David Walker's Linkedin Analytics

Promoting AI? Start with Internal Readiness Helping companies adopt AI isn’t always about the most powerful multi-agent approach; it’s about understanding where a company is on its AI journey. Some are eager to innovate, but others are held back by outdated systems, siloed data, or a lack of clear strategy. In fact, 59% of organizations cite legacy tech as a significant barrier to AI adoption. However, technology alone isn’t the only hurdle; organizational culture plays a critical role in determining whether AI initiatives succeed or stall. Companies that foster experimentation and cross-functional collaboration are far more likely to realize AI’s potential than those operating in rigid, siloed environments. If you really want to be successful, you need to tailor your approach: • For companies with limited AI experience, your approach should focus on education, helping them define realistic use cases, run pilot programs, and prove ROI. • For companies with fragmented infrastructure, discussions should revolve around data strategy, IT modernization, and cloud integration to lay the foundation for AI adoption. • For AI-mature organizations, the conversation shifts to scaling AI solutions, automation, and competitive differentiation. Even the best AI solution will fall flat if the organization isn’t ready. 💡 The best AI conversations start with one key question: “Where are you today on your AI journey and what’s your next step forward?”

MLOps Isn’t Optional Anymore You wouldn’t bring a new drug to market without regulatory oversight. So why are so many machine learning models deployed without any operational discipline? Here’s what we see far too often: -Promising models sitting idle, no deployment pipeline -Science teams hand off models, DevOps can’t scale -No monitoring. No feedback loops. No retraining strategy This isn’t a tech problem. It’s a process problem. MLOps is the missing link, the bridge between AI prototypes and real-world impact. It’s how you make models secure, scalable, and production-ready. At Quantori, we help life sciences and biotech companies operationalize AI, from proof of concept to production, with deep expertise in cloud, data science, and pharmaceutical R&D. If your models aren’t making it into production, or aren’t improving once they’re there, let’s talk. #MLOps #AI #PharmaTech #Quantori #LifeSciencesAI #MachineLearning

The AI Disruption Frontier: When AI Becomes the Business AI is evolving, and so is the conversation. For years, we’ve discussed AI as a tool for efficiency, automation, and augmentation. But now, we’re entering new territory. AI isn’t just improving business functions; it’s starting to replace them. From pharma to finance, AI vendors are no longer just selling software; they’re delivering AI-powered services that replace traditional workflows, departments, and even entire business units. This shift raises critical questions: -Will companies operate AI functions in-house or outsource them to AI-native vendors? -How do we rethink job roles when AI moves from a support tool to a core operator? -What happens when AI vendors don’t just provide the tech but also become the business function? 👉 For AI professionals, the conversation is no longer just about optimization; it’s about transformation. The winners in this next wave will help clients redefine their business models, not just improve their margins. Are you ready for that level of disruption? #AITransformation #FutureOfWork #AIAdoption #MasteringAISales #EnterpriseAI#QuantoriAI

The AI Commercial Role of 2030: What Will Change? As AI becomes more integrated into every aspect of business, the AI commercial role is set to undergo a massive transformation. Here’s my forecast for how this role will evolve over the next decade: 1️. From AI Salesperson to AI Strategist AI sales will evolve from product pitching to strategic co-creation. Professionals will act as trusted advisors, shaping clients' long-term AI roadmaps and innovation strategies. 2️. Deep Industry and Business Insight Success in AI commercialization will hinge on a firm grasp of industries, challenges, pain points, and goals. AI professionals must understand how AI can revolutionize specific processes and align solutions with measurable business outcomes. 4️. The importance of Ethical & Responsible AI Clients will demand more than performance, requiring assurances about transparency, fairness, and ethical AI use. AI professionals will lead conversations about responsible AI practices and guide clients through compliance and regulatory challenges. 5. Mastering AI-enhanced Sales Tools Future AI professionals will fully embrace AI to augment their workflows. They will leverage predictive analytics, sentiment analysis, and virtual assistants to anticipate client needs. ✨ What This Means for Today’s AI Professionals To stay ahead: • Evolve into an AI Strategist Position yourself as a trusted advisor, driving transformative value. • Develop Deep Industry and Business Insight Gain a thorough understanding of target industries. Map AI solutions to specific processes and ensure they deliver measurable business outcomes. • Champion Ethical and Responsible AI and lead discussions on AI transparency, fairness, and ethics. • Leverage AI-Enhanced Sales Tools Utilize predictive analytics, sentiment analysis, and virtual assistants to refine workflows. 🌟 Your Thoughts? What do you think the AI commercial role will look like in 2030?

