Ep 12: Augmenting Human Capabilities Responsibly with Hernan Chiosso

Bob chats with his fellow IBM alum Chuck Hamilton, Chief Innovation Officer at MShaped and Chief Learning Officer at MeetAmi, about the intersection of technology, innovation, and people throughout his career. He shares his experience in crowdsourcing innovation, as well as his work in mentoring startups. Chuck emphasizes the importance of continuous learning and upskilling in the age of AI. He discusses the need for personalized and just-in-time learning, as well as the role of AI in streamlining and condensing information. He also addresses the challenges organizations face in investing in L&D and the importance of developing talent within the company. The conversation explores the use of generative AI in higher education and the benefits it brings to graduates. It also delves into the importance of mentoring and the value of cross-pollination of skills between experienced professionals and newcomers. The discussion touches on the challenges of building effective teams and the potential of AI to optimize team formation. The conversation concludes with a discussion on the impact of blockchain on portable digital credentialing and the need for individuals to continuously learn and adapt in the evolving world of AI. Keywords technology, innovation, people, crowd innovation, mentoring, startups, learning, upskilling, AI, personalized learning, just-in-time learning, talent development, generative AI, higher education, mentoring, cross-pollination, team formation, blockchain, portable digital credentialing, continuous learning Takeaways Continuous learning and upskilling are crucial in the age of AI. AI can assist in streamlining and condensing information for faster learning. Organizations need to invest in L&D and talent development to stay competitive. Personalized and just-in-time learning is essential for effective upskilling. Generative AI in higher education can equip graduates with valuable skills and make them more productive and effective in the workforce. Mentoring and cross-pollination of skills between experienced professionals and newcomers can lead to better problem-solving and skill development. AI can assist in team formation by matching complementary skills and considering factors like personality and work style. Blockchain has the potential to revolutionize portable digital credentialing and improve trust and transparency in hiring and sourcing. Continuous learning and exploration of AI technologies are essential for individuals to elevate their AIQ and stay relevant in the changing landscape. Sound Bites "There probably wasn't a problem we couldn't solve if we could get the right people at the right time in the room at the same time or in the space at the same time." "No matter where you are on the learning spectrum, there's always a part in your journey, which is I need to know." "There's an overabundance of information now, and that information needs to be culled and sorted in a way it can be spit back to you so you can absorb it and learn it faster." "You could actually combine generative AI inside a company with your ability to use it, to be productive and effective and to make data informed decisions." "We're teaching them to bring more to the table." "Two in a box, we're going to cross-pollinate our skills. We're going to tackle problems in a cooperative way and solve that problem and we'll work on problems that we can learn from each other." Chapters 00:00 Introduction and Background 03:11 The Intersection of Technology, Innovation, and People 07:07 Crowd Innovation and Mentoring Startups 13:09 The Role of AI in Learning and Upskilling 16:18 Challenges in Investing in L&D and Talent Development 26:09 Mentoring and Cross-Pollination of Skills 27:06 AI's Potential in Team Formation 28:34 Blockchain and Portable Digital Credentialing 50:28 Continuous Learning to Elevate AIQ Chuck Hamilton: https://www.linkedin.com/in/chuck-hamilton1 Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Pulver chats with Ainesh Ravi and Victoria Sakal from Wonder, a startup that combines human expertise with AI to provide market research solutions. They discuss the evolution of Wonder, the challenges and benefits of incorporating AI into their workflow, and the importance of human expertise in the research process. They also touch on the potential risks and advantages of using AI tools and the need for a strong moat in the market. The speakers discuss the importance of adaptability, investing in people, and leveraging AI tools to enhance productivity. They also touch on the challenges of bias in AI output and the need for cognitive diversity in decision-making. The conversation concludes with discussions on the future of research, the role of strategic insights, and advice on how individuals can incorporate AI tools into their daily lives. Keywords market research, AI, human expertise, workflow, moat, strategic insights, AI, future of work, adaptability, investing