Ravi Mudigonda

In today's data-driven world, #healthcare and #life sciences (HCLS) organizations are facing an unprecedented surge in #data #volume and #complexity. To harness this data effectively and gain a #competitive edge, establishing a robust Center of Excellence (CoE) for #Data & #Analytics is becoming increasingly crucial. Last 12 months I interacted with over 15 CIO/CTO from HCLS building their CoE for D&A ! Working closely with them helped me put some thoughts together for companies looking to start on this journey. Imagine a world where: ➡️ Data-driven decision making enhanced customer experience, risk management, and product development. ➡️ Streamlined operations through data automation and optimized workflows lead to significant efficiency gains. ➡️ Reduced costs by minimizing data redundancy, optimizing resource allocation, and potentially reducing reliance on external data analysis services. But where do you start? How do you build a centralized hub to turn insights into action? A well-structured Data & Analytics CoE can make this vision a reality. A successful #CoE implementation requires a collaborative approach. Working closely with your #HCLS organization to understand specific needs, define CoE goals, and establish a clear roadmap for success. ➡️ It's a marathon, not a sprint - 82% of healthcare orgs may have analytics initiatives, but many lack a cohesive CoE approach. Don't get discouraged by complexity. Start small, focus on early wins. ➡️ The "why" matters most - Don't build a CoE just because it's trendy. Find a burning pain point – better patient outcomes, streamlined costs, etc. – to rally stakeholders around. ➡️ It's a team sport - Doctors, data scientists, IT – a successful CoE breaks down silos. Get buy-in early, make everyone feel like co-creators. The Data & Analytics CoE - A Strategic Framework Aligned with Best Practices I recommend a structured approach to CoE establishment. Here's a framework to consider; ➡️ Key Teams and Roles 📍Data Governance Council: Senior leaders who set the strategic direction for data. 📍Data Management Team: Ensures data quality, security, and accessibility. 📍Analytics Team: Data scientists, analysts, and business intelligence specialists who develop models, analyze data, and generate insights. 📍CoE Leader: An experienced data professional who oversees the CoE's operations and alignment with business goals. ➡️ Key Functions 📍Data #Governance: Establish policies and procedures to ensure data quality, consistency, and accessibility. 📍Data #Architecture and #Infrastructure: Design a scalable and secure data architecture to support the CoE's needs. 📍Data #Skills Development: Build data literacy and analytical skills across the organization through training programs. Where are you in this journey & what are some key challenges you have faced ? Comment below 👇🏽Teqfocus Jas Kaur Aditya Arora

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LLMs: The Power Behind Multi-Agent Collaboration Large Language Models (LLMs) are transforming the landscape of AI, ushering in a new era of intelligent collaboration and problem-solving. By integrating LLMs into multi-agent architectures, we unlock unparalleled potential. Agents, utilizing LLMs, are like smart applications that dynamically generate inputs for various functions, leveraging natural language for enhanced user interaction and task completion. In a Multi-Agent Architecture, independent entities collaborate towards a common objective, tackling intricate tasks collectively. LLMs serve as the "brains" of these agents, facilitating effective communication, reasoning, and understanding of natural language. From customer service to finance and healthcare, multi-agent systems powered by LLMs find applications in handling inquiries, analyzing trends, optimizing investments, and aiding in healthcare services. The future holds promise for more sophisticated multi-agent systems as LLMs evolve, revolutionizing work processes for increased efficiency and innovation. Excited about the potential of multi-agent systems with LLMs? Let's connect to explore how this technology can elevate your business and pave the way for intelligent collaboration. #AI #LLMs #MultiAgentSystems #Collaboration #Innovation

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Kudos to the wonderful panel on 'How GenAI enables IT modernization'! The insights shared by Sanjeev "Sonny" Bhagowalia Michael Chappell Sujit Mohanty, and Robert McNamara were truly valuable. Their expertise and perspectives shed light on the transformative power of Generative AI in IT modernization. Thank you for sharing your knowledge with us. #GenAI #ITModernization #Innovation @potomic #poccio5 #vtechsolution

