How Tech Companies Are Addressing AI Demand

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

As enterprises accelerate their deployment of GenAI agents and applications, data leaders must ensure their data pipelines are ready to meet the demands of real-time AI. When your chatbot needs to provide personalized responses or your recommendation engine needs to adapt to current user behavior, traditional batch processing simply isn't enough. We’re seeing three critical requirements emerge for AI-ready data infrastructure. We call them the 3 Rs: 1️⃣ Real-time: The era of batch processing is ending. When a customer interacts with your AI agent, it needs immediate access to their current context. Knowing what products they browsed six hours ago isn't good enough. AI applications need to understand and respond to customer behavior as it happens. 2️⃣ Reliable: Pipeline reliability has taken on new urgency. While a delayed BI dashboard update might have been inconvenient, AI application downtime directly impacts revenue and customer experience. When your website chatbot can't access customer data, it's not just an engineering problem. It's a business crisis. 3️⃣ Regulatory compliance: AI applications have raised the stakes for data compliance. Your chatbot might be capable of delivering highly personalized recommendations, but what if the customer has opted out of tracking? Privacy regulations aren't just about data collection anymore—they're about how AI systems use that data in real-time. Leading companies are already adapting their data infrastructure to meet these requirements. They're moving beyond traditional ETL to streaming architectures, implementing robust monitoring and failover systems, and building compliance checks directly into their data pipelines. The question for data leaders isn't whether to make these changes, but how quickly they can implement them. As AI becomes central to customer experience, the competitive advantage will go to companies with AI-ready data infrastructure. What challenges are you facing in preparing your data pipelines for AI? Share your experiences in the comments 👇 #DataEngineering #ArtificialIntelligence #DataInfrastructure #Innovation #Tech #RudderStack

This week has been a perfect storm. As if Diwali, Halloween, and month-end weren’t keeping us on our toes, the Tech Titans threw in their earnings for good measure. The big takeaway is this: for the cloud giants — Google, Microsoft, and Amazon—the AI trend has come with both a trick and a treat. 👻 On the one hand, they’re seeing accelerating cloud revenue as companies rush to adopt AI. On the other, they’re being handed the bill. Meeting this demand requires infrastructure—a lot of infrastructure—and that means some eye-popping capex projections. 🥇 Google kicked things off with a bang. Google Cloud’s 35% surge to $11.35 billion signals the AI hype is translating into real dollars. Overall revenue up 15% to $88.3 billion. Sundar Pichai dropped a fun stat for us in the earnings call - 25% of new code at Google is AI-generated. 🥈 Microsoft came in hot, but guidance left investors cold. Microsoft’s Azure posted a solid 29% growth, hitting $24.1 billion, but then the stock took a hit when they projected slower. Satya Nadella’s take? “We are seeing more demand for AI than we can keep up with.” Translation: the market wants AI now, but Microsoft’s pace is held back by its own infrastructure buildup. 🥉 Amazon had a massive quarter too, with AWS posting 19% growth to $27.5 billion and total revenue up 13% to $158.9 billion. But it’s Andy Jassy’s “once-in-a-lifetime opportunity” language on AI that’s notable. He talks about it like it’s a rare planetary alignment, so naturally, they’re investing accordingly. Their CAPEX is substantial, especially for AWS, and Amazon’s approach seems to be, “Spend now, explain to shareholders later.” The bigger picture here is that Alphabet, Microsoft, and Amazon are collectively bracing to drop over $200 billion by 2025 on the infrastructure needed to support AI. The market might flinch a bit at that figure, but there’s a certain inevitability to it. They aren’t just reacting to demand—they’re building the AI economy’s plumbing, making sure they’re the pipes. 🔌

