Throughout the past year, we've seen AI go from a nice-to-have, to a must-have in almost every large organization’s boardroom. There’s been more and more focus deploy AI  by leadership teams, and as a result, there's never been more pressure on the data team to deliver with AI. However, as the pressure to deliver with AI grows, the need to build safe and trustworthy experiences has also never been more important. But how do we balance between innovation and building these trustworthy experiences? How do you make responsible AI practical? Who should we get into the room when scoping safe AI use-cases?  Beena Ammanath is an award- winning senior technology executive with extensive experience in AI and digital transformation. Her career has spanned leadership roles in e-commerce, finance, marketing, telecom, retail, software products, service, and industrial domains. She is also the author of the ground breaking book, Trustworthy AI. Beena currently leads the Global Deloitte AI Institute and Trustworthy AI/ Ethical Technology at Deloitte. Prior to this, she was the CTO-AI at Hewlett Packard Enterprise. A champion for women and multicultural inclusion in technology and business, Beena founded Humans for AI, a 501c3b non-profit promoting diversity and inclusion in AI. Her work and contributions have been acknowledged with numerous awards and recognition such as 2016 Women Super Achiever Award from World Women’s Leadership Congress and induction into WITI’s 2017 Women in Technology Hall of Fame. Beena was honored by UC Berkeley as 2018 Woman of the Year for Business Analytics, by the San Francisco Business Times as one of the 2017 Most Influential Women in Bay Area and by the National Diversity Council as one of the Top 50 Multicultural Leaders in Tech. In the episode, Beena and Adel delve into the core principles of trustworthy AI, the interplay of ethics and AI in various industries, how to make trustworthy AI practical, who are the primary stakeholders for ensuring trustworthy AI, the importance of AI literacy when promoting responsible and trustworthy AI, and a lot more. Links mentioned in the Show Trustworthy AI by Beena AmmanathDeloitte AI InstituteHumans for AIData Literacy by Design, with Valerie Logan, CEO of the Data Lodge[Course] Implementing AI Solutions in Business[Webinar - October 19th 2023] Building a Capability Roadmap for AI

In today's AI landscape, organizations are actively exploring how to seamlessly embed AI into their products, systems, processes, and workflows. The success of ChatGPT stands as a testament to this. Its success is not solely due to the performance of the underlying model; a significant part of its appeal lies in its human-centered user experience, particularly its chat interface. Beyond the foundational skills, infrastructure, and tools, it's clear that great design is a crucial ingredient in building memorable AI experiences. How do you build human-centered AI experiences? What is the role of design in driving successful AI implementations? How can data leaders and practitioners adopt a design lens when building with AI? Here to answer these questions is Haris Butt, Head of Product Design at ClickUp. ClickUp is a project management tool that's been making a big bet on AI, and Haris plays a key role in shaping how AI is embedded within the platform. Throughout the episode, Adel & Haris spoke about the role of design in driving human-centered AI experiences, the iterative process of designing with large language models, how to design AI experiences that promote trust, how designing for AI differs from traditional software, whether good design will ultimately end up killing prompt engineering, and a lot more.

It's been almost a year since ChatGPT was released, mainstreaming AI into the collective consciousness in the process. Since that moment, we've seen a really spirited debate emerge within the data & AI communities, and really public discourse at large. The focal point of this debate is whether AI is or will lead to existential risk for the human species at large. We've seen thinkers such as Elizier Yudkowski, Yuval Noah Harari, and others sound the alarm bell on how AI is as dangerous, if not more dangerous than nuclear weapons. We've also seen AI researchers and business leaders sign petitions and lobby government for strict regulation on AI.  On the flip side, we've also seen luminaries within the field such as Andrew Ng and Yan Lecun, calling for, and not against, the proliferation of open-source AI. So how do we maneuver this debate, and where does the risk spectrum actually lie with AI? More importantly, how can we contextualize the risk of AI with other systemic risks humankind faces? Such as climate change, risk of nuclear war, and so on and so forth? How can we regulate AI without falling into the trap of regulatory capture—where a select and mighty few benefit from regulation, drowning out the competition in the meantime? Trond Arne Undheim is a Research scholar in Global Systemic Risk, Innovation, and Policy at Stanford University, Venture Partner at Antler, and CEO and co-founder of Yegii, an insight network with experts and knowledge assets on disruption. He is a nonresident Fellow at the Atlantic Council with a portfolio in artificial intelligence, future of work, data ethics, emerging technologies, and entrepreneurship. He is a former director of MIT Startup Exchange and has helped launch over 50 startups. In a previous life, he was an MIT Sloan School of Management Senior Lecturer, WPP Oracle Executive, and EU National Expert. In this episode, Trond and Adel explore the multifaceted risks associated with AI, the cascading risks lens and the debate over the likelihood of runaway AI. Trond shares the role of governments and organizations in shaping AI's future, the need for both global and regional regulatory frameworks, as well as the importance of educating decision-makers on AI's complexities. Trond also shares his opinion on the contrasting philosophies behind open and closed-source AI technologies, the risk of regulatory capture, and more.  Links mentioned in the show: Augmented Lean: A Human-Centric Framework for Managing Frontline Operations by Trond Arne Undheim & Natan LinderFuture Tech: How to Capture Value from Disruptive Industry Trends Trond Arne UndheimFuturized PodcastStanford Cascading Risk StudyCourse: AI Ethics