Founders: Don’t Build Alone. Co-Create with Clients. The best AI products I’ve seen? They weren’t built in isolation. They were co-created with real users in real-world conditions. Your early customers are your most valuable product managers. Ask them: • “What do you wish this actually did?” • “What’s missing from the workflow or UX?” • “What would get this funded internally, today?” When you build with your clients, not just for them, you gain more than product feedback. You earn trust. You accelerate adoption. And you turn buyers into champions. Founders who co-create aren’t just building software; they’re building alignment, buy-in, and business value. #AIProduct #FounderStrategy #CustomerDriven #GoToMarket #AISales #EarlyAdopters #B2BFounders

💡 Building Enterprise AI Buy-In Isn’t Just About Technology: It’s About Alignment Why? Because you’re navigating up to eight stakeholder groups, each with their agenda and varying levels of AI understanding: • Business wants measurable GenAI impact • IT wants secure, scalable integration • Data Science wants accuracy and transparency • Ops wants fast, actionable results • Legal wants compliance and governance • Finance wants precise ROI and cost control • Procurement wants vendor fit • Executives want innovation, and they want it yesterday The tech? That’s the easy part. The real challenge is alignment. So, how do you drive alignment and move the organization forward? Here’s what’s worked for me: ✔️ Facilitated cross-functional workshops to unify priorities ✔️ Built a joint POC team across business and technical groups ✔️ Developed tailored education for each stakeholder audience ✔️ Led with transparency to build trust and open dialogue The result? Silos broke down. Alignment took root. The organization didn’t just adopt AI; they rallied behind it. In B2B AI today, success means connecting, aligning, and guiding. 🔍 Want to dive deeper into this approach? Chapter 4 of Mastering AI Sales breaks it all down: 📖 Available here: Mastering AI Sales – Amazon Link #AI #EnterpriseSales #GenAI #AIConsulting #TrustedAdvisor #AIAdoption #SalesLeadership

Why AI in Life Sciences Takes More Than Just Engineers AI in pharma isn’t plug-and-play. You can’t just throw algorithms at complex scientific problems and expect breakthroughs. It takes more than engineering talent, it takes people who deeply understand the science behind the data. At Quantori, we bring together AI engineers, data scientists, and PhDs in computational chemistry, biology, and bioinformatics. Our teams don’t just build models; they understand how those models drive discovery, translational research, and ultimately impact patient outcomes. That’s why leading pharma and biotech companies trust us with AI initiatives that go far beyond chatbots and prototypes. In the life sciences, real innovation occurs when AI meets genuine domain expertise. #Quantori #LifeSciencesAI #ScientificComputing #AIConsulting #R&DInnovation #PharmaAI

Why Trust Is the Ultimate Differentiator in AI Commercialization You can have the most advanced AI platform, the flashiest demo, or the sharpest pitch deck, but if your clients don’t trust you, none of it will matter. AI is a paradigm shift. And in this high-stakes environment, companies aren’t looking for vendors; they’re looking for trusted advisors to guide them through complexity, risk, and change. After years of working in AI commercialization, here are three principles I’ve seen make all the difference: 🔍 Transparency Wins – Be honest about what your AI can, and can’t, do. That’s how you build real credibility. 🧠 Understand Before You Solve – Deeply grasp industry, pressures, pain points, and success metrics. Solutions only work if they’re grounded in reality. 🤝 Play the Long Game – Lasting relationships come from consistency, successfully delivering, and showing up when it matters. In AI, trust isn’t optional; it’s everything. When clients believe in you, they invite you into their most important decisions and strategic bets. That’s the real win. So, if you’re in the business of AI, whether as a founder, consultant, or enterprise sales leader, make trust your go-to-market strategy. Let’s stop selling hype. Let’s start building trust. #AIcommercialization #TrustedAdvisor #EnterpriseAI #AIstrategy #CustomerSuccess

The Traditional Enterprise Sales Playbook Doesn’t Work for AI We’re in a new era. Commercializing AI isn’t about pitching features, unveiling roadmaps, or name-dropping multi-agent systems. It’s about driving fundamental enterprise transformation, solving concrete business problems, and delivering measurable value. And to do that, you need a different kind of playbook. One that blends: • Technical fluency • Strategic alignment • Political and stakeholder savvy AI commercialization isn’t just a tech challenge. It’s an enterprise change challenge. If you're commercializing or scaling AI solutions and still using the old SaaS sales playbook, you’re already behind. Dive deeper in my new book: Mastering AI Sales: Essential Knowledge for B2B Professionals. A practical guide to navigating the complexity of AI adoption, stakeholder dynamics, and modern enterprise selling. 👉 Grab your copy on Amazon https://lnkd.in/eaYqymT4 Curious how internal power dynamics and stakeholder politics influence AI buying decisions? Let’s connect. I’d love to compare notes.