in people, AI tools, productivity, bias, cognitive diversity, AI literacy Takeaways Wonder combines human expertise with AI to provide market research solutions. The incorporation of AI into the research process requires a behavioral change in how the company thinks about and structures its teams. Prompt engineering and understanding the limitations of AI models are crucial for delivering high-quality research. Wonder focuses on serving both large companies and smaller clients, offering a simpler and more cost-effective alternative to traditional research firms. The combination of strategic value, IP, and process expertise creates a strong moat for Wonder in the market. The future of research lies in the integration of AI tools and human expertise, allowing for higher-quality insights and more strategic decision-making. Adaptability is key in the future of work, and individuals should invest in developing their skills and staying relevant. AI tools can enhance productivity and efficiency in various tasks, but it's important to choose the right tools and understand their limitations. Bias in AI output is a concern, and organizations should strive for cognitive diversity in decision-making to mitigate potential biases. A culture of curiosity and a mindset of continuous learning are essential for navigating the evolving landscape of AI and the future of work. Sound Bites "The incorporation of AI into the research process requires a behavioral change in how the company thinks about and structures its teams." "Prompt engineering and understanding the limitations of AI models are crucial for delivering high-quality research." "There's a bunch of different ways to think about it as your prompts might be recipes and you've got to, not everyone who uses the same ingredients, the output's not going to be the same." Chapters 00:00 Introduction and Background of Wonder 08:07 The Behavioral Change in Incorporating AI into Research 13:10 The Importance of Prompt Engineering and Understanding AI Limitations 23:29 Serving Both Large Companies and Smaller Clients 26:00 Building a Strong Moat with Strategic Value, IP, and Process Expertise 28:40 Adaptability and Investing in People in the Future of Work 35:05 Enhancing Productivity with AI Tools 46:15 Addressing Bias and Promoting Cognitive Diversity 54:53 Elevating AI Literacy: Starting Small and Embracing Curiosity Ainesh Ravi: https://www.linkedin.com/in/aineshravi/ Victoria Sakal: https://www.linkedin.com/in/victoriasakal/ Wonder: askwonder.com Wonder workshop on how to apply AI to your workflows (recording & resources): https://askwonder.com/insights-hub/tap-genai-to-accelerate-your-work Wonder's thought leadership, research and POVs (subscribe for more): https://askwonder.com/insights-hub Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Pulver chats with AI expert Daan van Rossum, CEO and co-founder of FlexOS, about the future of work and the role of AI. They discuss the challenges and opportunities of adopting AI tools in the workplace, the importance of employee autonomy and agency, and the need for training and guidance on using AI ethically. They also touch on the impact of remote work and the consumerization of IT. The conversation highlights the need for companies to embrace AI as a new coworker and to foster a culture of trust and collaboration. Bob and Daan explore the future of AI and its impact on work and productivity. They discuss the role of AI in different industries and the need for interoperability among AI agents. The conversation emphasizes the importance of responsible AI adoption and the need for companies to develop their own AI solutions. It also highlights the potential of AI to reduce tedious tasks and increase employee engagement. They conclude the discussion by encouraging companies to start their AI journey and evolve alongside AI technology. Keywords future of work, AI adoption, employee autonomy, ethical AI, remote work, consumerization of IT, trust and collaboration, AI, future, work, productivity, interoperability, responsible AI, adoption, toil, employee engagement Takeaways AI adoption in the workplace requires a balance between quantitative and qualitative approaches to evaluating and selecting AI solutions. Companies should view AI as a new coworker and focus on creating a culture of trust and collaboration with employees. Training and guidance on using AI ethically and responsibly are crucial for successful AI integration. The consumerization of IT and the rise of shadow IT highlight the need for companies to be agile and responsive to employee needs and preferences. The future of work requires a shift in mindset from productivity-focused metrics to a focus on autonomy, agency, and meaningful work. AI is a powerful tool that can enhance productivity and reduce toil in the workplace. Companies should consider developing their own AI solutions to leverage their unique data and gain a competitive advantage. Responsible AI adoption