Transitioning to New Business Models

Surveys say over half of companies have deployed a GenAI app or feature and I’m not buying it. Deployed = adopted, and I can tell you from experience, adopted is the harder problem. Half of companies still don’t trust their data enough to act on it. Now you’re telling me that they have magically deployed and gotten users to adopt GenAI? Every AI problem is a data problem until the model hits user and customer hands. Then it transforms into a people problem. Users only adopt GenAI when it’s seamlessly integrated into the apps they already use. Don’t underestimate the difficulty of getting users to change. AI Product Design 101: The closer the model supported experience is to the original workflow, the better adoption rates. For example, most business workflows that involve data, use tabular data and LLMs don’t handle that well. SAP only released 1 LLM this week…and it works with tabular data. It has a conversational interface for users to ask questions about spreadsheets, price quotes, and financial reports because that’s what customers are used to doing. Users can work with familiar data types and still get the ease of the new interface and simpler data querying. Familiarity is the smartest approach to adoption. In the LLM-supported products I have worked on, once users adapt their workflows to leverage the new interface, they quickly form new habits. The hard part is getting them to start, and most companies don’t realize how big that behavioral change barrier is. I’m an SAP partner because they build stuff that works and gets adopted. Those surveys would be believable if more companies followed its lead. #GenAI #SAPSapphire

Want to future-proof your career and start leveraging AI? Here's how I did it, ranked from easiest to most ambitious: 1️⃣ 𝗥𝗲𝗮𝗱 𝘂𝗽 𝗼𝗻 𝗔𝗜 𝘁𝗿𝗲𝗻𝗱𝘀, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝘂𝘀𝗲, 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗴𝗲𝘁 𝗮𝗰𝗾𝘂𝗮𝗶𝗻𝘁𝗲𝗱 • 𝗥𝗘𝗔𝗗: https://lnkd.in/eT-nzYP9 I recommend Heather Murray 's AI for Non-Techies Newsletter. It's a fun treasure trove of useful information. • 𝗥𝗘𝗦𝗣𝗢𝗡𝗦𝗜𝗕𝗟𝗘 𝗨𝗦𝗘: AI (Generative AI especially) is not infallible. Learn about the mistakes it can make, the issues it can cause, and how to navigate them. • 𝗧𝗘𝗦𝗧 (𝗜𝗻 𝗧𝗵𝗲 𝗙𝗹𝗼𝘄 𝗼𝗳 𝗪𝗼𝗿𝗸): For $15/mo, Canva is an amazing option because you can test alot of current capabilities. For $20/mo, Microsoft Copilot Pro can be added to your Office 365 account. Also for $20/mo, Google offers AI premium for your workspace (GMail, Docs, Sheets, etc). 2️⃣ 𝗔𝗽𝗽𝗹𝘆 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗶𝗻𝗴 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜-𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘀𝗸𝗶𝗹𝗹𝘀. If your company offers access to AI tools, get access and use them according to their use policy. If not, create sample scenarios at home and practice. 3️⃣ 𝗙𝗶𝗻𝗱 𝗮𝗻 𝗔𝗜 𝗺𝗲𝗻𝘁𝗼𝗿 𝘄𝗵𝗼 𝗵𝗮𝘀 𝗺𝗮𝗱𝗲 𝗮 𝘀𝗶𝗺𝗶𝗹𝗮𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻. Share that you're interested in learning more in your field. Ask if coworkers or your LinkedIn network if anyone incorporated AI into their work. Offer to continue to learn together. 4️⃣ 𝗔𝘁𝘁𝗲𝗻𝗱 𝗔𝗜 𝘄𝗲𝗯𝗶𝗻𝗮𝗿𝘀 𝗮𝗻𝗱 𝗲𝘃𝗲𝗻𝘁𝘀 𝘁𝗼 𝗼𝗽𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘆𝗲𝘀 𝘁𝗼 𝗻𝗲𝘄 𝗽𝗼𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀. There's no shortage of free webinars, conferences, etc. talking about AI. Get involved. 5️⃣ 𝗘𝗻𝗿𝗼𝗹𝗹 𝗶𝗻 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝗮𝗻𝗱 𝗴𝗲𝘁 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗶𝗻 𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗳𝗶𝗲𝗹𝗱. Professional organizations and technology vendors offer lots of free training for specific use cases. 6️⃣ 𝗝𝗼𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁. Talk to your manager about opportunities. Make it one of your professional goals to stand out. If they aren't there, contact your professional or volunteer organizations. 7️⃣ 𝗣𝗶𝘁𝗰𝗵 𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝗼 𝗴𝗲𝘁 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Use what you've learned to pitch an opportunity to create value at your company, your professional, or your volunteer organizations. Do these make sense for you? How are you going about it? #artificialintelligence #innovation #changemanagement #technology #digitaltransformation