AI may be smart, but it can’t listen like you can. In AI commercialization, it’s not your tech stack or ppt deck that sets you apart. It’s your ability to truly listen. Picture two meetings: 1️⃣ One person talks non-stop, rattling off specs and jumping straight to a demo. 2️⃣ Another leans in, asks sharp questions, mirrors key concerns, and draws out deeper insights. Which one builds trust? The difference is active listening, one of the most underrated (and powerful) skills in AI commercialization. What does active listening look like? • Asking layered questions to uncover root problems • Mirroring the client’s language to confirm understanding • Bridging responses into deeper dialogue, not pitching • Listening for what’s not being said (hesitations, concerns, unstated priorities) 📘 If you want to master the art of AI B2B sales and consistently win complex enterprise deals: Stop talking, start listening, and grab a copy of....Mastering AI Sales: Essential Knowledge for B2B Professionals https://lnkd.in/eDz2qRyg #AICommercialization #ConsultativeSelling #TrustedAdvisor #EnterpriseAI #AIStrategy

From SMILES to Graphs: Ushering in a New Era of AI-Driven Molecular Design For decades, cheminformatics has leaned on SMILES , an innovative, compact way to represent molecules as text. But as machine learning pushes boundaries in drug discovery, SMILES is starting to show its limits. At Quantori, in collaboration with Nebius, we’ve developed a molecular generation pipeline powered by graph neural networks (GNNs) and equivariant diffusion models (EDMs). Why it matters: 🔹 SMILES works well for basic tasks and NLP-inspired modeling. 🔹 But graph-based approaches more accurately capture 3D geometry, stereochemistry, and molecular connectivity. 🔹 Using EDMs, we generate chemically valid 3D structures, starting from noise and shaping molecules to match target geometries, opening the door for scalable structure-based drug design. Trained on 1.6 million ChEMBL compounds using 8x H200 GPUs, our model delivers: ✅ Molecules in \~2 seconds each ✅ Over **99.8% uniqueness ✅ Strong 3D shape similarity scores This isn’t just a research milestone; it’s a powerful new framework for the future of AI-powered molecular discovery. 🔗 Read the full blog post by our Principal Cheminformatics Engineer, Dr. Denis Sapegin: https://lnkd.in/eXY6Tert #Cheminformatics #DrugDiscovery #GraphNeuralNetworks #DiffusionModels #GenerativeAI #AIinScience #Quantori #MolecularDesign #ML4DrugDiscovery

I watch AI founders make the same expensive mistake over and over... They demo the tech. Buyers nod and say, "Cool." Then... crickets. Here's the brutal truth: Cool tech doesn't get traction or close deals. Impact does. “We use advanced AI to process documents faster." or "We just saved our last client 15 hours a week and $200K in legal fees." One gets you a polite "we'll be in touch." The other gets you more conversations and discussions. When you flip this switch, everything changes: • You're suddenly talking to decision makers • Sales cycles shrink because the ROI is clear • You become the solution, not just another vendor The reality? It’s not AI that organizations want; it’s solutions to their most significant problems. Lead with the outcome, not the algorithm.

Trust, Tech and Tension: The Stakeholder Challenge in AI Commercialization One thing that consistently stands out in AI projects is the quiet tension between stakeholder groups. Here’s a pattern I see all the time: The data science team is maturing, trying to position itself as the internal AI center of excellence. They’re more focused on models, validation, and infrastructure, and then trying to meet the needs of the business stakeholders. Meanwhile, the business stakeholders are chasing outcomes. They’ve sat through vendor pitches, read the hype, and want real-world results, fast. Now here’s the real disconnect: 🔍 The data team evaluates vendors based on precision, transparency, and ML pipeline robustness. 💬 The business team evaluates vendors based on confidence, clarity, and whether they believe the vendor truly understands their problem. And neither side is wrong. However, they often have different conversations, with varying priorities, definitions of value, and timelines, all while the business problem is still being defined in real time. This kind of disconnect is not unusual; it’s a defining challenge of AI commercialization. I unpack how to navigate these dynamics in Chapter 4 of my book: Mastering AI Sales: Essential knowledge for B2B professionals. 💬 If this resonates and you’d like a free preview chapter, just send me a DM. Have you seen this kind of disconnect in your own work? #AI #AICommercialization #EnterpriseAI #StakeholderAlignment #DigitalTransformation