From the dawn of humanity, decisions, both big and small, have shaped our trajectory. Decisions have built civilizations, forged alliances, and even charted the course of our very evolution. And now, as data & AI become more widespread, the potential upside for better decision making is massive. Yet, like any technology, the true value of data & AI is realized by how we wield it.  We're often drawn to the allure of the latest tools and techniques, but it's crucial to remember that these tools are only as effective as the decisions we make with them. ChatGPT is only as good as the prompt you decide to feed it and what you decide to do with the output. A dashboard is only as good as the decisions that it influences. Even a data science team is only as effective as the value they deliver to the organization.  So in this vast landscape of data and AI, how can we master the art of better decision making? How can we bridge data & AI with better decision intelligence? ​​Cassie Kozyrkov founded the field of Decision Intelligence at Google where, until recently, she served as Chief Decision Scientist, advising leadership on decision process, AI strategy, and building data-driven organizations. Upon leaving Google, Cassie started her own company of which she is the CEO, Data Scientific. In almost 10 years at the company, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Cassie also previously served in Google's Office of the CTO as Chief Data Scientist, and the rest of her 20 years of experience was split between consulting, data science, lecturing, and academia.  Cassie is a top keynote speaker and a beloved personality in the data leadership community, followed by over half a million tech professionals. If you've ever went on a reading spree about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers.  In the episode Cassie and Richie explore misconceptions around data science, stereotypes associated with being a data scientist, what the reality of working in data science is, advice for those starting their career in data science, and the challenges of being a data ‘jack-of-all-trades’.  Cassie also shares what decision-science and decision intelligence are, what questions to ask future employers in any data science interview, the importance of collaboration between decision-makers and domain experts, the differences between data science models and their real-world implementations, the pros and cons of generative AI in data science, and much more.  Links mentioned in the Show: Data scientist: The sexiest job of the 22nd centuryThe Netflix PrizeAI Products: Kitchen AnalogyType one, Two & Three Errors in StatisticsCourse: Data-Driven Decision Making for BusinessRadar: Data & AI Literacy...

In data science, the push for unbiased machine learning models is evident. So much effort is made into ensuring the products we create are done thoughtfully and correctly, but are we investing the same effort in ensuring our teams, the very architects of these models, are diverse and inclusive? Bias in data can lead to skewed results, and similarly, a lack of diversity in teams can result in narrow perspectives. As we prioritize building diversity and inclusion into our data, it's equally crucial to embed these principles within our teams. So, who is best equipped to guide us in integrating DEI from a data perspective? Tracy Daniels is the Chief Data Officer for Truist Financial Corporation. She leads the team responsible for Truist’s enterprise data capabilities, including strategy, governance, data platform delivery, client, master & reference data, and the centers of excellence for business intelligence visualization and artificial intelligence & machine learning. She is also the executive sponsor for Truist’s Enterprise Technology & Operations Diversity Council. Daniels joined Truist in 2018. She has more than 25 years of banking and technology experience leading high performing technology portfolio, development, infrastructure and global operations organizations. Tracy enjoys participating in civic and philanthropic endeavors including serving on the Georgia State University Foundation Board of Trustees. She has been recognized as a National 2013 WOC STEM Rising Star award recipient, the 2017 Working Mother magazine Mother of the Year recipient, and a 2021 Women In Technology (WIT) Women of the Year in STEAM finalist. In the episode Tracy and Richie discuss Truist's approach to Diversity, Equity, and Inclusion (DEI) and its alignment with the company's purpose and values, the distinction between diversity and inclusion, the positive outcomes of implementing DEI correctly, the importance of not missing opportunities both externally with customers and internally with talent, the significance of aligning diversity programs with business metrics and hiring to promote DEI, considerations for job advertisements that appeal to a diverse audience, and much more.  Links mentioned in the show: McKinsey on Diversity and InclusionBrookings Piece on Mitigating Bias in DataAlgorithmic Justice LeagueEuropean Legislation on Data and DiversityCourse: AI EthicsRadar: Data & AI Literacy Edition

