How Companies Are Advancing AI Strategy

Most companies are using AI for efficiency. Some are accelerating value creation. A great case study is how Colgate-Palmolive is driving innovation. Here are specific ways they are embedding GenAI across innovation processes to substantlly improve research and product development. These come from an excellent article in MIT Sloan Management Review by Tom Davenport and Randy Bean (link in comments). 💡 AI-Driven Product Concept Generation Accelerates Ideation By linking one AI system that surfaces consumer needs with another that crafts product concepts, Colgate-Palmolive can swiftly generate creative ideas like novel toothpaste flavors. This AI-augmented workflow produces a broader product funnel and allows rapid iteration, enabling more employees to participate in the innovation process under guided human oversight. 🔍 Retrieval-Augmented Generation Enhances Data Reliability The firm’s use of retrieval-augmented generation (RAG) integrates company-specific research, syndicated data, and real-time trends from sources like Google search data. This approach minimizes the risk of hallucinations and ensures that responses are deeply grounded in verified, internal content—delivering more accurate market analysis and trend detection. 🤖 Digital Consumer Twins Validate and Refine Concepts Moving beyond traditional focus groups, the company has developed “digital consumer twins”—virtual representations of real consumer behavior. These digital twins rapidly test hundreds of AI-generated product ideas. Early evaluations show a high level of agreement between virtual feedback and actual consumer responses. This innovation speeds up early-stage concept validation and reduces reliance on slower, more limited human panels. 🔐 Democratizing AI Through a Secure Internal AI Hub Colgate-Palmolive’s AI Hub provides employees with controlled access to advanced AI tools (including models from OpenAI and Google) behind corporate firewalls. Mandatory training on responsible AI use, including guardrails and prompt engineering best practices, ensures that employees harness these tools safely and effectively. Built-in surveys and KPI tracking further enable the company to measure improvements in creativity, productivity, and overall work quality. 🌐 Bridging Traditional Analytics with Next-Gen AI for Measurable Impact By integrating traditional machine learning with cutting-edge generative AI, Colgate-Palmolive is not only boosting operational efficiencies but also driving strategic growth. This seamless blend supports tasks ranging from market research and innovation to marketing content creation—demonstrating a holistic, value-driven approach to adopting AI that is a model for other organizations.

From MIT SMR - how 14 companies across a wide range of industries are generating value from generative AI today: McKinsey built Lilli, a platform that helps consultants quickly find and synthesize information from past projects worldwide. The system integrates with over 40 internal sources and even reads PowerPoint slides, leading to 30% time savings and 75% employee adoption within a year. Amazon deploys AI across multiple divisions. Their pharmacy division uses an internal chatbot to help customer service representatives find answers faster. The finance team employs AI for everything from fraud detection to tax work. In their e-commerce business, they personalize product recommendations based on customer preferences and are developing new GenAI tools for vendors. Morgan Stanley empowers their financial advisers with a knowledge assistant trained on over a million internal documents. The system can summarize client video meetings and draft personalized follow-up emails, allowing advisers to focus more on client needs. Sysco, the food distribution giant, uses GenAI to generate menu recommendations for online customers and create personalized scripts for sales calls based on customer data. CarMax revolutionized their car research pages with GenAI, automatically generating content and summarizing thousands of customer reviews. They've since expanded to use AI in marketing design, customer chatbots, and internal tools. Dentsu transformed their creative agency work with GenAI, using it throughout the creative process from proposals to project planning. They can now generate mock-ups and product photos in real-time during client meetings, significantly improving efficiency. John Hancock deployed chatbot assistants to handle routine customer queries, reducing wait times and freeing human agents for complex issues. Major retailers like Starbucks, Domino's, and CVS are implementing GenAI voice interactions for customer service, moving beyond traditional phone menus. Tapestry, parent company of Coach and Kate Spade, uses real-time language modifications to personalize online shopping, mimicking in-store associate interactions. This led to a 3% increase in e-commerce revenue. Software companies are integrating GenAI directly into their products. Lucidchart allows users to create flowcharts through natural language commands. Canva integrated ChatGPT to simplify creation of visual content. Adobe embedded GenAI across their suite for image editing, PDF interaction, and marketing campaign optimization. For more information on these examples and to gain insight into how companies are transforming with GenAI, read the full article here: https://lnkd.in/eWSzaKw4 images: 4 of the 20 I created with Midjourney for this post. #AI #transformation #innovation

