Tag Archives: Artificial Intelligence

We Must Rethink the Future of Technology

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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Sickcare AI Field Notes

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:

  1. AI has a way to go before it can substitute for physician judgment, intuition, creativity and empathy
  2. 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.
  3. When it comes to dissemination and implementation, culture eats strategy for lunch.
  4. 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.
  5. 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.
  6. 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.
  7. The value proposition of AI includes improving workflow and improving productivity
  8. AI requires large, clean data sets regardless of applications
  9. It will take a while to create trust in technology
  10. There needs to be transparency in data models
  11. 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
  12. AI is enabling both the clinical and business models of value based care
  13. Cloud based AI is changing diagnostic imaging and pattern recognition which will change manpower dynamics
  14. 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.
  15. We are in the second era of AI that is based on deep learning v rules based algorithms
  16. Value based care requires care coordination, risk stratification, patient centricity and managing risk
  17. Machine learning is being used, like Moneyball, to pick startup winners and losers, with a dose of high touch.
  18. 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.
  19. 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.
  20. 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
  21. The promise of using AI to get more done with less conflicts with the paradox of productivity
  22. 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.
  23. Nurses, pharmacists, public health professionals and veterinarians were under represented
  24. Payers were scarce
  25. Patients were scarce
  26. Students, residents and clinicians were looking for ways to get side gigs, non-clinical careers and exit ramps if need be.
  27. 70% of AI applications are in radiology
  28. AI is migrating from shiny to standard, running in the background to power diverse remote care modalities
  29. Chronic disease management and behavioral health have replace infectious disease as the global care management challenges
  30. AI education and training in sickcare professional schools is still woefully absent but international sickcare professional schools are filling the gaps
  31. 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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How AI is Shaping the Future of Innovation

GUEST POST from Chateau G Pato

As a human-centered change and innovation thought leader, I’ve always been fascinated by the intersection of technology and creativity. Today, we stand at the cusp of a revolutionary era, driven by the rapid advancement of Artificial Intelligence (AI). AI is not just a tool; it’s a catalyst, reshaping the very fabric of innovation across industries. It’s moving beyond automation, becoming a partner in the ideation and development process.

The essence of human-centered innovation lies in understanding and addressing human needs. AI empowers us to do this at scale, by analyzing vast datasets to uncover patterns and insights that would otherwise remain hidden. It’s about augmenting human intelligence, not replacing it. This synergy allows us to create solutions that are not only technologically advanced but also deeply resonant with human values and experiences.

One of the most profound impacts of AI is its ability to accelerate the ideation phase. AI algorithms can generate novel ideas by combining existing concepts in unexpected ways. This capability is particularly valuable in industries facing complex challenges, where traditional problem-solving approaches may fall short. By providing a diverse range of starting points, AI can help us break free from cognitive biases and explore uncharted territories.

Furthermore, AI-powered prototyping tools are democratizing innovation. They enable rapid iteration and testing, allowing us to validate ideas quickly and efficiently. This agility is crucial in today’s fast-paced market, where speed and adaptability are key to success. AI’s ability to simulate and predict outcomes can significantly reduce the risk associated with innovation, making it more accessible to a wider range of organizations.

However, the ethical considerations surrounding AI cannot be ignored. As we integrate AI into our innovation processes, we must ensure that it is used responsibly and transparently. Fairness, accountability, and privacy must be at the forefront of our minds. We must also consider the potential impact on the workforce and proactively address the need for reskilling and upskilling.

Case Studies

Case Study 1: Personalized Medicine with AI

In the healthcare sector, AI is revolutionizing personalized medicine. Companies are using AI algorithms to analyze patient data, including genetic information, medical history, and lifestyle factors, to develop tailored treatment plans. This approach goes beyond one-size-fits-all solutions, optimizing therapies for individual patients and improving outcomes. For example, AI-driven platforms are being used to predict patient responses to cancer treatments, allowing oncologists to select the most effective therapies from the outset. This not only enhances patient care but also reduces healthcare costs by minimizing ineffective treatments. Furthermore, AI is accelerating drug discovery by analyzing vast databases of molecular structures and predicting the efficacy of new compounds. This is significantly shortening the time it takes to bring life-saving drugs to market, addressing urgent medical needs more rapidly. By combining AI with human expertise, healthcare providers are delivering more precise, efficient, and compassionate care.