It's been a year since ChatGPT burst onto the scene. It has given many of us a sense of the power and potential that LLMs hold in revolutionizing the global economy. But the power that generative AI brings also comes with inherent risks that need to be mitigated.  For those working in AI, the task at hand is monumental: to chart a safe and ethical course for the deployment and use of artificial intelligence. This isn't just a challenge; it's potentially one of the most important collective efforts of this decade. The stakes are high, involving not just technical and business considerations, but ethical and societal ones as well. How do we ensure that AI systems are designed responsibly? How do we mitigate risks such as bias, privacy violations, and the potential for misuse? How do we assemble the right multidisciplinary mindset and expertise for addressing AI safety?  Reid Blackman, Ph.D., is the author of “Ethical Machines” (Harvard Business Review Press), creator and host of the podcast “Ethical Machines,” and Founder and CEO of Virtue, a digital ethical risk consultancy. He is also an advisor to the Canadian government on their federal AI regulations, was a founding member of EY’s AI Advisory Board, and a Senior Advisor to the Deloitte AI Institute. His work, which includes advising and speaking to organizations including AWS, US Bank, the FBI, NASA, and the World Economic Forum, has been profiled by The Wall Street Journal, the BBC, and Forbes. His written work appears in The Harvard Business Review and The New York Times. Prior to founding Virtue, Reid was a professor of philosophy at Colgate University and UNC-Chapel Hill. In the episode, Reid and Richie discuss the dominant concerns in AI ethics, from biased AI and privacy violations to the challenges introduced by generative AI, such as manipulative agents and IP issues. They delve into the existential threats posed by AI, including shifts in the job market and disinformation. Reid also shares examples where unethical AI has led to AI projects being scrapped, the difficulty in mitigating bias, preemptive measures for ethical AI and much more.  Links mentioned in the show: Ethical Machines by Reid BlackmanVirtue Ethics ConsultancyAmazon’s Scrapped AI Recruiting ToolNIST AI Risk Management FrameworkCourse: AI EthicsDataCamp Radar: Data & AI Literacy

For the past few years, we've seen the importance of data literacy and why organizations must invest in a data-driven culture, mindset, and skillset. However, as generative AI tools like ChatGPT have risen to prominence in the past year, AI literacy has never been more important. But how do we begin to approach AI literacy? Is it an extension of data literacy, a complement, or a new paradigm altogether? How should you get started on your AI literacy ambitions?  Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is a data analytics, AI, and BI thought leader and an expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy.

Cindi was previously a Gartner Research Vice President, the lead author for the data and analytics maturity model and analytics and BI Magic Quadrant, and a popular keynote speaker. She introduced new research in data and AI for good, NLP/BI Search, and augmented analytics, bringing both BI bake-offs and innovation panels to Gartner globally. She’s frequently quoted in MIT, Harvard Business Review, and Information Week. She is rated a top 12 influencer in big data and analytics by Analytics Insight, Onalytca, Solutions Review, and Humans of Data.

In the episode, Cindi and Adel discuss how generative AI accelerates an organization’s data literacy, how leaders can think beyond data literacy and start to think about AI literacy, the importance of responsible use of AI, how to best communicate the value of AI within your organization, what generative AI means for data teams, AI use-cases in the data space, the psychological barriers blocking AI adoption, and much more. 