According to recent insights from BofA Global Research, we're witnessing a significant shift in #AI investments, with #enterprises increasingly channeling funds toward AI Agents. This pivot is a strong signal of optimism, driving a positive surge in overall AI spend. 📈 The data reveals that 90% of enterprises are planning to boost their AI budgets, with a notable focus on AI Agents—tools designed to automate tasks and enhance digital workforce capabilities. What’s particularly intriguing is the breakdown of adoption stages: 72% of enterprises are already leveraging AI to enhance productivity, primarily in the "Assist" phase, with a gradual move toward "Augment" and "Transform" stages. This evolution promises to reshape how we work, leveraging generative AI to mimic human-like decision-making. One key takeaway? AI Agents demand much higher inference compute, which not only scales with test-time compute but also opens new opportunities for tech infrastructure providers. While current AI Agent technologies aren’t yet at full commercial readiness, the trajectory is clear—they’re poised to improve significantly over time. This incremental progress could redefine enterprise efficiency, with 52% of workloads expected to be handled by AI Agents in the near future. The rise of AI Agents also brings challenges and opportunities. Businesses are investing heavily in #data readiness (57% of AI spend), recognizing that robust data foundations—whether enterprise-wide or function-specific—are critical. Yet, competition from software companies remains a key risk, with 50% of enterprises viewing it as a watch area. On the flip side, this shift could evolve commercial IT models into a digital labor framework, potentially boosting IP-led revenues. What does this mean for specialists and vendors? Enterprises are keen to expand their AI vendor ecosystem (40% net expansion), while also consolidating partners in mature categories like CRM and ERP. For those in the industry, it’s a call to adapt and innovate. EmpowerEdge Ventures

The AI business model is undergoing a fundamental transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout," where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.

AI doesn't wait for your yearly review. Neither should your strategy. Static roadmaps are being replaced by living, evolving systems. The shift isn't about more meetings or bigger decks. It's about embedding agility into the core of how strategy is created, tested, and refined in the age of AI. Here are 13 ways leaders are leveraging AI to shape their strategic planning: 1/ Real-Time Monitoring Systems ↳ AI-powered dashboard integration ↳ Automated trend detection 💡Pro tip: Set up 15-minute daily stand-ups focused solely on emerging AI trends. 2/ Rolling Quarter Framework ↳ 90-day action sprints ↳ Monthly strategy refinements 💡Pro tip: Keep 70% of resources committed, 30% flexible. 3/ Scenario Planning Networks ↳ Multiple future state mapping ↳ Risk-opportunity matrices 💡Pro tip: Create 3 scenarios for every major decision: baseline, accelerated AI adoption, and disruption. 4/ Digital Twin Strategies ↳ Virtual strategy modeling ↳ Quick iteration cycles 💡Pro tip: Test strategic changes in digital environments before real-world implementation. 5/ Adaptive Team Structures ↳ Fluid role assignments ↳ Skills-based reorganization 💡Pro tip: Rotate 20% of team members quarterly across departments for fresh perspectives. 6/ AI Intelligence Streams ↳ Automated competitor analysis ↳ Market sentiment tracking 💡Pro tip: Set up AI alerts for both direct competitors and adjacent industry innovations. 7/ Micro-Learning Systems ↳ Just-in-time training ↳ Adaptive learning paths 💡Pro tip: Schedule 20-minute weekly team sessions on new AI tools. 8/ Decision Velocity Framework ↳ Rapid testing protocols ↳ Fast-fail mechanisms 💡Pro tip: Define your "reversal cost threshold" - the point at which a decision needs more review. 9/ Stakeholder Feedback Loops ↳ Continuous alignment checks ↳ Dynamic priority adjustment 💡Pro tip: Create a weekly survey that takes less than 30 seconds to complete. 10/ Resource Fluidity Models ↳ Dynamic budget allocation ↳ Skill-based resourcing 💡Pro tip: Keep 25% of your innovation budget unallocated for emerging AI opportunities. 11/ Crisis-Ready Culture ↳ Rapid response protocols ↳ Distributed decision rights 💡Pro tip: Run monthly "AI disruption simulations" with different teams leading each time. 12/ Data-Driven Pivots ↳ Automated trend analysis ↳ Predictive modeling 💡Pro tip: Define specific metrics that automatically initiate strategy reviews. 13/ Continuous Communication ↳ Strategy visualization tools ↳ Real-time progress tracking 💡Pro tip: Use AI tools to create strategy briefings under 2 minutes. The most resilient teams aren’t the ones with the perfect plan. They’re the ones built to adapt in real time. Continuous strategy isn’t a trend; it’s the new baseline for staying competitive in an AI-driven market. Which of these shifts are you implementing? Share below 👇 _____ Follow Carolyn Healey for more AI and leadership content. Repost to your network if they will find this valuable.