requires a people-centric approach and a focus on ethics and transparency. AI has the potential to increase employee engagement and satisfaction by automating mundane tasks and allowing more time for strategic work. Companies that embrace AI early and foster a culture of experimentation and learning will have a significant advantage in the future. Sound Bites "This is not software, right? This is not software in the sense that, software was always something that you could install or use in the cloud and it had a button." "You can't just look at historical patterns of decisions and say, well, I see, I know what you're going to do. So I'll just take care of it. Like, whoa, whoa, whoa, whoa. No, that's not exactly how it works." "If there was ever an age for autonomy and agency with employees, it's now." Chapters 00:00 Introduction to Flex OS and the Mission of Creating a Happier Future of Work 02:14 Evaluating and Reviewing AI Solutions: Balancing Quantitative and Qualitative Approaches 05:22 The Impact of AI on Work and the Need for Trust and Collaboration 09:09 The Challenges of AI Adoption and the Role of HR in Guiding Employees 13:35 The Rise of Shadow IT and the Importance of Agility in IT 31:08 The Future of AI and Its Impact on Work and Productivity 32:05 The Need for Interoperability Among AI Agents 32:35 The Importance of Responsible AI Adoption 33:15 Reducing Toil and Increasing Employee Engagement with AI 34:09 Starting the AI Journey and Evolving Alongside AI Technology Daan van Rossum: https://www.linkedin.com/in/daanvanrossum/ FlexOS: https://www.flexos.work/ Lead With AI: https://www.flexos.work/leadwithai Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Pulver catches up with advisor, executive coach, entrepreneur, and NY Times best selling author Charlene Li about AI-driven transformation and the importance of remaining human-centric. Charlene has written numerous books on this subject (her seventh book is coming soon!) and is continuously educating leaders and organizations worldwide on how to get it right. They spoke about AI-driven transformation and how it differs from prior transformation initiatives. Charlene shared invaluable insights on strategic alignment and leadership buy-in to shrink the ‘knowing-doing-leading' gap. Importantly, Charlene and Bob dig into the crucial role of Responsible AI in ensuring positive outcomes for all stakeholders. Charlene also shares examples of organizations that have successfully embraced AI and offers practical advice for gaining AI knowledge and skills. How are you bridging the knowing-doing-leading gap? How will AI help you ask better questions, and think differently? Charlene's extensive expertise in digital transformation and human-centric approaches to technology make this episode a must-listen for anyone interested in the future of work and how AI can augment and empower us (humans, that is). Keywords AI-driven transformation, human-centric, digital transformation, strategic alignment, leadership buy-in, responsible AI, data literacy, data governance Takeaways AI-driven transformation is an opportunity to change the way organizations operate and upend the status quo. Strategic alignment and leadership buy-in are crucial for successful AI implementation. Responsible AI requires a focus on data literacy, data governance, and ethical use of AI. Customization and personalization of AI tools can enhance their effectiveness and impact. Building AI tools in-house can make organizations more knowledgeable buyers and ensure solutions that solve real problems. Sound Bites "AI upends the way we work and changes the way we relate to each other." "AI transformation requires strategic alignment and thinking big." "Executives need AI literacy and must transform themselves before leading AI implementation." "Until you can see the power of AI directly benefiting you, you can't begin to think about how to lead an organization in using AI." Chapters 00:00 Introduction and Background 02:21 Comparing AI-driven Transformation to Previous Transformations 04:39 Strategic Alignment and Leadership Buy-in 06:16 Becoming AI Literate as a Leader 09:14 Responsible AI: Data Literacy and Governance 13:18 Responsible AI: Speeding Things Up with Clear Guidelines 16:11 Example of Successful AI Implementation: AARP 18:05 Responsible AI: Setting Up AI Ethics Committees 20:24 Responsible AI: Responsible Use of Data and Access 22:25 Building AI Tools In-house: Customization and Personalization 26:04 Building AI Tools In-house: Minimal Viable Team 29:32 Responsible AI: Imagination and Curiosity 32:28 Responsible AI: Responsible Use of All AI Technologies 40:33 Customization and Personalization of AI Tools 46:25 Building AI Tools In-house: Becoming Knowledgeable Buyers Charlene Li: https://charleneli.com/ Charlene's books: https://charleneli.com/books/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Justin Strharsky, co-founder of humyn.ai, joins Bob Pulver to discuss the power of collective