Links Mentioned in the Show: The Data Chief Podcast  ThoughtSpot Sage  BloombergGPT  Radar: Data & AI Literacy Course: AI Ethics  Course: Generative AI Concepts Course: Implementing AI Solutions in Business 

With September and International Literacy Day (September 8th) upon us, we’re dedicating the entire month to cover the ins and outs of data & AI literacy. Make sure to sign up for the events we have in store, and to tune in for this month’s episodes. Data & AI Literacy MonthDataCamp Radar: Data & AI Literacy Edition

The mainstreaming of data & AI is fundamentally altering the way we work and operate. But with rising innovation, comes rising ambiguity and complexity. How can leaders effectively navigate the path ahead? How can leaders adopt data-driven decision-making and learn from their mistakes? How can leaders use data to look inward, and become what today’s guest describes as “meta-leaders”?  Constance Dierickx is an internationally recognized expert in high-stakes decision-making who has advised leaders and delivered speeches in more than 20 countries. Founder and president of CD Consulting Group, her clients include Fortune 20 companies, private equity firms, and large not-for-profits around the globe. She is a contributor to Harvard Business Review, Forbes, Chief Executive, and others, and has taught strategic decision-making at Skolkovo Institute of Science and Technology in Moscow, Russia.  In the episode, Richie and Constance delve into what meta-leadership is, the nuances of meta-leadership, the pivotal role of data in leadership, the importance of recognizing subtle behavioral cues, the implications of cognitive biases (particularly overconfidence), and the essence of wisdom in decision-making. Constance also shares insights from her clinical psychology background, highlighting the application of biofeedback mechanisms in managing chronic pain and much more.  Links From the Show: Meta-Leadership by Constance Dierickx High-Stakes Leadership by Constance Dierickx The Merger Mindset by Constance Dierickx Design the Life You Love: A Step-by-Step Guide to Building a Meaningful Future Book by Ayse Birsel Introducing The State of Data Literacy Report 2023 Data-Driven Decision Making for Business

Throughout history, small businesses have consistently played a pivotal role in the global economy, serving as its foundational backbone. As we navigate the digital age, the emergence of large corporations and rapid technological advancements present new challenges. Now, more than ever, it's imperative for small businesses to adapt, embracing a data-driven approach to remain competitive and sustainable. In this evolving landscape, we need champions dedicated to guiding these businesses, ensuring they harness the full potential of modern tools and insights to ensure a fair and varied marketplace of goods and services for all.  Dr Kendra Vant, Executive General Manager of Data & AI Products at Xero, is an industry leader in building data-driven products that harness AI and machine learning to solve complex problems for the small-business economy. Working across Australia, Asia and the US, Kendra has led data and technology teams at companies such as Seek, Telstra, Deloitte and now Xero where she leads the company's global efforts using emerging practices and technologies to help small businesses and their advisors benefit from the power of data and insights. Starting with doctoral research in experimental quantum physics at MIT and a stint building quantum computers at Los Alamos National Laboratory, Kendra has made a career of solving hard problems and pushing the boundaries of what's possible. In the episode, Kendra and Richie delve into the transformative impact of data science on small businesses, use-cases of data science for small businesses, how Xero has supported numerous small businesses with data science. They also cover the integration of AI in product development, the unexpected depth of data in seemingly low-tech sectors, the pivotal role of software platforms in data analysis and much more.  Links Mentioned in The Show: Xero Analyzing Business Data in SQL Financial Modeling in Spreadsheets Implementing AI Solutions in Business Generative AI Concepts

Generative AI is here to stay—even in the 8 months since the public release of ChatGPT, there are an abundance of AI tools to help make us more productive at work and ease the stress of planning and execution of our daily lives among other things.  Already, many of us are wondering what is to come in the next 8 months, the next year, and the next decade of AI’s evolution. In the grand scheme of things, this really is just the beginning. But what should we expect in this Cambrian explosion of technology? What are the use cases being developed behind the scenes? What do we need to be mindful of when training the next generations of AI? Can we combine multiple LLMs to get better results? Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage. Bal is also the Co-Founder of Aedeon—the first hyper-scale Marketplace for Data Analytics and AI talent. In the episode, Richie and Bal explore common use cases for generative AI, how it's evolving to solve enterprise problems, challenges of data governance and the importance of explainable AI, the challenges of tracking the lineage of AI and data in large organizations. Bal also touches on the shift from general-purpose generative AI models to more specialized models, fascinating use cases in the manufacturing industry, what to consider when adopting AI solutions in business, and much more. Links mentioned in the show: PulsarTrifactaAWS Clarify[Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business[Course] Generative AI Concepts