AI is the easy part. Organizational alignment is the real challenge. After 20+ years working with advanced technologies, including AI. I’ve learned that the biggest roadblocks to AI adoption aren’t always technical. More often, they’re organizational. Take, for example, a Head of research who is frustrated with a newly deployed AI data platform. It didn’t meet their needs. It wasn’t intuitive. It wasn’t gaining traction with the team. Meanwhile, the Head of IT was glowing with pride over the same platform, celebrating a successful launch. Two stakeholders. One product. Completely different realities. This is the disconnect that stalls AI adoption, and why success requires more than technical delivery. It requires cross-functional alignment from day one. This kind of disconnect between IT and scientific users is all too common. And bridging that gap is where AI success either flourishes or fails. 👉 If you're facing this kind of AI adoption challenge, let’s connect. Quantori has helped R&D teams and IT organizations align on AI initiatives, and I’d be happy to share what we have learned. #AIAdoption #QuantoriAI #DigitalTransformation #LifeSciences #OrganizationalChange #AIDrivenR&D #Leadership #TechAlignment

At Google Cambridge today for “Accelerating Biotech Innovation with Google Cloud.” Great to see the focus on how cloud technology is transforming life sciences, faster data analysis, scalable AI infrastructure, intelligent agents, and secure collaboration environments that enable true innovation. At Quantori, we’re proud to be a Google Cloud partner, helping biotech and pharma companies harness these tools to accelerate drug discovery, disease modeling, and real-world data insights. If you’re exploring how to scale your AI/ML or cloud-driven research efforts, let’s connect! #GoogleCloud #LifeSciences #BiotechInnovation #AIinBiotech #Quantori #DrugDiscovery #QuantoriAI

Thank you to everyone who joined last night’s AI Connect event, AI in Biopharma: Success Stories & Lessons from the Front Line. A special thank you to our panelists, Parantu Shah, Mark Brenckle, Ryan Pehrson , for sharing your valuable insights. It was a pleasure to see such a diverse and engaged audience, including biotech founders, pharma R&D leaders, data scientists, and domain experts, all focused on advancing the application of AI in life sciences. Our discussions highlighted several key themes: ✅ With 45% of AI projects failing post-POC, the critical importance of having a coherent AI platform to drive successful execution ✅ LLM's have the potential to replace 50% of tasks in BioPharma, and the rapid evolution from LLMs to multi-agent systems, and how these capabilities are being applied in pharma R&D ✅ The pivotal role of people and process, including the need to engage stakeholders in their language, using metaphors they understand, e.g., multiple agents = GTP with phone a friend. Technology alone is not enough ✅ The importance of securing corporate legal and compliance alignment early in AI initiatives The Q&A sparked thoughtful dialogue on how AI is reshaping competitive advantage across techbio, biotech, and pharma. While core AI capabilities are becoming increasingly standardized, true differentiation will come from innovative business models and flawless execution. Thank you again to our speakers and to everyone who attended. I look forward to continuing these conversations at our next event this fall. David Sedlock, Yuriy Gankin, PhD, Robert Kovalev, Elizaveta Agafonova #AI #Biopharma #LifeSciences #AIinR&D #AILeadership #QuantorAI

Excited to be heading to San Francisco next week for the Databricks Data + AI Summit, along with Arun Nayar, VP R&D Data Science, and Digital Solutions Databricks has become a foundational platform for many of our BioPharma clients, helping them unify data, analytics, and AI workflows and accelerate the deployment of AI-powered solutions at scale. At Quantori, we are proud to be a Databricks Partner, and even prouder to have two colleagues who are Databricks Champions, the highest level of Databricks certification for service delivery. We work extensively with Databricks across: • Advanced data engineering • Real-world evidence generation • Applying AI to scientific R&D in pharma and biotech If you’ll be at the #DataAndAISummit next week, we’d love to connect. Feel free to reach out! #Databricks #DataAndAISummit #AIinLifeSciences #BioPharma #AIinR&D #Quantori #AI #LifeSciences #PharmaInnovation