Insite AI

Most organizations assume their biggest challenge is making better decisions. In reality, the bigger challenge is making decisions fast enough to matter. Across the average CPG organization, there is no shortage of intelligence. Sales has a recommendation. RGM has a recommendation. Category has a recommendation. Finance has a recommendation. Supply chain has a recommendation. The problem is that every team is working from a slightly different view of the business. A pricing recommendation needs alignment. A promotion needs review. A forecast needs validation. An assortment change needs approval. None of these activities are wrong. But collectively, they create a hidden tax on decision making. Most executives think this friction costs time. The real cost is opportunity. While teams are aligning internally, competitors are adjusting prices. Retailers are changing plans. Consumer behavior is shifting. Market conditions are evolving.  But increasingly, the biggest threat isn't another brand.  It's organizational latency. By the time consensus arrives, the market has often already moved. The organizations pulling ahead are not necessarily making dramatically better decisions. They are reducing the distance between insight and action. The organizations pulling ahead are not removing humans from the process. They are reducing the number of handoffs, reviews, reconciliations, and iterations required to move from recommendation to action.  Because in today's market, a slightly imperfect decision made today is often worth more than a perfect decision made next month. Read our full analysis: https://lnkd.in/dnKncw6e

The Most Valuable Data Doesn't Exist Yet Most CPG organizations are focused on collecting, integrating, and governing data that already exists.  Retailer POS. Syndicated data. Shipment data. Inventory data. Financial data. These assets are important.  But increasingly, they are inputs, not advantages.   The next generation of competitive advantage is being created from them. A forecast is not just a prediction. It influences manufacturing, inventory, retailer planning, and financial expectations. A price elasticity is not just an analytical output. It influences pricing decisions across an entire business. A promotional response model is not just an insight. It influences where millions of trade dollars are invested. A demand transfer model is not just data. It influences assortment decisions across categories and retailers. These assets do not simply describe the business - they help run it. Most organizations think of proprietary data as something they collect.  Leading organizations are building systems that continuously create new decision intelligence through forecasting, optimization, simulation, execution, and learning. Retailer data can be purchased.  Syndicated data can be purchased.  Cloud infrastructure can be purchased.  Software can be purchased. But the forecast models, elasticity curves, switching relationships, optimization outputs, and execution learnings that emerge from your business cannot.  That is the data your competitors cannot buy. At Insite AI, we help organizations create that data. Every forecast, elasticity, decomposition model, optimization recommendation, and execution outcome contributes to a growing body of proprietary commercial intelligence that becomes more valuable over time. The future advantage will not come from owning more historical data than your competitors. It will come from creating better data than they can. Read the full breakdown here: https://lnkd.in/dypscVhb

Get-your-data-right. Few pieces of advice have slowed AI adoption more than this. For years, organizations have been told that meaningful AI capability comes after data harmonization, system integration, and transformation projects are complete. The result? Many teams have spent months (or years) preparing for AI while delaying the business outcomes they hoped AI would help achieve. Part of the problem is that much of the market still treats data readiness as the finish line. The assumption is that once retailer POS, syndicated, shipment, financial, and supply chain data are connected and governed, insights will naturally emerge. But data alone does not create commercial intelligence. Connecting data is fundamentally different from understanding how pricing, promotions, assortment, forecasting, and retailer behavior interact to drive business outcomes. Data platforms solve a data problem. Commercial AI solves a decision problem. Organizations need both. Across the customers and prospects we speak with, the goals are remarkably consistent: improve forecast accuracy, optimize trade spend, make smarter pricing decisions, strengthen retailer planning, and respond faster to market volatility. The organizations moving fastest are not waiting for perfect data before they start. They are creating value while continuing to improve their data foundation. At Insite AI, we help organizations navigate this balance by combining embedded commercial intelligence with each company's unique data, processes, and business context. The question is no longer: "Is our data good enough to start?" The question is: "What can AI do for me right now?” (Check out our full breakdown on Why you don’t need perfect data to start in the first comment).

