Category Archives: Technology

What Latest Research Reveals About Innovation Management Software

GUEST POST from Jesse Nieminen

Our industry of innovation management software is quite an interesting one. It’s been around for a while, but it’s still not a mainstay that every organization would use, at least not in the same way as CRM and team communication software are.

Hence, there’s quite little independent research available out there to prove its efficacy, or even for determining which parts of it are the most valuable.

So, when I saw a new study, conducted jointly by a few German universities, come out on the topic, I was naturally curious to learn more.

In this article, I’ll share the key findings of the study with you, as well as some personal thoughts on the how and why behind these findings. We’ll also wrap up the discussion by considering how these findings relate to the wider trends within innovation management.

About the Study

Before we get to the results, let’s first briefly cover what the study was actually about and how it was conducted.

First, the focus of the study was to analyze the role of Innovation Management Software (IMS) adoption for New Product Development (NPD) effectiveness and efficiency, as well as the factors (software functionality and offered services) that actually led to successful adoption of said innovation management software.

The data was collected with an online questionnaire that was answered by innovation managers from 199 German firms of varying sizes, 45% of which used an Innovation Management Software, and 55% of which didn’t.

While this is the largest independent piece of research I’ve yet seen on innovation management software, we should remember that all research comes with certain limitations and caveats, and it’s important to understand and keep these in mind.

You can read the paper for a more detailed list, but in my opinion, this boils down to a few key things:

First, the study uses NPD performance as a proxy for innovation outcomes. This is an understandable choice to make the research practical, but in reality, innovation is much more than just NPD.
Second, while the sample size of companies is respectable, the demographic is quite homogenous as they are all German companies that employ an innovation manager, which obviously isn’t representative of every organization out there.
Third, the results are analyzed with regression analyses, which always brings up the age-old dilemma: correlation doesn’t imply causation. In other words, the study can tell us the “what”, not the “why” or “how”.
And finally, while the chosen variables are based on validated prior research, the questions still require subjective analysis from the respondent, which can introduce some bias to the results.

So, let’s keep these in mind and move on to the actual findings.

The Main Findings of the Study

The authors have done a great job in summarizing the hypothesis and respective results in a table, which you’ll also find reproduced below.

Innovation Management Software Research Results

Let’s break the results down by hypothesis and cover the main takeaways for each.

Innovation Management Software Adoption Leads to Better NPD Performance

The first hypothesis was that using an Innovation Management Software would lead to better New Product Development performance. This can further be broken down into two parts: efficiency and effectiveness.

The results show that IMS adoption does indeed improve NPD efficiency, but the impact on NPD effectiveness wasn’t significant.

Innovation Management Software improves New Product Development efficiency, but the impact on effectiveness isn’t significant.

Intuitively, this makes sense and is also well in line with our experience. Innovation, especially in terms of NPD, is hard and requires a lot of work and difficult decisions, usually in the face of significant uncertainty. No software can magically do that job for you, but a good tool can help keep track of the process and do some of the heavy lifting for you.

This naturally helps with efficiency which allows innovators to focus more of their efforts on things that will lead to better results, but those results still aren’t a given.

Functionality That Leads to Higher IMS Adoption

The second hypothesis is focused on the functionality provided by the innovation management software, and the impact of said functionality on overall IMS adoption.

To be more specific, the respondents were asked how important they considered each functionality to be for their firm.

Here, Idea Management was the only functionality that had an impact for these firms.

Idea Management was the only functionality that had a significant positive impact for the surveyed firms.

Again, that intuitively makes sense and is well in line with our experience. Idea management is the part that you embed in the organization’s daily processes and use across the organization to make ideation and innovation systematic. And as mentioned, it’s the part that does a lot of the heavy lifting, such as increasing transparency, communication and collecting and analyzing data, that would otherwise take up a lot of time from people running innovation, which naturally helps with efficiency.

So, while Strategy and Product Management capabilities do have their uses, they are not nearly as essential to IMS adoption, or innovation success for that matter.

In our experience, this primarily comes down to the fact that most companies can manage those capabilities just fine even without an IMS. The value-add provided by the software just isn’t nearly as high for most organizations there.

Services That Lead to Higher IMS Adoption

The third and final hypothesis focused on the importance of the services offered by IMS vendors for the respective firms.

Here the spectrum covered consulting, training, customer support, customizations, as well as software updates and upgrades.

Here, the only factor that made a positive difference for the respondents was software updated and upgrades. This category includes both minor improvements as well as new functionality for the software.

Interestingly enough, for consulting that relationship was negative. Or as the authors put it, adopters more alienate than appreciate such services.

Software updates and upgrades were the only service with a positive impact, whereas consulting actually had a negative one.

Let’s first cover the updates and upgrades as that is probably something everyone agrees on.

Good software obviously evolved quickly and as most companies have embraced the Software as a Service (SaaS) model, they’ve come to expect frequent bug fixes, usability and performance improvements, and even new features for free. Over the lifetime of the product, these make a huge difference.

Thus, most understand that you should choose a vendor that is committed and capable of delivering a frequent stream of updates and new capabilities.

Let’s then move on to consulting and discuss why it is detrimental to adoption.

While we’ve always kept professional services to a minimum at Viima, this still came as a bit of a surprise for me. As I’ve raised this point up in discussions with a couple of people in the industry, that do offer such services, they seem to respond with varying degrees of denial, dismissal, and perhaps even a hint of outrage. When such emotions are at play, it’s always a good time for an innovator to lean in and dig a bit deeper, so let’s do that!

Looking at this from the point of view of the customer, there are a few obvious problems:

Misaligned incentives
… which leads to focusing on the wrong issues
Lack of ownership

Each of these could be discussed in length, but let’s focus on covering the keys here.