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📢 Exciting insights from Google CEO Sundar Pichai in his recent interview on the future of AI! 🌐 In this compelling discussion, Pichai delves into Google's evolving AI strategies, emphasizing the immense potential of generative AI and the importance of ethical and responsible deployment. Google's journey from a search engine pioneer to a comprehensive Cloud and Enterprise platform underscores its adaptability and innovation in the tech landscape. As the internet has grown, so has Google's role, shaping not just how we search for information, but how businesses leverage technology for growth. For those interested in the intersection of AI, cloud computing, and enterprise solutions, this interview is a must-watch! Check it out here: https://lnkd.in/ea_eEJtP #AI #CloudComputing #TechInnovation #GoogleAI #FutureOfAI #EnterpriseSolutions #Leadership #DigitalTransformation

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#Data and #reason will win the day. #AI will be a powerful tool in a few areas of tedious human endeavor. It's not going to have the impact that the hypesters and doomers fancy. Does this mean it's a waste? No. AI will be a fundamental shift in how we approach certain problems. It will shift some jobs from one thing to another, just as multiple technological achievements have in the past. #Innovators will find practical uses not yet even imagined. Problems will be solved in creative and surprising ways. All we need to do now is to set aside our irrational fears and our wild-eyed fantasies of creating artificial general (i.e. human) #intelligence as some kind of apocalyptic replacement for humanity.

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This article by the venture capital firm Andreessen Horowitz "16 Changes to the Way Enterprises Are Building and Buying Generative AI", provides an insightful look at how leaders from the Fortune 500 and other major global companies were planning to scale and use generative AI at the enterprise level (it was interesting to read that last year there were pessimists that thought it wasn’t even possible to scale generative AI into the enterprise).   The changes listed in the article reflect a more optimistic outlook going forward, and provide a good summary of what executives have to consider (e.g., resourcing, models, and use cases) when planning and implementing enterprise level generative AI strategies. Linking you to the article via this post by Allie K. Miller (Fortune 500 AI advisor ) because she added comments to the post which were very insightful (not to mention entertaining – her readers’ reactions to her “acid reflux” comment alone were great). #ai #artificialintelligence #modernIT

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Join us on T-Talks for an exclusive conversation with Gurvinder Singh Ahluwalia, a tech visionary whose journey took him from aspiring Air Force pilot to leading innovations in AI, blockchain, and digital platforms. As the founder and CEO of Digital Twin Labs and former CTO of IBM North America, Gurvinder has spearheaded groundbreaking technological advancements and strategic digital platforms. In this episode, he shares his inspiring story, discussing the pivotal moments and market forces that shaped his path, and offers insights into the intersection of passion and market demand. Don’t miss this deep dive into the life and career of a global technology leader. Link in comments! #TechCEO #FutureTech #TechInnovations #DigitalStrategy #LeadershipJourney #BusinessInnovation #IndustryInsights #DigitalEconomy #TJWalia #TTalks #TTalksepisode21

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Unique peak into the inner workings of an AI Model (Claude Sonnet LLM) from the Anthropic team. Link: https://lnkd.in/gXJXpKEC Highlights: - Unveiling the Black Box: Achieving some interpretability of the model and its represented concepts, marking the initial step towards understanding how LLM function. - Utilizing Dictionary Learning: Employing classical machine learning techniques to identify patterns and features, showing the value of this methodology and its future scalability. - Constructing a Conceptual Map: Fascinating view of how various concepts such as inner conflict or landmarks are stored. Revealing nascent concepts safeguarded today to mitigate undesirable behavior. However, their existence poses potential risks of manipulation by malicious actors for misuse. Access to the full paper is provided in the article as well, for further reading.

