How Companies Are Adopting AI Tools

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

𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐧 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬: 𝐅𝐨𝐮𝐫 𝐋𝐞𝐯𝐞𝐥𝐬 𝐟𝐫𝐨𝐦 𝐂𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲 𝐭𝐨 𝐀𝐈-𝐍𝐚𝐭𝐢𝐯𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 Most companies think they are further along in AI adoption than they actually are.  This framework maps four distinct levels and being honest about where you sit is the first step to moving up. LEVEL 1: INDIVIDUAL USAGE (Curiosity-Driven) Goal: Individuals experiment with AI to save time. • Quick Tasks: Used for emails, brainstorming, and summaries • No AI Strategy: No formal company policy or direction • Personal Tools: Employees use different AI tools individually • Manual Workflows: Outputs are copied manually between tools • Early Exploration: High curiosity but inconsistent results • No Data Governance: Sensitive data may be shared without safeguards LEVEL 2: TEAM-LEVEL EXPERIMENTATION (Process Exploration) Goal: Teams begin applying AI to real work processes. • AI Content Creation: Used for emails, posts, reports, and documents • Meeting Automation: AI summarizes meetings and extracts action items • Workflow Automation: Simple AI chains automate repetitive tasks • AI Research Support: Helps analyze competitors and summarize reports • Tool Consolidation: Teams narrow down to a few preferred AI tools • Manager-Driven Adoption: Leaders encourage AI adoption LEVEL 3: DEPARTMENTAL AI INTEGRATION (Structured + Scalable) Goal: AI use becomes standardized across teams. • AI Playbooks: Defined workflows for each department • Data Pipelines: Clean, structured data feeds AI systems • Prompt Libraries: Shared prompts ensure consistent results • AI Team Champions: Each team has someone responsible for AI adoption • Security Controls: Data protection policies and tool vetting in place • ROI Tracking: Teams measure productivity gains and cost savings LEVEL 4: AI-NATIVE OPERATIONS (Autonomous + Self-Improving) Goal: AI is embedded in every workflow and continuously improves. • AI-Driven Decisions: AI guides strategy, hiring, pricing, forecasting • Connected AI: AI systems across teams work together automatically • Self-Learning: Models improve continuously using new data • AI Governance: Policies ensure ethical and secure AI use • Custom Models: Internal data trains specialized AI models • Revenue from AI: AI creates new products and services MY RECOMMENDATION At Level 1: Establish an AI strategy and basic data governance immediately. At Level 2: Consolidate tools and appoint AI champions per team. At Level 3: Build data pipelines and prompt libraries before scaling further. At Level 4: Focus on connected AI systems and self-learning loops. Which level best describes your organization right now? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #EnterpriseAI #AgenticAI #AIGovernance

A few months ago, AI felt too intimidating for most of the company. Today, 85% use it daily. Every CEO talks about adopting AI. But very few share the behind-the-scenes, or actually quantify the progress. So here's what we did: 1. Put the tools in everyone's hands We onboarded every single employee onto the company Claude account. 2. Created the AI Club (a dedicated Slack channel) The home base for all conversations related to AI. Simply seeing what other people were building was helpful to inspire new ideas. 3. Launched AI Office Hours on Mondays Claude was still too intimidating for a lot of people. This was the chance to unblock them and offer engineering support. 4. Scheduled AI Show and Tell on Fridays So people could demo their projects to the entire company. I scheduled it Friday afternoon hoping the demos would inspire people to build over the weekend. 5. Hosted a 36-hour AI Hackathon at our offsite The entire company participated. More than 40 teams presented, and many of those projects have since become live features or internal tools we use frequently. // & 4 months since I began heavily pushing AI adoption, I'm pretty pleased with the results: > 85% of employees use AI at least daily. One-third use it “constantly”. > Two-thirds of the team saves 4+ hours per week. A quarter saves 8+. > Almost every team has custom-built tools and dashboards that didn't exist before. We've even deferred hires we otherwise would have made. Very interested to hear what's actually helped with AI adoption at your company? (asking for a friend)