First, it’s important to understand that every software company makes most of their profits from software licenses. Thus, while generally speaking modern SaaS models do incentivize the vendor to make you successful, that isn’t the whole picture. The focus is actually on keeping the customer using the software. With the right product, that will lead to good outcomes, but that isn’t necessarily always the case.

However, when you add consulting to the mix, it’s only natural that it focuses primarily on the usage of the software because that’s what they know best, and what’s also in their best interest.

And, while making the most out of the software is important, it’s usually not the biggest challenge organizations have with their innovation efforts. In our experience, these are usually in topics such as organizational structure, resource allocation, talent, culture, as well as leadership buy-in and understanding.

And, even if the vendor would focus more on some of these real challenges the customer has, they rarely are the best experts in these matters due to their experience coming from matters related to the product.

Advice on Innovation Management

Now, once you have a consultant come in, you of course want to listen to them. However, a consultant’s job is to give advice, it isn’t to get to the outcomes you want or need, and there’s a big difference there. That is one of the fundamental challenges in using consultants in general, and a big reason for why many don’t like to use them for long-term issues that are core to your future success, such as innovation.

Having said that, if you do use consultants, you can’t lose track of the fact you still need to take ownership for delivering those results. The consultant might be able to help you with that, or they might not. It’s still your job to make the decisions and execute on the chosen plan.

Put together, these reasons are also why we have been reluctant to do much consulting for our customers. We simply think the customer is best served by taking ownership of these matters themselves. We do, on the other hand, seek to provide them with the information, materials and advice they might need in navigating some of these decisions – with no additional cost through channels such as this blog and our online coaching program.

How do these findings relate to wider IMS trends?

Now that we’ve covered the key findings, let’s discuss how these are present in the wider trends within the Innovation Management Software industry.

In addition to what we hear in our discussions with customers and prospects, we’ve also discussed the topic quite extensively with industry analysts and would break these down into a few main trends.

Focus on enterprise-wide innovation

One of the big trends we see is that more and more companies are following in the footsteps of the giants like Tesla, Amazon, Apple and Google, and are moving innovation from separate silos to become more of a decentralized organization-wide effort.

This isn’t always necessary for pure NPD performance, which is what the study was focused on, but it is certainly key for scaling innovation in general, and one where efficient idea management can play a key role.

Once you embark on that journey, you’ll realize that your innovation team will initially be spread very thin. In that situation, it’s especially important to have easy-to-use tools that can empower people across the organization and improve efficiency.

Simultaneous need for ease of use and flexibility

That enterprise-wide innovation trend is also a big driver for the importance of intuitiveness, ease of use, and flexibility becoming more important.

In the past, you could have an innovation management software that is configured to match your stage-gate process for NPD. You might still need that, but it’s no longer enough. You probably want more agile processes for some of your innovation efforts, and more lightweight ones for some of the more incremental innovation many business units need to focus on.

If people across the organization don’t know how to use the software, or require extensive training to do so, you’ll face an uphill battle. What’s more, if you need to call the vendor whenever you need to make a change to the system, you’re in trouble. Top innovators often run dozens or even hundreds of different simultaneous innovation processes in different parts of the organization, so that quickly becomes very tedious and expensive.

Reducing operational complexity and costs

A big consideration for many is the operational complexity and running costs associated in running and managing their infrastructure and operations.

Extensive configuration work and on-premises installations significantly add to both of these, so even though they can be tempting for some organizations, the costs do pile up a lot over time, especially since it requires a lot more attention from your support functions like IT to manage.

What’s more, if you want to make changes or integrate these systems with new ones you may introduce, typically you only have one option: you need to turn to your IMS vendor.

As IMS tools have matured and off-the-shelf SaaS services have become much more capable, the compromises in increased rigidity, complexity and running costs, as well as less frequent updates are no longer worth it and off-the-shelf SaaS is now the way to go for almost everyone. With SaaS, you benefit immensely from economies of scale, and you are no longer held captive by the sunk cost fallacy of up-front license payments and extensive configuration and training work.

Commoditization in Idea Management

As the study pointed out, idea management is at the core of most innovation management software. However, in the last decade, the competition in the space has increased a lot.

There are now native SaaS platforms, like Viima, that are able to offer extremely competitive pricing due to efficient operations and a lean organizational structure. This has put a lot of pressure on many vendors to try to differentiate themselves and justify their higher price tags with additional professional services, as well as adjacent products and capabilities.

In our experience, while these might sound good on paper, they aren’t often leading to more value in real life, and the respondents of this study would seem to concur.

Conclusion

So, to conclude, what did we learn from the research?

In a nutshell, no innovation management software or vendor will miraculously turn you into a successful innovator. A good software, however, will help you become more efficient with your innovation efforts, as well as lead to softer benefits such as improvements in communication, knowledge transfer and culture. Put together, these can make your life a lot easier so that you can focus on actually driving results with innovation.

What then should you consider when choosing your innovation management vendor?

Well, the evidence shows that you should focus on idea management, as that’s where the biggest impact on the factors mentioned above come from. And therein, you should focus on vendors that continuously update and evolve their software with the help of modern technology and that has made all the above so easy and intuitive that they don’t need to sell you consulting.

And of course, ask them the tough questions. Ask to test the software in real life. If you can’t, that is a red flag in and of itself. See how flexible and easy-to-use their software really is. Does it require consulting or configuration by the vendor?

This article was originally published in Viima’s blog.