intelligence and the role of AI in solving complex problems. He explains how humyn.ai uses competition and collaboration to bring together a global community of data scientists to solve data challenges. Humyn.ai's crowdsourcing approach has proven very effective, where multiple independent solutions can lead to higher confidence and the discovery of valuable outlier answers. Strharsky emphasizes the importance of constraints and the right incentives to maximize contributions and ensure the best outcomes. It's a wide-ranging and insightful discussion on myriad topics, each relevant to the future of work design and dynamic workforce ecosystems. Keywords collective intelligence, AI, data science, competition, collaboration, constraints, incentives, outcomes Takeaways Collective intelligence, where multiple independent solutions are combined, can lead to higher confidence and the discovery of valuable outlier answers. Constraints and the right incentives are important in maximizing contributions and ensuring the best outcomes. AI can function as a thought partner, stimulating creativity and enhancing human problem-solving abilities. The power of AI lies in its ability to free up time and resources, solve mundane problems, and create a world of abundance. It is important to have a clear understanding of the problem at hand and to critically evaluate AI outputs to avoid being overwhelmed or misled. Sound Bites "More answers are better than one." "Constraints matter in maximizing contributions and outcomes." "Generative AI is fantastic for finding commonalities and driving decisions." Chapters 00:00 Introduction and Background 02:16 Competition and Collaboration in Solving Data Challenges 07:17 IP Ownership and Collaboration Opportunities 09:17 The Power of Cognitive Diversity and Subject Matter Expertise 13:59 Finding the Right Constraints for Optimal Solutions 22:15 Transparency, Observability, and Intellectual Property 25:04 The Need for Responsible and Ethical AI Use 28:33 The Potential of AI in Enhancing Human Thinking 31:39 The Importance of Engaging with AIQ and Avoiding Over-Optimism 36:05 The Value of Solving Mundane Problems and Creating a World of Abundance 39:01 The Future of AI and Collective Intelligence 42:10 Navigating the AI Landscape and Ensuring Clear Understanding 45:17 The Importance of Critical Evaluation and Avoiding Overwhelm 46:33 Elevating AIQ: Engaging with Tools Practically and Focusing on Problem Solving Justin Strharsky: https://www.linkedin.com/in/justin-strharsky https://humyn.ai Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Pulver speaks with Dr. Shiran Danoch, CEO and Founder of Informed Decisions, an interview intelligence platform, about the use of AI in the hiring process. They discuss the importance of data-driven and skills-based hiring, as well as the need for infrastructure and guidance to mitigate bias in AI algorithms. They also emphasize the value of feedback for candidates and the role of responsible AI in improving decision-making. Shiran shares her favorite AI tools for content writing and data analysis, as well as her advice for upskilling in AI and staying curious. Keywords AI in hiring, data-driven hiring, skills-based hiring, bias mitigation, feedback for candidates, responsible AI, AI tools, upskilling in AI Takeaways Data-driven and skills-based hiring are becoming more prevalent in the recruitment process. Infrastructure and guidance are necessary to mitigate bias in AI algorithms. Providing feedback to candidates is crucial for their growth and improvement. Responsible AI can enhance decision-making and improve diversity and inclusion efforts. Experimenting with AI tools and staying curious are key to upskilling in AI. Sound Bites "You can't manage what you don't measure." "We want to learn from what [interviewers] are doing." "Set up an infrastructure that will allow you to understand something, to collect data and to understand something about the effectiveness of your interview practices." Chapters 00:00 Introduction and Background 03:16 Shiran's Journey in the HR Tech Space 08:55 Mitigating Bias in AI Algorithms 13:43 The Importance of Feedback in the Hiring Process 21:39 The Role of Responsible AI in Decision-Making 28:14 The Future of AI in Hiring and DEI 38:01 Favorite AI Tools and Use Cases 42:34 Incorporating AI Use in Interviews 46:21 Advice for Upskilling in AI Shiran Danoch: https://www.linkedin.com/in/shirandanoch Informed Decisions: https://informedecisions.io Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Pulver and Gordon Ritchie, Principal Consultant and Skills Architect at Skill Collective, discuss the challenges and complexities of skills in the workplace. They explore topics such as skills assessments, skills ownership, skills taxonomy, and the role of AI in skills inference. They also touch on the importance of durable skills and the need for a shift in how skills are evaluated and matched in the hiring process. The conversation explores the