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💡Scaling Generative AI: Technical Insights from Experience (Part 1) 🚀 As I've been working with customers on their Generative AI (GenAI) initiatives, I've noticed some familiar challenges surfacing - reminiscent of the early cloud adoption days. As enterprises are gearing up to launch their first few GenAI workloads in production, they want to get ahead of potential roadblocks for smooth scaling and adoption across lines of business (LoBs). Here are a few crucial considerations based on recent engagements: Robust Identity and Access Management (IAM) has surfaced a few times already. IAM is key in ensuring users can securely access only the necessary resources (LLMs, APIs, Vector Databases, Data, etc.) - most LoBs don't openly share data/assets across BU boundaries. Additionally, tracking and allocating resource consumption for each line of business (LOB) promotes fairness and manages costs across departments. Learning from the Cloud adoption days, organizations want the usual FinOps best practices on 'Metering Usage', 'Show Backs' and 'Charge Backs' sorted before mass adoption. Organizations are starting to realize the need early on for a flexible enterprise GenAI platform that supports both commercial and open-source Large Language Models (LLMs) - allows users to choose the best-suited models for their specific use cases, and selecting the most cost-performant LLM based on business needs is important. Who wants to pay $$$ for vacation policy lookup? Also, ability for the organization to evaluate LLMs based on performance, accuracy, and relevance is emerging as an essential capability for enterprise customers. This is because enterprises are already getting humbled by the various creative ways in which employees/LoBs are considering leveraging GenAI. So much variety warrants choice and flexibility! Enterprises want to be able to put necessary guardrails in place for what prompts can be sent to the LLM and what outcome can be shared back with the employees. As such, platform's capability to assessing the quality, ensuring safety of prompts, and adherence to corporate guidelines is critical for GenAI production launch. Likewise, implementing thorough testing and validation automation is surfacing as a core capability for path to production. To adhere with audit and compliance, organizations want to implement Large Language Model Operations (LLMOps) practices for model management, tuning, deployment, and monitoring streamlines operations. Stay tuned for Part 2 of this post, where I'll share additional technical insights that my customers are considering to help build a robust, scalable, and secure enterprise GenAI platform. 💬 Do share your GenAI experiences and technical learnings on the path to production in the comments below! 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7  #genai #ai #aws

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Met with Quantexa CEO Vishal Marria last week at their offices in the Rockafella Center where we discussed the rise of AI and the future. However, beyond that, I wanted to take a moment to acknowledge the remarkable work happening across various industries including healthcare, construction, transportation, infrastructure and more. This is an extraordinary time to reinvent oneself as an individual or an organization. Let's embrace the opportunities and push the boundaries of what's possible. #AI #ArtificialIntelligence

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A new Boston Consulting Group (BCG) report on generative AI in the workplace shows that Job Security has risen to the top of the priority list. You need a plan: Coming from a top business school in NYU Stern School of Business with a big focus on generative AI and how it will impact the workforce, my colleagues and I think a TON about career pathing and job security. Here's what I would tell you from our perspective: In the age of generative AI, job security is more in your control than it has been in a very long time. That's because you have an opportunity to radically improve not just your productivity and workflows, but to bring that to your team, to your leadership, and to your company. I recently did a video on Four Steps to AI-Proof your Career. (It doesn't come from keeping AI out, but rather going all in on genAI.) Here's the link in case it's helpful: https://lnkd.in/e8Y2cSig Some big announcements coming soon on how I can help you on this front- stay tuned! But remember - there are a lot of people to learn from here on LinkedIn. If you're a coach, consultant, share this BCG report with your people. Let them know that you can help them in this journey. Here's the link to the report: https://lnkd.in/epAAb9ef ++++++++++++++++++ Okay, let's TLDR this report: How Work Preferences Are Shifting in the Age of GenAIKey insights from a global survey of 150,000+ workers across 188 countries: 1. Job security is now the #1 priority for workers, jumping from 7th place in previous years 2. Work-life balance remains crucial, ranking as the second most important factor 3. Career development and learning opportunities have gained importance, reflecting workers' focus on long-term employability 4. 57% of workers are ready for reskilling to stay competitive in the age of AI 75% believe GenAI will disrupt the workplace, but only 5% think it will replace their jobs 5. 60% anticipate needing significant reskilling due to AI advancements 6. 40% wouldn't work for companies lacking mental health support or having a negative societal impact Talent folks, you know what to do. Get after it. ++++++++++++++++++++ Want to follow the top AI news and become a genAI ninja in the process? Go to www.conorgrennan.com to sign up for the free AI Mindset Newsletter.

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A few thoughts on #LLM usage in the small, prompted by my toying with #llama3 and #phi-3 on consumer hardware.

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TIAA, the financial services organization overhauled its #customerservice operations, weaving together #machinelearning models, #generativeAI, and customer #journeymapping to improve #customerexperience and predict trouble points. See Paula Rooney's writeup on this based on the insights shared by Sastry Durvasula, chief information and client services officer at TIAA on CIO Online. Durvasala says “We are now for the first time giving our customer service reps a subway map so they can see where a customer is in a journey and may have questions,” he says, noting that retirement planning requires many steps and TIAA could not locate a commercial offering that would give its customers access to all the data they need to develop a plan. To make that possible, the Journey and Service Operations Center (JSOC) initiative reaches into the company’s cloud infrastructure to access data using a range of technologies, including AWS Gateway, Aurora, OpenShift, Secrets Manager, Athena, and Kafka, to name a few. Raymond Bellucci, head of recordkeeping and chief administrative officer for retirement solutions at TIAA, says JSOC is already having an impact, making it easier to identify complexities in TIAA participants interactions so the company’s customer agents can proactively address their challenges. “The simplification of these complex customer journeys helps instill confidence in our participants,” he says. Click below to read more. Geeta Pyne :: Vijay Kumar :: Oindrila Majumdar https://lnkd.in/g7-krVfK