The CPG AI Divide: Why So Many Teams Feel Stuck Most CPG organizations are aligned on what they want from AI: better decisions, made faster. Across the customers and prospects we speak with, the priorities are remarkably consistent: more accurate forecasting, smarter pricing, stronger trade promotion, better retailer planning, and faster responses to market volatility. The challenge is rarely identifying the business problem. The challenge is figuring out where to start. The instinct is understandable: pick one application, prove value, then scale. A forecasting pilot. A pricing tool. A copilot for analytics or reporting. In a prior generation of enterprise software, that approach made sense. The challenge is that today’s commercial decisions are increasingly connected. Forecasting shapes promotions. Pricing influences demand. Assortment impacts retailer execution, supply chain complexity, and financial outcomes. Point solutions can improve productivity, but they often optimize locally while missing broader business impact. That is why many organizations are realizing that copilots, dashboards, and LLM wrappers are not the transformation executives are actually seeking. Executives are not asking for faster PowerPoints. They are trying to improve trade profitability, pricing confidence, forecast accuracy, retailer planning, and the speed at which teams move from signal to action. Doing that requires more than technology. It requires harmonized, decision-grade data, connected analytics, and workflows that move recommendations into execution. Just as importantly, it requires both deep CPG operating expertise and technical know-how, something many organizations struggle to bring together. At Insite AI, we help organizations navigate this shift by combining technical capability with deep CPG context, aligning data, analytics, and decision making around measurable business outcomes. (Check out our full breakdown on how to evaluate and select your next RGM technology partner in the first comment). #CPG #RevenueGrowth #AI #InsiteAI #EnterpriseTech

The CPG AI Divide: Why So Many Teams Feel Stuck Most CPG organizations are aligned on what they want from AI: better decisions, made faster. Across the customers and prospects we speak with, the priorities are remarkably consistent: more accurate forecasting, smarter pricing, stronger trade promotion, better retailer planning, and faster responses to market volatility. The challenge is rarely identifying the business problem. The challenge is figuring out where to start. The instinct is understandable: pick one application, prove value, then scale. A forecasting pilot. A pricing tool. A copilot for analytics or reporting. In a prior generation of enterprise software, that approach made sense. The challenge is that today’s commercial decisions are increasingly connected. Forecasting shapes promotions. Pricing influences demand. Assortment impacts retailer execution, supply chain complexity, and financial outcomes. Point solutions can improve productivity, but they often optimize locally while missing broader business impact. That is why many organizations are realizing that copilots, dashboards, and LLM wrappers are not the transformation executives are actually seeking. Executives are not asking for faster PowerPoints. They are trying to improve trade profitability, pricing confidence, forecast accuracy, retailer planning, and the speed at which teams move from signal to action. Doing that requires more than technology. It requires harmonized, decision-grade data, connected analytics, and workflows that move recommendations into execution. Just as importantly, it requires both deep CPG operating expertise and technical know-how, something many organizations struggle to bring together. At Insite AI, we help organizations navigate this shift by combining technical capability with deep CPG context, aligning data, analytics, and decision making around measurable business outcomes. (Check out our full breakdown on how to evaluate and select your next RGM technology partner in the first comment). #CPG #RevenueGrowth #AI #InsiteAI #EnterpriseTech

A call to the innovators across CPG: the brands pulling ahead with AI are not simply experimenting faster, they are fundamentally changing how decisions get made. Piecemeal tools, disconnected dashboards, and isolated pilots may drive incremental gains, but they are not transformation. Wrapping an LLM around fragmented data and disconnected analytics does not create competitive advantage.  Fortunately, the path forward is not about starting over, it is connecting what matters: better data, smarter applications, and faster decisions across the business. The organizations we work with are building connected decision systems across brand, customer, pricing, trade, supply chain, and finance. While others optimize one function at a time, leaders are accelerating how quickly the business can move from signal → decision → action. This is not about better reporting. It is about building a faster, smarter operating model. What does that look like in practice? 🔹 Harmonized, always-ready data infrastructure Winning organizations start with decision-grade data. POS, syndicated, retailer, shipment, TPM, manufacturing, supply chain, pricing, financial, and customer signals are unified into a single environment and refreshed dynamically as data becomes available. Clean, current, and ready for action. 🔹 Connected applications, not disconnected tools Forecasting, pricing, trade promotion, assortment, and scenario planning cannot operate in silos. Leading organizations are connecting these capabilities so decisions improve together, not independently. 🔹 Optimization grounded in business reality The future is not static reporting. It is optimization that balances growth, margin, retailer strategy, trade spend, inventory, pricing elasticity, supply constraints, and promotional effectiveness simultaneously through business-defined rules. 🔹 Cross-functional decision alignment Commercial, finance, supply chain, category, and customer teams operate from the same intelligence layer, reducing friction and aligning decisions across brands, channels, and customers. 🔹 Faster loops from data to action The leaders of tomorrow will not simply react faster, they will operate ahead of the pace of the customer. Optimization increasingly feeds planning systems directly, shortening the cycle from signal → recommendation → execution and enabling more automated decision making. The companies pulling away in CPG aren't just using more AI. They are building the infrastructure to act instantly. The future of CPG isn't in retroactive reporting. It’s in predictive execution. What is the biggest bottleneck in your current RGM tech stack? Are your AI tools giving you reports, or actual decisions? Let's discuss below. 👇 (Check out our full breakdown on how to evaluate and select your next RGM technology partner in the first comment). #CPG #RevenueGrowth #AI #InsiteAI #EnterpriseTech

