Vin Siegfried

Just finished the course “Artificial Intelligence and Business Strategy: Case Studies” by Anil Gupta and Haiyan Wang! Check it out: https://lnkd.in/e8d3gaCK #artificialintelligenceforbusiness #businessstrategy.

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Gen AI continues to be a pervasive topic among leaders while the ripple effects of poor implementation are being felt across nearly every facet of the economy. Thinking differently about the use cases for AI requires stepping back to assess how technology reduces the instances of error and increases the likelihood of positive embrace. #AI #dataleadership

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Very grateful to have been nominated by Corinium Global Intelligence as the top 100 #data leaders for 2024. It’s been a extremely busy but a fantastic learning experience in the data industry over the past 12-24 months with ever changing regulatory landscape and an advancement in the technology enablers that support business outcomes. Here is an interesting conversation we recently had about building trust with data. I was asked this question on what steps we can take to improve trust in our data - I wanted to share just one perspective of many. ⭐️ Firstly I compared to building Trust in data in the same way we build trust with our fellow human beings - why so? ⭐️ Well if you don’t know someone very well on day 1 you are unlikely to have much trust with them ⭐️ As you get to know them better you know what their strengths are and what you can work with them on for example you would trust me to be a running coach but it’s unlikely you would trust me to sing in front of a large audience ⭐️ Data is the same; once we get to know and understand our data we will be able to trust what it can and cannot be used for. Data has to be used in context to a particular outcome. ⭐️ So the first step of building Trust is to understand our data better by knowing what it means and what is was designed to be used for before we start learning in which scenarios we can trust it and in which scenarios it is not fit for purpose. A big thank you to all my colleagues at Standard Chartered for supporting my learning and growth over the past two decades in my journey. #ai #data #analytics

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Exploring the AI Jungle: For our June episode of the Data Book Club, we debated insight in AI strategy and breakthroughs from AI/ML research from six bestsellers on the topic of #AI. 1. Architects of Intelligence - The truth about AI by the people building it 2. The Book of Why - The new science of Cause and Effect 3. The Coming Wave - AI, Power & the 21st Century 4. Generative Deep learning - Teaching machines to Write, Paint, Compose & Play 5. Genius Makers - The Mavericks who brought AI to the world 6. The Master Algorithm - The quest for the Ultimate Learning Machine We discussed AI strategic themes that included: ▪️AI DNA – How do we build prior knowledge into AI? Avoid training from scratch. ▪️AI Learning Strategies – how do we generalise and remember better? ▪️Emotional AI – approaches for managing 'what' and 'how' we goal seek with machine intelligence. ▪️ Causal AI – how do we move from correlation to understand cause and effect relationships in the world around us? ▪️Imaginative AI – how do we create new possibilities, form new ideas and concepts through to testing them? ▪️Unified AI – how do we combine a unified view across the contemporary machine-learning paradigms? AI/ML research is progressing on many fronts for these questions - its a big jungle out there. 🌴 Examples of ideas we discussed: ▫️Learning by Algorithm competition with the evolution of GAN architecture. ▫️Model-free vs. Model-based reinforcement learning using simulation ▫️Causal AI for understanding cause and effect for true explain-ability and counter factual reasoning. ▫️xLM The many flavours of Language Models - from large to small to visual language models. ▫️Human aware AI - from Artificial Social Intelligence, Agentic AI to Large Action models. ▫️and so much more.   Takeaways ● Prepare for an uncertain and fast moving roller-coaster so 'Built for Change' principal will be important for agile, adaptable, open & flexible architecture. 🤸‍♀️ ● Invest in your AI/ML skills. We are still in the early stages of AI adoption; lot of options out there and still lots to be done. 💪 ● #GIGO remain as true today as ever. Without Good data there is no Good AI. ℹ️ ● Learning could become our most important asset & resource (so how are you managing & securing)? 🎓 A fascinating set of books that recommend as AI will be a central force in our future. So much insight we had to break up over two episodes. What AI strategic themes and research ideas are you most interested in? Our #westpac data book club, is our member-driven community for the curious, data inspired and passionate continuous learners. 📚 #databookclub #ArchitectsofIntelligence #GeniusMakers #BookofWhy #ComingWave #MasterAlgorithm #GenerativeDeepLearning #ArtificialIntelligence #DataScience #AdvancedAnalytics #MachineLearning #DeepLearning

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Thanks to Frances Stoor, lead Data and AI governance architect, for representing Jackson so well on DATAVERSITY’s recent panel on “The Impacts of AI on Data Architecture,” and touching on the need for efficient collaboration and communication between AI engineers, delivery teams and leadership. #dataarchitecture #AI #data #datamodeling #dataquality

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Great discussion with my fellow panel members about data self-serve using AI. https://lnkd.in/etBN5gbn