A new World Economic Forum report, written in collaboration with Accenture, offers one of the clearest pictures of what it takes to move from AI experimentation to real impact.   What stands out most is how sharply the gap is widening between organizations that are still running pilots and those that are now delivering measurable business value. The differentiator isn’t model performance or access to technology. It’s whether leaders can align their organizations around AI as a core capability, not a side project.   The companies pulling ahead are doing a few things differently. They’re embedding AI into strategic decision‑making, redesigning workflows so people and AI can collaborate meaningfully, and investing in the foundations that make scale possible: data, platforms, responsible governance, and modern engineering practices. They treat AI less as a promise and more as a system they are actively building. This is exactly what we’re seeing with Copilot across customers of every size. When strategy, data, security, operations, and culture all move together, AI creates compounding value. See the full report: 

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

For many, AI has become synonymous with efficiency. Across industries, AI has proven its ability to reduce costs, streamline workflows, and deliver services faster and cheaper—whether it’s automating customer service, optimizing marketing campaigns, or enhancing sales processes. These productivity gains are invaluable and should undoubtedly be embraced. But focusing solely on productivity misses the bigger picture. The true potential of AI lies in its ability to drive top-line growth by powering innovation that resonates with consumers. Consumers Want AI-Driven Innovation The demand for AI goes beyond speed and savings. According to a survey by Prophet, 69% of consumers are excited about brands that use generative AI tools to improve their experience. This excitement reflects a shift in expectations: people aren’t just looking for brands that are faster or more cost-efficient. They’re looking for brands that innovate in meaningful, transformative ways—brands that redefine the customer experience and create entirely new value propositions through AI. To truly differentiate and grow, companies must go further by embedding AI into their core strategies for innovation. Here are three ways to do that: 1. Reimagine the Customer Experience AI offers unprecedented opportunities to personalize and elevate customer interactions. Generative AI, for example, can create hyper-personalized recommendations, design immersive virtual experiences, or enable entirely new ways for customers to interact with products and services. Think of AI not just as a tool for answering questions or speeding up processes, but as a catalyst for delighting customers in ways they’ve never experienced before. 2. Drive Breakthrough Product Innovation From drug discovery to sustainable materials, AI is enabling breakthroughs that were previously unimaginable. Companies that integrate AI into their R&D processes can bring truly novel products to market faster, setting themselves apart from competitors focused solely on incremental improvements. 3. Create New Business Models AI can help companies move beyond traditional revenue streams. For example, manufacturers can leverage AI-powered predictive analytics to shift from selling products to offering subscription-based services. Retailers can use AI to build immersive digital environments that blend physical and virtual shopping experiences. By using AI to rethink what they offer and how they offer it, companies can unlock entirely new growth opportunities. Leveraging AI for top-line growth requires more than just adopting the latest tools. It demands a mindset shift—a willingness to experiment, take risks, and think beyond the obvious. Bold leadership will define the winners of this new era.

Our latest Bain Brief finds that fewer than 20% of companies have meaningfully scaled GenAI. The reason many struggle is because they're treating AI primarily as a technology initiative rather than using it as a catalyst to fundamentally reimagine their business. The organizations getting it right start by simplifying core processes. They align technology directly to clear strategic priorities and empower their people to confidently put AI to work. At Bain, we're fully committed to this approach, with our leadership team driving it hands-on, every day. We're reimagining how we deliver work, co-developing solutions closely with our technology partners, piloting new human-elevated and agentic teaming models, and continuously investing in and upskilling our people. It's inspiring to see how our teams have embraced this mindset, creating over 20,000 GPTs in a single year by experimenting, innovating, and transforming the way we work. Read the full Bain Brief and discover practical ways to shift your AI approach from isolated experiments to lasting transformation: https://lnkd.in/e6r8eY7S

A recent study revealed some major insights: 🔹 68% of organizations now run custom AI solutions in production 🔹 81% spend at least $1M annually on AI 🔹 25% invest over $10M per year But turning ambition into impact isn’t easy. Data quality issues, training challenges, and integration delays remain common. Nearly 70% of organizations report at least one AI project running behind schedule. The focus is also shifting. Beyond chatbots, companies are applying AI to software development, fraud detection, and predictive analytics. Generative AI is a top priority, but most are taking a multi-model approach to stay flexible. Interestingly, more businesses are moving away from full cloud setups. With growing concerns around data sovereignty and control, 67% plan to shift to on-prem or hybrid environments. The confidence in AI is strong, but success will depend on building systems that are not just powerful, but also transparent and trustworthy.