I’m watching public company earnings to identify early trends in the software market to inform startups’ plans for 2023. At last, we see a change in slope in the annual growth rates of the cloud services. Both Google & Microsoft announced growth rates in GCP & Azure that held steady from one quarter to the next. There are two forces in tension : overall cost reduction efforts by companies & the desire to invest in AI. The desire for AI is broad. Microsoft’s Azure Open AI customer base grew 4x by count, up from 2500 last quarter : "We have great momentum across Azure OpenAI Service. More than 11,000 organizations across industries, including IKEA, Volvo Group, Zurich Insurance…" The same is true for the engineering productivity solution, GitHub Copilot "More than 27,000 organizations, up 2x quarter-over-quarter, have chosen GitHub Copilot for Business to increase the productivity of their developers." Microsoft’s Robotic Process Automation business, Power Automate is growing quickly, too. UIPath doesn’t share MAU count & Microsoft didn’t break out revenue, so comparing the two businesses’ size directly isn’t possible. "Finally, Power Automate now has 10 million monthly active users at companies like Jaguar Land Rover, Repsol, Rolls-Royce, up 55% year-over-year." 365 CoPilot, the Microsoft Office AI Upsell product has some good initial customer pull. "With about 365m Office users & a price point of $29 per user per month, a 10% cross-sell into the customer base would add $12.7b of revenue to the Office suite & more than $100b in market cap, assuming constant multiples." We are now rolling out Microsoft 365 Copilot to 600 paid customers through our early access program, and feedback from organizations like Emirates NBD, General Motors, Goodyear and Lumen is that it’s a game changer for employee productivity. Microsoft is investing in data centers to support the needs of companies like Meta who have used Azure to train Llama2, a positive sign of increasing spend for the ecosystem. "The acceleration is really quite broad. It’s both on – both the data centers and a physical basis plus CPUs and GPUs and networking equipment, think of it in a broad sense as opposed to a narrow sense. So it’s overall increases of acceleration of overall capacity." Satya Nadella’s view on technology? The cloud migration is about halfway as percentage of GDP. "You’ve heard me talk about this as a percentage of GDP, what’s going to be tech spend? If you believe that, let’s say, the 5% of GDP is going to go to 10% of GDP, maybe that gets accelerated because of the AI wave." Overall, the demands of AI seem to be stabilizing infrastructure spend, something we haven’t seen in 7 quarters. It may be a sign that we’ve hit the bottom but the remaining earnings calls of the top infrastructure companies later this month will be telling.

Influencers hyping an AI bubble burst are cooked. It’s earnings season, and AI’s impact on revenue growth is being quantified for all to see. Atlassian’s AI platform grew 25X in just a year, helping the company increase subscription revenue by 30%. IBM reported $5 billion in generative AI bookings so far. AI demand helped increase its software segment growth by 10%. Palantir’s AI platform drove a 64% increase in US commercial revenue, and its stock is at an all-time high today. Spotify Wrapped (a SQL query and a dashboard) was one of its biggest user engagement drivers of 2024, helping it post its first annual profit and proving that business leaders can’t overlook innovations built on simple data products. ✅ Here are the biggest takeaways ✅ AI is much more powerful as a revenue driver than a cost saver. Business leaders must shift their focus to customer-facing AI products. Don’t go straight to AI because data and #analytics are significant revenue drivers. Build for the future, deliver incrementally, and get paid today. An aligned data and AI product roadmap is more critical than ever. Mid-tier tech companies have massive opportunities. AI isn’t just for the Magnificent 7 and Big Tech. #Data and AI teams should present opportunities that align with and amplify the current business model. Startups can leverage low-cost AI features and even data products to accelerate their path to profitability. AI isn’t just for large corporations. Business leaders at SMEs don’t need to wait on the sidelines. Satya Nadella said that #AI is a new input for growth, and the evidence supporting his thesis keeps growing.