challenges and considerations of skills-based hiring, the impact of automation on talent acquisition, and the importance of internal mobility and reskilling. It also delves into the use of assessments in evaluating human skills and the need for responsible AI practices. Bob and Gordon discuss the limitations of AI assessments and the importance of multiple sources of research. The conversation concludes with advice to explore and experiment with AI tools and to embrace continuous learning and adaptation. Keywords skills, assessments, ownership, taxonomy, AI, durable skills, hiring process, skills-based hiring, automation, talent acquisition, internal mobility, reskilling, assessments, responsible AI, research, experimentation, continuous learning Takeaways Skills assessments can help individuals and organizations identify aptitudes and capabilities. Ownership of skills is a challenge, as different HR verticals and solution providers vie to be the system of record for skills. Skills taxonomy and global skill standards are difficult to create and maintain, and may not be the most effective approach. Skills inference using AI can help identify skills based on descriptions and tasks, but it is important to consider the limitations and biases of AI. Durable skills, including critical thinking and problem-solving, are essential in a rapidly changing work environment. Skills-based hiring often falls short, with skills-based sourcing being more common. Inconsistencies in skills evaluation and matching persist in the hiring process. Skills-based hiring may not always lead to better outcomes and behavior. Hiring managers should focus on the tasks that need to be done, rather than just the skills required. Automation should be approached with consideration for the impact on culture, engagement, and retention. Internal mobility and reskilling can provide opportunities for employees and help retain valuable talent. Assessments in the AI space vary in reliability and validity, and caution should be exercised in their use. Continuous learning and experimentation with AI tools can help individuals elevate their AIQ. Sound Bites "Skills assessments can help individuals and organizations identify aptitudes and capabilities." "Skills taxonomy and global skill standards are difficult to create and maintain, and may not be the most effective approach." "Skills inference using AI can help identify skills based on descriptions and tasks, but it is important to consider the limitations and biases of AI." "And so that looks like a great vanity metric to measure the success of skills-based hiring on, but it hasn't actually changed the outcome and behavior." "What is it that needs doing? Because that's the business of the business. And that's what a job architecture needs to mirror or mimic." "You don't automate jobs, you automate tasks." Chapters 00:00 Introduction and Background 09:48 Challenges of Skills Taxonomy and Standards 19:12 Skills Inference with AI 25:19 The Importance of Durable Skills 33:27 Skills-Based Hiring Challenges 35:37 The Limitations of Skills-Based Hiring 39:23 Considering the Impact of Automation on Talent Acquisition 45:22 The Value of Internal Mobility and Reskilling 58:53 The Challenges of Assessments in the AI Space 01:06:07 Embracing Continuous Learning and Experimentation with AI Gordon Ritchie: https://www.linkedin.com/in/gordon-m-ritchie Skill Collective: https://skillcollective.co.uk Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob is joined by Dr. Charles Handler to discuss various topics related to AI, skills-based hiring, and the challenges of keeping up with information overload. They explore the importance of defining skills in skills-based hiring and the need for verification at scale. They also touch on the value and risks of AI in the hiring process, particularly in the assessment phase. Dr. Handler shares his thoughts on reducing friction in hiring, the paradox of AI, and the potential future of assessment with automated inferential assessments and high-fidelity simulations. The conversation explores the challenges and potential of AI adoption and responsible AI practices. Bob and Charles discuss the timeline for mass adoption of AI, the role of AI in simulations and training, the need for policies and governance around AI, and the impact of AI on diversity, equity, and inclusion. They emphasize the importance of understanding the technical aspects of AI and recommend educational resources for building AI literacy. Keywords AI, skills-based hiring, information overload, verification, friction reduction, accuracy, fairness, assessment, inferential assessments, simulations, AI adoption, responsible AI, mass adoption, simulations, training, policies, governance, diversity, equity, inclusion, AI literacy Takeaways Defining skills is crucial in skills-based hiring to ensure a shared understanding of what constitutes a skill. Verification of skills at scale is a challenge in skills-based hiring, and finding a