situation, your nervous system and brain feel safe.

That makes sense, but it’s not easy.  We’re trained to hide our emotions and always appear calm.  I can’t tell you how many times I’ve heard and said, “Be a duck.  Calm on the surface and paddling like hell below it.”

And that is not congruent.  But congruence doesn’t mean you act out like a toddler, either.

Step one in creating safety is calming your nervous system by verbalizing your feelings. If you say, “This conversation is overwhelming for me. I need a break. Let me get some water,” you’re safe and regulated at that moment. There’s nothing wrong.

But when you can’t verbalize what you’re experiencing and freeze, that’s a sign you’re no longer in a safe state. Your body starts pumping cortisol and adrenaline, preparing for whatever it perceives as a threat.

Even if you feel overwhelmed, if you’re aware of that feeling and can take some breaths or a short break and return to the conversation, you’re in a safe, regulated state.

I can’t imagine admitting to feeling overwhelmed or asking for a break! Plus, I work with so many people who say, “I feel overwhelmed, but I can’t take a moment for myself.  I need to plow through and get this done.”

It takes a tremendous amount of self-awareness. If you want to create safety and emotional intelligence, you must know what you’re feeling and be able to name it. You also need to sense what others are feeling and understand your emotional impact on them.

For example, if you say, “I’m feeling overwhelmed right now,” and I respond calmly and slow my cadence of speech, your nervous system receives the message that everything is okay.  However, if I’m in “fight or flight” mode and you’re overwhelmed, we’ll end up in a chaotic and unproductive cycle.

Self-awareness and understanding are essential to safety. Unfortunately, many organizations I speak with need help with this.

Amen, sister,


Stay tuned for next week’s exciting conclusion, 3 Steps to Building a Psychologically Safe Environment or The No-Cost, No-Hug Secret to Smarter Teams

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AI as an Innovation Tool – How to Work with a Deeply Flawed Genius!

AI as an Innovation Tool - How to Work with a Deeply Flawed Genius!

GUEST POST from Pete Foley

For those of us working in the innovation and change field, it is hard to overstate the value and importance of AI.   It opens doors, that were, for me at least, barely imaginable 10 years ago.  And for someone who views analogy, crossing expertise boundaries, and the reapplication of ideas across domains as central to innovation, it’s hard to imagine a more useful tool.

But it is still a tool.  And as with any tool, leaning it’s limitations, and how to use it skillfully is key.  I make the analogy to an automobile.  We don’t need to know everything about how it works, and we certainly don’t need to understand how to build it.  But we do need to know what it can, and cannot do. We also need to learn how to drive it, and the better our driving skills, the more we get out of it.

AI, the Idiot Savant?  An issue with current AI is that it is both intelligent and stupid at the same time (see Yejin Chois excellent TED talk that is attached). It has phenomenal ‘data intelligence’, but can also fail on even simple logic puzzles. Part of the problem is that AI lacks ‘common sense’ or the implicit framework that filters a great deal of human decision making and behavior.  Chois calls this the  ‘dark matter’ common sense of decision-making. I think of it as the framework of knowledge, morality, biases and common sense that we accumulate over time, and that is foundational to the unconscious ‘System 1’ elements that influence many, if not most of our decisions. But whatever we call it, it’s an important, but sometimes invisible and unintuitive part of human information processing that is can be missing from AI output.    

Of course, AI is far from being unique in having limitations in the quality of its output.   Any information source we use is subject to errors.  We all know not to believe everything we read on the internet. That makes Google searches useful, but also potentially flawed.  Even consulting with human experts has pitfalls.   Not all experts agree, and even to most eminent expert can be subject to biases, or just good old fashioned human error.  But most of us have learned to be appropriately skeptical of these sources of information.  We routinely cross-reference, challenge data, seek second opinions and do not simply ‘parrot’ the data they provide.

But increasingly with AI, I’ve seen a tendency to treat its output with perhaps too much respect.   The reasons for this are multi-faceted, but very human.   Part of it may be the potential for generative AI to provide answers in an apparently definitive form.  Part may simply be awe of its capabilities, and to confuse breadth of knowledge with accuracy.  Another element is the ability it gives us to quickly penetrate areas where we may have little domain knowledge or background.  As I’ve already mentioned, this is fantastic for those of us who value exploring new domains and analogies.  But it comes with inherent challenges, as the further we step away from our own expertise, the easier it is for us to miss even basic mistakes.  

As for AI’s limitations, Chois provides some sobering examples.  It can pass a bar exam, but can fail abysmally on even simple logic problems.  For example, it suggests building a bridge over broken glass and nails is likely to cause punctures!   It has even suggested increasing the efficiency of paperclip manufacture by using humans as raw materials.  Of course, these negative examples are somewhat cherry picked to make a point, but they do show how poor some AI answers can be, and how they can be low in common sense.   Of course, when the errors are this obvious, we should automatically filter them out with our own common sense.  But the challenge comes when we are dealing in areas where we have little experience, and AI delivers superficially plausible but flawed answers. 