Case Study 2: AI-Driven Sustainable Product Development

The urgency of addressing climate change has spurred a wave of sustainable innovation. AI is playing a critical role in this transformation by optimizing product design and manufacturing processes for environmental sustainability. Companies are using AI to analyze the environmental impact of materials and manufacturing methods, identifying opportunities to reduce waste and carbon emissions. For example, AI-powered tools are being used to design packaging that minimizes material usage while maintaining product integrity. AI is also helping to create circular economy models by optimizing recycling and reuse processes. By analyzing consumer behavior and product lifecycles, AI can help companies design products that are not only sustainable but also meet consumer needs and preferences. Furthermore, AI-driven simulations are helping to optimize supply chains, reducing transportation costs and environmental impact. This holistic approach to sustainable product development is ensuring that innovation contributes to a healthier planet. This is not only about reducing negative impact, but creating a positive, regenerative impact.

Conclusion

AI is not just a technological advancement; it’s a paradigm shift in how we approach innovation. By augmenting human intelligence and enabling us to tackle complex challenges with greater efficiency and creativity, AI is unlocking new possibilities across industries. However, it’s crucial that we embrace AI responsibly, ensuring that it serves humanity’s best interests. As we navigate this transformative era, we must remain focused on creating solutions that are not only innovative but also ethical, sustainable, and deeply human-centered. The future of innovation is not about replacing human ingenuity, but about amplifying it with the power of AI.

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: Pixabay

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Innovative Approaches to Accessibility in Technology

Innovative Approaches to Accessibility in Technology

GUEST POST from Chateau G Pato

In the rapidly evolving landscape of technology, the importance of accessibility remains a crucial focus. As we advance into a world increasingly intertwined with digital tools and platforms, ensuring that all individuals, regardless of ability, can operate these technologies is more important than ever. Creating technology that is accessible not only serves those with disabilities but enriches the user experience for everyone. In this article, we explore innovative approaches to accessibility and offer insights into how companies are successfully integrating these strategies to create a more inclusive digital environment.

Redefining Accessibility

Traditional accessibility in technology often focused on compliance-driven adaptations, which, while necessary, sometimes missed the bigger picture of user experience and inclusivity. Innovative approaches begin with empathy and a deep understanding of diverse user needs, leading to solutions that are not only compliant but also delightful to use.

Universal Design Principles

Universal design, a concept originally from architecture, has transcended into the tech sphere, emphasizing that solutions should be usable by everyone to the greatest extent possible, without the need for adaptation. By applying universal design principles, designers and developers can create products that are inherently accessible right out of the gate. This approach fosters innovation as teams are challenged to think outside the box and create interfaces and interactions that are intuitive for all users.

Artificial Intelligence and Machine Learning

Advancements in artificial intelligence (AI) and machine learning (ML) are paving the way for more insightful accessibility solutions. AI can automate and enhance accessibility features such as voice recognition, real-time translation, and image recognition, thus opening new realms of possibility for people with disabilities. By training AI models on diverse and inclusive datasets, accessibility can become more personalized and responsive to individual user needs.

Case Study: Microsoft’s AI for Accessibility

Microsoft’s commitment to accessibility is prominently showcased through its ambitious “AI for Accessibility” program. Launched in 2018, the initiative invests in leveraging AI technologies to amplify human capabilities for those with disabilities, focusing on employment, daily life, and communication.

One of the flagship outputs of this initiative is the Seeing AI app, designed for visually impaired individuals. This app utilizes AI to narrate the world around the user using a smartphone camera, identifying objects, reading text, and recognizing faces. Seeing AI delivers on multiple fronts of accessibility, offering an intuitive user experience underpinned by cutting-edge technology.

“By augmenting human abilities with artificial intelligence, we can achieve more inclusive outcomes and ensure that technology empowers all users,” says Jenny Lay-Flurrie, Microsoft’s Chief Accessibility Officer.

Microsoft’s dedication to inclusive design highlights not just the potential of AI, but also the importance of a commitment across the organization. By fostering a culture of accessibility from leadership to product teams, companies can ensure that accessibility is not an afterthought but an integral part of the innovation process.