Image credits: Unsplash, Viima

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AI-Powered Tools for Creative Industries

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AI-Powered Tools for Creative Industries

GUEST POST from Chateau G Pato

The creative industries are experiencing a transformation, thanks to artificial intelligence (AI) tools that enhance productivity, spark innovation, and expand creative possibilities. From content creation to design, AI-powered tools are reshaping the way artists, designers, and thinkers work. This article explores these advancements, featuring real-world case studies that illustrate the impact of AI on creative processes.

The Rise of AI in Creative Processes

AI is equipped to handle tasks that traditionally required significant human effort, such as pattern recognition and data analysis. However, its influence on creativity isn’t about replacing human artistry—it’s about augmenting it. AI can handle repetitive tasks, allowing creatives to focus on what they do best: innovating and ideating.

Case Study 1: AI in Music Composition

AI Platform: AIVA (Artificial Intelligence Virtual Artist)

AIVA is an AI-based composer that’s been used by artists and musicians around the world to enhance and inspire music production. Trained on a wide range of classical compositions, AIVA can create original scores and suggest enhancements to existing compositions. By iterating with composers, AIVA helps create music that resonates emotionally with audiences.

Outcome: AIVA was employed in film scoring, leading to a fusion of human creativity and AI precision. Composers reported a 30% reduction in time spent on initial drafts, allowing more time to focus on intricacy and expression.

Tools Transforming the Industry

Beyond music, AI tools are influencing numerous sectors within creative industries. They provide everything from generative design and content curation to audience engagement analytics. Let’s explore another example where AI tools have significantly impacted creativity.

Case Study 2: AI in Graphic Design

AI Platform: Adobe Sensei

Adobe Sensei uses AI to boost productivity and creativity for graphic designers by automating mundane tasks such as object detection and layering. Designers can create more complex visuals in less time with AI assistance. Tools like Adobe’s “Content-Aware Fill” leverage AI algorithms to enhance or alter images seamlessly.

Outcome: A marketing agency integrated Adobe Sensei into their workflow, reducing their design time for digital advertising campaigns by 40%. Designers reported feeling less creatively fatigued, leading to a rise in innovative concepts and overall client satisfaction.

Conclusion

Artificial intelligence has carved out an invaluable role within the creative industries, not as a replacement, but as a powerful ally. The potential for AI to enhance creative output lies in its ability to handle intensive tasks, providing creatives with the freedom to push boundaries. As AI continues to evolve, so too will the possibilities for innovation, ensuring that the marriage between human creativity and machine precision leads to exciting new frontiers.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Microsoft CoPilot

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We Must Rethink the Future of Technology

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We Must Rethink the Future of Technology

GUEST POST from Greg Satell

The industrial revolution of the 18th century was a major turning point. Steam power, along with other advances in areas like machine tools and chemistry transformed industry from the work of craftsmen and physical labor to that of managing machines. For the first time in world history, living standards grew consistently.

Yet during the 20th century, all of that technology needed to be rethought. Steam engines gave way to electric motors and internal combustion engines. The green revolution and antibiotics transformed agriculture and medicine. In the latter part of the century digital technology created a new economy based on information.

Today, we are on the brink of a new era of innovation in which we will need to rethink technology once again. Much like a century ago, we are developing new, far more powerful technologies that will change how we organize work, identify problems and collaborate to solve them. We will have to change how we compete and even redefine prosperity itself.

The End of the Digital Revolution

Over the past few decades, digital technology has become almost synonymous with innovation. Every few years, a new generation of chips would come out that was better, faster and cheaper than the previous one. This opened up new possibilities that engineers and entrepreneurs could exploit to create new products that would disrupt entire industries.

Yet there are only so many transistors you can cram onto a silicon wafer and digital computing is nearing its theoretical limits. We have just a few generations of advancements left before the digital revolution grinds to a halt. There will be some clever workarounds to stretch the technology a bit further, but we’re basically at the end of the digital era.

That’s not necessarily a bad thing. In many ways, the digital revolution has been a huge disappointment. Except for a relatively brief period in the late nineties and early aughts, the rise of digital technology has been marked by diminished productivity growth and rising inequality. Studies have also shown that some technologies, such as social media, worsen mental health.

Perhaps even more importantly, the end of the digital era will usher in a new age of heterogeneous computing in which we apply different computing architectures to specific tasks. Some of these architectures will be digital, but others, such as quantum and neuromorphic computing, will not be.

The New Convergence

In the 90s, media convergence seemed like a futuristic concept. We consumed information through separate and distinct channels, such as print, radio and TV. The idea that all media would merge into one digital channel just felt unnatural. Many informed analysts at the time doubted that it would ever actually happen.

Yet today, we can use a single device to listen to music, watch videos, read articles and even publish our own documents. In fact, we do these things so naturally we rarely stop to think how strange the concept once seemed. The Millennial generation doesn’t even remember the earlier era of fragmented media.

Today, we’re entering a new age of convergence in which computation powers the physical, as well as the virtual world. We’re beginning to see massive revolutions in areas like materials science and synthetic biology that will reshape massive industries such as energy, healthcare and manufacturing.

The impact of this new convergence is likely to far surpass anything that happened during the digital revolution. The truth is that we still eat, wear and live in the physical world, so innovating with atoms is far more valuable than doing so with bits.

Rethinking Prosperity

It’s a strange anachronism that we still evaluate prosperity in terms of GDP. The measure, developed by Simon Kuznets in 1934, became widely adopted after the Bretton Woods Conference a decade later. It is basically a remnant of the industrial economy, but even back then Kuznets commented, “the welfare of a nation can scarcely be inferred from a measure of national income.”

To understand why GDP is problematic, think about a smartphone, which incorporates many technologies, such as a camera, a video player, a web browser a GPS navigator and more. Peter Diamandis has estimated that a typical smartphone today incorporates applications that were worth $900,000 when they were first introduced.