'Software is eating the world’ is a truism coined by Mark Andreesen, General Partner at Andreesen Horowitz. This was especially evident during the shift from analog mediums to digital at the turn of the century. Software companies have essentially usurped and replaced their non-digital predecessors. Amazon was the largest bookseller, Netflix was the largest movie "rental" service, Spotify or Apple were the largest music providers. Today, AI is starting to eat the world. However, we are still at the early start of the AI revolution, with AI set to become embedded in almost every piece of software we interact with. An AI ecosystem that touches every aspect of our lives is what today’s guest describes as ‘Ambient AI’. But what can we expect from this ramp up to Ambient AI? How will it change the way we work? What do we need to be mindful of as we develop this technology? Daniel Jeffries is the Managing Director of the AI Infrastructure Alliance and former CIO at Stability AI, the company responsible for Stable Diffusion, the popular open-source image generation model. He’s also an author, engineer, futurist, pro blogger and he’s given talks all over the world on AI and cryptographic platforms. In the episode, Adel and Daniel discuss how to define ambient AI, how our relationship with work will evolve as we become more reliant on AI, what the AI ecosystem is missing to rapidly scale adoption, why we need to accelerate the maturity of the open source AI ecosystem, how AI existential risk discourse takes away focus from real AI risk, and a lot lot more.

Links Mentioned in the Show Daniel’s Writing on MediumDaniel’s SubstackAI Infrastructure AllianceStability AIFrancois CholletRed Pajama DatasetRun AIWill Superintelligent AI End the World? By Eliezer Yudkowsky Nick Bostrom’s Paper Clip MaximizerThe pessimist archive [Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business

In a time when AI is evolving at breakneck speeds, taking a step back and gaining a bird's-eye view of the evolving AI ecosystem is paramount to understanding where the field is headed. With this bird's-eye view come a series of questions. Which trends will dominate generative AI in the foreseeable future? What are the truly transformative use-cases that will reshape our business landscape? What does the skills economy look like in an age of hyper intelligence? Enter Joanne Chen, General Partner at Foundation Capital. Joanne invests in early-stage AI-first B2B applications and data platforms that are the building blocks of the automated enterprise. She has shared her learnings as a featured speaker at conferences, including CES, SXSW, WebSummit, and has spoken about the impact of AI on society in her TED talk titled "Confessions of an AI Investor." Joanne began her career as an engineer at Cisco Systems and later co-founded a mobile gaming company. She also spent many years working on Wall Street at Jefferies & Company, helping tech companies go through the IPO and M&A processes, and at Probitas Partners, advising venture firms on their fundraising process. Throughout the episode, Richie and Joanne cover emerging trends in generative AI, business use cases that have emerged in the past year since the advent of tools like ChatGPT, the role of AI in augmenting work, the ever-changing job market and AI's impact on it, as well as actionable insights for individuals and organizations wanting to adopt AI. Links mentioned in the show: JasperAIAnyScaleCerebras[Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business[Course] Generative AI Concepts

Spreadsheets have been the unsung heroes of the data world for many decades now. Yet, despite their ubiquity and importance, they've seen little disruption or evolution. The grid of cells we interact with today isn't far removed from the ones our predecessors used in the 1980s. However, the winds of change have started to blow. As we stand on the cusp of a new era in data and AI, the humble spreadsheet is poised for transformation. The coming changes could redefine how we interact with data, derive insights, and how we make decisions. The implications are vast given the popularity and dependence we have on spreadsheets, and the potential impacts could ripple through every corner of the professional world.  Hjalmar Gislason is the founder and CEO of GRID, with their main product being a smart spreadsheet with an interactive data visualization layer and integrated AI assistance. Hjalmar previously served as VP of Product Management at Qlik. He was the founder and CEO of DataMarket, founded in 2008 and sold to Qlik in 2014. A career data nerd and entrepreneur, GRID is Hjalmar’s fifth software startup as a founder.  In the episode, Richie and Hjalmar explore the integral role of spreadsheets in today's data-driven world, the limitations of traditional Business Intelligence tools, and the transformative potential of generative AI in the realm of spreadsheets.