A call to the innovators across CPG: the brands pulling ahead with AI are not simply experimenting faster, they are fundamentally changing how decisions get made. Piecemeal tools, disconnected dashboards, and isolated pilots may drive incremental gains, but they are not transformation. Wrapping an LLM around fragmented data and disconnected analytics does not create competitive advantage.  Fortunately, the path forward is not about starting over, it is connecting what matters: better data, smarter applications, and faster decisions across the business. The organizations we work with are building connected decision systems across brand, customer, pricing, trade, supply chain, and finance. While others optimize one function at a time, leaders are accelerating how quickly the business can move from signal → decision → action. This is not about better reporting. It is about building a faster, smarter operating model. What does that look like in practice? 🔹 Harmonized, always-ready data infrastructure Winning organizations start with decision-grade data. POS, syndicated, retailer, shipment, TPM, manufacturing, supply chain, pricing, financial, and customer signals are unified into a single environment and refreshed dynamically as data becomes available. Clean, current, and ready for action. 🔹 Connected applications, not disconnected tools Forecasting, pricing, trade promotion, assortment, and scenario planning cannot operate in silos. Leading organizations are connecting these capabilities so decisions improve together, not independently. 🔹 Optimization grounded in business reality The future is not static reporting. It is optimization that balances growth, margin, retailer strategy, trade spend, inventory, pricing elasticity, supply constraints, and promotional effectiveness simultaneously through business-defined rules. 🔹 Cross-functional decision alignment Commercial, finance, supply chain, category, and customer teams operate from the same intelligence layer, reducing friction and aligning decisions across brands, channels, and customers. 🔹 Faster loops from data to action The leaders of tomorrow will not simply react faster, they will operate ahead of the pace of the customer. Optimization increasingly feeds planning systems directly, shortening the cycle from signal → recommendation → execution and enabling more automated decision making. The companies pulling away in CPG aren't just using more AI. They are building the infrastructure to act instantly. The future of CPG isn't in retroactive reporting. It’s in predictive execution. What is the biggest bottleneck in your current RGM tech stack? Are your AI tools giving you reports, or actual decisions? Let's discuss below. 👇 (Check out our full breakdown on how to evaluate and select your next RGM technology partner in the first comment). #CPG #RevenueGrowth #AI #InsiteAI #EnterpriseTech

At Shoptalk, one thing was clear: AI in consumer goods is moving from pilots to real operational impact. That came through strongly in CGT - Consumer Goods Technology's coverage from the event. Across conversations on discovery, digital content, innovation, and decision-making, the message was consistent: AI only creates value when connected data, business context, and execution come together. That is why the perspective from Shaveer M., Co-Founder & CEO at Insite AI, stood out. His point was simple, but powerful: what once took years of transformation, large teams, and major systems can now happen far faster. Messy, fragmented data can be harmonized and enriched with intelligence around buyers, cross-shopping behavior, product availability, and other signals that help the business make better decisions. At Insite AI, we see that as the real unlock for CPG and consumer brands. The opportunity is no longer just to gather more data or run more AI experiments. It is to turn fragmented signals into decision-ready intelligence that improves how the business actually runs across growth, planning, commerce, and execution. That is where durable competitive advantage will be built. Appreciate Liz Dominguez for capturing these perspectives in the Shoptalk coverage: https://lnkd.in/eP_qZ6zi #CPG #AI #Shoptalk

Key Microsoft Consumer Goods & Retail and M12, Microsoft's Venture Fund Announcement! Insite AI has been recognized as one of the very few organizations globally to receive Microsoft’s Retail & Consumer Goods AI Certified Software Designation. This distinction is awarded only to partners who meet the highest standards of enterprise-grade AI for the Retail and CPG sectors. Why does this matter to organizations? After extensive and rigorous evaluation by Microsoft’s AI, Engineering, and Azure leadership teams, they assessed the global partner ecosystem; looking deeply at AI code, solution architecture, and customer references to identify partners that demonstrate: • Consistently superior, measurable impact across revenue management, marketing, data harmonization, and other AI-enabled decision areas • Strong alignment with CPG and Retail enterprise architectures and operating models, with proven scalability for CIOs, CDOs, CTOs, and technology leaders • Advanced capabilities that enable intelligent automation and streamlined enterprise workflows, helping organizations operate with greater precision, speed, and reliability We are grateful for this recognition and look forward to partnering even more closely with Microsoft’s and M12, Microsoft's Venture Fund's leadership to support the next wave of AI-driven transformation in Retail and CPG. Shaveer M. Matthew Horne Nicholas Leeper Gopalakrishna Tadiparthi Jasmeet Sraw