Case Study: Apple’s VoiceOver

Apple has long been a pioneer in integrating accessibility features directly into its products. VoiceOver, a screen reader built into iOS and macOS, is a prime example of innovation in this space. Unlike traditional screen readers, which are often third-party applications that must be purchased and installed separately, VoiceOver comes pre-installed and integrated deeply with the operating systems.

VoiceOver utilizes gesture-based navigation with touch commands on iOS devices, allowing visually impaired users to explore their devices in an intuitive manner. What makes VoiceOver particularly innovative is its synergy with Apple’s ecosystem, enhancing the overall accessibility across different devices, including Mac, iPhone, iPad, and Apple Watch.

This integrated approach has far-reaching implications for user empowerment and independence. It reflects Apple’s belief that accessibility should be central to the user experience, not a mere add-on. By equipping all of its devices with robust accessibility features, Apple ensures that users with disabilities have the tools they need to thrive in an increasingly digital world.

Design Thinking for Accessibility

Integrating accessibility into the design thinking process is crucial for creating solutions that truly meet user needs. This begins with empathy and understanding, engaging with people with disabilities in the research phases of product development. Through methods like journey mapping and prototyping with diverse populations, teams can uncover unique insights and innovate in ways that standard testing may not reveal.

Inclusive Testing and Feedback Loops

To ensure that accessibility is woven into the fabric of technology solutions, businesses must incorporate inclusive testing and feedback loops. Involving users with varying abilities in testing stages ensures that products are genuinely accessible and valuable. Continuous feedback loops enable organizations to iterate on their products, continuously refining and enhancing accessibility features.

Future Directions

As we forge ahead, the future of accessibility in technology is promising yet requires commitment from all stakeholders. Educating teams within organizations about the importance and techniques of accessibility will drive innovation. Furthermore, as technologies like augmented reality (AR) and virtual reality (VR) continue to evolve, they hold the potential to significantly enhance accessibility, creating immersive experiences that are accessible to all.

Moreover, as global connectivity increases, collaboration across industries and borders will be instrumental in developing universal accessibility standards. By working together, sharing knowledge, and championing inclusivity, we can cultivate a digital world where technology serves as a bridge to opportunity rather than a barrier.

Conclusion

The journey towards accessible technology is ongoing and demands an innovative mindset. By embracing emerging technologies, conducting empathetic research, and fostering inclusive design, we can create digital environments that are not only accessible but also empowering for all users. As technology leaders, it’s our responsibility to champion accessibility as a core value, ensuring that everyone has the opportunity to thrive in our connected world.

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: Unsplash

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Should You Have a Department of Artificial Intelligence?

Should You Have a Department of Artificial Intelligence?

GUEST POST from Arlen Meyers, M.D.

Several hospitals, academic medical centers and medical schools are creating artificial intelligence organizational centers, institutes and programs. Examples are Stanford, the University of Colorado , Children’s Hospital of Orange County and Duke.

If you are contemplating doing the same, think about what is the best organizational structure? There’s a lot of debate about where AI and analytics capabilities should reside within organizations. Often leaders simply ask, “What organizational model works best?” and then, after hearing what succeeded at other companies, do one of three things: consolidate the majority of AI and analytics capabilities within a central “hub”; decentralize them and embed them mostly in the business units (“the spokes”); or distribute them across both, using a hybrid (“hub-and-spoke”) model. We’ve found that none of these models is always better than the others at getting AI up to scale; the right choice depends on a firm’s individual situation.

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The decision will depend on:

  1. What problems are you trying to solve? Form follows function.
  2. What resources do you have? People, money, processes, intrastructure, IP protection?
  3. What is your level of digital transformation?
  4. What is the level of your organizational innovation readiness?
  5. What are the underlying hypotheses of your intrapreneurial business model canvas and what evidence to you have that they are valid?
  6. How will you overcome the barriers to dissemination and implementation?
  7. What processes do you have in place to scale?
  8. Do you have the right people?
  9. Do you have a culture of innovation silos and, if so, how will you break them down?