Data and AI are advancing at an unprecedented rate—and while the jury is still out on achieving superintelligent AI systems, the idea of artificial intelligence that can understand and learn anything—an “artificial general intelligence”—is becoming more likely. What does the rise of AI mean for the future of software and work as we know it? How will AI help reinvent most of the ways we interact with the digital and physical world? Bob Muglia is a data technology investor and business executive, former CEO of Snowflake, and past president of Microsoft's Server and Tools Division. As a leader in data & AI, Bob focuses on how innovation and ethical values can merge to shape the data economy's future in the era of AI. He serves as a board director for emerging companies that seek to maximize the power of data to help solve some of the world's most challenging problems. In the episode, Richie and Bob explore the current era of AI and what it means for the future of software. Throughout the episode, they discuss how to approach driving value with large language models, the main challenges organizations face when deploying AI systems, the risks, and rewards of fine-tuning LLMs for specific use cases, what the next 12 to 18 months hold for the burgeoning AI ecosystem, the likelihood of superintelligence within our lifetimes, and more. Links from the show: The Datapreneurs by Bob Muglia and Steve HammThe Singularity is Near by Ray KurzweilIsaac AsimovSnowflakePineconeDocugamiOpenAI/GPT-4The Modern Data Stack

Today's government agencies face unprecedented complexities, and when thinking about the role of government in driving positive change for society at large, data & AI stand out as key levers to empower government agencies to do more with less. However, the road to government data & AI transformation is fraught with risk, and is full with opportunity. So how can government data leaders succeed in their transformation endeavors?  Steve Orrin is Intel’s Federal Chief Technology Officer. He leads Public Sector Solution Architecture, Strategy, and Technology Engagements and has held technology leadership positions at Intel where he has led cybersecurity programs, products, and strategy. Steve was previously CSO for Sarvega, CTO of Sanctum, CTO and co-founder of LockStar, and CTO at SynData Technologies. He was named one of InfoWorld's Top 25 CTO's, received Executive Mosaic’s Top CTO Executives Award, is a Washington Exec Top Chief Technology Officers to Watch in 2023, was the Vice-Chair of the NSITC/IDESG Security Committee and was a Guest Researcher at NIST’s National Cybersecurity Center of Excellence (NCCoE). He is a fellow at the Center for Advanced Defense Studies and the chair of the INSA Cyber Committee. Throughout the episode, we talked about the unique challenges government face when driving value with data & AI, how agencies need to align their data ambitions with their actual mission, the nuances between data privacy laws between the united states, Europe, and China, how to best approach launching pilot projects if you are in government, and a lot more.

About 10 years ago, Thomas Davenport & DJ Patil published the article "Data Scientist: The Sexiest Job of the 21st Century" in the Harvard Business Review. In this piece, they described the bourgeoning role of the data scientist and what it will mean for organizations and individuals in the coming decade. As time has passed, data science has become increasingly institutionalized. Once seen as a luxury, it is now deemed a necessity in every modern boardroom. Moreover as technologies like AI and systems like ChatGPT keep astonishing us with their capabilities in handling data science tasks, it raises a pertinent question: Is Data Science Still the Sexiest Job of the 21st Century? In this episode, we invited Thomas Davenport on the show to share his perspective on where data science & AI are at today, and where they are headed. Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. One of HBR’s most frequently published authors, Thomas has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence. Throughout the episode, we discuss how data science has changed since he first published his article, how it has become more institutionalized, how data leaders can drive value with data science, the importance of data culture, his views on AI and where he thinks its going, and a lot more. Links from the Show: Working with AI by Thomas Davenport The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas Davenport Harvard Business Review New Vantage Partners CCC Intelligent Solutions Radar AI

In the realm of Applied Intelligence, Accenture leads the way in harnessing the power of data and AI to transform industries. From consumer products to life sciences, retail, and aerospace, Accenture's influence is far-reaching. But what drives the organization? How does it navigate the complex landscape of data modernization and transformation? And more importantly, how does it leverage technology not just as an enabler, but as a catalyst for innovation?  Tracy Ring leads Accenture’s Applied Intelligence Products Category Group, in this role she has leadership across Consumer and Industrial Products, Automotive, Life Sciences, Retail and Aerospace and Defense. As the CDO and Global Generative AI lead for Life Sciences, she personally anchors the NA Applied Intelligence Life Sciences practice of more than 500 practitioners. Tracy has created solutions for Generative AI, Data led transformation, Artificial Intelligence, Data and Cloud Modernization, Analytics, and the organization and operating model strategies for next-generation adoption and AI fluency.  In the episode, Tracy initially clarifies the difference between data modernization and data transformation, highlighting their distinct meanings and why the terms aren’t interchangeable.  Tracy also emphasizes the importance of involving business end-users from the outset of data projects as well as advocating for a product-oriented approach to data. The discussion also covers the topic of team diversity and inclusivity. Tracy shares practical advice on how to build diverse teams and create an environment that encourages curiosity and open dialogue. Tracy also shares her perspective on the future of work and the importance of fostering meaningful conversations in the workplace. She advocates for an attitude of infinite curiosity within teams. In the context of life sciences, Tracy highlights the high stakes involved and underscores the need for responsible AI, data sharing, and data privacy. She also points out that the challenges in this field are more similar than dissimilar to those in other industries. Tune in for a wealth of insights from a seasoned leader in the field of Applied Intelligence.