How are enterprises adopting and consuming AI? Here is a framework to understand the consumption of AI in the enterprise. At the foundational level, Co-Pilots and Chatbots are the initial AI interactions and workflows, serving as frontline AI applications that enhance productivity and customer engagement. These are AI co-pilot and chatbot products from usual suspects: Anthropic, Microsoft, Google and others. Next up, Enterprise Applications, from in-house solutions to SaaS and Collaboration Platforms, now have embedded AI capabilities to drive smarter workflows, analytics, notifications and decision-making processes. These are traditional enterprise applications and SaaS players ranging from Atlassian, Salesforce, to Workday, and their peers. For organizations seeking a more tranformative approach, building a Custom AI Stack is becoming increasingly prevalent. This includes Commercial and Open Source LLMs (Large Language Models), which are providing unparalleled customization in AI applications. Data Pipelines and RAG (Retrieval-Augmented Generation) systems are vital for managing the vast inflow of data, while Hyperscaler Stacks ensure scalability and robust infrastructure. There is a ton of players in this space as opportunities abound, ranging from OpenAI to Mistral AI, and hyperscalers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Each layer of this model represents a step towards AI maturity, from basic automation to strategic AI-driven innovation. It's a pathway that businesses are navigating with keen investment, reshaping industry paradigms and redefining what's possible. What are your thoughts on enterprise AI adoption? #GenAI #AIAdoption #EnterpriseTechnology #ArtificialIntelligence #BusinessStrategy #Innovation

In my work at ServiceNow, we continuously host executives in our global innovation centers. AI and AI use cases are the top topics everyone wants to explore. Innovative leaders seek to understand how AI can help them reimagine operational and business models entirely. I wanted to share some interesting examples with you in this edition of AInsights. 💡 (Link at the end 👀) The world’s largest mining company used ChatGPT to analyze its leadership framework and improve employee experiences. IKEA deployed AI in customers service to optimize customer service and reduce costs, reskilled call center employees as interior designers, and generated net new revenue. Klarna AI assistant handles two-thirds of customer service chats in its first month. NVIDIA and Simulation Solutions use computer vision and AI to monitor end-to-end supply chain operations. NVIDIA and Omniverse use AI to create digital twins of manufacturing plants and distribution centers to design and optimize workflows 24/7 without having to purchase equipment or build new facilities. NVIDIA's BioNeMo AI platform helps researches analyze cell structures and dynamics to recreate cells and virtually test responses to disease pathways and drug efficiency. 👉 https://lnkd.in/g_T3UYfP

Breaking Analysis | How chasing AI shifts tech spending patterns In another big week we saw the #AI battles continue to escalate. Google reorged to better focus on AI, Meta’s Llama 3 was released, TSM is doubling its manufacturing capacity for NVIDIA high end chips, Samsung Semiconductor got a $6.4B tranche of the Chips Act money, a Microsoft paper describes VASA-1 that turns photos into a kinda creepy talking head, Mistral AI is doing a $1/2B raise at more than 2X its last valuation from just four months ago and SAS introduced industry models in a sign that demand for domain-specific AI is taking shape. Since the AI awakening in November of 2022, the spending climate for enterprise tech has transformed. Customers are scraping money from other budgets to fund AI and running experiments in the desperate race for monetization. Names that were virtually unknown in early 2022 - like OpenAI, Meta Llama and Anthropic, are vying with the cloud scale companies to get a piece of the pie. Notably, based on the latest ETR survey data, while Google remains a distant third in cloud computing spend overall, it has dramatically accelerated its position in the all important AI sector, closing the gap with Amazon Web Services (AWS) in terms of account penetration. In this (abbreviated) #BreakingAnalysis we’ll show you how the spending patterns have changed since early 2022, prior to the launch of ChatGPT and we’ll share where customers are putting their bets on AI platforms. With new survey data from ETR (Enterprise Technology Research) showing spending patterns in AI for Microsoft, AWS, Google, Databricks IBM Watson, Oracle, Anthropic & Meta.  #techspending #budgets #CIO #machinelearning #GenAI https://lnkd.in/eRKfUWtj