10. How will you measure results? Dr Anthony Chang, the co- founder of the American Board of Artificial Intelligence, suggests that the following are some helpful metrics to measure the artificial intelligence capabilities of the health system in the context of an individual AI project:

AI Project Score

The projects that involve machine learning and artificial intelligence, either clinical oradministrative, can be followed in stages (with each stage being scored 1 point each to a maximumof 5 points) and scored to keep track as well as maintain momentum:

Stage 1: Ideation. The project is first discussed and brought to a regular meeting for input from all stakeholders. This is perhaps the most important part of an AI project that is often not regularly done with enough discussion and consideration.

Stage 2: Preparation. After approval from the group, the data access and curation takes place in order to perform the ML/AI steps that ensue. The team should appreciate that this stage takes the most effort and will require sufficient resources.

Stage 3: Operation. After the data is curated and managed, this stage entails a collaborative effort during the feature engineering and selection process. Using the ML/AI tools, the team then creates the algorithms that will lead to the models that will be used later on in the project.

Stage 4: Presentation. Upon completion of the model with real world data, the project is presented in front of the group and depending on the nature of the project, it is either presented only or is also presented at a regional or national meeting or advanced to be published in a journal.

Stage 5: Implementation. Beyond the presentation and publication, it is essential for the AI project to be implemented in the real world setting using real world data. This project still requires continual surveillance and maintenance as model and data often fatigue.

11. Are you connected to the other parts of the healthcare AI ecosystem?

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12. Are you prepared to overcome the ethical, legal, social, economic and privacy issues?

Feeding the organizational beasts that are resistant to change is hard. They have an insatiable appetite. Be sure your pantry is well stocked.

Image credit: Pixabay

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Three Steps to Digital and AI Transformation

Three Steps to Digital and AI Transformation

GUEST POST from Arlen Meyers, M.D.

In his book, The Four Steps to the Epiphany, Steve Blank described what has become the gospel of lean startup methodologies: Customer validation, customer discovery, customer creation and company building

The path to sickcare digital transformation is a bit shorter, but certainly no less difficult and plagued by failure: Personal innovation readiness, organizational innovation readiness and digital/AI transformation.

PERSONAL INNOVATION READINESS

Are you prepared to innovate? Here’s what you should know about innovation.

Before you start, prepare yourself with these things:

MINDSET

Starting down the entrepreneurship path means that you will not only have to change your mind about things, more importantly, you will have to change your mindset. Don’t make these rookie mindset mistakes. Here’s what it means to have an entrepreneurial mindset. There is a difference between a clinical and an entrepreneurial mindset. Innovation starts with the right mindset.

Here is how to cope in a VUCA world.

MOTIVATION

Organizational behavior gurus have been studying how to motivate employees for a very long time. Most have failed.

Indeed, most of your ideas will fail. Consequently, you will need a source of intrinsic motivation to keep you going. Make it personal, but don’t take it personally. Find the right mentors and sponsors to keep you on track and support you when you are down. Create a personal advisory board. Develop these entrepreneurial habits. Practice the power of negative entrepreneurial thinking.

MEANING

Meaning should drive what you are about to do. Practice virtuous entrepreneurship and find your ikigai. Instead of starting with the end in mind, start with the why in mind. Prune. Let go of the banana.

MEANS

Once these attitudes are in place, then focus on building your entrepreneurial knowledge, skills, behaviors and competencies. Take a financial inventory. Start accumulating the physical, human and emotional resources you will need to begin and sustain your journey. In addition to knowledge, you will need resources, networks, mentors, peer support and non-clinical career guidance.

METRICS

What are some standards and metrics you can us to measure your innovation readiness e.g. in the use of artificial intelligence in medicine?

The American National Standards Institute (ANSI) has released a new report that reflects stakeholder recommendations and opportunities for greater coordination of standardization for artificial intelligence (AI) in healthcare. The report, “Standardization Empowering AI-Enabled Systems in Healthcare,” reflects feedback from a 2020 ANSI leadership survey and national workshop, and pinpoints foundational principles and potential next steps for ANSI to work with standards developing organizations, the National Institute of Standards and Technology, other government agencies, industry, and other affected stakeholders.

The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

As an important step to ensure successful integration of AI and avoid unnecessary investments and costly failures, better consideration should be given to: (1) Needs and added-value assessment; (2) Workplace readiness: stakeholder acceptance and engagement; (3) Technology-organization alignment assessment and (4) Business plan: financing and investments. In summary, decision-makers and technology promoters should better address the complexity of AI and understand the systemic challenges raised by its implementation in healthcare organizations and systems.