🤖 How you can uplevel on AI/ML as a Product Manager: (from easy to hard) 0️⃣ Play with AI products and find ways to leverage AI into your daily tasks. As a Product Manager, you're probably more interested in effective applications of AI and less the nitty gritty technical details. Playing with AI products helps you build a sense of a good AI product experience vs a bad one. AI products I played with that impressed me: - Canva video generation for clips for my YouTube videos - Leveraging Teal to help rewrite my resume How I leverage AI into my daily tasks: - As a hobbyist translator, ChatGPT is my goto for language translation - I use ChatGPT to help me study for product design interviews by asking it to come up with multiple alternative solutions I've never thought of 1️⃣ Learn the basic building blocks so that you're using industry jargon correctly. SUPER EASY basic courses I’ve taken and enjoyed: - The basics & jargon you need to know: https://lnkd.in/ddN6FN4j (got this rec from Chantal Cox) - Prompt engineering fundamentals (1 hour): https://lnkd.in/dsecmjdB 2️⃣ Following AI creators and role models. Find role models you admire and want to learn from that also teach about AI. My current faves: - Tina Huang - Project ideas & how to leverage AI effectively (https://lnkd.in/dXabURpV) - Peter Yang - AI concept breakdowns, AI applications; he's currently running a 7-day intro to AI course through his newsletter (https://lnkd.in/dSD4hm5y) - Andrew Ng - This guy feels like the Godfather of AI yet explains everything so simply (https://lnkd.in/dkUnSrku) 3️⃣ Build personal projects based off your interests & hobbies. Tutorials I've personally used and recommend: Easy intro project ideas: Tina Huang (https://lnkd.in/dxs27pB6) Build an app with ChatGPT: Peter Yang (https://lnkd.in/dvqCFaMC) Build a chat bot: Andrew Ng (https://lnkd.in/dsecmjdB) 4️⃣ Iterate on this list. 🔄 If you have any recommendations of resources or how to more efficiently learn about AI/ML, I'm all ears! 👩🏻💻

If you’re leading a legacy business in transition, here’s one thing to remember: Transformations don’t happen all at once. They happen in steps. A recurring theme I see in companies trying to pivot: leaders swing too hard toward either aspiration or urgency but rarely balance both…. or they can’t push themselves out of their comfort zone and end up never swinging at all. The most successful growth strategies? They start where you are and build to where you want to go. That means crafting a multi-step transformation: Step 1: Generate momentum and capital. Identify high-confidence, low-risk growth opportunities that leverage what you already do well. These early wins fund the future and build belief. Step 2: Reposition the business. Lay down the scaffolding for the company you’re becoming, new business models, new capabilities, new customer value. Step 3: Transform. Make bold, foundational bets that open up your total addressable market, create defensible moats, and reposition your relevance for the next decade. But here’s the tough part: Transformation is as much about what you stop doing as what you start. Many companies fail at transforming because they simply just add more to their business vs focusing investments and execution. Focus is your friend! Great leaders don’t just set vision. They create sequencing, resourcing, and culture that makes change possible. Your first move doesn’t have to be your final one. But it has to be intentional, confidence-building, and value-creating or you’ll never get to phase two. If you’re a transformation leader, I’d love to hear how you’re approaching sequencing change and lessons learned. #Leadership #BusinessTransformation

