AI ROI and Preparing Your Organization for Adoption

The hard reality for AI adoption in Life Sciences AI is ready to massively augment workflows in life sciences. So why isn’t uptake matching what we see in legal, customer support, or finance? Three levers have to click at the same time for take-off: - Budgeting: AI blurs the line between “software” and “services.” Procurement paths need to blur too—or approvals stall. - Change management: Tools must slide into existing processes, not force new ones. Niche, life‑science‑focused apps will win over generic chatbots every time. - Data integration: Start narrow. Classify your “spectrum of secrets” and decide what can safely feed an AI today vs. tomorrow. ROI for AI adoption in life sciences is clear. We’ve done extensive evaluations and AI today can drive down costs while increasing speed and quality across tons of workflows: Competitive intelligence Knowledge management Med affairs insights across field notes Literature Review Primary market research,  Disease landscaping & unmet need analysis Here is how we get there: 1. How to pay: AI is blurring traditional software with services and budgets and procurement needs to start to blur too. Traditionally these budgets follow different approval paths. And pulling from multiple budgets is an organizational challenge. Leadership has to get ahead of these challenges and consider changing internal processes to speed up adoption. 2. Change Management: products need to fit seamlessly into existing workflows. Teams trying to adopt general AI solutions like ChatGPT are struggling to see ROI. This is where life sciences focused AI companies will make a huge impact. Applications have to work backward from how things are done today. Ease of use, onboarding, and customer support will be critical. Starting narrow allows everyone to focus and push these changes forward. 3. Data Integrations: data stewards need to define their crawl‑walk‑run sharing model. Integrating all internal data for an organization is an obvious no-go. And different data sources have different levels of sensitivity. For example, a history of tracked competitor activity might be something teams are willing to share today, but draft clinical protocols for your upcoming trial might be a heavily guarded secret. Teams have to start internal discussion TODAY about their “Spectrum of Secrets” and decide on what is shareable and what is not. The hard reality – change will need the entire ecosystem to move in lockstep. Big pharma, biotechs, consultancies, and startups need to work together to solve these challenges. From my vantage point the ecosystem is close. We’re primed for a big 2H of 2025 and 2026 will be the year of mass adoption.

“We spent millions on AI and have nothing to show for it.” That’s what the CEO told me. And they weren’t wrong… The results were underwhelming. Deadlines kept slipping. The board was asking tough questions. But instead of agreeing to pull the plug, I said something that surprised them: "Before you give up, let's take three steps back." I emphasized that AI can deliver exceptional outcomes, but only when you're rooted in what's actually achievable. Here's what I mean: STEP ONE: Know exactly what you're dealing with - The current state of your data quality - How prepared your infrastructure really is - What capabilities your team actually possesses STEP TWO: Balance your aspirational AI goals (what could be possible) with the reality of what you can deliver today (what is practical). Success in AI comes from marrying honest evaluation with executable strategy. So that’s exactly what we did: we stepped back, rethought the goal, and simplified the approach. We kept their ambitious vision but completely changed the execution: → Redefined success metrics to be measurable and achievable. → Broke their "moonshot" goal into 6 smaller milestones. → Started with one use case in a smaller capacity that could demonstrate clear ROI Six weeks later, they had their first AI success story. Not the revolutionary transformation they originally envisioned, but something better: proof that AI could work in their environment. - That early win gave the team confidence.  - The board renewed their commitment.  - And now they're scaling systematically. So the lesson here isn't about scaling back your vision. It's about finding the right path forward. Sometimes that means starting smaller to eventually go bigger. Big AI transformations don't happen overnight. They happen when you break them into manageable pieces and prove value incrementally. Start practical. Then scale ambitious. Have you ever had to shift from moonshot thinking to practical execution in AI? How did it go?

With the constant stream of AI updates, announcements, analyses, and 'cheat sheets,' it's easy to feel overwhelmed and confused. The fear of missing out on AI adoption is real. Here's the thing, though - AI adoption does not require you to be an AI expert. It does, however, require you to have a deep understanding of your processes and your domain. Here is a framework that I've been following to adopt AI in my role: a) Understand your work success metrics thoroughly. What are the key goals and KPIs you need to hit to keep moving forward? For example, in sales, closing deals is probably the most important KPI. b) Identify some of the biggest challenges you face that prevent you from achieving those goals. Write these down for clarity. E.g., preparing well for a 30-minute prospect meeting. c) Get granular, break down those challenges further, and identify the core issues. d) Once you break down the challenges, create hypotheses about where AI can help, e.g., prospect and persona research and their role in the company's growth. e) Once you've reached this point, experiment with tools (like GPT, Gemini, Claude, etc.) to get the best possible output for the challenges you identified. This will require some prompting and tweaking. f) Repeat this step across multiple instances to see the correlation. Observe and, if possible, quantify the impact. g) Finally, collate the results and create a map of areas where AI can have the most impact in your role. Very highly likely that you don't need every new tool/feature out there to get there. Focus on the outcome, and the tool will follow.

