Artificial Intelligence in Business

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 ��𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

I’ve been headhunting in the CPG industry for the past decade, and I’ve never seen a post-inflation market like we’re in right now. For the past three years, customers have been capitulating to price hikes by extending their budgets. But now, they’re at a breaking point. American families, already tethering on edges of their budgets, do not have the ability or the desire to expand their budget in order to accommodate increased prices. I’m sure you’d agree with this, because my family certainly does. With grocery bills through the roof, we’d rather skip on groceries and essentials rather than paying a premium right now. A couple things led us here, starting the pandemic and the post-pandemic impact on spending and savings. Secondly, the wave of AI and tech developments that caught us off guard. So, where do the companies go now? Once the “price increase” playbook is done, CPG brands can only win in both value and volume by shifting gears. In my chats with executives, I’m sensing a change in tone. To stay competitive, they’re looking for ways to shift from the post-pandemic survival mindset to a growth-focused one that accommodates the customer as well. Rather than hiking prices, the focus is now on bringing down costs, and getting to terms with consumer’s limited budgets and increasing product choices. Layoffs aren’t the only way to bring down costs. In my view, CPG companies do have the leeway to embrace data-driven innovation and efficiency to cut costs. Here are some of the ways in which companies can use AI and ML to achieve targets in 2025 and beyond: 1/ Predicting the demand: Post-pandemic behavior is tough to predict, especially in CPG markets. With AI, the companies can now leverage real-time insights from sources like point-of-sale systems, social media, and even economic indicators to see future trends more clearly. PepsiCo, uses Tastewise to track what consumers are eating across 60+ million touchpoints and making decisions that align with local preference. 2/ Inventory management: With AI-powered predictive analytics, companies are now turning inventory management into a science. Procter & Gamble’s Supply Chain 3.0 initiative is one example of this shift. 3/ Increased personalization: Leaders are tapping into geographical intelligence to connect meaningfully with audiences. Estée Lauder has a voice-enabled makeup assistant for visually impaired customers, reaching a new market while boosting brand loyalty. Bottom line is: customers are no longer meeting brands where they’re at. It’s high time that companies start caring about customers and their shrinking bottom lines. Are you excited to see your grocery bill go down in the next few months? #CPG #AI #ML #fmcg #marketing #trending

The 7 Layers of the LLM Stack — A Complete Map for Building with AI When most people think of Large Language Models (LLMs), they picture just the model (like GPT, LLaMA, or Claude). But in reality, an entire stack of 7 interconnected layers is what makes enterprise-grade AI systems possible. Here’s how the stack unfolds: 🔴 Layer 1 – Data Sources & Acquisition Everything begins with data pipelines. Web scraping, APIs, enterprise systems, logs, documents, IoT sensors — this is the raw material. Without diverse, high-quality data, everything above it crumbles. 🔵 Layer 2 – Data Preprocessing & Management -Raw data is rarely usable. This layer handles cleaning, normalization, chunking, embeddings, governance, and secure storage. Think of it as turning unstructured chaos into structured knowledge. 🟡 Layer 3 – Model Selection & Training This is where the AI “brain” is formed: -Choosing foundation models (GPT-4, LLaMA, etc.) -Fine-tuning with LoRA/QLoRA -Adding safety layers, distillation, and multimodal prep -RLHF/RLAIF for alignment It’s where raw capability is transformed into fit-for-purpose intelligence. 🟣 Layer 4 – Orchestration & Pipelines Models don’t live in isolation. They need agents, memory, planning, guardrails, and workflows (LangChain, CrewAI, Airflow). This layer ensures your AI can interact with tools, APIs, and other agents in a safe, repeatable, and scalable way. 🟠 Layer 5 – Inference & Execution The “runtime engine.” It covers real-time/batch inference, caching, rate limiting, multimodal support, determinism controls, and safety filters. This is what keeps systems both fast and reliable. 🔵 Layer 6 – Integration Layer How does AI connect with the rest of the business? Through APIs, SDKs, connectors (Slack, Salesforce, Jira), identity/auth, billing, and event buses. This is what makes AI plug-and-play across enterprise ecosystems. 🔴 Layer 7 – Application Layer Finally, the visible part: copilots, chatbots, RAG apps, workflow automation, forecasting, domain-specific agents (healthcare, legal, support). This is where end-users experience the value. The key insight: LLMs are not standalone magic. They’re part of a layered architecture where each layer adds stability, trust, and scalability. Skip a layer, and your AI solution risks collapsing under real-world demands. For builders, leaders, and enterprises — knowing where you sit in this stack clarifies: What to build yourself vs. integrate, Where to invest for differentiation, And how to future-proof as the ecosystem evolves.