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How Philips spinoffs outperformed the parent company! Over the last few decades Philips has undergone significant transformations and multiple restructurings. Philips transformative journey from an electronics giant, with 350,000 employees, to a medical company, with 60,000 employees, was enabled by a series of major divestitures and spinoffs (as well as acquisitions). Reflecting on Philips’s journey, it is remarkable to see how its spinoffs not only have thrived and become industry leaders, but significantly outperformed the parent company in terms of value creation.   TSMC Founded in 1987 as a joint venture between Philips (28%), the Taiwanese government (48%), and private investors, TSMC has grown to become world's 2nd largest semiconductor company, right after NVIDIA, and a critical player in the global tech ecosystem. Today TSMC has an astronomical valuation of over $850B. Had Philips kept its stake in TSMC, it would have been worth over $200B today. That is 10x higher than its own valuation. But Philips exited its position in 2007, when its stake was worth $9B.   ASML Spun off from Philips' advanced lithography division in 1995 with $1B valuation, ASML is now a dominant player in the photolithography equipment industry, essential for producing ICs. With market cap of over $400B, today, ASML is 16x more valuable than Philips.   NXP Semiconductors Spun off in 2006, NXP has carved out a niche in high-performance mixed-signal electronics and have become pivotal in the automotive and other industries. With market cap of $70B, today, NXP is 3x more valuable than Philips.   Signify Spun off from Philips’s lighting division in 2016, with valuation of over $3B, Signify continues to innovate in the lighting industry.   The success stories of these companies are not just tales of growth and financial gain; they are prime examples of how spinoffs can unlock immense value and result in industry-leading innovations. Philips' decision to spin off these divisions allowed each to focus on their own specific technologies and markets, something that may not have been possible under the broader corporate structure. Prior to spinoff, TSMC, NXP and ASML were mainly internal suppliers for Philips electronics products. After spinoff, they could all operate independently, become supplier and technology partner for other major players, and attract new investors to fund their growth.   As for Philips itself, it will be interesting to see where the company goes from here. One option could be to spin off its healthcare division as well, potentially replicating the success of its former spinoffs, and focus mainly on fundamental research and IP, something that Philips historically has been very good at. But a careful look at the recent changes at Philips suggests that the company is going in the opposite direction, by focusing more on its healthcare division, and reducing its fundamental research efforts instead. Time will tell what future holds for Philips.

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#PendlerPod Natural language models are the future. For successful AI adoption, a dedicated internal change manager should oversee implementation, as an external hire may face integration delays and cultural barriers. Agility is key; companies must adapt to new technologies and ensure processes are future-proof. Maintaining a human-in-the-loop approach for critical decisions ensures AI complements human judgment. Zack Kass visits Inspari’s #Dataklubben with pod hosts Heino Lærke Vigsø and Rosa Ryberg. #transformation

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How can #blockchain transform healthcare data management? Abderahman Rejeb, Mike G., Anjum Khurshid, Alex Norta, Jim Poteet, Suresh Sivagnanam and I believe that the changes will be #disruptive and profound. In our newest publication in Blockchain in Healthcare Today - DMV Chapter, we identify important areas related to current #challenges, #stakeholders, #legal environment, #trust and #privacy, #benefits, #data #integrity, data #security, data #interoperability, data #sharing and #transfer as well as numerous #future #developments. This paper should not only help #researchers to identify interesting areas but is also relevant for #practitioners to better understand the potentials of blockchain for the #healthcare #industry. The open access paper can be found here: https://lnkd.in/dBrgqJ7r This paper is based on a #panel #discussion that can be watched here: https://lnkd.in/dQamzKNp We hope that we can raise awareness and contribute to a more efficient #healthcaresystem that will benefit #patients and the #industry. Many thanks to John Russo and Tory Cenaj for their ongoing support.

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Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

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