ORGANIZATIONAL INNOVATION READINESS

Improvement readiness is not the same as innovation readiness.

Giffford Pinchot, who originated the term “intrapreneur”, has suggested that you rate your organization in several domains to see whether your innovation future looks bright or bleek:

  1. Transmission of vision and strategic intent
  2. Tolerance for risk, failure and mistakes
  3. Support for intrapreneurs
  4. Managers who support innovation
  5. Empowered cross functional teams
  6. Decision making by the doers
  7. Discretionary time to innovate
  8. Attention on the new, not the now
  9. Self- selection
  10. No early hand offs to managers
  11. Internal boundary crossing
  12. Strong organizational culture of support
  13. Focus on customers
  14. Choice of internal suppliers
  15. Measurement of innovation
  16. Transparency and truth
  17. Good treatment of people
  18. Ethical and professional
  19. Swinging for singles, not home runs
  20. Robust external open networks

If you ask a sample of people to rate these in your company on a scale of 1-10, don’t be surprised if the average equals somewhere between 2-4. Few organizations, you see, are truly innovative or have a truly innovative culture. Most don’t even think about how to bridge the now with the new, let alone measure it.

Do a cultural audit. Creating a culture of innovation must include SALT and PRICES

AND

  • Process
  • Recognition
  • Incentives
  • Champions
  • Encouragement
  • Structure

Here is a rubrick that might help get you started

Learn from companies in other industries who transformed. Here are some tips from Levi Strauss.

DIGTAL/AI TRANSFORMATION

Develop and deploy the 6Ps:

  1. Problem seeking
  2. Problem solving
  3. People
  4. Platform/infrastructure
  5. Process/Project management
  6. Performance indicators that meet clinical, operational and business objectives and achieve the quintuple aims.

Here are some sickeare digital transformation tips.

The path to the end of the rainbow is filled with good intentions and lots of shiny new objects. Stay focused, use your moral compass to guide you and follow the yellow brick road.

Image Credit: Pixabay

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Harnessing AI for Breakthrough Innovation

Harnessing AI for Breakthrough Innovation

GUEST POST from Art Inteligencia

In the rapidly evolving digital landscape, Artificial Intelligence (AI) is not just a tool for optimization, but a catalyst for breakthrough innovation. Organizations worldwide are leveraging AI to transform industries, redefine customer experiences, and create unprecedented value. In this article, we explore how AI can drive innovative growth and provide real-world case studies demonstrating its potential. We also include links to additional resources for those looking to deepen their understanding of this transformative technology.

Case Study 1: AI in Healthcare – Revolutionizing Diagnosis

The healthcare industry stands to gain immensely from AI, particularly in improving diagnostic accuracy and efficiency. One standout case is that of Google’s DeepMind, which has partnered with Moorfields Eye Hospital in London to develop an AI system capable of diagnosing complex eye diseases as accurately as world-leading experts. Utilizing deep learning algorithms, the system analyzes thousands of retinal scans to detect conditions like diabetic retinopathy and age-related macular degeneration.

This breakthrough has not only increased diagnostic speed but also enhanced accessibility to expert-level care, thereby improving patient outcomes. The AI’s ability to learn and improve from vast datasets ensures continuous innovation in diagnostic technology, underscoring AI’s game-changing role in healthcare.

Case Study 2: AI in Retail – Personalizing Customer Experience

Retail is another sector where AI is reshaping business models and consumer engagement. Consider the case of Stitch Fix, an online personal styling service that combines data science and human expertise to deliver personalized fashion recommendations. By analyzing customer preferences, purchasing history, and social media behavior, Stitch Fix’s AI system curates clothing options tailored to each individual’s taste.

The system not only predicts customer preferences with remarkable accuracy but also helps the company optimize inventory, reducing waste and costs. This approach has enabled Stitch Fix to offer a highly customized shopping experience, setting a new standard in the retail industry and highlighting AI’s potential to innovate traditional business practices.