So, you can see the potential for smartphones to massively deflate GDP. First of all, the price of the smartphone itself, which is just a small fraction of what the technology in it would have once cost. Then there is the fact that we save fuel by not getting lost, rarely pay to get pictures developed and often watch media for free. All of this reduces GDP, but makes us better off.

There are better ways to measure prosperity. The UN has proposed a measure that incorporates 9 indicators, the OECD has developed an alternative approach that aggregates 11 metrics, UK Prime Minister David Cameron has promoted a well-being index and even the small city of Somerville, MA has a happiness project.

Yet still, we seem to prefer GDP because it’s simple, not because its accurate. If we continue to increase GDP, but our air and water are more polluted, our children less educated and less healthy and we face heightened levels of anxiety and depression, then what have we really gained?

Empowering Humans to Design Work for Machines

Today, we face enormous challenges. Climate change threatens to pose enormous costs on our children and grandchildren. Hyperpartisanship, in many ways driven by social media, has created social strife, legislative inertia and has helped fuel the rise of authoritarian populism. Income inequality, at its highest levels since the 1920s, threatens to rip shreds in the social fabric.

Research shows that there is an increasing divide between workers who perform routine tasks and those who perform non-routine tasks. Routine tasks are easily automated. Non-routine tasks are not, but can be greatly augmented by intelligent systems. It is through this augmentation that we can best create value in the new century.

The future will be built by humans collaborating with other humans to design work for machines. That is how we will create the advanced materials, the miracle cures and new sources of clean energy that will save the planet. Yet if we remain mired in an industrial mindset, we will find it difficult to harness the new technological convergence to solve the problems we need to.

To succeed in the 21st century, we need to rethink our economy and our technology and begin to ask better questions. How does a particular technology empower people to solve problems? How does it improve lives? In what ways does it need to be constrained to limit adverse effects through economic externalities?

As our technology becomes almost unimaginably powerful, these questions will only become more important. We have the power to shape the world we want to live in. Whether we have the will remains to be seen.

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

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Measuring Success in Agile Transformations

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Measuring Success in Agile Transformations

GUEST POST from Art Inteligencia

Agile transformations are sweeping through organizations worldwide, promising enhanced flexibility, faster time to market, and greater responsiveness to change. However, while many companies embark on this journey, the measure of success can often seem elusive. To truly gauge the effectiveness of an agile transformation, one must look beyond surface-level metrics and delve into deeper, more meaningful indicators.

In this exploration, we’ll delve into what it means to measure success in agile transformations, enriched by real-world case studies that illustrate successful implementations.

Understanding Agile Success

Agile transformation is not a destination but a journey. Success isn’t simply about adopting Scrum or any other agile framework. It’s about fostering a culture of continuous improvement, collaboration, and responsive adaptation to change.

To assess success, consider the following dimensions:

Customer Satisfaction: Are customers happier and are their feedback loops tighter?
Employee Engagement: Are team members more engaged and empowered to innovate?
Quality Improvement: Are defects reduced and is quality improving?
Time to Market: Are products and services hitting the market faster?
Value Delivery: Is there a clear, measurable increase in value delivered to stakeholders?

Case Study 1: TechCorp’s Agile Journey

Background

TechCorp, a mid-sized software company, embarked on an agile transformation to improve product development speed and enhance customer satisfaction.

Approach

The company started by forming cross-functional teams and implementing Scrum. Leaders invested in training and coaching, emphasizing a shift in mindset toward customer-centricity and collaboration.

Outcomes

Within a year, TechCorp saw a 30% reduction in time to market, with customer satisfaction scores increasing by 20%. Employee engagement surveys revealed a 25% boost in morale, and the defect rate in software releases dropped by 15%.

Continual retrospectives and adaptations became part of the culture, allowing TechCorp to sustain and build upon these gains.

Case Study 2: HealthFirst’s Transformation

Background

HealthFirst, a healthcare provider, sought to transform its operations to improve patient outcomes and operational efficiency.

Approach

The transformation began with the integration of agile methodologies across various departments, from IT to patient care management. A focus was placed on iterative improvement and adopting a data-driven decision-making process.

Outcomes

After two years, HealthFirst reported a 40% reduction in patient wait times and a substantial increase in patient satisfaction scores. Operational costs decreased by 15%, and employee turnover rates dropped by 10%.

The organization’s commitment to measuring patient-centric outcomes allowed for a more rounded view of success, blending agile practices with core healthcare principles.

Key Takeaways

Agile transformations can yield impressive results when approached with a comprehensive understanding of success metrics. Organizations should focus on aligning agile processes with broader strategic goals to ensure meaningful change.

By closely monitoring both qualitative and quantitative outcomes — from customer feedback to engagement levels — companies can create a consistent feedback loop to guide ongoing improvement. The true measure of success lies not only in adhering to agile principles, but in fostering a dynamic, responsive culture that can thrive in a rapidly changing world.

Are you ready to embark on your agile journey? Remember, success is measured not just in numbers, but in transformed lives and lasting impact.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Sickcare AI Field Notes

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Sickcare AI Field Notes

I recently participated in a conference on Artificial Intelligence (AI) in healthcare. It was the first onsite meeting after 900 days of the pandemic.