I recently wrote that AI is not just a technology shift – it's a work shift. So, how does that play out? First, AI changes how we do tasks. Next, it changes how we do our jobs. Then, it changes entire functions. The result? A brand new way of getting work done and thinking about growth. Step 1: AI transforms tasks: AI works with you. It helps you do what you’ve always done — just faster. A marketer drafts blog posts in minutes. A rep writes emails with higher personalization, less effort. A support leader summarizes tickets in seconds. This is where most teams are today: AI as a productivity booster. Step 2: AI transforms jobs. AI works for you. It starts delivering outcomes. A content agent spins one blog into a full campaign. A prospecting agent books qualified meetings without human touch. A customer agent handles most Tier 1 support tickets. The job itself starts to evolve. You spend less time doing — and more time creating, optimizing, and scaling. Step 3: AI transforms functions. As agents take on entire workflows, the structure of departments begins to shift: Support shifts from to proactive experience design. Marketing shifts to creative strategy. Sales shifts to high-impact closing. Role ratios change. Skillsets shift. We are not quite here but we can see the path. The result for scaling businesses? A whole new way of approaching work, structuring teams, and thinking about growth.

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

Everyone is talking about #AI in logistics. Some still believe logistics is simply about moving goods from A to B. And now headlines around the world are asking: Can logistics be replaced by AI-driven software? The answer is both simple and incomplete. ▶️ AI enables us to process billions of data points in real time. ▶️ It anticipates risk before it materialises. ▶️ It increases transparency across global networks. ▶️ It reduces manual errors while accelerating throughput. In short: AI drives efficiency. And further: There is no future for logistics without AI. But here is the real question: Will AI make supply chains more efficient or more human? Yes, you read correctly: human. Because efficiency alone is not the benchmark. #CustomerExperience is. Let me explain this by looking into the status quo. Already today, we use AI to: Predict more reliable ETAs by real-time recalculation. Detect disruptions earlier allowing for proactive route and capacity planning. Automate end-to-end workflows, reducing manual work, errors, and processing time across core operations. This is not theory, it’s no longer experimental, it’s daily practice. And there is a lot more to come. Yet, what matters most is this: The more powerful AI becomes, the more decisive the #HumanExpertise becomes. In an AI-driven world, customers will not differentiate us by who has access to technology. Technology will become mainstream. Customers will differentiate us by: ▶️ Who explains complexity clearly. ▶️ Who takes ownership when disruption hits. ▶️ Who anticipates consequences, not just data patterns. ▶️ Who acts as a strategic partner, not just a service provider. AI allows us to be faster. Customer experience requires us to be better. The real opportunity for our industry is not to automate relationships but to elevate them. AI can process billions of data points. But trust is built through clarity, reliability, and accountability. Kuehne+Nagel’s ambition is simple: Lead in AI. Lead in customer experience. Because the future of logistics will not be defined by algorithms alone but by how intelligently and responsibly we use them to serve our customers. We’ll share further insights into our AI strategy during the Kuehne+Nagel Conference Call on March 3, 2026.

I've put my last 6 months building and selling AI Agents I've finally have "What to Use Framework" LLMs → You need fast, simple text generation or basic Q&A → Content doesn't require real-time or specialized data → Budget and complexity need to stay minimal → Use case: Customer FAQs, email templates, basic content creation RAG: → You need accurate answers from your company's knowledge base → Information changes frequently and must stay current → Domain expertise is critical but scope is well-defined → Use case: Employee handbooks, product documentation, compliance queries AI Agents → Tasks require multiple steps and decision-making → You need integration with existing tools and databases → Workflows involve reasoning, planning, and memory → Use case: Sales pipeline management, IT support tickets, data analysis Agentic AI → Multiple specialized functions must work together → Scale demands coordination across different systems → Real-time collaboration between AI capabilities is essential → Use case: Supply chain optimization, smart factory operations, financial trading My Take: Most companies jump straight to complex agentic systems when a simple RAG setup would solve 80% of their problems. Start simple, prove value, then scale complexity. Take a Crawl, Walk, Run approach with AI I've seen more AI projects fail from over-engineering than under-engineering. Match your architecture to your actual business complexity, not your ambitions. P.S. If you're looking for right solutions, DM me - I answer all valid DMs 👋 .

Just published my analysis on the legal industry's $900B repricing event - how AI is ending the billable hour and creating the biggest disruption in professional services history. Here is the full analysis: https://lnkd.in/gWXKEBbY While most focus on AI tools helping lawyers work faster, the real revolution is AI-native law firms replacing the entire business model. BigLaw convinced clients that time spent = value delivered, creating the only major industry where efficiency threatens profitability. That protection is about to expire. We're witnessing a fork that will split the legal landscape into two distinct futures: 🌑 Legacy BigLaw: - Revenue tied to inputs (hours worked, not outcomes delivered) - Scale driven by associate leverage (junior lawyers billing at senior rates) - Efficiency treated as enemy (faster work = lower revenue) Partnership economics make long-term AI bets impossible 🌕 AI-Native Law Firms: - Fixed, outcome-based pricing at 50% of BigLaw rates - End-to-end automation with 60%+ gross margins - Proprietary datasets that improve with every engagement - Software-like scaling without linear cost increases The math is brutal: A $1.5B firm faces $450M in revenue pressure as AI compresses 30-60% of billable work into minutes. Most vulnerable: M&A diligence, regulatory compliance, patent prosecution, contract lifecycle management. $45B+ in annual fees where "complexity" is often manufactured scarcity. This creates a 10x market expansion - 32M underserved SMBs can now access elite-quality legal work previously exclusive to Fortune 500 companies. The transition is client-driven. GCs are already demanding change: "We expect AI to make things less expensive. Figure it out or we're paying you 20% less next year." ⚡ This transformation represents the largest opportunity in legal services history. ⚡ The next Cravath won't be a partnership - it'll be a platform company with global reach and SaaS-like margins. Let me know if you're building in legal AI. The industry won't have another window this wide open in our lifetime.

Nvidia & the story of intentionally stumbling on innovation Sometimes, the most transformative innovations don’t come from your product plan, they come from actively listening to how customers use your product in unexpected ways. Nvidia’s rise to becoming one of the most valuable companies on earth is exactly that story. As a kid, I remember saving money so I can buy an Nvidia graphics card so I can play the games I loved. Back then, Nvidia was gaming to me. Fast forward to today, most people probably associate Nvidia with AI, not video games. The story behind that shift is incredible. From the book “Chip War”:  “In the early 2010s, Nvidia—the designer of graphic chips—began hearing rumors of PhD students at Stanford using Nvidia’s graphics processing units (GPUs) for something other than graphics. GPUs were designed to work differently from standard Intel or AMD CPUs, which are infinitely flexible but run all their calculations one after the other. GPUs, by contrast, are designed to run multiple iterations of the same calculation at once. This type of “parallel processing,” it soon became clear, had uses beyond controlling pixels of images in computer games. It could also train AI systems efficiently. Where a CPU would feed an algorithm many pieces of data, one after the other, a GPU could process multiple pieces of data simultaneously. To learn to recognize images of cats, a CPU would process pixel after pixel, while a GPU could “look” at many pixels at once. So the time needed to train a computer to recognize cats decreased dramatically. Nvidia has since bet its future on artificial intelligence.” This wasn’t just luck. Actively listening to your customers requires intentionality…spending time with them…asking questions…leading with curiosity. What massive opportunities could you unlock if you listened more closely to how people use your product today?

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.