The Strategic Framework for AI-Driven Innovation

To harness AI for breakthrough innovation, organizations need a strategic framework that integrates AI into the core of their operations. Here are key steps to consider:

  1. Identify Opportunities: Begin with a comprehensive exploration of areas where AI can create the most impact. Look for patterns, inefficiencies, and unmet needs within your industry.
  2. Leverage Data: AI thrives on data. Ensure your organization has a robust data infrastructure to gather, store, and analyze relevant data.
  3. Foster Collaboration: Encourage cross-disciplinary teams, combining AI expertise with industry know-how, to identify and implement innovative solutions.
  4. Iterate and Scale: Start with pilot projects, learn from iterations, and scale successful innovations across the organization.

Further Reading

For those looking to explore more about the intersection of AI and innovation, I recommend checking out the following articles:

Conclusion

AI holds the potential to drive transformative change across industries by enabling breakthrough innovations. By intelligently integrating AI into strategic operations, organizations can unlock new value, create sustainable competitive advantages, and embark on unprecedented growth trajectories. The case studies of Google’s DeepMind and Stitch Fix exemplify how AI can be harnessed to revolutionize industries and enhance user experiences. As we continue to explore the possibilities, the role of AI in shaping the future of innovation becomes increasingly vital.

This article provides a comprehensive analysis of how AI can be utilized for breakthrough innovation, supplemented by two case studies and links to further resources on this website.

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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The Future of Agile

Trends and Innovations

GUEST POST from Art Inteligencia

The Future of Agile

Introduction to the Evolving Landscape of Agile

As thought leaders in human-centered change and innovation, we must continuously adapt and evolve. Agile methodologies have transformed how organizations operate, focusing on flexibility, collaboration, and customer-centric solutions. As we look to the future, several trends and innovations are expected to reshape the Agile landscape.

Emerging Trends in Agile

The Agile landscape is ever-evolving, responding to technological advancements and shifts in organizational culture. Here are the trends that are gaining momentum:

  • Agile Beyond Software Development: Agile principles are now being applied across various sectors, from marketing to finance, embracing a more holistic approach to organizational agility.
  • Remote and Distributed Teams: With the rise of remote work, Agile practices are evolving to support distributed teams, emphasizing virtual collaboration and digital tools.
  • AI and Machine Learning Integration: Agile processes are increasingly integrating AI and machine learning, optimizing workflows, and enhancing decision-making.

Case Studies: Leading the Agile Revolution

Case Study 1: Spotify’s Squad Model

Spotify has become synonymous with Agile innovation through its unique approach known as the ‘Squad Model.’ This framework promotes team autonomy and accountability, empowering ‘squads’ to operate as self-contained units focusing on specific objectives. Each squad is cross-functional, enhancing collaboration and efficiency.

The success of Spotify’s model highlights the importance of customizing Agile practices to fit organizational needs and culture, fostering an environment conducive to rapid innovation and experimentation.

Case Study 2: ING’s Agile Transformation

In the financial services sector, ING has demonstrated the power of Agile transformation. Through the adoption of Agile principles, ING restructured its operations, breaking down silos and fostering a collaborative, customer-focused culture.

This transformation involved training over 3,500 employees in Agile methodologies, integrating Agile teams across multiple departments to enhance efficiency and speed to market. ING’s journey underscores the potential for Agile practices to drive significant organizational change, even within highly regulated industries.

Innovations Driving the Future of Agile

As Agile continues to evolve, several innovations are expected to shape its future:

  • Agile at Scale: Large organizations are increasingly seeking ways to implement Agile at the enterprise level, integrating Agile methodologies across all facets of their operations.
  • Agility in Strategic Leadership: Leadership teams are adopting Agile practices to enhance strategic decision-making and responsiveness to market dynamics.
  • Hybrid Models: Many companies are blending Agile with traditional project management methodologies to create hybrid models that leverage the strengths of both approaches.

Conclusion

The future of Agile is bright, driven by the need for organizations to remain competitive in an ever-changing environment. By embracing these trends and innovations, companies can not only survive but thrive in a landscape marked by constant change.

For more insights into organizational change, explore our article on Agile Leadership and discover strategies for effective Digital Transformation.