Here is a report from the front:

AI has a way to go before it can substitute for physician judgment, intuition, creativity and empathy
There seems to be an inherent conflict between using AI to standardize decisions compared to using it for mass customization. Efforts to develop customized care must be designed around a deep understanding of what happens at the ground level along the patient pathway and must incorporate patient engagement by focusing on such things as shared decision-making, definition of appointments, and self-management, all of which are elements of a “build-to-order” approach.
When it comes to dissemination and implementation, culture eats strategy for lunch.
The majority of the conversations had to do with the technical aspects and use cases for AI. A small amount was about how to get people in your organization to understand and use it.
The goal is to empower clinical teams to collaborate with patient teams and that will take some work. Moving sick care to healthcare also requires changing a sprint mindset to a marathon relay race mindset with all the hazards and risks of dropped handoffs and referral and information management leaks.
AI is a facilitating technology that cuts across many applications, use cases and intended uses in sick care. Some day we might be recruiting medical students, residents and other sick care workers using AI instead of those silly resumes.
The value proposition of AI includes improving workflow and improving productivity
AI requires large, clean data sets regardless of applications
It will take a while to create trust in technology
There needs to be transparency in data models
There is a large repository of data from non-traditional sources that needs to be mined e.g social media sites, community based sites providing tests, like health clubs and health fairs, as well as post acute care facilities
AI is enabling both the clinical and business models of value based care
Cloud based AI is changing diagnostic imaging and pattern recognition which will change manpower dynamics
There are potential opportunities in AI for quality outcome stratification, cost accounting and pricing of episodes of care, determining risk premiums and optimizing margins for a bundled priced procedure given geographic disparities in quality and cost.
We are in the second era of AI that is based on deep learning v rules based algorithms
Value based care requires care coordination, risk stratification, patient centricity and managing risk
Machine learning is being used, like Moneyball, to pick startup winners and losers, with a dose of high touch.
It is encouraging to see more and more doctors attending and speaking at these kinds of meetings and lending a much needed perspective and reality check to technologists and non-sick care entrepreneurs. There were few healthcare executives besides those who were invited to be on panels.
Overcoming the barriers to AI in sick care have mostly to do with changing behavior and not dwelling on the technicalities, but, rather, focusing on the jobs that doctors need to get done.
The costs of AI , particularly for small, independent practitioners, are often not affordable, particularly when bundled with crippling EMR expenses . Moore’s law has not yet impacted medicine
The promise of using AI to get more done with less conflicts with the paradox of productivity
Top of mind problems to be solved were how to increase revenuces, cut costs , fill the workforce pipelines and address burnout and behavioral health employee and patient problems with scarce resouces.
Nurses, pharmacists, public health professionals and veterinarians were under represented
Payers were scarce
Patients were scarce
Students, residents and clinicians were looking for ways to get side gigs, non-clinical careers and exit ramps if need be.
70% of AI applications are in radiology
AI is migrating from shiny to standard, running in the background to power diverse remote care modalities
Chronic disease management and behavioral health have replace infectious disease as the global care management challenges
AI education and training in sickcare professional schools is still woefully absent but international sickcare professional schools are filling the gaps
Process and workflow improvements are a necessary part of digital and AI transformation

At its core, AI is part of a sick care eco-nervous system “brain” that is designed to change how doctors and patients think, feel and act as part of continuous behavioral improvement. Outcomes are irrelevant without impact.

AI is another facilitating technology that is part and parcel of almost every aspect of sick care. Like other shiny new objects, it remains to be seen how much value it actually delivers on its promise. I look forward to future conferences where we will be discussing how, not if to use AI and comparing best practices and results, not fairy tales and comparing mine with yours.

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Designing Products for a Global Audience

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Designing Products for a Global Audience

GUEST POST from Chateau G Pato

In today’s interconnected world, designing products for a global audience isn’t just a strategy; it’s a necessity. As companies expand their reach across borders, understanding the diverse needs, cultural contexts, and user behaviors becomes critical. To successfully innovate on a global scale, a deep commitment to human-centered design is paramount.

Understanding Diverse Needs

Designing for a global market requires acknowledging and embracing diversity. Considerations such as language, cultural nuances, local regulations, and technological infrastructure can make or break a product’s success overseas. Understanding these elements can help avoid missteps and create products that resonate with users worldwide.

Key Principles of Global Product Design

Empathy and Research: Conduct exhaustive research to understand user needs in different regions. Employ methodologies like ethnographic studies and immersive local experiences.
Localization: Go beyond mere translation. Consider cultural customs, color symbolism, and local trends.
Flexibility and Scalability: Design products that can evolve with changing user needs and technological advancements.
Collaborative Design: Involve local designers and experts to bring authentic perspectives into the design process.

Case Study: Airbnb

Airbnb’s success as a global platform lies in its commitment to localization and user-centric design. When expanding into new markets, Airbnb goes beyond text translation. They consider local travel behaviors and integrate culturally relevant elements into their platform.

For instance, in Asian markets, where personal relationships and trust are paramount, Airbnb enhanced its platform with features that allow hosts and guests to exchange more information upfront, fostering trust through transparency. They also adjusted their business model in China to cater to the unique regulatory environment and partnered with local payment providers.

Case Study: Coca-Cola

Coca-Cola’s approach to global product design is a testament to the power of local personalization within a global brand framework. Coca-Cola adapts its marketing strategies and product offerings to suit local tastes and preferences.

In Japan, Coca-Cola introduced more than 100 new products annually, experimenting with local flavors such as matcha and shiso. They focused on understanding local taste trends and innovating accordingly, making them a key player in regional markets.

Challenges in Designing for a Global Audience

Despite the benefits, designing for a global audience entails certain challenges:

Cultural Sensitivity: Misinterpretations can lead to alienation. Cultural sensitivity in design choices is crucial.
Regulatory Compliance: Navigating varied regulatory environments requires careful planning and flexible design frameworks.
Technological Disparities: Varying levels of technology adoption necessitate adaptable designs that work in both high-tech and low-tech environments.