A lot of the times when we walk into a supermarket, we don't necessarily think about the impact data science had in getting these products on shelves. However, as you’ll learn in today's episode, it's safe to say there's a myriad of applications for data science in the FMCG industry. Whether be that supply chain use-cases that leverage time-series forecasting techniques, to computer vision use-cases for on-shelf optimization—the use-cases are endless here. So how can data scientists and data leaders maximize value in this space? Enter Anastasia Zygmantovich. Anastasia is a Global Data Science Director at Reckitt, which is most known for products like Airwick, Lysol, Detol, and Durex. Throughout the episode, we discuss how data science can be used in the FMCG industry, how data leaders can hire impactful data teams in this space, why FMCG is a great place to work in for data scientists, some awesome use-cases she's worked on, how data scientists can best maximize their value in this space, what generative AI means for organizations, and a lot more.

When we think about video games like Call of Duty, Fifa, or Fortnite, our minds often turn to creative artists, software developers, designers, and producers. These are the people who make our favorite games a reality. But behind the scenes, data & AI actively shape our experience with our favorite video games. From the quality of video games, the accessibility of maps and worlds, even the go to market, data & AI play an impactul role in making or breaking the success of a video game. Marie de Léséleuc is an accomplished game industry professional with over a decade of experience. Marie started her career as a data analyst, and has since risen through the ranks to a data leader in the gaming industry. She's worked at companies such as Ubisoft, Warner Brothers, and most recently at Eidos, the company most well known for games such as Guardians of the Galaxy and Tomb Raider. Throughout the episode, we discuss how data science can be used in gaming, the unique challenges data teams face in gaming from really low data volumes to massive changes to production schedules and game vision. We also spoke about the difference between "AI" as we know it in data science, and AI in gaming, which informs how NPCs behave in a video game world—and a lot more.

Although many have been cognizant of AI’s value in recent months, the further back we look, the more exclusive this group of people becomes. In our latest AI-series episodes of DataFramed, we gain insight from an expert who has been part of the industry for 40 years. Joaquin Marques, Founder and Principal Data Scientist at Kanayma LLC has been working in AI since 1983. With experience at major tech companies like IBM, Verizon, and Oracle, Joaquin's knowledge of AI is vast. Today, he leads an AI consultancy, Kanayma, where he creates innovative AI products. Throughout the episode, Joaquin shares his insights on AI's development over the years, its current state, and future possibilities. Joaquin also shares the exciting projects they've worked on at Kanayma as well as what to consider when building AI products, and how ChatGPT is making chatbots better. Joaquin goes beyond providing insight into the space, encouraging listeners to think about the practical consequences of implementing AI, with Joaquin sharing the finer technical details of many of the solutions he’s helped build. Joaquin also shares many of the thought processes that have helped him move forward when building AI products, providing context on many practical applications of AI, both from his past and the bleeding edge of today.   The discussion examines the complexities of artificial intelligence, from the perspective of someone that has been focused on this technology for more than most. Tune in for guidance on how to build AI into your own company's products.