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It was a privilege to participate in the Databricks’ Data + AI Executive Series, sharing Discovery Bank's journey in data-driven financial services. Discovery Bank has always been a data driven organization, and I am passionate about how we leverage data and AI to enhance our clients’ financial health. As we leverage generative AI, a robust data ecosystem as the foundation is crucial for success. The Databricks Intelligence Platform (Databricks) has enabled this foundation, allowing our team to deliver high quality data products for our clients. https://dbricks.co/4dRoQLm Dael Williamson, Richard Wylie, Abdul Ibrahim

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I came across this study from McKinsey that states that AI adoption is surging to a record high, and Gen AI is performing well, but data barriers and risk need to be addressed (maybe not such a big surprise). What I like in this survey is the message that AI and Gen AI have begun to create real business value and don't only exist as garage projects in an IT organisation; I notice that Risk, Legal, and Compliance reports increased revenue and that HR reports the most significant cost decrease. I'm realistic, but this is a good way to start the value conversation. That is a strong signal that business adoption is moving in the right direction. Unsurprisingly, the industry technology is most likely to invest in AI. I think financial services are forced to adopt AI to be cost-effective and comply with regulations. Interestingly, energy and materials are more prominent than media. Given the insights from the McKinsey study, I'm keen to explore the AI landscape in the Nordics. Localised research could shed light on any potential lagging of AI adoption in our region.

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Excited to see the conversation around AI and data governance continuing to grow at events like #CDOIQ2024. The insights shared by leaders like Richard Wang highlight the critical role that Chief Data Officers play in shaping the future of AI-driven innovation. Looking forward to seeing how these discussions translate into actionable strategies that drive value across industries. #AI #DataGovernance #Leadership

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AI at Wells Fargo — An AI Product Manager’s Take on a Public Story (Part 3-1) Title: Wells Fargo, artificial intelligence, and you https://lnkd.in/e3YC72fd The AI revolution is coming. It is impacting every industry, and financial services are no exception. As an AI Product Manager, I have seen how AI is shaping the future of a company like Wells Fargo. Let me walk you through a few key questions about AI adoption based on my high level interpretation of the article referenced above. - The Why: AI’s Role in Financial Services 1. Data is NOT enough; insights become the default: AI helps us make sense of massive amounts of data, turning it into actionable insights that drive customer experience and operational efficiency improvements like never before. 2. Immediate gratification at scale is expected: Personalization is now a basic requirement, and AI enables it at scale. Customers expect fast, relevant solutions, and AI makes that possible. 🏃‍♂️💨 - The What: Opportunities in AI for FS 1. Virtual assistants: Tools like Fargo™ offer a banking copilot experience, answering questions and helping customers navigate their accounts. 2. Fraud detection: AI can predict and prevent fraud in real-time, safeguarding customer assets. 3. Personalized services: AI-driven insights can tailor services to individual customer needs, offering real-time advice and solutions. - The How: Unlocking Value from AI Opportunities 1. AI infrastructure: Wells Fargo has built the Enterprise Open Source Data Science Platform, accelerating AI value realization. 2. AI safety is a top priority: Responsible AI is critical. Wells Fargo supports the AI Bill of Rights, ensuring ethical, privacy-respecting solutions. 🔒 #AI #ProductManagement #Innovation #ResponsibleAI #FinancialServices #MachineLearning #CustomerExperience

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AI misalignment can lead to discrimination, harmful content, and business failure. Yet, these risks are avoidable. Misaligned AI erodes trust among stakeholders, and without trust, enterprises can’t unlock AI’s full potential. Forrester's latest research introduces “align by design”—a proactive approach to ensure AI systems meet business goals while adhering to values and standards. By aligning people, technology, and processes from the start, organizations can mitigate risks and maximize the impact of AI investments. Read more: https://lnkd.in/eJN2fsp6 #AI #DigitalTransformation #ForresterResearch #AIAlignment #Trust

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Here's a link to access my article on the Graph Database, please let me know your comments and suggestions. “The Power of Graph Databases: Unlocking Complex Relationships in Data“ by Kausik Kumar on Medium:

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Oracle HeatWave GenAI is 30X faster than Snowflake, 18X faster than Google BigQuery, and 15X faster than Databricks for vector processing. 💪 Learn more in this great post! https://lnkd.in/e4yPUyC4

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Gartner’s #AI #Leaders team: “Navigating #GenAI #Production and #Evaluation Challenges” Pleased to join our recent roundtable, with select peers in the AI Leaders group, on the above very engaging topic, expressing my personal views. We explored: Understanding GenAI production strategies * How do you define and prioritize best practices when implementing GenAI production strategies? * What considerations are essential when selecting workflows for managing GenAI models? * How do you balance the need for innovation and experimentation with the necessity of maintaining stability and reliability in GenAI production? GenAI model evaluation * Beyond traditional metrics, what additional factors do you consider when evaluating the performance of GenAI models? * How do you monitor and evaluate the performance of GenAI models in production for drift or degradation? * How do you approach the validation and verification of data sources used to train GenAI models? Integration Hurdles * What are the primary challenges you've encountered when integrating GenAI production into your existing development pipelines? How are you overcoming them?
 Can you share any "aha" moments or unexpected discoveries that have occurred during integration efforts, and how have they influenced your approach going forward? * Are there any regulatory or compliance considerations that have posed challenges during integration, and how do you address them? * What role does change management play in successfully integrating GenAI production into existing workflows, and how do you ensure smooth adoption by end users? As always, such conversations with senior leaders practicing AI and Generative AI are thought provoking and, in my view, enable us to go beyond the typical expectation of what is possible. Thank you Gartner, for hosting this event, and for your kind invitation. #GenerativeAI #Strategy #LLMs #Innovation #transformation #artificialintelligence #AI #GenAI #ChatGPT #disruptivetechnologies #cxos #ceos #cfos #ctos #caios #cios #cdos #excellence #GenAINY

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David Steffa of Snowflake and Jessica S. of Dropbox sharing their perspectives on a San Francisco #Treasury Symposium #sfts panel about how to deal with the current #rate environment. There are things that are outside of your control (e.g., #Fed rate cuts/hikes) but you can focus on things that are within your control (e.g., solid #cashflowforecasting). And, like also heard on other panels today, #ESG investing came up as a passion project for many, but not all, Treasurers.

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Successfully integrating generative AI into your organization requires a strategic approach amidst its disruptive impact. Here are key steps to maximize its value and foster innovation: ✳️ Set a Holistic Gen AI Strategy: Align AI, automation, and data management to support enterprise goals. ✳️ Identify and Prioritize Use Cases: Compile and prioritize use cases based on business impact and feasibility. ✳️ Experiment with Purpose: Encourage controlled experiments to harness innovation potential. ✳️ Share Organizational Guardrails: Establish clear guidelines to manage AI risks and foster innovation. ✳️ Focus on ROI Early: Develop a robust business case emphasizing potential ROI and operational efficiencies. By following these practices, organizations can navigate the complexities of generative AI adoption effectively, driving strategic investments and sustainable innovation. #GenerativeAI #AIStrategy #Innovation #ROI #DigitalTransformation

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🚀 Unlocking Success: Delving into the Vital Role of a Well-Crafted Operating Model for Data Governance in Enterprises! 📊 Don't miss out on understanding how this strategic backbone fuels organizational efficiency and success. ➡️ Let's delve into the innovative concept of data mesh and its pivotal role in fostering decentralized data democratization. 📝 This approach revolutionizes how data is managed and governed across distributed teams in organizations ➡️ I cannot stress enough the necessity for a comprehensive data governance operating model and roadmap. 📝 This strategic framework is essential for guiding organizations towards effective data governance practices and achieving their objectives. ✅ Key Aspects of Data Governance Operating Model: ----------------- ✴️ Organizational Scope, Data Scope, and Domain Scope Management: ↗️ Define the scope of data governance within the organization. ↗️ Identify stakeholders, responsibilities, and coordination mechanisms. ✴️ Decision-Making for Data-Related Matters: ↗️ Determine the organizational placement of decision-making authority. ↗️ Consider antecedents, contingencies, and impacts on actively managed data. ✴️ Motivation, Goals, and Performance Assessment: ↗️ Establish motivation factors and goals for data governance. ↗️ Develop a performance assessment plan to measure progress and report. ✴️ Standardizing Processes and Procedures: ↗️ Discover and standardize processes based on policies and guidelines. ↗️ Implement change control with a stakeholder communication strategy. ✴️ Implementation Roadmap and Risk Management: ↗️ Develop an implementation roadmap with a work breakdown structure. ↗️ Manage risks, values, and benefits throughout the process. https://lnkd.in/dQ8YHqVD #DataGovernance #BusinessStrategy #DataManagement #Networking #Data #Datascience #Datamanagement #Management #Innovation #Technology #Strategy #Business #Artificialntelligence #Digital

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Harnessing the power of generative AI can transform the way we work. From analyzing meeting transcripts, summarizing regulatory guidance to analyzing business financials for lending risks GenAI offers many efficiency benefits. However, it's critical to use it safely. Here are some tips: 1. Data Privacy: Ensure sensitive information is protected and avoid inputting personal or confidential data into AI systems that train on your data. 2. Bias Mitigation: Be aware of potential biases in AI outputs and strive for inclusivity. if you AI is driven from stored database, ensure that the output has no inherent bias by ensuring prompts are detailed and don't include filters for protected classes. 3. Human Oversight: Always review AI-generated content to maintain accuracy and relevance. Check back random facts and figures and ask the system to include references. Yes, GenAI can give you facts and also explain how it came up with them. 4. Ethical Use: Use AI responsibly, respecting intellectual property and avoiding harmful applications. AI is coming, embrace the technology and learn how to securely use this new and exciting technology (No, this isnt a sales post) How are YOU using AI?

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