Bottom line: 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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Addressing Ethical Concerns

Ensuring AI-powered Workplace Productivity Benefits All

Addressing Ethical Concerns: Ensuring AI-powered Workplace Productivity Benefits All

GUEST POST from Art Inteligencia

In today’s fast-paced world, artificial intelligence (AI) has become an integral part of workplace productivity. From streamlining processes to enhancing decision-making, AI technologies have the potential to revolutionize the way we work. However, with great power comes great responsibility, and it is essential to address the ethical concerns that come with the widespread adoption of AI in the workplace.

One of the primary ethical concerns surrounding AI in the workplace is the potential for bias in decision-making. AI algorithms are only as good as the data they are trained on, and if this data is biased, the AI system will perpetuate that bias. This can lead to discriminatory outcomes for employees, such as biased hiring decisions or performance evaluations. To combat this, organizations must ensure that their AI systems are trained on diverse and unbiased datasets.

Case Study 1: Amazon’s Hiring Algorithm

One notable example of bias in AI can be seen in Amazon’s hiring algorithm. The company developed an AI system to automate the screening of job applicants, with the goal of streamlining the hiring process. However, the system started to discriminate against female candidates, as it was trained on historical hiring data that favored male candidates. Amazon eventually scrapped the system, highlighting the importance of ethical considerations when implementing AI in the workplace.

Another ethical concern with AI in the workplace is the potential for job displacement. As AI technologies become more advanced, there is a fear that they will replace human workers, leading to job losses and economic instability. To address this concern, organizations must focus on re-skilling and up-skilling their workforce to prepare them for the changes brought about by AI.

Case Study 2: McDonald’s AI-powered Drive-thru

McDonald’s recently introduced AI-powered drive-thru technology in select locations, which uses AI algorithms to predict customer orders based on factors such as time of day, weather, and previous ordering patterns. While this technology has led to improved efficiency and customer satisfaction, there have been concerns about the impact on the workforce. To address this, McDonald’s has implemented training programs to help employees adapt to the new technology and take on more customer-facing roles.

Conclusion

The ethical concerns surrounding AI in the workplace must be addressed to ensure that the benefits of AI-powered productivity are distributed equitably. By focusing on diversity and inclusion in AI training data, as well as investing in reskilling and upskilling programs for employees, organizations can mitigate the potential negative impacts of AI on the workforce. By taking a proactive approach to ethics in AI, organizations can create a workplace that benefits all employees, customers, and stakeholders.

Bottom line: 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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AI and Employee Engagement

Improving Productivity and Job Satisfaction

AI and Employee Engagement: Improving Productivity and Job Satisfaction

GUEST POST from Art Inteligencia

In today’s fast-paced work environment, employee engagement plays a crucial role in driving productivity and job satisfaction. With the rapid advancements in artificial intelligence (AI) technology, organizations have a unique opportunity to leverage AI tools to enhance employee engagement and create a more productive and fulfilling workplace.

Case Study 1: Chatbots as Virtual Mentors

One innovative way organizations are using AI to improve employee engagement is through the use of virtual chatbots as mentors. These chatbots are programmed to provide guidance, support, and feedback to employees in real time, helping them navigate challenges and develop their skills.

For example, a large tech company implemented a virtual mentor chatbot for its customer service team. The chatbot was programmed to provide on-the-job training, answer questions, and offer personalized feedback based on the employee’s performance. As a result, employees felt more supported and engaged in their roles, leading to an increase in productivity and job satisfaction.

Case Study 2: AI-Driven Performance Management

Another way AI is transforming employee engagement is through AI-driven performance management systems. These systems use algorithms and data analytics to provide real-time insights into employee performance, leading to more personalized feedback and development opportunities.

A leading financial services firm implemented an AI-driven performance management system that analyzed employee data, such as productivity metrics and feedback, to identify areas for improvement and growth. The system then provided targeted feedback and recommendations to help employees enhance their skills and performance.

As a result, employees felt more engaged and empowered to take ownership of their development, leading to higher levels of job satisfaction and productivity across the organization.

Conclusion

AI has the potential to revolutionize employee engagement by providing personalized support, feedback, and development opportunities. By leveraging AI tools like virtual mentors and performance management systems, organizations can create a more engaging and fulfilling workplace that drives productivity and job satisfaction. It is essential for organizations to embrace AI as a tool to enhance employee engagement and create a more productive and successful work environment.

Bottom line: 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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