Reasons to Prioritize Targeted GenAI Adoption in Business

In preparing for a upcoming keynote speech on #genai and the impact on #work; I found these Insights global study by Google #Cloud and National Research Group some of the best I have seen. As a management consulting leader, I'm struck by the clear imperative for organizations to educate themselves on gen AI today. Here are some key takeaways: 1) 74% of enterprises using gen AI report ROI within the first year - faster than most #software deployments 2) 86% of organizations seeing revenue growth estimate a 6%+ increase in annual revenue (real revenue growth!) 3) 84% can move a gen AI use case from idea to production in under 6 months (once again, speed WINS) 4) 45% of organizations report employee productivity has doubled or more due to gen AI (maybe some technology to make our lives easier!) The message is clear: gen AI is not just another tech trend, but a key driver of business transformation and competitive advantage. The study also reveals a "gen AI #leadership gap" - only 16% of organizations are truly leading in this space. These leaders are seeing outsized gains in revenue, productivity, and innovation. To close this gap, organizations must prioritize gen AI education at all levels. This means: 1) Building unified C-suite support and vision for gen AI initiatives 2) Focusing gen AI efforts on core business functions 3) Investing in AI talent development across the organization 4) Prioritizing data quality and infrastructure to support gen AI It is more clear to me than ever that the time to act is now. Those who invest in understanding and strategically implementing gen #AI today will be best positioned to thrive in the AI-driven future of business. Link to the complete study if interested - https://lnkd.in/gmn-yAwE #GenerativeAI #BusinessStrategy #Innovation #Leadership Mercer Ravin Jesuthasan, CFA, FRSA JESS VON BANK #google Adriana O'Kain Ryan Malkes

The time of GenAI Proof of Concepts is coming to an end! This is a must read article to learn how a company like J&J is taking this to the next level. The article actually provides an implicit playbook. Important snippets: "the move ensures that the company allocates resources only to the highest-value generative AI use cases, while it cuts projects that are redundant or simply not working, or where a technology other than GenAI works better." "Now we’ve moved from the thousand flowers to a really prioritized focus on GenAI. The “thousand flowers” approach involved a number of use case ideas germinating from across the company, which made their way through a centralized governance board. At one point, employees were pursuing nearly 900 individual use cases, many that were redundant or simply didn’t work" "And as the company tracked the broad value of AI, including generative AI, data science and intelligent automation, it found that only 10% to 15% of use cases were driving about 80% of the value" --> Pareto Principle appears everywhere "J&J began its pivot last year, removing a centralized governance board responsible for vetting employee GenAI ideas. It then distributed governance responsibilities to various corporate functions, including commercial, supply chain and research, that had a better handle on whether the use cases were actually driving value in their area." --> Governance doesn't go away. It needs to be setup in order to be an enablement function. "J&J is drilling down into high-value generative AI use cases around drug discovery and supply chains, as well as an internal chatbot to answer questions on company policy." 💰 Sales (make money): "One example that is working is a “Rep Copilot,” which helps coach sales representatives on how to engage with healthcare professionals about new treatments." 🤝 Internal (reduce time/costs): "GenAI also is being used for an internal chatbot that ingests information about company policies and benefits to help reduce the some 10 million interactions employees have every year with the services team." 💉 Drug Discovery (their core business): "In drug discovery, the company is looking at whether GenAI can help researchers find the optimal moment to add a solvent to turn a liquid molecule into a solid." --> Very clear use cases on making money and saving money. "The company is tracking progress in three buckets: 1) the ability to successfully deploy and implement use cases; 2) how widely they are adopted; and 3) the extent to which they deliver on business outcomes." Success = deployed systems that make and save money. Duh 🤣

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.