The Road Ahead

The journey of designing products for a global audience is continuous and evolving. It requires a persistent commitment to learning, adaptation, and empathy. Companies that master this approach will not only thrive globally but will also forge deeper connections with their audiences, ultimately driving innovation and growth in unprecedented ways.

As we navigate the complexities of global markets, let us embrace the diversity that defines our world, channeling it into human-centered design innovations that are as varied and dynamic as the people we aim to serve.

Image credit: Unsplash

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Transformation Insights – Part Two

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Transformation Insights - Part Two

“The world needs stories and characters that unite us rather than tear us apart.”~ Gale Anne Hurd, Producer of Aliens and The Terminator

GUEST POST from Bruce Fairley

In my early years I was fortunate to spend some time on film sets. Unlike how the entertainment industry is portrayed in the Netflix series, The Movies that Made Us, I did not come to blows with any of my directors as Eddie Murphy apparently did with John Landis during the making of Coming to America. Nor did I witness an entire crew mutiny, as James Cameron did on Aliens. Instead, I often saw the same dynamic I’ve witnessed in the tech sector from the first moment I stepped off set and into I.T.

People coming together.

Skilled, diverse, passionate people hard at work fighting against miscommunication, technical issues, and time constraints – coming together to achieve something significant. I referred to this in my previous Transformation Insights post, The Future Always Wins as:

Collaboration Between Complementary Influencers.

This dynamic is as true of a film set as it is of a firm engaged in digital transformation. In both cases, expertise in various areas is required to create a successful whole, with C-Suite leaders in the corporate sphere tasked with providing the articulated vision at the helm. Of course, the success of any endeavor comes down to human-powered action and decision making at every level of execution. And while the challenges of a digital transformation project may not be as bone-breaking dangerous as the stunts in an action film, getting to greatness requires a similar fusion of mind and machine – of talent and technology.

If that sounds like The Terminator, consider that its box office success speaks to the fusion of mind and machine as an unstoppable trajectory – but those who deepen their humanity rather than succumb to machine rule are the heroes that triumph. This was mirrored in the making of the film, which was nearly shut down when the crew put down their tools. Addressing their humanity and acknowledging the value of their contribution changed the story from disaster to blockbuster.

Humans lead – technology serves. Not the other way around.

When that is reversed, dystopia ensues whether on screen or in the boardroom. Having witnessed many occasions in which technology was expediently obtained before its value to the user could be established, I am convinced we have lost the plot in telling a wider, corporate story. Technology was supposed to liberate not enslave. Instead, how many times have you attended a Zoom meeting or prepared weeks for a presentation only to discover the sound not working, the slide deck freezing, or even a hidden ‘on’ button? These may be simple examples, but they rob the intrepid hero of the corporate journey; the chance to shine and advance their creative talent much like the crew of Aliens putting down their tools. Now multiply that by the large scale digital transformation projects I’ve spearheaded, and it becomes clear how a broken axis between human-powered decision making and technology can break the bottom line.

Optimism and momentum towards a more positive, successful outcome hinges on more than technological expertise. It requires an understanding of the whole story – and how the team, tech, leadership, and consumers each play a role. The story you wish to tell about your corporate journey requires buy-in at every level of service – human and tech. Obstacles are not indictments, they are merely obstacles. But they do often require a third-party complementary collaborator that understands how to transform pitfalls into profits.

When I launched the Narrative Group I wanted to amplify the genius of C-Suite executives through the optimization of the business-tech relationship. Similarly to how I observed the inner workings of a set and how all the pieces had to fit together to create a screen success, I spent years observing digital transformation from the inside. Across continents and boardrooms, I learned, led, and transformed as well. This only increased my commitment to helping talented leaders tell their story successfully.

If you’re a C-Suite leader that would like to storyboard the trajectory of your corporate success, please feel free to reach out and continue the conversation at:

connect@narrative-group.com

Image Credit: The Narrative Group

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The Ethics of AI in Innovation

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The Ethics of AI in Innovation

GUEST POST from Chateau G Pato

In today’s rapidly evolving technological landscape, artificial intelligence (AI) plays a pivotal role in driving innovation. From healthcare and transportation to education and finance, AI’s potential to transform industries is unparalleled. However, with great power comes great responsibility. As we harness the capabilities of AI, we must also grapple with the ethical implications that accompany its use. This article delves into the ethical considerations of AI in innovation and presents two case studies that highlight the challenges and solutions within this dynamic field.

Understanding AI Ethics

AI ethics refers to the moral principles and guidelines that govern the development, deployment, and use of AI technologies. These principles aim to ensure that AI systems are designed and used in ways that are fair, transparent, and accountable. AI ethics also demand that we consider the potential biases in AI algorithms, the impact on employment, privacy concerns, and the long-term societal implications of AI-driven innovations.

Case Study 1: Healthcare AI – The IBM Watson Experience

IBM Watson, a powerful AI platform, made headlines with its potential to revolutionize healthcare. With the ability to analyze vast amounts of medical data and provide treatment recommendations, Watson promised to assist doctors in diagnosing and treating diseases more effectively.

However, the rollout of Watson in healthcare settings raised significant ethical questions. Firstly, there were concerns about the accuracy of the recommendations. Critics pointed out that Watson’s training data could be biased, potentially leading to flawed medical advice. Additionally, the opaque nature of AI decision-making posed challenges in accountability, especially in life-or-death scenarios.

IBM addressed these ethical issues by emphasizing transparency and collaboration with healthcare professionals. They implemented rigorous validation procedures and incorporated feedback from medical practitioners to refine Watson’s algorithms. This approach highlighted the importance of involving domain experts in the development process, ensuring that AI systems align with ethical standards and practical realities.

