Lawrence Y.

We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

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Nearly two years on from ChatGPT's release, we now have enough to understand Gen AI's impact within the enterprise. And research shows that while Gen AI has delivered incremental productivity in specific workflows, it comes with its own risks and challenges In Ema Unlimited's latest blog, seasoned digital ops & intelligent automation expert Ian Barkin analyzes this impact & the challenges, and why enterprises' focus might better be directed towards Agentic AI. • 𝗜𝗻𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝗶𝗲𝘀 𝗮𝗻𝗱 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀: Studies by McKinsey, Deloitte, Asana, and Anthropic all indicate that inaccurate outputs from Gen AI integrations are a major concern for enterprises. There is a need for high-quality training data and for customized models, leading to an increased emphasis on data governance and solutions that can be tailored to the enterprise.  • 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: A majority of orgs report that only 30% (or less) of their Gen AI experiments were taken into production, as these projects were plagued by scalability and deployment issues. Integrating AI into existing workflows without causing disruption to operations is a key problem. • 𝗥𝗼𝗜 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗔𝗜 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗚𝗮𝗽: Out of ~2770 enterprise leaders, 40% struggled to define and measure the impact of their Gen AI initiatives. Most companies do not have a formal AI strategy in place, leading to misalignment between business goals and AI projects. 𝗜𝗳 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝗴𝗼 𝗮𝗻 𝗼𝘃𝗲𝗿𝗵𝗮𝘂𝗹 𝘁𝗼 𝘀𝘂𝗰𝗰𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜, Ian Barkin 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝘀 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗹𝗼𝗼𝗸 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝗻𝘀𝘁𝗲𝗮𝗱. In addition to the above concerns, Gen AI can only deliver incremental productivity when done right. 𝗕𝘂𝘁 𝘄𝗶𝘁𝗵 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜, 𝘁𝗵𝗲 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲: Organizations will no longer need excessive human coordination across data silos with no accountability on workflows actually executed. Instead, collaborative, adaptive, intelligent Agentic AI systems can own and execute workflows end-to-end. By 2028, Gartner predicts 𝟯𝟯% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘄𝗶𝗹𝗹 𝗶𝗻𝗰𝗹𝘂𝗱𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜, autonomously handling 15% of day-to-day work. The enterprise of the future will be agentic. Read why in Ian Barkin's full blog for Ema Unlimited — linked in comments. #AgenticAI #EnterpriseAI #FutureOfWork #AI

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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7 RECOMMENDATIONS FOR HARDWARE STARTUPS It’s hard to understate the difficulty of the v1 launch of a consumer hardware company. I see a ton of new HW startups coming out. Here are some counterintuitive recommendations… 1) Target a small market for v1, not a large market. You need a small number of people to love your product and this will earn you the right to build a v2. You have to understand your v1 will not make you a successful company. Your v1 needs to prove that you deserve to exist in the first place. In the case of WHOOP, we started with pro athletes who were willing to put up with our v1’s short battery life and flaky Bluetooth connections because they so desperately needed Sleep and Recovery data. A broad consumer base would have rejected it. 2) Be great at no more than 1-3 things. If you try to be great at more than that, you will build a hardware that is mediocre at a wide range of things. You won’t think it’s going to be mediocre, but it will be. 3) You want a small launch. Not because you want small sales but because you want to have a steady ramp. It’s much better to do 100k of sales steadily over 12 months than in 2 months with a huge drop off. Keep in mind you need to manage your manufacturing partner and that there’s nothing better or more efficient than steady and predictable. 4) CAC is your enemy. Referrals are gold. The magic of referrals is that they signal high NPS and they help you grow steadily. Again everything that helps you have a steady HW manufacturing plan is good. Building a referral program for v1 launch is a good use of your time. 5) Be excellent at Software. Turns out being good at both Hardware and Software is a hard thing and something that your BIG COMPANY competitor is actually not great at (excluding Apple here). For some reason, there’s such a magnifying glass on the Hardware development, that Software is then overlooked by consumer startups. Don’t make that mistake. You can be great at both. 6) You should spend an unreasonable amount of time and money on ONE aspect of your Hardware. Could be the design or a technical breakthrough. But something needs to be SPECIAL. In our case, we designed a modular battery pack that allowed Whoop to be charged without taking it off your body. This made our development timeline much longer, but it allowed us to achieve something that no other wearable has achieved since: 24/7 health data. 7) Finally, have an unrealistic belief in yourself and your team. You are not supposed to succeed and the odds are stacked against you. Building hardware takes confidence and you’re up against big players who will rip you off. We went so far as to leave a message for our competitors on our circuit board (pictured below). In the case of Amazon and others, the prophecy came true. If you’re building consumer hardware, I wish you nothing but success. It will be super hard, but super rewarding. Good luck 👊🏼 #consumer #hardware #wearable #startup #whoop