solution for this is essential for accuracy. AI can reduce friction in the hiring process but may come at the cost of accuracy and fairness. Machine learning has been used in ATSs for resume parsing and matching, but there is still room for improvement. The future of assessment may involve automated inferential assessments based on digital exhaust and high-fidelity simulations. Mass adoption of AI may take time, but experimentation and progress are happening. AI can be used in simulations and training, such as military simulations and role-playing scenarios. Policies and governance around AI are necessary to ensure responsible AI practices. AI has the potential to impact diversity, equity, and inclusion in hiring and talent acquisition. Building AI literacy is important for understanding the technical aspects of AI and its implications. Sound Bites "Skills-based hiring is about accuracy and fairness." "There will be a sinkhole in assessment with automated inferential assessments and high-fidelity simulations." "Is AI a fad? This is not crypto. This is here to stay." "The future is people training the agents how to have good competencies." "The baseline of where we are in the moment is still incredible and game-changing." Chapters 00:00 Introduction and Background 03:03 Challenges of Keeping Up with AI and Information Overload 10:40 Skills-Based Hiring and the Half-Life of Skills 21:58 The Value and Risks of AI in the Hiring Process 22:18 AI's Role in Reducing Friction and the Paradox of Accuracy and Fairness 24:47 The Future of Assessment: Automated Inferential Assessments and High-Fidelity Simulations 25:59 The Timeline for Mass Adoption of AI 27:31 AI in Simulations and Training 34:18 The Need for Policies and Governance around AI 43:18 AI's Impact on Diversity, Equity, and Inclusion 48:13 Building AI Literacy Dr. Charles Handler: https://www.linkedin.com/in/drcharleshandler/ Rocket-Hire: http://www.rocket-hire.com Psych Tech @ Work: https://open.spotify.com/show/2euPiLjmRMoce5cvh5S3Jp?si=30050c48046e452d Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob has an engaging discussion with Gary Bolles, Chair for the Future of Work at Singularity University, author of the book, "The Next Rules of Work: The Mindset, Skillset and Toolset to Lead Your Organization through Uncertainty", keynote speaker, lecturer, and many other roles. We talk about his extensive background, including the ‘family business' and all his endeavors related to human potential, career development, and of course, the future of work. Gary and I discuss the intersection of trends and the potential impact of AI on various industries. We explore the challenges of regulating AI and finding the right balance between innovation and regulation. We hit on the automation maturity curve, and the need for individuals and organizations to adapt and transform in the face of exponential change. Gary emphasizes the importance of mindset and culture in driving successful transformation. The conversation explores the concept of collective intelligence and the combination of human intelligence and AI. We also touch on the importance of asking better questions and the evolving role of leaders in guiding teams. Gary highlights the need for organizations to embrace new models of social cohesion and education systems to empower individual learning. We wrap up with a discussion on the responsible and fair use of AI and the need to develop AI literacy in education. Keywords future of work, AI, trends, regulation, innovation, automation, transformation, mindset, culture, collective intelligence, human intelligence, AI, adaptability, growth, team metrics, cognitive diversity, AIQ, education, AI literacy Takeaways Exponential technologies like AI have the potential to bring both tremendous benefits and concerns. It is crucial to have a proactive approach to regulation and scenario planning to anticipate the impacts of new technologies. Automation is a continuum. Rather than focusing on job displacement, organizations should re-architect job roles and empower humans with technology. Successful transformation requires a growth mindset and a culture of continuous learning and adaptation. AI has the potential to augment collective intelligence by providing insights and sparking new ideas. Leaders should focus on asking better questions and empowering individuals to find solutions. Organizations should embrace new models of social cohesion to leverage collective intelligence. Education systems need to shift towards empowering individual learning and teaching AI literacy. Sound Bites "Exponential technologies often follow an exponential curve and accelerate each other along that curve." "The concern with exponential technologies is that they tend to create outsized players and monocultures." "Automation doesn't replace jobs, it automates tasks. The decision to eliminate jobs is a human decision." "So long as we help them to be able to continually adapt and grow, then you're going to