David Knea

New Creation... a small language model for Consulting https://lnkd.in/gVuqRQNP this is just a toy model to test the ability to create custom agents using the LLAMA3 70B model. This is an example of how open source is driving Innovation and speed... it really is a small exercise to create something simple. click the link and give it a try it's focused on Consulting based on a very simple instruction set and some very good Consultants who know how to explain things clearer than I do. you start with asking you to look at your company's website and give it a problem for to help solve. it will try and provide some instruction or some direction on Consulting techniques to solve that problem. for example... use the following prompt about a company called grow.com which is a pretty good company that does dashboards and Analytics. prompt: review my company website: https://grow.com assess the clarity of our messaging on the website and describe a summary of the business and our key customer segments along with any competitors we have in that segment. that should give you a good starting point.

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Need a data maturity assessment? Love using AI? Why not combine the 2 together? https://lnkd.in/gEW4QrRU Share your experiences with this approach below 👇

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When Logic, Part of Accenture engages with retailers - such as at #NRF2025 in a couple of days - we often start with the factors that indicate how much (or little) an organization values data and analytics. Specifically: 1. Organizational understanding of, and commitment to, Data & AI/ML 2. Data & AI/ML Strategy & Plan developed and signed off 3. Data & AI/ML Capabilities deployed and being used It doesn't tell us where the company should focus its data and AI investment. But it gives us a great perspective on how far they'll be able to progress in whatever timeline they're working with. Of course, our goal is to help retailers mature in their usage of data and AI, leveraging a strategy to outline their plan and relying on trusted solutions to realize value. #dataandAI #retailanalytics

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Reading and reflecting on a recent report by Richard Fletcher and Rasmus Kleis Nielsen, which surveyed people from Argentina, Denmark, France, Japan, the UK, and the US on their use of and opinions on generative AI. Their work revealed that #ChatGPT is the most well-known and used #AI tool for generating content. About half of the online population in these countries have heard of it, but only a small percentage (1-7%) use it daily. Many people have only tried it once or twice, and a significant portion (20-30%) haven't heard of any popular AI tools at all. Younger people, especially those aged 18-24, are more likely to use AI tools regularly compared to older generations. Most people believe that AI will have a big impact on various aspects of society within the next five years, from politics to news media and science. However, trust in different sectors to use AI responsibly varies greatly. About half of the people trust scientists and healthcare professionals to use AI responsibly, while less than a third trust social media companies, politicians, and news media to do so. When asked if AI will make their personal lives better or worse, more people in four out of the six countries said "better," but many don't have strong opinions, and some think it will make their lives worse. People are generally more pessimistic about AI's impact on society as a whole. There is a lot of optimism about how AI will affect science, healthcare, and daily activities like media and entertainment. However, people are worried about its impact on the cost of living, job security, and news. Many people haven't made up their minds about the overall impact of AI, with a large portion choosing neutral options or saying they don't know when asked about it. While some have strong opinions, others are still unsure about how AI will affect their lives and society in general. https://lnkd.in/efHB4FGp

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Rather than focusing solely on others, individuals should cultivate a profound curiosity for self-discovery, prioritize outcomes over individuals, nurture robust connections, and lead with guidance rather than authority. One fundamental aspect of a profoundly humble leader is their commitment to transparency through openly acknowledging their own errors and weaknesses. Lot to Practice!

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🌟 Intuition vs. AI: Why one is a game-changer and the other a liability 🌟 Intuition might have driven decisions in the past, but in the age of AI, it's a recipe for disaster. Here's why: 👉 Intuition is inherently biased. Personal experiences, emotions, and cognitive biases shape your gut feeling. These can cloud judgment and lead to poor decisions. 👉 AI provides unbiased analysis. Algorithms objectively process vast amounts of data, offering insights free from human bias. 👉 Intuition can't scale. As businesses grow, relying on human intuition becomes impractical. AI can handle terabytes of data. 👉 Data-driven decisions are more accurate. Studies show that decisions based on data outperform those made purely on intuition. 👉 The competitive edge. Companies leveraging AI can respond quicker to market changes and make informed decisions faster. 👉 Intuition lacks transparency. Data-driven decisions are transparent and can be justified with clear evidence. 👉 Future-proofing your business. Integrating AI and analytics into your decision-making process prepares your business for future challenges. While intuition will always have its place, relying solely on it in the age of AI is risky. Embrace data-driven decision-making to ensure accuracy, scalability, and a competitive edge. What do you think? Is intuition still relevant in today's AI-driven world? #booklaunch #achilleai #data #analytics #artificialintelligence #AI #keynote #speaker #consulting #careers #leadership #management ---------------------- Did you enjoy this post? If you’re looking to dive deeper into how data, AI, and tech can boost your business, here’s how you can stay in the loop:   🔹 Like this post and drop a comment below 🔹 Check out my profile for more insights 🔹 Hit ‘Connect’ to keep the conversation going   This will tell the LinkedIn algorithm to bring more data-driven strategies your way! 🤖

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Title: The AI Paradox: How Technology Can Enhance Humanity in the Customer Experience Authors: Amelia Dunlop, Charlie Woodward, Greg Melia, CAE, Helena Augenstein Published by: Deloitte Digital, Natter, and Customer Experience Professionals Association (CXPA) Executive Summary: This report examines the "AI Paradox," where AI improves efficiency yet offers the potential to elevate human connection in customer experiences (CX). It argues that AI, when applied correctly, enhances personalization and empathy in customer interactions, fostering deeper relationships between brands and customers. Key Topics: The AI Paradox in CX: AI, while typically associated with #automation, can enhance the #human touch in #CX, allowing for more personalized and emotionally resonant interactions. AI as an Enabler of Human Connection: AI enables #businesses to analyze #customerdata for deeper insights, tailoring experiences that anticipate and meet individual needs, strengthening emotional ties. Balancing Efficiency with Empathy: The report emphasizes the need for balance, using AI to improve processes while preserving the #emotionalintelligence and empathy that customers value. Case Studies and Examples: Real-world cases highlight how companies successfully integrate AI to enhance personalization and responsiveness while maintaining a human-centric approach. Challenges and Ethical Considerations: Ethical concerns like #dataprivacy and bias must be addressed, with #AIsystems being transparent and fair to maintain trust. Future of AI in CX: The report foresees AI becoming central to #CXstrategies, enhancing #humaninteractions without overshadowing them, provided ethical challenges are met. Conclusion AI holds the potential to both improve #efficiency and deepen #humanconnection in CX. By thoughtfully integrating #AI, #companies can enhance empathy and #personalization, ensuring that #technology strengthens 3humanrelationships, not replaces them.

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Executive Summary Capgemini’s “Gener(AI)ting the Future” report delves into the transformative impact of #generativeartificialintelligence (AI) across various industries, examining how it reshapes #businessoperations, #customerinteractions, and #productinnovation. Key Insights 1. Economic and Strategic Potential of Generative AI #GenerativeAI is positioned as a catalyst for economic transformation, potentially increasing global #GDP through process #automation, rapid #dataanalysis, and enhanced decision-making. The report emphasizes its role in fostering #innovation, particularly in sectors like #healthcare, #finance, and retail, where it can personalize offerings, reduce #costs, and create new value propositions. 2. Technological Innovations and Advancements Capgemini outlines major advancements in #generativeAItechnologies, including sophisticated machine learning models like transformers, #largelanguagemodels (LLMs), and advanced image and video generation. The report also addresses technical limitations, such as the substantial computing power required and the need for extensive datasets, which pose challenges for smaller enterprises. 3. Ethical and Regulatory Considerations The report underscores the importance of ethical AI development, advocating for transparent, unbiased, and accountable systems. Capgemini highlights the growing regulatory landscape, with governments and institutions moving toward guidelines that balance innovation with responsible use. Issues such as data privacy, copyright concerns, and AI-driven misinformation are highlighted as significant challenges that need immediate attention. 4. Operational Integration and Workforce Impact For effective adoption, generative AI must be seamlessly integrated into existing business processes. Capgemini advises on the need for upskilling employees and reshaping roles to work alongside AI-driven systems. The report notes a potential shift in workforce dynamics, where human-AI collaboration will become essential, requiring organizations to foster a culture of continuous learning and adaptability. 5. Future Outlook and Strategic Recommendations Capgemini provides a forward-looking perspective on generative AI’s role in the next decade, predicting a widespread adoption curve and substantial economic impact. The report suggests that businesses should develop AI-driven strategies that are both innovative and adaptable, invest in robust data infrastructure, and collaborate with regulatory bodies to ensure ethical compliance. Conclusion Capgemini’s report serves as a comprehensive guide for organizations aiming to leverage generative AI. While the potential of AI is vast, its success depends on thoughtful implementation, workforce readiness, and adherence to ethical principles. Capgemini’s analysis provides valuable insights for decision-makers, offering strategic recommendations that emphasize a balanced approach to harnessing generative AI’s transformative capabilities.

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The AI Product Manager's Handbook: https://amzn.to/3D6OyPd via Packt Publishing (greatly expanded and updated 2nd Edition, just released) 𝙒𝙝𝙖𝙩 𝙮𝙤𝙪 𝙬𝙞𝙡𝙡 𝙡𝙚𝙖𝙧𝙣: 🟠Plan your AI PM roadmap and navigate your career with clarity and confidence. 🟣Gain a foundational understanding of AI/ML capabilities. 🟠Align your product strategy, nurture your team, and navigate the ongoing challenges of cost, tech, compliance, and risk management. 🟣Identify pitfalls and green flags for optimal commercialization. 🟠Separate hype from reality and identify quick wins for AI enablement and GenAI. 🟣Understand how to develop and manage both native and evolving AI products. 🟠Benchmark product success from a holistic perspective. ———— #EnterpriseAI #DataScience #GenAI #MachineLearning #ML #DeepLearning #DataScientist #CDO #CTO #CMO #AIStrategy #GenerativeAI #AI

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McKinsey Research: AI could drive up to $660B in productivity value for the consumer goods sector. New solutions for Consumer Goods Cloud use AI grounded in unlocked and unified data to make planning and execution more efficient and intelligent:

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What if the future we imagine could be positive and full of possibility? In a powerful Voices of the Future conversation, Gerd Leonhard—an inspiring futurist with over 20 years of experience in predicting trends—shares why it’s time to reframe our outlook on tomorrow. For Gerd, envisioning the future isn’t about forecasting shiny new gadgets; it’s about humanity, purpose, and a collective good that prioritizes "People, Planet, Purpose, and Prosperity." As Gerd explains, we’re at a critical crossroads. Today’s rapid technological advances give us immense power, but how we choose to wield that power will shape society. And for Gerd, that choice must be rooted in optimism rather than fear. He points out that while social media and Hollywood often show us a dystopian future, we can choose to create a world where tech elevates humanity instead of eroding it. Gerd’s message is urgent: let’s build a “good future” together—one where businesses, individuals, and governments champion sustainability, digital rights, and social equity. If we approach these challenges with courage, the future can be brighter than we ever imagined. Catch Gerd’s full podcast in the comments to be inspired by his insights on creating a hopeful future that serves us all. 

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Get early access to the 2025 IBM IBV Consumer Study Report! Set to launch at #NRF2025, this new IBM Institute for Business Value (IBV) explores how organizations can harness AI to drive decisions, innovation, and strategies across customer experiences, product design, supply chains, and store operations. 📥 Pre-register now to receive early access to insights on: ·      Key findings on criteria for scaling AI to deliver value ·      Preparation needed to empower your workforce on the AI journey ·      Imperatives in safeguarding brand trust   Additionally, visit IBM at booth #4639 during #NRF2025 to explore enterprise-grade AI and hybrid cloud technology solutions. More details coming soon #AI #IBM #IBVStudy #RetailInnovation #NRF2025

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#Manufacturing is ripe for the potential benefits of #AI. Yet, for many manufacturing executives, the journey to leverage value out of AI solutions remains long and foggy. I’ve observed a common recurring theme asked by manufacturing leaders: what are practical ways to leverage AI? Why? First, manufacturing businesses tend to be complex organizations with many processes including purchasing, production, quality control, and logistics. For manufacturers with different product lines and plants, it gets difficult to find a solution that fits for the whole business. Secondly, there is a gap between theory and practice. Too many industry conferences and publications continue to have AI thought leaders and AI visionaries talking about AI in the theoretical sense. Instead of getting relevant, practical advice on how to leverage AI solutions, most manufacturing leaders feel even more lost than when they started. How can businesses overcome these challenges and start to leverage AI? 1. Your business is most likely already using AI – That’s right, you may already be using AI and not aware of it. ERP solutions are now coming with built-in AI technologies. Additionally, some of your employees are most definitely using a generative AI tool (whether you know it or not). 2. Focus on specific processes. Every organization needs an AP department. Leveraging an AP automation tool with AI capabilities is a great place to start. 3. Clean up your data – having bad data is a nightmare whether or not you want to use AI. AI leverages your data, make sure the data is clean and valuable. 4. Invest in talent – many manufacturers, especially in the midmarket, have not invested in a chief information officer (#CIO) or transformation executive. These leaders are responsible for planning and executing digital transformations. Focusing on the processes and bringing in the right people to plan and execute an AI-enabled transformation are some of the best ways organizations can leverage these solutions. How is your organization leveraging AI? #digital #transformation

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Rudy Agovic, PhD and I have recently discussed the rise of AI Agents and how they might apply to Pricing and revenue Growth Management. While LLMs (like Open AI's GPT-4, Claude's 3.5 Sonnet, or Google's Gemini models) excel at generating, summarizing, and analyzing text (and increasingly, data), AI Agents combine multiple data sources and real-time feedback loops to automate processes and decision-making and take proactive steps (much like a human domain expert would). But how can AI Agents help with Pricing & RGM problems? Many organizations face similar RGM struggles, from scattered data to gut-feel Pricing, sales & marketing decisions. - Inefficient Data & Analytics: Manual processes drain team & executive bandwidth and delay insights. - Lack of Integrated Pricing Analytics: Disjointed reports, basic dashboards, or underutilized BI tools limit margin improvements. - Weak Competitor Analysis: There is no automated price scraping, and reactive adjustments result in lost shares or $$$ left on the table. - Negative Promo ROI: Without systematic analysis and optimization capabilities, each promo investment is dilutive GP$. - Inconsistent Discounting Practices: Ad-hoc (or reactive, routine) discounts erode margins and brand value. And the list goes on. For example, AI Agents can already streamline data ingestion, harmonization, automatically scrape competitor pricing, build dashboards and deliver actionable insights to the right decision-makers. By combining predictive and optimization algorithms, they can recommend optimized prices, trigger discount recommendations or guardrails, and provide post-promotion ROI analyses. In short, most core data, process, and analysis activities that a Data Scientist / Pricing Analyst / Pricing Manager resource carries out can be done solely with purpose-built AI agents. While this sounds great in theory, it is still many years away from becoming a reality for most companies (especially in B2B). Large Enterprises benefit from vast data sets and greater resources to build sophisticated AI platforms. However, they often grapple with complex governance, siloed systems, and multi-regional compliance - slowing down any sort of true AI innovation. For small- and mid-cap companies, agility is their strong suit. However, they face tighter budgets, dirty & incomplete data with limited in-house expertise, and scalability issues. B2B vs. B2C GTM differences are also important to consider. Before jumping into AI Agents, ensure you have the fundamentals in place for Pricing & RGM: 1. Data Quality & Governance: Clean, harmonized data fuels accurate insights. 2. Dynamic Pricing Dashboards: Centralize visibility, track competitor moves, and measure the right Pricing KPIs. 3. Price Elasticity Models: Know your price sensitivities for scenario analyses, optimization, and segmentation. 4. Segmentation & Strategy: Segment customers & products to refine pricing tactics for each group. #revenue_growth_analytics

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Must see this new article from Prophecy >> The Case for Data Transformation Copilots (#AI-powered companions that leverage #GenerativeAI to boost productivity) — How they will change ETL by evolving far beyond conversational interfaces: https://lnkd.in/eidJ69YK ➕ See the Prophecy Data Transformation Copilot in Action at the Databricks Data + AI Summit June 10-13!

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