Case Study 2: Autonomous Vehicles – Google’s Waymo Journey

Waymo, Google’s self-driving car project, embodies the promise of AI in redefining urban transportation. Autonomous vehicles have the potential to enhance road safety and reduce traffic congestion. Nevertheless, they also bring forth ethical dilemmas that warrant careful consideration.

A key ethical challenge is the moral decision-making inherent in self-driving technology. In complex traffic situations, these AI-driven vehicles must make split-second decisions that could result in harm. The “trolley problem”—a classic ethical thought experiment—illustrates the dilemma of choosing between two harmful outcomes. For instance, should a self-driving car prioritize the safety of its passengers over pedestrians?

Waymo addresses these ethical concerns by implementing a robust ethical framework and engaging with stakeholders, including ethicists, regulators, and the general public. By fostering open dialogue, Waymo seeks to balance technical innovation with societal values, ensuring that their AI systems operate ethically and safely.

Principles for Ethical AI Innovation

As we navigate the ethical landscape of AI, several guiding principles can help steer innovation in a responsible direction:

Transparency: AI systems should be designed with transparency at their core, enabling users to understand the decision-making processes and underlying data.
Fairness: Developers must proactively address biases in AI algorithms to prevent discriminatory outcomes.
Accountability: Clear accountability mechanisms should be established to ensure that stakeholders can address any misuse or failure of AI technologies.
Collaboration: Cross-disciplinary collaboration involving technologists, ethicists, industry leaders, and policymakers is essential to fostering ethical AI innovation.

Conclusion

The integration of AI into our daily lives and industries presents both immense opportunities and complex ethical challenges. By thoughtfully addressing these ethical concerns, we can unleash the full potential of AI while safeguarding human values and societal well-being. As leaders in AI innovation, we must dedicate ourselves to building systems that are not only groundbreaking but also ethically sound, paving the way for a future where technology serves all of humanity.

In a world driven by AI, ethical innovation is not just an option—it’s a necessity. Through continuous dialogue, collaboration, and adherence to ethical principles, we can ensure that AI becomes a force for positive change, empowering people and societies worldwide.

Image credit: Microsoft CoPilot

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Cultural Shifts Required for Agile Success

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Cultural Shifts Required for Agile Success

GUEST POST from Art Inteligencia

In an era of rapid technological evolution and market dynamism, Agile has emerged as the go-to methodology for organizations seeking agility and resilience. However, the successful adoption of Agile is not just about implementing new processes or tools. At its core, Agile requires profound cultural shifts—a transformation in how individuals and teams think, interact, and operate.

The Imperative of Cultural Change

Agile methodologies promise speed, flexibility, and customer-centric approaches. However, many organizations fail to reap these benefits, primarily because they overlook the critical role of culture. For Agile to truly take root and flourish, organizations must embrace several key cultural shifts:

From Control to Empowerment: Agile thrives in environments where teams are empowered to make decisions. This requires a shift away from command-and-control management styles.
From Silos to Collaboration: Cross-functional collaboration is vital. Agile demands breaking down silos and fostering open communication and teamwork.
From Planning to Experimentation: Agile values iterative learning and adaptation over rigid planning.
From Risk Avoidance to Embracing Failure: Creating a culture where failure is seen as a learning opportunity is crucial for innovation.

Case Study 1: Spotify

Spotify’s success with Agile practices is well-documented and provides a compelling case study of cultural transformation. At Spotify, the organization is designed around cross-functional “squads,” each with end-to-end responsibility for their portions of the product. Here’s how Spotify navigated the cultural shifts:

Empowerment: Squads at Spotify are autonomous, empowering team members to experiment and make decisions without needing constant approval from higher management.
Collaboration: Cross-functional nature of squads ensures deep collaboration across disciplines, promoting knowledge sharing and holistic problem-solving.
Experimentation: Spotify encourages a “fail-friendly” culture where trying new ideas is embraced, and projects can pivot or stop based on what they learn quickly.

As a result, Spotify maintains a high capacity for innovation and adaptability, relevant to their fast-moving digital landscape.

Case Study 2: General Electric (GE)

General Electric, a company known for its traditional bureaucratic structure, embarked on an Agile transformation journey in its software development division to keep pace with technological changes and market demands.

From Control to Empowerment: GE overhauled their managerial approaches by adopting Lean Startup principles, which gave teams more autonomy to develop innovative solutions quickly.
Silos to Collaboration: GE’s Agile journey involved creating collocated, cross-functional teams tasked with tackling specific customer challenges, breaking down traditional silos.
Embracing Failure: Teams were encouraged to experiment and iterate, fostering a culture of learning from failure without the fear of repercussions.

While challenges existed, this cultural shift allowed GE to accelerate innovation and better respond to customer needs in their software products.

Navigating the Transition

Transitioning to an Agile culture is not without its challenges. Resistance to change, entrenched habits, and existing power dynamics can hinder progress. Here are strategies to navigate these challenges:

Leadership Buy-In: Securing support from leadership is crucial. Leaders must model Agile behaviors and champion cultural changes.
Change Agents: Identify and empower change agents who can advocate for and facilitate cultural shifts within teams.
Continuous Learning: Promote a culture of ongoing education and training to equip staff with the skills and mindset needed for Agile success.
Feedback Loops: Create mechanisms for regular feedback and reflection, allowing teams to learn and adapt continually.

Conclusion

Agile is not just a process but a mindset—a culture. The organizations that successfully navigate the transition to Agile do so by fundamentally reshaping their organizational culture. As seen in the examples of Spotify and GE, the journey to Agile success is challenging but ultimately rewarding, leading to more innovative, responsive, and resilient organizations.