🚀🔄 From Service to Software: My Entrepreneurial Evolution 💡💻 The transition from a service based business model (Dental Support Organizations) to a software business model (Unify) is no joke. Before I engaged in this transition, I under estimated the amount of difference I was going to experience. This shift isn't just a change in what I do; it's a complete transformation in how I think, operate, and envision business growth and success. 🌟 The Service-Based Chapter: Running a DSO When I first started my DSO, I quickly realized that the success of the business was less about building creative and new solutions but more about taking tested and true methodologies and scaling them in a cost effective manner. I learned the success of a DSO is truly about consistently providing an amazing patient experience. I learned the primarily skillset that a DSO needs to achieve a great patient experience at scale really boils down to mastering people management and change management. 🔀 The Shift to Start-up Software: Transitioning to a software business model has been akin to learning a new language while writing a novel in it. My focus had to shift from team management to identifying needs of many and building a product that can address those needs. It's a leap going from a clearly define revenue driver of dentistry to mastering innovation, product development, marketing, and sales in a vast, ever-evolving digital market. 📈 Looking Ahead: The transition is not just about a new business model; it's about adopting a new mindset. It's about envisioning a product that can impact a multitude at once. While the metrics of success have changed, the core principle remains – deliver value. Every day is a roller coaster and sometimes I wonder if this business model transition was the right move. Then there are days that I would never trade back for anything. For those of you in my network that have experienced both, what are your thoughts? Which one do you prefer? #Entrepreneurship #BusinessTransition #SoftwareDevelopment #Innovation #GrowthMindset #BusinessEvolution

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

I've worked as an AI PM at AWS, Meta, Etsy, and now Netflix... But if I worked at a resource-constrained company, here's how I'd build AI products: I'd start by leveraging proven solutions. There is no need to reinvent the wheel. I'd look at areas where industry leaders have already demonstrated ROI (e.g., recommender systems, customer support automation, etc.). Why? These are safe bets with known outcomes. In other words, it's all about focusing on low-risk, high-value cases. Here's the exact 4-step process I'd follow: 𝟭/ 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝘀 𝗵𝘂𝗺𝗮𝗻 𝘄𝗼𝗿𝗸 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗶𝘁. For example, help customer service reps with AI-generated suggestions or automate routine content tasks. This builds trust and comfort with AI in your organization. 𝟮/ 𝗧𝗵𝗲𝗻 𝘂𝘀𝗲 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝘁𝗼 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗶𝗱𝗲𝗮𝘀. Avoid wasting time and budget chasing uncertain “moonshots.” Start small, measure impact, then scale. 𝟯/ 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗻𝗼-𝗰𝗼𝗱𝗲 𝗼𝗿 𝗹𝗼𝘄-𝗰𝗼𝗱𝗲 𝘁𝗼𝗼𝗹𝘀 𝘄𝗵𝗲𝗻 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲. Speed up development and reduce engineering overhead by using accessible platforms. Test value first. Don’t aim for perfect systems on day one. 𝟰/ 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗼𝗻. Celebrate small wins, learn from failures, and continually optimize. AI is evolving, so your approach should too. 𝗧𝗵𝗲 𝗺𝗮𝗶𝗻 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Big players like Netflix, Meta, and others have the risk appetite to run hundreds of experiments. But smaller teams must be smarter with where and how they invest. Fortunately, they often share what's working and what's not. This is invaluable data for smaller teams to avoid throwing darts at the wall. ♻️ Share if you know someone building AI with limited resources. Follow me for more insights on practical AI product leadership.

Those who follow me know what I think about the forced EV transition. It's revealing to see auto industry bigwigs now voicing similar concerns. - GM's Mary Barra noted the transition to EV is proving "bumpy," with GM retracting its near-term EV production targets. - The Mercedes-Benz USA's CFO called the EV market "a pretty brutal space," having to discount EVs heavily to move them off lots. - Toyota's Akio Toyoda and Honda's Toshihiro Mibe also expressed skepticism towards the current push for EVs, emphasizing the reality check the industry is facing. - Ford Motor Company has delayed about $12 billion in EV investments due to softening demand for higher-priced electric vehicles, emphasizing the price pressure growing the losses in their EV business. These sentiments resonate with the broader dialogue on EV affordability, energy security, and environmental efficacy. Delving deeper into the American context reveals substantial barriers to EV adoption: - Cost: High upfront costs can deter average consumers. - Infrastructure: The inadequate charging infrastructure poses a significant challenge. - Range Anxiety: Range limitations and sparse charging stations exacerbate this issue. - Cultural Attachment: The love for gas-powered trucks and SUVs is strong. - Maintenance & Repair Knowledge: The switch demands a new learning curve for many. It's noteworthy to mention that the "greenest" vehicle is a used vehicle, as the energy to produce it has already been spent. Hybrids are proving to be a viable route—they mitigate most of the emissions without encountering the hurdles listed above. They blend the best of both worlds, offering a balanced approach towards a cleaner and more sustainable automotive future. #HybridVehicles #SustainableTransition #Commonsenseenergy #electricvehicles https://lnkd.in/g27JaSpY