This seems to be on everyone’s mind: how to operationalize your product team around AI. Peter Yang and I recently chatted about this topic and here’s what I shared about how we are doing this at Duolingo. For improving our product: -Using AI to solve problems that weren’t solvable before. One of the problems we had been trying to solve for years was conversation practice. With our Max feature, Video Call, learners can now practice conversations with our character Lily. The conversations are also personalized to each learner’s proficiency level. -Prototyping with AI to speed up the product process. For example, for our Duolingo Chess, PMs vibe-coded with LLMs to quickly build a prototype. This decreased rounds of iteration, allowing our Engineers to start building the final product much sooner. -Integrating AI into our tooling to scale. This allowed us to go from 100 language courses in 12 years to nearly 150 new ones in the last 12 months. For increasing AI adoption: -Building with AI Slack channels. Created an AI Slack channel for people to show and tell and share prototypes and tips. -“AI Show and Tell” at All-Hands meetings. Added a five‑minute live demo slot in every all hands meeting for people to share updates on AI work. -FriAIdays. Protected a two‑hour block every Friday for hands-on experimentation and demos. -Function-specific AI working groups. Assembled a cross-functional group (Eng, PM, Design, etc.) to test new tools and share best practices with the rest of the org. -Company-wide AI hackathon. Scheduled a 3-day hackathon focused on using generative AI. Here are some of our favorite AI tools and how we are using them: -ChatGPT as a general assistant -Cursor or Replit for vibe coding or prototyping  -Granola or Fathom for taking meeting notes -Glean for internal company search #productmanagement #duolingo

AI adoption didn’t start in the boardroom Most enterprise technologies follow a pattern: Leadership approves → IT deploys → employees adopt. AI broke that model. Research now shows AI adoption has been bottom-up, not top-down. Consumers and employees started using it first. Organizations are now trying to catch up. What the data shows: • ChatGPT reached ~100M users in about 2 months — one of the fastest adoption curves in tech (UBS analysis, 2023) • 75% of knowledge workers already use AI at work (Microsoft & LinkedIn Work Trend Index) • 78% of AI users bring their own AI tools to work • 60% of leaders say their company lacks a clear AI plan In other words: Employees are already adopting AI — whether the organization planned it or not. And that creates a new leadership challenge. If adoption is happening anyway, the job of leadership shifts from: • approvals → guardrails • tool rollout → workflow redesign • blocking shadow AI → channeling it safely Because shadow AI is already growing. Gartner warns 40% of enterprises may face incidents linked to unauthorized AI use by 2030. The organizations that win won’t be the ones that announce AI strategies. They will be the ones that formalize what employees already started. Where is AI already being used inside your organization — and what are you doing about it?

Wondering how enterprises are 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 using AI in 2025? Andreessen Horowitz asked 100+ CIOs across 15 industries — and what they shared might surprise you 👇 𝟭/ 𝗔𝗜 𝗯𝘂𝗱𝗴𝗲𝘁𝘀 𝗮𝗿𝗲 𝗲𝘅𝗽𝗹𝗼𝗱𝗶𝗻𝗴 - Enterprise AI budgets are already bigger than expected; predicted to grow ~75% in the next year. - Spend has moved from experimental “innovation budgets” to core operational IT line items. 𝟮/ 𝗠𝘂𝗹𝘁𝗶-𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗻𝗼𝗿𝗺 - Enterprises are using 5+ models in production use cases - A key driver for this is to optimize cost/performance - Model selections are also based on use case. E.g. for writing tasks: OpenAI is the choice for complex Q&A, Anthropic for brainstorming. 𝟯/ 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗶𝘀 𝗰𝗿𝗼𝘄𝗱𝗲𝗱, 𝗯𝘂𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 - OpenAI, Google, and Anthropic lead in enterprise adoption. - Many larger orgs prefer open source, with Meta and Mistral leading. - Newer players like xAI, DeepSeek are seeing traction right out of the gate. 𝟰/ 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗹𝗲𝘀𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 - Newer models are more intelligent with longer context windows. - Using just prompt engineering, teams can now get similar/better results. - This reduces the need for fine-tuning for strong model performance. - It also avoids model lock-in, allowing portability across models. 𝟱/ 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗮𝗿𝗲 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 “𝗯𝘂𝗶𝗹𝗱” 𝘁𝗼 “𝗯𝘂𝘆”  - The AI app ecosystem has been maturing. Fast.  - Off the shelf AI-native apps can outperform internal builds. - Companies are also finding internally developed tools difficult to maintain.  - Purpose-built apps allow companies to innovate faster, leading to better outcomes + happier users = better ROI 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲? Enterprise AI is moving fast. There’s real budgets. Real traction. Real focus on value. Real tools. And opportunity to create real impact! 🔗 Link to the full a16z report in comments. I'd rate it as a "must read". Save it. Study it. Share it. 🤔 Which of this surprised you the most? Or feels most urgent to act on? #EnterpriseAI #ArtificialIntelligence #AIforBusiness #GenAI