To truly thrive in today’s fast-paced world, organizations must embrace the cultural shifts that Agile demands, fostering environments where empowerment, collaboration, experimentation, and learning from failure are not just encouraged, but ingrained into the very fabric of daily operations.

Image credit: Pixabay

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An Innovation Action Plan for the New CTO

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Finding and Growing Innovation Islands Inside a Large Company

An Innovation Action Plan for the New CTO

GUEST POST from Steve Blank

How does a newly hired Chief Technology Officer (CTO) find and grow the islands of innovation inside a large company?

How not to waste your first six months as a new CTO thinking you’re making progress when the status quo is working to keep you at bay?

I just had coffee with Anthony, a friend who was just hired as the Chief Technology Officer (CTO) of a large company (30,000+ people.) He previously cofounded several enterprise software startups, and his previous job was building a new innovation organization from scratch inside another large company. But this is the first time he was the CTO of a company this size.

Good News and Bad

His good news was that his new company provides essential services and regardless of how much they stumbled they were going to be in business for a long time. But the bad news was that the company wasn’t keeping up with new technologies and new competitors who were moving faster. And the fact that they were an essential service made the internal cultural obstacles for change and innovation that much harder.

We both laughed when he shared that the senior execs told him that all the existing processes and policies were working just fine. It was clear that at least two of the four divisions didn’t really want him there. Some groups think he’s going to muck with their empires. Some of the groups are dysfunctional. Some are, as he said, “world-class people and organizations for a world that no longer exists.”

So, the question we were pondering was, how do you quickly infiltrate a large, complex company of that size? How do you put wins on the board and get a coalition working? Perhaps by getting people to agree to common problems and strategies? And/or finding the existing organizational islands of innovation that were already delivering and help them scale?

The Journey Begins

In his first week the exec staff had pointed him to the existing corporate incubator. Anthony had long come to the same conclusion I had, that highly visible corporate incubators do a good job of shaping culture and getting great press, but most often their biggest products were demos that never get deployed to the field. Anthony concluded that the incubator in his new company was no exception. Successful organizations recognize that innovation isn’t a single activity (incubators, accelerators, hackathons); it is a strategically organized end-to-end process from idea to deployment.

In addition, he was already discovering that almost every division and function was building groups for innovation, incubation and technology scouting. Yet no one had a single road map for who was doing what across the enterprise. And more importantly it wasn’t clear which, if any, of those groups were actually continuously delivering products and services at high speed. His first job was to build a map of all those activities.

Innovation Heroes are Not Repeatable or Scalable

Over coffee Anthony offered that in a company this size he knew he would find “innovation heroes” – the individuals others in the company point to who single-handedly fought the system and got a new product, project or service delivered (see article here.) But if that was all his company had, his work was going to be much tougher than he thought, as innovation heroics as the sole source of deployment of new capabilities are a sign of a dysfunctional organization.

Anthony believed one of his roles as CTO was to:

Map and evaluate all the innovation, incubation and technology scouting activities
Help the company understand they need innovation and execution to occur simultaneously. (This is the concept of an ambidextrous organization (see this HBR article).)
Educate the company that innovation and execution have different processes, people, and culture. They need each other – and need to respect and depend on each other
Create an innovation pipeline – from problem to deployment – and get it adopted at scale

Anthony was hoping that somewhere three, four or five levels down the organization were the real centers of innovation, where existing departments/groups – not individuals – were already accelerating mission/delivering innovative products/services at high speed. His challenge was to find these islands of innovation and who was running them and understand if/how they:

Leveraged existing company competencies and assets
Understand if/how they co-opted/bypassed existing processes and procedures
Had a continuous customer discovery to create products that customers need and want
Figured out how to deliver with speed and urgency
And if they somehow had made this a repeatable process

If these groups existed, his job as CTO was to take their learning and:

Figure out what barriers the innovation groups were running into and help build innovation processes in parallel to those for execution
Use their work to create a common language and tools for innovation around rapid acceleration of existing mission and delivery
Make permanent delivering products and services at speed with a written innovation doctrine and policy
Instrument the process with metrics and diagnostics

Get Out of the Office

So, with another cup of coffee the question we were trying to answer was, how does a newly hired CTO find the real islands of innovation in a company his size?

A first place to start was with the innovation heroes/rebels. They often know where all the innovation bodies were buried. But Anthony’s insight was he needed to get out of his 8th floor office and spend time where his company’s products and services were being developed and delivered.

It was likely that most innovative groups were not simply talking about innovation, but were the ones who rapidly delivering innovative solutions to customer’s needs.

One Last Thing

As we were finishing my coffee Anthony said, “I’m going to let a few of the execs know I’m not out for turf because I only intend to be here for a few years.” I almost spit out the rest of my coffee. I asked how many years the division C-level staff has been at the company. “Some of them for decades” he replied. I pointed out that in a large organization saying you’re just “visiting” will set you up for failure, as the executives who have made the company their career will simply wait you out.

As he left, he looked at a bit more concerned than we started. “Looks like I have my work cut out for me.”

Lessons Learned

Large companies often have divisions and functions with innovation, incubation and technology scouting all operating independently with no common language or tools

Innovation heroics as the sole source of deployment of new capabilities are a sign of a dysfunctional organization

Innovation isn’t a single activity (incubators, accelerators, hackathons); it is a strategically organized end-to-end process from idea to deployment

Somewhere three, four or five levels down the organization are the real centers of innovation – accelerating mission/delivering innovative products/services at high speed

The CTO’s job is to:

create a common process, language and tools for innovation
make them permanent with a written innovation doctrine and policy

And don’t ever tell anyone you’re a “short timer”

This article originally appeared in Fast Company

Image credit: Unsplash

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