Why is this a weak spot for AI?  At the root of this is that implicit knowledge is rarely articulated in the data AI scrapes. For example, a recipe will often say ‘remove the pot from the heat’, but rarely says ‘remove the pot from heat and don’t stick your fingers in the flames’. We’re supposed to know that already. Because it is ‘obvious’, and processed quickly, unconsciously and often automatically by our brains, it is rarely explicitly articulated. AI, however, cannot learn what is not said.  And so because we don’t tend to state the obvious, it can make it challenging for an AI to learn it.  It learns to take the pot off of the heat, but not the more obvious insight, which is to avoid getting burned when we do so.  

This is obviously a known problem, and several strategies are employed to help address it.  These include manually adding crafted examples and direct human input into AI’s training. But this level of human curation creates other potential risks. The minute humans start deciding what content should and should not be incorporated, or highlighted into AI training, the risk of transferring specific human biases to that AI increase.   It also creates the potential for competing AI’s with different ‘viewpoints’, depending upon differences in both human input and the choices around what data-sets are scraped. There is a ‘nature’ component to the development of AI capability, but also a nurture influence. This is of course analogous the influence that parents, teachers and peers have on the values and biases of children as they develop their own frameworks. 

But most humans are exposed to at least some diversity in the influences that shape their decision frameworks.  Parents, peers and teachers provide generational variety, and the gradual and layered process that builds the human implicit decision framework help us to evolve a supporting network of contextual insight.  It’s obvious imperfect, and the current culture wars are testament to some profound differences in end result.  But to a large extent, we evolve similar, if not identical common sense frameworks. With AI, the narrower group contributing to curated ‘education’ increases the risk of both intentional and unintentional bias, and of ‘divergent intelligence’.     

What Can We do?  The most important thing is to be skeptical about AI output.  Just because it sounds plausible, don’t assume it is.  Just as we’d not take the first answer on a Google search as absolute truth, don’t do the same with AI.  Ask it for references, and check them (early iterations were known to make up plausible looking but nonsense references).  And of course, the more important the output is to us, the more important it is to check it.  As I said at the beginning, it can be tempting to take verbatim output from AI, especially if it sounds plausible, or fits our theory or worldview.  But always challenge the illusion of omnipotence that AI creates.  It’s probably correct, but especially if its providing an important or surprising insight, double check it.    

The Sci-Fi Monster!  The concept of a childish super intelligence has been explored by more than one Science Fiction writer.  But in many ways that is what we are dealing with in the case of AI.  It’s informational ‘IQ’ is greater than the contextual or common sense ‘IQ’ , making it a different type of intelligence to those we are used to.   And because so much of the human input side is proprietary and complex, it’s difficult  to determine whether bias or misinformation is included in its output, and if so, how much?   I’m sure these are solvable challenges.  But some bias is probably unavoidable the moment any human intervention or selection invades choice of training materials or their interpretation.   And as we see an increase in copyright law suits and settlements associated with AI, it becomes increasingly plausible that narrowing of sources will result in different AI’s with different ‘experiences’, and hence potentially different answers to questions.  

AI is an incredible gift, but like the three wishes in Aladdin’s lamp, use it wisely and carefully.  A little bit of skepticism, and some human validation is a good idea. Something that can pass the bar, but that lacks common sense is powerful, it could even get elected, but don’t automatically trust everything it says!

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80% of Psychological Safety Has Nothing to Do With Psychology

or Why the Lack of Psychological Safety Makes You Dumber

80% of Psychological Safety Has Nothing to Do With Psychology

GUEST POST from Robyn Bolton

It’s been over 20 years since “Psychological Safety” exploded onto the scene and into the business lexicon.  But as good as it sounded, I always felt like it was one of those “safe space, everyone gets a trophy, special snowflake” things we had to do to make the Millennials (and subsequent generations) happy.

Then I read Alla Weinberg’s book, A Culture of Safety, and realized I was very, very wrong.

It’s not the equivalent of an HR-approved hug and high-five. 

It’s the foundation of what we do. Without it, there is no productivity, creativity, or progress.

Needing to know more, I reached out to Alla, who graciously agreed to teach me.


Thanks for speaking with me, Alla.  Let’s get right to the point: why should I, or any business leader, care about psychological safety?

The short answer is that without psychological safety, you are dumber.  When you feel unsafe, your operating IQ, which you use for daily tasks, drops in half.