With the advances in AI products and the explosion of ChatGPT in recent months, it is becoming easier to imagine a world where AI and humans work seamlessly together—revolutionizing how we solve complex problems and transform our daily lives. This is especially the case for data professionals. In this episode of our AI series, we speak to Sarah Schlobohm, Head of AI at Kubrick Group. Dr. Schlobohm leads the training of the next generation of machine learning engineers. With a background in finance and consulting, Sarah has a deep understanding of the intersection between business strategy, data science, and AI. Prior to her work in finance, Sarah became a chartered accountant, where she honed her skills in financial analysis and strategy. Sarah worked for one of the world's largest banks, where she used data science to fight financial crime, making significant contributions to the industry's efforts to combat money laundering and other illicit activities. Sarah shares her extensive knowledge on incorporating AI within data teams for maximum impact, covering a wide array of AI-related topics, including upskilling, productivity, and communication, to help data professionals understand how to integrate generative AI effectively in their daily work. Throughout the episode, Sarah explores the challenges and risks of AI integration, touching on the balance between privacy and utility. She highlights the risks data teams can avoid when using AI products and how to approach using AI products the right way. She also covers how different roles within a data team might make use of generative AI, as well as how it might effect coding ability going forward. Sarah also shares use cases for those in non-data teams, such as marketing, while also highlighting what to consider when using outputs from GPT models. Sarah shares the impact chatbots might have on education calling attention to the power of AI tutors in schools. Sarah encourages people to start using AI now, considering the barrier to entry is so low, and how that might not be the case going forward. From automating mundane tasks to enabling human-AI collaboration that makes work more enjoyable, Sarah underscores the transformative power of AI in shaping the future of humanity. Whether you're an AI enthusiast, data professional, or someoone with an interest in either this episode will provide you with a deeper understanding of the practical aspects of AI implementation.

With the advent of any new technology that promises to make humans lives easier, replacing concious actions with automation, there is always backlash. People are often aware of the displacement of jobs, and often, it is viewed in a negative light. But how do we try to change the collective understanding to one of hope and excitement? What use cases can be shared that will change the opinion of those that are weary of AI?  Noelle Silver Russell is the Global AI Solutions & Generative AI & LLM Industry Lead at Accenture, responsible for enterprise-scale industry playbooks for generative AI and LLMs. In this episode of our AI series, Noelle discusses how to prioritize ChatGPT use cases by focusing on the different aspects of value creation that GPT models can bring to individuals and organizations. She addresses common misconceptions surrounding ChatGPT and AI in general, emphasizing the importance of understanding their potential benefits and selecting use cases that maximize positive impact, foster innovation, and contribute to job creation. Noelle draws parallels between the fast-moving AI projects today and the launch of Amazon Alexa, which she worked on, and points out that many of the discussions being raised today were also talked about 10 years ago. She discusses how companies can now use AI to focus both on business efficiencies and customer experience, no longer having to settle for a trade-off between the two. Noelle explains the best way for companies to approach adding GPT tools into their processes, which focusses on taking a holistic view to implementation. She also recommends use-cases for companies that are just beginning to use AI, as well as the challenges they might face when deploying models into production, and how they can mitigate them.  On the topic of the displacement of jobs, Noelle draws parallels from when Alexa was launched, and how it faced similar criticisms, digging into the fear that people have around new technology, which could be transformed into enthusiasm. Noelle suggests that there is a burden on leadership within organizations to create a culture where people are excited to use AI tools, rather than feeling threatened by them.

ChatGPT has leaped into the forefront of our lives—everyone from students to multinational organizations are seeing value in adding a chat interface to an LLM. But OpenAI has been concentrating on this for years, steadily developing one of the most viral digital products this century. In this episode of our AI series, we sit down with Logan Kilpatrick. Logan currently leads developer relations at OpenAI, supporting developers building with DALL-E, the OpenAI API, and ChatGPT. Logan takes us through OpenAI’s products, API, and models, and provides insights into the many use cases of ChatGPT.  Logan provides fascinating information on ChatGPT’s plugins and how they can be used to build agents that help us in a variety of contexts. He also discusses the future integration of LLMs into our daily lives and how it will add structure to the unstructured nature and difficult-to-leverage data we generate and interact with on a daily basis. Logan also touches on the powerful image input features in GPT4, how it can help those with partial sight to improve their quality of life, and how it can be used for various other use cases. Throughout the episode, we unpack the need for collaboration and innovation, due to ChatGPT becoming more powerful when integrated with other pieces of software. Covering key discussion points with regard to AI tools currently, in particular, what could be built in-house by OpenAI and what could be built in the public domain. Logan also discusses the ecosystem forming around ChatGPT and how it will all become connected going forward. Finally, Logan shares tips for getting better responses from ChatGPT and the things to consider when integrating it into your organization’s product.  This episode provides a deep dive into the world of GPT models from within the eye of the storm, providing valuable insights to those interested in AI and its practical applications in our daily lives.

From May 8-11, discover expert insights from four industry leaders from OpenAI, Accenture, Kubrick Group, and Kanayma LLC on how to navigate the era of AI.