be much more likely to have the organization have that capacity." "We're still in very early days, especially with regenerative AI and large language models." "We will define the organization by its level of social cohesion." Chapters 00:00 Introduction and Background 05:20 Exploring the Intersection of Trends and the Impact of AI 08:52 Finding the Balance Between Innovation and Regulation 16:28 Navigating the Automation Maturity Curve 25:10 The Importance of Mindset and Culture in Transformation 29:08 Adaptability and Growth 31:46 Augmenting Collective Intelligence with AI 35:12 Asking Better Questions for Insights 44:00 Rethinking Organizational Models 51:11 Empowering Individual Learning Gary Bolles: https://www.linkedin.com/in/gbolles The Next Rules of Work: https://www.koganpage.com/general-business-interest/the-next-rules-of-work-9781398601635 LinkedIn Learning: https://www.linkedin.com/learning/instructors/gary-bolles Singularity University: https://su.org Learn more about your ad choices. Visit megaphone.fm/adchoices

Bob Pulver talks with Ioanna Onasi, CEO and co-founder of Dextego, about the role of AI in coaching, the importance of personalized learning, and the value of experiential education. They also explore the impact of AI on job displacement and the potential for AI to enhance decision-making and collaboration. The conversation highlights the need for a balance between automation and human skills, and the importance of soft skills and practical application in education. Bob and Ioanna explore the challenges and opportunities of creating a learning culture within organizations. Ioanna emphasizes the importance of investing in employees' growth and creating incentives for learning. They touch on the need for mindset shifts to embrace AI and automation. Bob and Ioanna discuss the evolving nature of careers and the need for organizations to adapt their talent strategies. They highlight the importance of building a culture that attracts and retains talent and the role of AI in supporting personalized learning and productivity. They wrap things up with thoughts on how individuals can elevate their AIQ by joining communities and connecting with others in their field. Keywords AI, personalized learning, experiential education, decision-making, automation, human skills, soft skills, education, learning culture, workforce transformation, growth mindset, talent strategy, personalized learning, AIQ Takeaways Personalized learning is crucial for effective skill development, as it provides a safe space for practice and feedback. AI can enhance decision-making and collaboration by providing insights and guidance based on individual needs and preferences. The future of work will require individuals to adapt and continuously learn, leveraging their transferable skills and embracing AI as a co-pilot. Education systems need to evolve to include soft skills and practical use, preparing students for the complex and changing work environment. Investing in employees' growth and creating incentives for learning are crucial for creating a learning culture within organizations. Mindset shifts are necessary to embrace AI and automation in the workforce. Organizations need to adapt their talent strategies to the evolving nature of careers and build a culture that attracts and retains talent. AI can support personalized learning and productivity by providing customized support to individuals. Individuals can elevate their AIQ by joining communities and connecting with others in their field. Sound Bites "Today with the power of Gen. AI, we can actually create a very personalized coach" "There's so much more that could be done... AI can help you in how you make decisions" "It's a little ironic that we use AI to help people develop skills so that they beat AI" "Are you investing in me? And do you recognize that I have the potential to be successful in one of those other roles if only we fill this particular gap?" "Learning cultures are created by a combination of things, not just by one person leading an LMS." "There's only 1-2% of people in your company that truly will do everything for their learning because they have the drive. Everybody else needs to know that the effort will have an external reward." Chapters 00:00 Introduction and Background 02:39 The Power of Personalized Learning and Experiential Education 05:09 AI as a Co-Pilot: Enhancing Decision-Making and Collaboration 08:25 Balancing Automation and Human Skills in the Future of Work 13:28 The Need for Soft Skills and Practical Application in Education 19:42 Driving Sales Enablement and Onboarding with AI 27:34 Adapting Talent Strategies to Evolving Careers 29:37 Building a Culture that Attracts and Retains Talent 35:30 AI for Personalized Learning and Productivity 41:07 Elevating AIQ: Joining Communities and Connecting with Others Ioanna Onasi: https://www.linkedin.com/in/joannemantzouridou Dextego: https://dextego.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices