Examples of companies with custom AI systems

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.

Johnson & Johnson, the world’s biggest pharmaceutical company by revenue, revealed details on its AI strategy. After a year of experimentation with over 900 AI applications, they kept on using the ones that drove the most value: A life sciences division uses a generative AI sales assistant that delivers compliant, product-specific insights tailored to each customer. It’s now being adapted for complex medtech sales like robotics and implants. AI is speeding up pharma R&D—from optimizing chemical synthesis steps to spotting promising compounds using image-based models. A predictive AI tool scans for disruptions in the supply chain from fires to material shortages so managers can act before delays hit. Clinical trials are getting a boost from AI: algorithms now match diverse patients to studies faster and even double enrollment rates in some programs. A company-wide chatbot is helping employees navigate HR policies and benefits with instant answers and direct links. Separate AI and data governance units ensure ethical development and scalability, while staff receive hands-on training, including in generative AI. Do you know about other similar use cases at pharma companies? Source: https://lnkd.in/evfrcTaq

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

In recent times, #AgenticAI has emerged as a transformative force within India's IT sector, marking a significant evolution from traditional AI applications. Unlike earlier systems, #AgenticAI focuses on creating autonomous, context-aware agents capable of interacting seamlessly with complex workflows, thereby enhancing efficiency and productivity across various industries. Major Indian IT companies are actively integrating #AgenticAI into their service offerings: Tata Consultancy Services (TCS): TCS has embedded AI into a majority of its transformation deals, viewing AI adoption as core to business strategies. The company emphasizes moving beyond initial chatbot phases to incorporate AI into automation and intelligence. Infosys: Infosys is leveraging small language models and multi-agent AI to automate processes and improve efficiency. Generative AI is now a part of all large programs, transformations, and cost-efficiency initiatives, transitioning from pilots to full-scale enterprise adoption. Wipro: Focusing on reskilling employees and building AI-driven consulting, Wipro categorizes its projects into AI-led, AI-infused, and AI-powered solutions, viewing AI as a net positive for the industry. HCLTech: Through partnerships with AWS, Google, and NVIDIA, HCLTech is expanding its AI offerings, particularly through initiatives like AI Force and AI Labs, to meet the growing demand for AI and GenAI solutions. LTIMindtree: LTIMindtree is making AI accessible across industries, reporting significant AI wins in manufacturing, finance, and energy sectors, and ensuring AI fluency across the company through workforce training. Tech Mahindra: Distinctively, Tech Mahindra is developing its own AI models, positioning itself uniquely in the market by building sovereign AI models from scratch, aiming to redefine enterprise AI beyond chatbots to autonomous workflows. Mid-sized IT firms are also embracing Agentic AI through strategic investments and acquisitions. For instance, LTIMindtree committed $6 million to Voicing AI, a U.S.-based startup specializing in human-like AI #voiceagents, aiming to enhance conversational, contextual, and emotional intelligence across more than 20 languages. India's tech ecosystem is poised to play a critical role in the advancement of #AgenticAI. With a robust engineering talent pool and a focus on application-driven innovation, Indian startups and established firms are at the forefront of developing AI agents that integrate seamlessly into business processes. Companies like Kore.ai are utilizing platforms such as #Redis to power their #virtual #AIagents, exemplifying India's contribution to this evolving field. In conclusion, #AgenticAI is not merely a technological upgrade but a fundamental transformation in the Indian IT landscape, promising to enhance efficiency, drive innovation, and maintain global competitiveness. Image Credit : AIM Magazine #generativeai #aiagents #futuretrends #transformation #servicenow

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

It’s been over 2 years since ChatGPT launched. But there are several AI companies that were innovating even before then. Open this to see who’s innovating: ______________ In this thread, I'm sharing the company's focus, why it matters, and one bonus element. OpenAI is clearly one of the market leaders but we'll talk about several others. Starting with: 1. Anthropic (Founded 2021) Focus: “Constitutional AI” and safer large language models. Why it matters: Their Claude model aims to make AI more transparent and aligned with human values, which is crucial as these tools go mainstream. Recommended for: Tech leaders focused on ethical AI development. And their latest release, "Computer Use," has been a remarkable success. ______________ 2. Cohere (Founded 2019) Focus: Enterprise-grade LLMs and NLP solutions with privacy-focused APIs. Why it matters: By delivering stable, customizable models, they help businesses integrate AI smoothly without big-tech lock-in. Recommended for: IT managers and developers in need of secure, customizable AI solutions. ____________ 3. Inflection AI (Founded 2022) Focus: AI companions that converse, reason, and assist in daily life. Why it matters: Backed by Reid Hoffman, they’re creating AI that feels personal and helpful, bridging tech and human interaction. Recommended for: Consumer tech companies and product designers enhancing AI interaction. ___________ 4. Mistral AI (Founded 2023) Focus: Modular, open-source LLMs that are easy to fine-tune. Why it matters: Engineers from DeepMind and Hugging Face joined forces, aiming to democratize access and customization. Recommended for: AI developers and researchers looking for flexible, open-source models. __________ 5. Runway (Founded 2018) Focus: AI for creatives, video editing, image generation, special effects. Why it matters: Known for co-creating Stable Diffusion, Runway’s pushing generative video tools that could shape film, ads, and social media. Recommended for: Creatives in media and advertising pushing boundaries with AI. __________ 6. Stability AI (Founded 2020) Focus: Open-source generative models for text, images, and beyond. Why it matters: Their transparent approach invites global input, fueling faster innovation and trust. Recommended for: Digital creators and community projects favoring open-source innovation. ___________ 7. CharacterAI (Founded 2021) Focus: AI characters with unique personalities for entertainment and learning. Why it matters: They’re turning AI into a cultural product, moving beyond utility to narrative and fun. Recommended for: Game developers and content creators exploring AI-driven narratives. ___________ Companies are investing $$ in AI infrastructure, tools and the future. Follow me to stay updated on how businesses and leaders of tomorrow will use AI. ♻️ Repost to share with your network if you found this valuable.