Your AI journey shouldn’t start with models. I’ve helped several enterprises avoid one of the biggest AI pitfalls: → Jumping straight to advanced models before building a strong foundation. Instead, we follow a proven "Crawl → Walk → Run" framework  to help you scale Enterprise AI Automation the right way. Here’s how it works👇🏻 𝗣𝗵𝗮𝘀𝗲 1: 𝗖𝗿𝗮𝘄𝗹 – 𝗕𝘂𝗶𝗹𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 Start with low-cost, low-risk projects.  Your goal? Learn fast and build core capabilities. ✅ Automate routine tasks using RPA (invoice processing, data entry) ✅ Organize and clean your data for downstream AI use 📌Key Insight: Don’t chase ROI yet. Chase readiness. Train teams. Prove small wins. 𝗣𝗵𝗮𝘀𝗲 2: 𝗪𝗮𝗹𝗸 – 𝗔𝗱𝗱 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗦𝘁𝗮𝗿𝘁 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Once your foundation is solid, step into mid-complexity AI with moderate investment. ✅ Predictive Maintenance: Reduce equipment failures with ML ✅ AI Chatbots: Improve CX while lowering support costs 📌Key Insight: Let technical and business teams work closely together. Use real learnings from the crawl phase to guide decisions. 𝗣𝗵𝗮𝘀𝗲 3: 𝗥𝘂𝗻– 𝗗𝗿𝗶𝘃𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗪𝗶𝗱𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 Now you’re ready for high-stakes, high-reward transformation. ✅ Personalization Engines: Tailored experiences = loyal customers ✅ Executive Decision Support: Fast insights for strategic calls 📌Key Insight: Establish strong governance. Track ROI. Let AI shift from a support role to a strategic driver. Skipping these foundations breaks momentum. This approach is sustainable, and that’s how real AI transformation happens. Curious where your organization stands in this journey? Let’s connect… happy to share how we approach this at Pronix Inc #AIAutomation #AutomationStrategies #PronixInc

As we look back on the last few years of ambitious analytics initiatives in B2B, the emerging narrative isn't about the 30x ROI from analytics COEs anymore—it's about pragmatic solutions, realistic implementations, and sustainable growth. A common theme, underscored by former AI/ML executive and current hedge fund manager Pratik Kodial's insights, concerns last-mile delivery (i.e., adoption and impact, with a wide gap between strategic analytics initiatives and actual end-user uptake). Despite successful AI/ML-enabled commercial analytics deployments across functions like Pricing, Supply Chain, and Marketing, actual ROI was often negative. Many Analytics/ Data Science teams set out with a broad scope influenced by high expectations from CXOs, hoping to address various business challenges through AI/ML. However, this often leads to an overcommitment that might impress on paper and make for a good section on the Annual Report but needs to improve substantially in practice. The crucial lesson here is the importance of focused, smaller-scale projects directly influencing Revenue and Gross Profit drivers. For Analytics leaders, the challenge is dual: Balancing the pressure to engage in transformational, high-visibility projects against smaller projects they know will deliver immediate, measurable value to commercial teams. It is imperative to spearhead practical, scalable analytics solutions that stakeholders will adopt that will demonstrably impact the bottom line. For those new to leading Analytics or Data Science teams, consider this approach: 1. Narrow Focus: Select fewer initiatives with a high potential impact on key financial metrics. Work your way down the Income Statement, and those areas will be your most significant opportunities to attack with AI/ML-enabled solutions and strategies. 2. Stakeholder Engagement: Ensure that projects are supported by senior executives at all levels, fostering broader buy-in. 3. Expert Partnerships: Differentiate between what can be outsourced and what should be developed internally, leveraging experienced external firms where beneficial. 4. Collaborative Development: Engage a core team from various organizational levels to build solutions that are as much 'theirs' as 'yours.' Value-Driven Development: Delay coding until the problem and its value are fully understood and broken down into manageable parts. Areas for early focus include Price Optimization, Customer Churn Reduction, Cross-Sell Optimization, Promotion and Discount Management, and Procurement/Logistics Optimization. These areas promise immediate returns and build a strong foundation for more extensive, transformative projects. Dive deeper into this approach in our previous LinkedIn article, and subscribe to join over 3,500 revenue management and commercial analytics professionals who regularly read our content. https://lnkd.in/eszpvrp4 #revenue_growth_analytics