Think about all the people you work with or all the people in your company.  They’re there because they’re smart, have experience, and demonstrated that they can do the job.  But then something goes wrong, and you wonder why they didn’t anticipate it or plan appropriately to avoid it.  You start to question their competence when, in fact, it may be that they feel unsafe, so parts of their brain have gone offline.  Their operating IQ isn’t operating at 100%.

I am so guilty of this.  When things go wrong, I assume someone didn’t know what to do, so they need to be trained, or they did know what to do and decided not to do it. It never occurs to me that there could be something else, something not logical, going on.

We all forget that human beings are biological creatures, and survival is the number one evolutionary trait for all living beings. Our body and mind are wired to ensure our continued existence.

A part of the brain – the prefrontal cortex, responsible for planning, executive thought, and analysis – is unique to humans, and it goes offline when our body feels unsafe. 

When we experience extreme stress, our body and mind cannot distinguish an impending deadline from a lunging tiger.  Our body and mind prioritize survival, so we experience all the biological responses to a threat, like getting tunnel vision, losing peripheral vision, and perceiving limited options.

So, when you’re trying to meet a deadline, and your manager or supervisor asks why you didn’t consider alternatives or complete a specific task, it’s because you physically couldn’t think of it at that moment. This is how human beings operate.

My first reaction is to wonder who can’t tell the difference between a deadline and a tiger because if you can’t tell the difference between the two, you may have bigger problems.  But when you mentioned the inability to perceive options, I immediately thought of something that happened yesterday.

I was on a call with a client, someone I’ve worked with for years and consider a friend, and we were trying to restructure a program to serve their client’s needs better.  I didn’t feel under threat…

Consciously.  You didn’t consciously feel under threat.

Right, I didn’t feel consciously under threat. But I froze.  I absolutely couldn’t think.  I put my head in my hands and tried to block out all the light and the noise, and I still couldn’t think of any option other than what we were already doing.  My brain came to a screeching halt.

That’s your nervous system, and it’s a huge driver of psychological safety.  80% of the information our brain receives comes from our nervous system.  So, while you didn’t consciously feel unsafe, your body felt unsafe and sent a signal to your brain to go into survival mode, and your brain chose to freeze.

But it was a Zoom call.  I was sitting alone in my office. I wasn’t unsafe.  Why would my nervous system think I was unsafe?

Your nervous system doesn’t think. It perceives and reacts.  Let me give you a simple illustration that we’ve all experienced.  When you touch something hot, your hand immediately pulls away.  You say “ouch” after your hand is away from the heat source.  When you felt the hot object, your nervous system entered survival mode and pulled away your hand.  Your brain then had to catch up, so you saw “Ow” after the threat was over.

Hold up.  We’re talking about psychological safety.  What does my nervous system have to do with this?

I define psychological safety as a state of our nervous system with three states: safe, mobilized (fight or flight), and immobilized (freeze response). The tricky part is not psychological but neurobiological. You cannot think your way to safety or unfreeze yourself. The rational mind has no control over this. Mantras and mindsets won’t make you feel safe; it’s a neurobiological process.

That is a plot twist I did not see coming.


Stay tuned for Part 2:

How to Use Your Nervous System to Feel Psychologically Safe, or “Why Mandating a Return to the Office Destroys Safety”

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Continuous Improvement vs. Incremental Innovation

Are They the Same?

Continuous Improvement vs. Incremental Innovation

GUEST POST from Robyn Bolton

“Isn’t continuous improvement the same as incremental innovation?  After all, both focus on doing what you do better, faster, or cheaper.”

Ooof, I have a love-hate relationship with questions like this one.

I hate them because, in the moment, they feel like a gut punch.  The answer feels obvious to me – no, they are entirely different things – but I struggle to explain myself clearly and simply.

I love them because, once the frustration and embarrassment of being unable to offer a clear and simple answer passes, they become a clear sign that I don’t understand something well enough or that *gasp* my “obvious” answer may be wrong.

So, is Continuous Improvement the same as Incremental Innovation?

No. They’re different.

But the difference is subtle, so let’s use an analogy to tease it apart.

Imagine learning to ride a bike.  When you first learn, success is staying upright, moving forward, and stopping before you crash into something.  With time and practice, you get better.  You move faster, stop more quickly, and move with greater precision and agility.

That’s continuous improvement.  You’re using the same solution but using it better.

Now, imagine that you’ve mastered your neighborhood’s bike paths and streets and want to do more.  You want to go faster, so add a motor to your bike.  You want to ride through the neighboring forest, so you change to off-road tires.  You want a smoother feel on your long rides, so you switch to a carbon fiber frame.

That’s incremental innovation.  You changed an aspect of the solution so that it performs better.

It all comes down to the definition of innovation – something different (or new) that creates value.