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Looking forward to sharing how we at basys.ai are leveraging Generative AI to tackle healthcare’s biggest challenges. PS: Also looking forward to sporting my basys vest in the #BayArea PPS: What I’m not looking forward to is the second cross-country flight in less than a week. #GenAI #BasysAI #GenerativeAI

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The most downloaded datasets from the California HHS site? If you follow me on LinkedIn, you've probably guessed it - the Medi-Cal enrolled provider list and Suspended and Ineligible list are the top two, followed by other datasets by a wide margin. The California Health & Human Services Agency (Cal HHS) has a nice data hosting site that resembles data.cms.gov. The site looks slick and provides interesting insights, including visitor statistics and download metrics. It's fascinating to see how people are interested in provider eligibility and exclusion data for Medi-Cal billing. Looking at how organizations use these public datasets, compliance emerges as the dominant use case. This makes sense - when accessing government data portals, users are typically verifying facts related to government programs. The user base spans health plans, hospitals, IPAs, medical groups, and even commercial payers, as Medicaid suspensions often trigger reviews across the healthcare ecosystem. What's interesting is that this pattern extends beyond healthcare. On data.ca.gov, which hosts all kinds of government data, the most downloaded dataset is the directory of certified electricians. This shows a consistent theme - whether it's healthcare providers or electricians, people come to government sites primarily to verify professional credentials and eligibility. This raises an intriguing observation about how government data is used. The way organizations use these datasets - downloading lists to verify credentials and eligibility - is functionally similar to what modern Identity Provider (IdP) systems do, but through a much more manual process. What are your thoughts on the future of government data in healthcare identity verification? #Healthcare #DigitalTransformation #Compliance #HealthTech #GovTech #Identity

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As the CEO of an AI-driven health-tech company, Labops, I’m constantly evaluating how technology can create the most impact. While Large Language Models (LLMs) are making headlines, I believe Small to Medium-sized Language Models (SMLs) deserve more attention—especially for businesses operating in niche domains like healthcare. Why do I say this? 1️⃣ Tailored Solutions: In healthcare, where precision and specificity are critical, SMLs (40–50B parameters) are more adaptable to domain-specific needs. 2️⃣ Cost-Effectiveness: For startups, running and fine-tuning a SML is more economical than relying on large, resource-intensive LLMs. 3️⃣ Ownership & Flexibility: Open-source SMLs allow businesses to build and deploy models on their terms, giving greater control over data and performance. At Labops, where we’re building solutions to transform diagnostic services, the ability to leverage AI that aligns with our unique requirements is crucial. SMLs strike the right balance between scalability and practicality, making them a natural fit for startups like ours. LLMs have their place, but they’re not always the answer for every use case—especially in sectors where focus and efficiency matter more than sheer size. If you’re in healthcare or another specialized industry, I encourage you to explore the potential of SMLs. They might just be the competitive edge you’re looking for. What’s your perspective on SML vs. LLM? Let’s discuss! 💬 #AI #HealthTech #LLM #SML #Startups #Innovation #Labops

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California Law SB 1120 has gone into effect, and prohibits health insurance providers from denying claims solely based on an algorithmic decision. This means that "yes" decisions can be automated, but "no" decisions must be routed to a human expert reviewer for a final decision. The law is intended to disallow mass automated rejections, as has happened before, and matches how most health insurers already operate. https://lnkd.in/gKDx_XVa #healthcare #ai #regulation

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🌉 San Francisco's Surprising Comeback: From 'Doom Loop' to Renaissance? Recent data suggests that San Francisco, once trapped in a pandemic-induced 'doom loop', is showing strong signs of recovery. Here's what's changing: 📈 Positive Trends: - Population Growth: Net gains over the past two years, with domestic in-migration positive for the first time since 2012. - Housing Market: Median home sale prices up 1.2% YoY to $1,427,500 in June. - Job Growth: The Bay Area added 7,400 jobs between January and June. Downtown Revival: Foot traffic beyond typical work hours has recovered to over 90% of pre-pandemic levels. - Crime Reduction: Property crime down 32% and violent crime down 14% in Q1 2024 compared to Q1 2023. - AI Boom: The city is positioning itself as a hub for the AI revolution. 🚧 Ongoing Challenges: - High office vacancy rates - Homelessness and drug crisis - Lagging tourism and business travel 🔑 Key Initiatives: - "Vacant to Vibrant" program offering free rent to businesses in empty storefronts - Increased police patrols and encampment sweeping - Crackdown on open-air drug dealing 💡 Expert Insight: "The rising AI empire is giving the city a new edge. Funding, talents, and culture related to the AI revolution will help the city turn the corner." - Lu Chen, Senior Economist at Moody's Analytics 🤔 What's Your Take? Are these positive trends sustainable, or is it too early to declare a comeback? How can other cities learn from San Francisco's recovery strategies? What role will AI play in reshaping urban economies, particularly in tech hubs? https://lnkd.in/gCnPCBRU