Riddle me this: If #AI is the leading #techtrend of 2024, why isn't it being implemented at a higher rate? So... what do you think? I'll go first: AI is THE leading trend in #business #technology, underscored by its new ranking as the third top priority for CIO Online in the #2024 "State of the CIO" survey, just behind #digitaltransformation (yay!). Despite its prominence, a CBI Economics survey reveals that only 16% of businesses have #implemented AI in their operations, with a significant 58% not adopting #AI technology at all. Here's what happens:  📢 An #executive aiming to #innovate directs their #IT team to explore AI. 💼 The IT team evaluates options and presents a business case. 📃 Contracts are executed, and the system is implemented. ⚙️ The system may function but with limited capacity. 😵💫 The outputs are nonsensical, other systems remain unintegrated, and the system fails to deliver value. 🆘 Consequently, the business incurs losses. This pattern stems from a lack of practical business use cases for AI and, honestly, is a complete misunderstanding of what #artificialintelligence actually is. To avoid this technical department, businesses must clearly attach #ROI to AI projects. This involves CIOs collaborating closely with business-side project sponsors to validate anticipated ROIs. Here's how to calculate the ROI of an #AIsystem designed to optimize #inventorymanagement in a #retail setting: Step 1: Define Costs Initial Costs: purchasing / developing #AIsoftware, hardware, and necessary integrations. How does it work with core #ERP? What is the total cost of ownership within the tricky subscription (#sass) model?  Operational Costs: #softwareupdates, system maintenance, and additional #training. Implementation Costs: integrating and deploying the system, including any custom #integrations. Step 2: Identify Benefits Reduced Inventory Cost: optimizes stock levels, minimizing overstock and understock. Increased Sales: inventory accuracy enhances product availability, reducing missed opportunities. Efficiency Gains: replaces manual inventory management Step 3: Quantify Benefits Cost Savings: reduction in inventory costs compared to previous years. Increased Revenue: increased sales due to better product availability. Labor Cost Savings: decreased time spent on inventory management Step 4: Calculate ROI The ROI calculation should not be seen as a standalone metric but as a tool to ensure the AI #investment aligns with broader #strategic objectives. This strategic vision is crucial for the successful implementation of AI in #businessoperations. #MachineLearning #DeepLearning #DataScience #Robotics #python #deeplearning #programming #tech #robotics #innovation #bigdata #computerscience #data #dataanalytics #businesst #software #automation #analytics #ml #pythonprogramming #innovation #coding #development #erpsoftware #crm #sap #erpsystem #erpsolutions #erpsolution #cloud #clouderp #saphana #dynamics #pos #hana

🤔 Businesses around the world are seeing massive AI investments, yet many still stall in pilots. The recent IBM, Oxford Economics, and Dubai Future Foundation study makes clear why: translating AI from proofs-of-concept into scalable, measurable value requires dedicated leadership. Among 2,300 organizations surveyed, only 26% have a Chief AI Officer (CAIO). And those that do report 10% greater ROI on AI spend and are 24% more likely to outperform peers on innovation. The study shows what drives impact: • Authority matters: 57% of CAIOs report directly to the CEO or Board, and 76% are consulted by other CxOs on critical AI decisions. • Operating model matters: Centralized or hub-and-spoke models under CAIO leadership deliver 36% higher ROI than decentralized approaches. • Measurement matters: 72% say the lack of robust impact metrics risks leaving them behind, yet 68% still launch initiatives without full measurement frameworks. For healthcare organizations, this is a structural imperative. Complexity—multiple models, fragmented vendor ecosystems, disconnected data—demands orchestration. A CAIO with the right mandate can unify governance, align investments, and build the operating model that turns pilots into enterprise-scale value. Full report: Solving the AI ROI Puzzle https://lnkd.in/gU9gJPSB Image: Figure 4, Solving the AI ROI Puzzle Study.