Both continuous improvement and incremental innovation create value. 

The former does it by improving what exists. The latter does it by changing (making different) what exists.

Got it. They are entirely different things.

Sort of.

Think of them as a Venn diagram – they’re different but similar.

There is evidence that a culture committed to quality and continuous improvement can lead to a culture of innovation because “Both approaches are focused in meeting customer needs, and since CI encourages small but constant changes in current products, processes and working methods its use can lead firms to become innovative by taking these small changes as an approach to innovation, more specifically, incremental innovation.”

Thanks, nerd.  But does this matter where I work, which is in the real world?

Yes.

Continuous Improvement and Incremental Innovation are different things and, as a result, require different resource levels, timelines, and expectations for ROI.

You should expect everyone in your organization to engage in continuous innovation (CI) because (1) using CI helps the organizations change adoption and risk taking by evaluating and implementing solutions to current needs” and (2) the problem-solving tools used in CI uncover “opportunities for finding new ideas that could become incremental innovations.”

You should designate specific people and teams to work on incremental people because (1) what “better” looks like is less certain, (2) doing something different or new increases risk, and (3) more time and resources are required to learn your way to the more successful outcome.

What do you think?

How do you answer the question at the start of this post?

How do you demonstrate your answer?

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Is AI Saving Corporate Innovation or Killing It?

Is AI Saving Corporate Innovation or Killing It?

GUEST POST from Robyn Bolton

AI is killing Corporate Innovation.

Last Friday, the brilliant minds of Scott Kirsner, Rita McGrath, and Alex Osterwalder (plus a few guest stars like me, no big deal) gathered to debate the truth of this statement.

Honestly, it was one of the smartest and most thoughtful debates on AI that I’ve heard (biased but right, as my husband would say), and you should definitely listen to the whole thing.

But if you don’t have time for the deep dive over your morning coffee, then here are the highlights (in my humble opinion)

Why this debate is important

Every quarter, InnoLead fields a survey to understand the issues and challenges facing corporate innovators.  The results from their Q2 survey and anecdotal follow-on conversations were eye-opening:

  • Resources are shifting from Innovation to AI: 61.5% of companies are increasing the resources allocated to AI, while 63.9% of companies are maintaining or decreasing their innovation investments
  • IT is more likely to own AI than innovation: 61.5% of companies put IT in charge of exploring potential AI use cases, compared to 53.9% of Innovation departments (percentages sum to greater than 0 because multiple departments may have responsibility)
  • Innovation departments are becoming AI departments.  In fact, some former VPs and Directors of Innovation have been retitled to VPs or Directors of AI

So when Scott asked if AI was killing Corporate Innovation, the data said YES.

The people said NO.

What’s killing corporate innovation isn’t technology.  It’s leadership.

Alex Osterwalder didn’t pull his punches and delivered a truth bomb right at the start. Like all the innovation tools and technologies that came before, the impact of AI on innovation isn’t about the technology itself—it’s about the leaders driving it.

If executives take the time to understand AI as a tool that enables successful outcomes and accelerates the accomplishment of key strategies, then there is no reason for it to threaten, let alone supplant, innovation. 

But if they treat it like a shiny new toy or a silver bullet to solve all their growth needs, then it’s just “innovation theater” all over again.

AI is an Inflection Point that leaders need to approach strategically

As Rita wrote in her book Seeing Around Corners, an inflection point has a 10x impact on business, for example, 10x cheaper, 10x faster, or 10x easier.  The emergence and large-scale adoption of AI is, without doubt, an inflection point for business.

Just like the internet and Netscape shook things up and changed the game, AI has the power to do the same—maybe even more. But, to Osterwalder’s point, leaders need to recognize AI as a strategic inflection point and proceed accordingly. 

Leaders don’t need to have it all figured out yet, but they need a plan, and that’s where we come in.

This inflection point is our time to shine

From what I’ve seen, AI isn’t killing corporate innovation. It’s creating the biggest corporate innovation opportunity in decades.  But it’s up to us, as corporate innovators, to seize the moment.

Unlike our colleagues in the core business, we are comfortable navigating ambiguity and uncertainty.  We have experience creating order from what seems like chaos and using innovation to grow today’s business and create tomorrow’s.

We can do this because we’ve done it before.  It’s exactly what we do,

AI is not a problem.  It’s an opportunity.  But only if we make it one.

AI is not the end of corporate innovation —it’s a tool, a powerful one at that.

As corporate innovators, we have the skills and knowledge required to steer businesses through uncertainty and drive meaningful change. So, let’s embrace AI strategically and unlock its full potential.

The path forward may not always be crystal clear, but that’s what makes it exciting. So, let’s seize the moment, navigate the chaos, and embrace AI as the innovation accelerant that it is.

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