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I’ve spoken to 50+ CMIOs/CIOs over the past year. Here are 5 thoughts about AI Scribes I'm hearing that many people are NOT talking about👇 1️⃣ Distribution matters more than Product. Most are just buying Nuance because it’s easier to buy. It’s a known entity. It’s an add-on to the Microsoft contract which means less internal hoops. Key question for who wins long-term: will the startups get distribution faster than the incumbent gets innovation? Will the EHRs eventually just release their own native AI scribes? (my belief is yes… which Oracle/Cerner has already done). 2️⃣ There is a clear “phenotype” for the clinicians who actually use AI Scribes: it’s clinicians who don’t care to have control over how their notes look and sound. But for those who really care about controlling the output? Those folks will keep writing their own notes. 3️⃣ The impact of AI scribes on medical education and trainees is very unclear. Some believe trainees should get exposure to AI scribes early on. Others worry that trainees who don’t learn to write notes are going to miss a critical skill that helps clinicians think. And what happens if there’s a cybersecurity incident and the EHR goes down? Will future clinicians not know how to document manually? 4️⃣ ROI is still hard to truly measure. Few see any improvement in “productivity” (i.e. more patients seen). The real value is in improving clinician well-being. However it’s hard to prove AI scribes are lowering staff churn since that is a lagging indicator many steps away and affected by many other factors. 5️⃣ The per clinician pricing model used by most vendors breaks down at scale. If I have clinicians who would only use the AI scribe for 30% of their patient encounters, am I going to buy a license for them? Probably not. What other less talked about learnings or insights are you hearing behind the scenes?

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John Snow Labs #Healthcare #NLP & #LLM 5.3 is now out! This release completely overhauls entity resolution - mapping medical entities in text to standard medical ontologies, in context - greatly improving accuracy. Full release notes: https://lnkd.in/gsiGF26i Install software: https://lnkd.in/gsTqkWKT #ai #generativeai #healthcareai #healthai #datascience #ontologies #medicalcoding

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LLMs are great, but we need to broaden our perspectives on what AI can do for humanity. Such as predictive healthcare and disease prevention, smart agriculture, or education personalization – the list goes on. If we only look at AI from the narrow scope of LLMs, we’re hamstringing our creative understanding of what’s coming and how to prepare. This is exactly what HumanX is all about: broadening our viewpoints and understanding of AI. I hope to see you there!

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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There is a tendency for many first time founders to overbuild an MVP. The reasons vary, but most often it stems from improper/lack of customer discovery and results in an improperly scoped build. The role of an MVP is not to capture the entire workflow of a potential user, rather it is to build a solution for a very specific white hot pain point that allows you to build trust with your customer and over time deploy more product and capture more wallet share. When a startup truly understands their target customer, and has isolated top priority problem areas, they are often able to zero in on a fairly narrow scope for an MVP. Oftentimes this solution can be fairly crude with a V1 UI/UX and not built to scale, but because it is meeting such a pressing need it generates robust usage from an initial cohort of users. On the other hand, when a startup fails to perform robust customer discovery, they almost always fall victim to the "feature fallacy" and overbuild initial products thinking that quantity of features will lead to adoption. It's the classic problem solving technique of throwing spaghetti at the wall until something sticks, but in early stage startups with very limited resources, this is often a path to failure. Founding teams need to balance the feature set of an MVP with their ability to ship product within the existing runway of the business - which in many cases is the sweat equity of the founding teams living off of savings or friends and family money they've raised. As such, the composition of an initial founding team is crucial, and having engineering talent on the founder team is most often necessary to bridge the gap. Founding teams lacking engineering talent and reliant on outside development contractors to stand up initial products put a startup in a suboptimal position to succeed. Investors are increasingly unwilling to fund first time founders to build out an initial product. As such, teams need to focus on a way to get a product in market and prove some degree of traction within their customer base before realistically targeting institutional capital. Raising money before deploying an initial product is possible, most notably by second time founders or early executives at high growth startups, but in many cases this subset already realizes the importance of customer discovery and getting an initial version of product in market, and optimizes for that before looking to capitalize the business. There are certain types of market opportunities where a narrow feature set is an unrealistic option for initial deployment - examples being an embedded payroll offering or a core infrastructure layer. In these cases, a founding team will need to raise capital to build an MVP. Second-time founders or founders with deep domain experience/networks who can demonstrate robust customer discovery proof points have a reasonable chance of raising capital, but most founders will struggle to launch these types of businesses.