Over the past year, AI has moved from experimentation to execution inside engineering teams. At Dropbox, we’ve learned that adopting AI tooling for its own sake is meaningless. It must tie directly to business outcomes. As we built products like Dropbox Dash, we became active users of AI in our own workflows, from tools like Claude Code and Cursor. The real shift came when we made AI adoption a company-level priority, reduced friction for teams to experiment, and invested in enablement rather than simply providing access to tools. Today, most of our developers use at least one AI tool. We track PR throughput per engineer as one of the core productivity metrics, and we see a strong correlation between deeper AI engagement and more code shipped. Sentiment toward AI tooling continues to trend strongly positive. At our CTO Executive Roundtable in December, these themes came into sharper focus through conversations with peer engineering leaders. A few patterns were clear. • Productivity gains must be balanced with long-term quality and maintenance • Leadership plays a decisive role in shaping responsible AI use • Formalizing AI competency signals strategic commitment One open question remains. If AI creates more capacity, where should it go? For us, that capacity is flowing into tech debt reduction, migrations, and reliability. In 2026, our focus is tightening the link between engineering productivity and end-to-end product velocity. We are still early in this journey, but AI is no longer just a developer tool. It is an organizational leadership challenge. https://lnkd.in/gKXQBxK7

Last week, I had the privilege of facilitating a session on AI Use Cases and Practical Applications for professionals at Hexaware Technologies Hyderabad. One insight stood out throughout the discussion: Access to AI is becoming common. Effective use of AI is becoming the differentiator. Across software development, testing, project delivery, customer support, knowledge management, and enterprise operations, teams are exploring how AI can improve productivity, accelerate innovation, and support better decision-making. But the organizations creating real value are focusing on more than tools. Here are five key takeaways from the session: #1. AI adoption is a people strategy before it is a technology strategy. Successful organizations invest in confidence, capability, and responsible usage alongside platform deployment. #2 Practical use cases create momentum. The fastest wins often come from: • Workflow automation • Documentation support • Coding assistance • Knowledge retrieval • Productivity enhancement #3 When employees see value in their daily work, adoption accelerates. Prompting is becoming a core professional skill. The ability to provide context, ask better questions, and evaluate AI outputs is quickly becoming a workplace advantage. #4 Human judgment remains the competitive edge. AI can increase speed and scale. Strategic thinking, creativity, ethical reasoning, and decision-making remain human responsibilities. #5 Governance and adoption must evolve together. Organizations need frameworks that encourage experimentation while ensuring security, responsibility, and business alignment. The biggest takeaway? Organizations will not gain a competitive advantage simply because they have access to AI. They will gain advantage because their people know how to use AI effectively, responsibly, and strategically. Thank you to the Hexaware team for the engaging conversations, thoughtful questions, and commitment to preparing for the future of work. At Mindacks and the Mindacks Human-Centred AI Institute (MHCAI), we continue to support organizations in building: • AI-ready leaders • AI-capable workforces • Responsible AI adoption strategies • Measurable business outcomes Mind First. Future Ready. What do you believe is the biggest barrier to AI adoption today: Technology, Skills, Culture, or Governance? #AI #ArtificialIntelligence #AIAdoption #HumanCentredAI #FutureOfWork #AIGovernance #AILiteracy #DigitalTransformation #Leadership #Innovation #WorkforceTransformation #LearningAndDevelopment

AI adoption by US businesses has surged over the last 18 months, from just over 6% to 42%.   A major turning point came earlier this year, coinciding with the release of stronger, more capable mainstream models. But since then, the growth rate has flattened.   This pause might not reflect disinterest, but rather the reality of enterprise change: once the experimentation phase ends, companies face harder questions about integration, workflows, and most importantly, people.   Because despite the tech headlines, the diffusion of generative AI in a non-tech company is less about models and more about mindset. It’s not an “IT project.” It’s a corporate culture and capability shift. Companies that recognize this, who build trust, provide access, and encourage exploration across teams, will be the ones that translate AI potential into business value.

Companies don’t fail at AI because of bad tools. They fail because of bad implementation. The typical approach looks like this: → Buy enterprise AI licenses → Announce “We are now AI-powered” → Conduct one training session → Hope productivity improves magically What follows? → Everyone uses AI differently → Prompts are random → Outputs are inconsistent → No one knows what “good” looks like → Leadership sees no measurable ROI The problem is not with the AI tools. The problem is that the underlying workflows were never redesigned. Here’s what actually works. Step 1: Map the Work Break down every key process step by step. Clarity before tools. Step 2: Identify Repeatable Tasks Find tasks that are structured and recurring. Reports. Emails. Research. Content. Analysis. Documentation. Step 3: Identify Tool for Each Task Select AI tools based on the task. Not based on hype. Step 4: Build a Standard Prompt Library Create approved prompts for each repeatable task. So outputs become predictable, not personality-driven. Step 5: Stack Tools into Workflows Instead of using just one tool for everything, stack different tools for each task. Now AI becomes a system. Step 6: Measure Before vs After Time saved Error reduction Revenue impact Decision speed If you cannot measure it, you cannot scale it. Step 7: Scale and Review Roll it across teams. Run periodic audits. Continuously optimize. AI adoption is not a tool decision. It is a workflow decision. And workflow decisions are leadership decisions. DM me if you want to seriously explore how to make your organization AI-ready in a structured, and measurable way. Let’s discuss.