This case study from C.H. Robinson is the perfect example of building advanced AI systems in the right way. Getting an AI system to complete over three million shipping tasks is no small feat! Why is it a good example of building AI solutions right? They didn't build the system all at once but instead chose to start small. They began with common, repetitive tasks. Built trust. Then expanded the system step by step. Each improvement layered on the last. From task prediction, to decision support, to full automation. This is what real AI adoption looks like inside a business. Not a one-off pilot. Not a chatbot at the edge. A system that grows with the maturity of the people and their understanding of how to best evolve the system. Built in the right way, an agent can incrementally understand more data, with more advanced logic that completes more pieces of a process. It moves from assisting a person to orchestrating outcomes. Because real AI capability compounds, it creates systems that learns and evolve as the ability of the organisation to adopt innovation evolves in parallel. https://lnkd.in/dFujbBiU

If your data is difficult to access and leverage, then you fail at AI. Which leads me to Excel. Bear with me. “Almost everything was done through Excel or on paper.” This is from a Mendix customer, Vivix, a glass manufacturer based out of Brazil. Aristoteles Terceiro Neto, their Industrial Transformation Manager, talks all about the challenges of industrial digital transformation in their story. https://lnkd.in/gGxn4rVT Excel and paper have a place in the enterprise, obviously. But manual data entry can take up as much as 60 hours per month per employee. That’s slow. It’s messy data, especially in an era of AI. Organizations need to tighten up their data architecture and think of new ways to create, record, search for, and find their data. That’s crucial for AI. It’s crucial to make any sort of custom software that matches your unique processes and needs. This is why I’m impressed with how Vivix approached their shift away from Excel and paper. They created a digital solution that helps them analyze the quality of glass and adapt their product as needed. The solution’s function alone is impressive. More so is the link it has to other quality assurance applications and Vivix’s legacy systems of record. Even better: Vivix is infusing AI into that solution, integrating with several data sources and Amazon Bedrock that’s creating a virtual engineer who gathers product and process data and offers suggestions for addressing customer complaints. What Vivix is doing is exactly what successful digital transformation looks like. ✅ Good data practices ✅ Integrated custom software ✅ AI

My latest in today's print edition of The Wall Street Journal: It’s a Legacy Agriculture Company—And Your Newest AI Vendor. Bayer is known for selling seeds. Now it sells seeds and AI. Microsoft this week announced it is working with the German pharmaceutical-and-agricultural group and other companies on specialized AI models fine-tuned on industry-specific data. The companies can now list and monetize those models on Microsoft’s online model catalog. For Bayer that means an AI model fine-tuned with its data and designed to provide answers on agronomy and crop protection is available to be licensed by its distributors, new AgTech startups, and even potentially competitors. The model can answer questions about ingredients in an insecticide or whether a product could be applied to cotton, for example. “A lot of folks have the same pain points that we have,” said Sachi Desai, Bayer’s VP of AI Go to Market and Partnerships. “There’s a lot of ways to not only amortize our own cost by allowing others to collaborate off the same platforms or build on it, but also to uplevel the outcomes for our customers.” Industrial automation provider Rockwell Automation, compliance tech provider Saifr, which is part of Fidelity Investments, manufacturing analytics provider Sight Machine, automotive software company Cerence Inc. and Siemens Digital Industries Software, a unit of Siemens, are also launching their industry models today. When it comes to enterprise adoption of AI, “Industry-specific and domain-specific models are going to be the game-changer,” said IDC's Ritu Jyoti. What do you think? Read the full story here: https://lnkd.in/eDYpMPXs #tech #ai #artificialintelligence #cio