In 2015, Volkswagen faced one of the largest corporate scandals ever, risking billions in fines and the trust of millions. Their emission cheat threatened to derail over 80 years of brand legacy. Yet, a few years down the line, they're not just surviving, they're thriving... In 2015, the Environmental Protection Agency (EPA) found that Volkswagen cars being sold in America had "defeat device" software in its diesel engines. This could detect when they were being tested and change performance accordingly to improve results. Volkswagen intentionally installed software to cheat emissions tests in its 2009-15 diesel cars. The software reduced emissions only during testing, otherwise, they were up to 40x the legal limit. Within days of the EPA findings, VW Group CEO Martin Winterkorn resigned after nearly a decade. Share prices then dropped 37% in 2 weeks, wiping out $26B in market value. VW set aside over $7B for direct costs, with 11M affected cars. VW skillfully handled this crisis. VW's new CEO Matthias Müller offered a full apology, saying the company had "broken the trust of our customers and the public." He admitted they "totally screwed up" and vowed to take responsibility. Müller committed to an external investigation, cooperating fully with authorities to understand the crisis and prevent it from happening again. VW America's CEO Michael Horn also apologized before Congress. Beyond apologies, VW pledged sweeping reforms focused on sustainability and transparency. It launched an independent testing program, support for TDI owners, and a US$2 billion investment in EV charging infrastructure and zero-emission vehicles. Today, VW has bounced back remarkably from the scandal and is thriving, especially in the electric vehicle space. The company invested over €30 billion in the transition to EVs and plans to stop developing new gas engines by 2026. Sales of electric models like the ID.4 and Porsche Taycan are accelerating. VW's share price has recovered and now exceeds pre-scandal levels. By fully owning mistakes, charting a bold new course, and backing words with action, VW transformed disaster into opportunity. The biggest lessons from how VW handled its emissions crisis: 1. Take full accountability without deflection or excuses. 2. Act swiftly and decisively to chart a new direction. 3. Pledge real change beyond just words. Back it up with concrete investments and actions. 4. Stay committed for the long haul. Rebuilding trust requires sustained action and industry leadership over time. Don't just do the minimum needed. 5. Have courage to be transparent and fully own mistakes. Companies can bounce back stronger through accountability. That’s a wrap. Hope you enjoyed this.

When acquiring a small business as an entrepreneur, the transition period can feel like a high-wire balancing act. On one hand, you likely have bold ideas and a vision for where you want to take the company. On the other, you need to keep the trains running on time and avoid disrupting existing operations. It's tempting to want to put your stamp on things quickly, but proceeding too hastily can undermine short-term performance. This delicate dance is maybe the single hardest thing I see new searcher CEOs deal with. ⚠️ So here are some ways I've seen searchers balance growth and execution during the transition. 👇 1. Listen and Learn First Your employees have tremendous institutional knowledge. Before making changes, invest time upfront simply listening and learning. Ask lots of questions to understand why things are done in a certain way. There may be good reasons! This will build trust and help you identify improvement opportunities. 2. Communicate the 'Why' When introducing changes, clearly explain the rationale and how they link to the company's overall vision. Your team will be more receptive if they see the purpose behind the change, rather than just being told to do something different. 3. Pilot Test Changes Big, sweeping changes all at once are jarring. Instead, pilot test changes on a small scale first. Work out the kinks and get employee feedback. Then, if successful, scale the change more broadly. This gradual approach reduces disruption. 4. Prioritize Quick Wins Focus initial changes on easy, high-impact areas. For example, refresh branding, enhance sales collateral, or fix glaring inefficiencies. Early wins build momentum and belief that your vision is sound. 5. Preserve What's Working Avoid change for change's sake. If certain areas of the business are thriving, don't mess with success! Let those parts of the organization be a source of stability amidst other changes. 6. Get Aligned with Your Team Your employees are your partners in growth. Spend time aligning them to your vision. Ensure they feel heard and included in shaping plans. An aligned team will embrace changes rather than resist them. 7. Accept Some Disruption Growing pains are inevitable during transitions. Be empathetic, but don't let short-term dips in performance derail your vision. With the right balance of patience and persistence, you'll turn the corner.