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In my experience of interviewing and working with many product leaders, a common trait among the great ones is treating their craft more as a science than an art. Delighting users, creating magical experiences, and ultimately developing software that users love to use is a science. An immediate red flag during interviews is when product leaders can't express details beyond "talking to customers" on how they build lovable products. While talking to customers is paramount, I listen for the structure and tools they use to become customer-centric and experts in the users they serve. Here’s what I look for: Methods to Listen For When Evaluating How Product Leaders Create Lovable Products: 🗺️ Journey Mapping: Understand what users do before and after they interact with your product or attempt to accomplish their job. 🔑 Key Moments Identification: Reveal the key moments that matter in the user journey. 😌 Emotional State Awareness: Understand the emotional states of users and how they change as they progress through the journey. 📊 Data Collection and Analysis: Collect and analyze data to understand funnels, drop-off rates, attrition, etc. I’m also on the lookout for specific techniques on how the product leader builds high-performing product teams. Here are some of the practices to listen for: 🏆 Recognition and Rewards: Establish clear KPIs that define success, create accountability, and drive alignment. Implement a system for recognizing and rewarding team achievements and individual contributions. 🤝 Consistent 1-on-1s: Conduct regular one-on-one meetings to mentor and guide the career and development of the product management team. 👥 Team Collaboration: Allow teams to craft their process and evolve it over time. My favorite team performance ceremony is the retrospective, where the team can gather in a safe place and collectively define how they will improve. 🔄 Cross-Functional Alignment: Create alignment between product, engineering, sales, marketing, and leadership. This involves regular cross-functional meetings, transparent communication of goals and progress, and a unified vision that ties the efforts of all departments together. Ensuring that all teams are moving in the same direction is crucial for driving consistent and sustainable growth. Overall, there are many important items across both themes that define effective product leadership. By listening for a product leader who employs these structured methods, companies can make a better hire to turn insights into actionable strategies, ensuring that the user experience is not left to chance but meticulously crafted and continuously improved, while building and managing high-performing teams that drive success.

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McKinsey Says that Product Marketing Managers (PMMs) are the Quiet Force Behind Software Success A new McKinsey study (link in the comments, https://lnkd.in/gE9vyDxx) has put PMMs in the spotlight, emphasizing their growing importance in the software industry. Its findings reinforce the ideas I've shared in my previous posts on the evolving role of PMMs in the AI era (https://lnkd.in/ggjfSawh) and their pivotal role in driving personalization and efficient growth (https://lnkd.in/erV6Nipd). Here are some key insights from the study: ► Instrumental in Product Launches - PMMs are essential in launching successful products in today's competitive market. ► Revenue Growth - Companies in the top quartile in revenue growth have a 25-30% higher ratio of PMMs to PMs compared to bottom quartile companies, reinforcing the value of well-resourced PMM teams. ► Bridging Gaps - PMMs align product value with market needs, bridge gaps between teams, and orchestrate launch processes. As Goksu Nebol-Perlman, VP of Business Product Marketing at Meta, said, "Product marketing is the glue that keeps the product, the market, and the customer together." Why are PMMs critical more than ever? ► Market Differentiation - With changing market dynamics and increased competition, PMMs help products stand out and succeed. ► AI-Powered Analytics - As data and AI-powered analytics become essential for decision-making, PMMs use these tools to guide strategies and gain deep customer insights. ► Efficient Growth - AI helps PMMs with analytics to make smart decisions and target efficiently for sustained growth. ► Cross-Functional Collaboration - In a world where teamwork is key, PMMs are at the center of collaboration to create better customer experiences. By leveraging AI to understand customers deeply, make data-driven decisions, and deliver personalized experiences at scale, PMMs are driving efficient growth and success in the software industry. Visionary companies that recognize and invest in empowering their PMMs will be better positioned to thrive in the AI era. Your thoughts? ► How is the role of PMMs evolving in your organization? Are they being recognized as more than just the Masters of the One-Pager and Battle Card Wizards? ► Have you empowered your PMMs to drive strategic initiatives? ► How are they using AI to understand the market, analyze product-market fit, prioritize top segments, craft defensible positioning and messaging, develop personas and buyer's journeys, launch products, enable sales, and align cross-functional teams? Interested in collaborating and learning how to inspire PMMs with what's possible with AI? Please feel free to DM me. #ProductMarketing #PMM #StrategicMarketing #AIInMarketing #AI Michael Conn Product Marketing Alliance Rowan Noronha Product Marketing Community GrowthPath Partners

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