AI-Enhanced Business Intelligence Tools

"We need an AI strategy!" 𝘙𝘦𝘤𝘰𝘳𝘥 𝘴𝘤𝘳𝘢𝘵𝘤𝘩 Hold up. That's the wrong question. The right question? "What business problem are we actually trying to solve?" I've sat in countless board meetings where executives demand AI initiatives – not because they've identified a problem AI can solve, but because they're afraid of being left behind. This FOMO-driven approach is precisely how companies end up in what I call "perpetual POC purgatory" – running endless proofs of concept that never see production. Here's the uncomfortable truth: Your goal isn't to use AI for the sake of AI. Your goal is to solve real business problems. Sometimes the best solution is a regular hammer, not a sledgehammer. So when leadership pushes AI without purpose, redirect the conversation: → "What business outcome are we trying to drive?” → “What’s the actual problem we’re solving?” → “Is AI the most effective tool for that — or just the most exciting one?” Next, how do you determine if AI is the right solution? I recommend this straightforward approach that keeps business problems at the center: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 - What specifically are you trying to solve? The more precisely you can articulate the problem, the easier it becomes to evaluate whether AI is appropriate. 2. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁 - Could existing technology or processes handle this faster, cheaper, and more reliably? 3. 𝗟𝗲𝗮𝗻 𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 - If the problem seems AI-suitable, validate it with people who’ve delivered outcomes — not just hype. 4. Be brutally realistic about your organization's maturity - Do you have the data infrastructure, talent, and risk tolerance necessary for an AI implementation? Remember this fundamental truth: AI is not a silver bullet. Even seemingly simple AI projects require time, focus, alignment, and resilience to implement successfully. The companies winning with AI aren't the ones with the flashiest technology. They're the ones methodically solving pressing business challenges with the most appropriate tools—AI or otherwise. 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂: What business problem are you trying to solve that might (or might not) actually need AI?

If you are an AI engineer, thinking how to choose the right foundational model, this one is for you 👇 Whether you’re building an internal AI assistant, a document summarization tool, or real-time analytics workflows, the model you pick will shape performance, cost, governance, and trust. Here’s a distilled framework that’s been helping me and many teams navigate this: 1. Start with your use case, then work backwards. Craft your ideal prompt + answer combo first. Reverse-engineer what knowledge and behavior is needed. Ask: → What are the real prompts my team will use? → Are these retrieval-heavy, multilingual, highly specific, or fast-response tasks? → Can I break down the use case into reusable prompt patterns? 2. Right-size the model. Bigger isn’t always better. A 70B parameter model may sound tempting, but an 8B specialized one could deliver comparable output, faster and cheaper, when paired with: → Prompt tuning → RAG (Retrieval-Augmented Generation) → Instruction tuning via InstructLab Try the best first, but always test if a smaller one can be tuned to reach the same quality. 3. Evaluate performance across three dimensions: → Accuracy: Use the right metric (BLEU, ROUGE, perplexity). → Reliability: Look for transparency into training data, consistency across inputs, and reduced hallucinations. → Speed: Does your use case need instant answers (chatbots, fraud detection) or precise outputs (financial forecasts)? 4. Factor in governance and risk Prioritize models that: → Offer training traceability and explainability → Align with your organization’s risk posture → Allow you to monitor for privacy, bias, and toxicity Responsible deployment begins with responsible selection. 5. Balance performance, deployment, and ROI Think about: → Total cost of ownership (TCO) → Where and how you’ll deploy (on-prem, hybrid, or cloud) → If smaller models reduce GPU costs while meeting performance Also, keep your ESG goals in mind, lighter models can be greener too. 6. The model selection process isn’t linear, it’s cyclical. Revisit the decision as new models emerge, use cases evolve, or infra constraints shift. Governance isn’t a checklist, it’s a continuous layer. My 2 cents 🫰 You don’t need one perfect model. You need the right mix of models, tuned, tested, and aligned with your org’s AI maturity and business priorities. ------------ If you found this insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content ❤️

One reason AI initiatives stall? Few execs use AI in their own work. In 3 hours, I take leaders from “I don’t know” to a POV (co-developed with AI!) on how AI can support key strategic initiatives. To crack the code on exec adoption we: >> Focus on Strategic Use Cases that Click with Execs << To get experience with high value use of AI, we dive into cases that directly enhance executive decision-making and strategic thinking. This tends to be a major eye-opener—most leaders don't realize AI can elevate their highest-level work. Once executives experience immediate personal value, they better understand how AI can have immediate impact across the organization. >> Reframe Mental Models << Generative AI operates fundamentally differently from anything we've seen before, so we need to identify why and how digital change playbooks must shift to leverage this moment. I go straight to the heart of the silent organizational barriers that prevent productive adoption, and how to navigate a path forward. >> Start with the Business, Not the Tech << We don’t begin with AI—we begin with your business. We anchor the process with the breakthroughs that will drive real impact—and to get there, we go analog with brainstorming, whiteboards, and post-its, working to envision what advancement could look like. What could be possible if cognitive limits were lifted? What long-standing friction could finally be overcome? This surfaces a library of meaningful, business-driven opportunities. Then, using proven filters and frameworks, we zero in on the highest-impact places to start applying AI. >> Use AI to Develop AI Strategy << We then—on the spot—collaborate with AI to develop executive viewpoints on how AI can accelerate those strategic priorities. This is hands-on work with AI tools to co-create a path forward, often culminating in each group sharing a lightning talk (co-developed with AI) with the broader team. This approach fast tracks execs to: 1️⃣ Build readiness: Gain deep understanding of the new landscape of use cases today’s AI offers, and the organizational structures needed to effectively harness it. 2️⃣ Map use cases: Develop a prioritized library of strategic use cases ready for immediate collaboration with technology and data teams. 3️⃣ Accelerate alignment: Establish common language and jump-start cross-functional alignment on tackling high-impact opportunities. 4️⃣ Hands-on understanding: Acquire hands-on experience with AI tools they can immediately apply to their most challenging strategic work. What do my clients say about this approach? That their teams shift from skepticism to enthusiasm—hungry for more, and from uncertainty to clarity about the next steps. It’s a remarkable change, especially in a few hours. ➡️ Want to learn more? Let’s talk. #AIworkshop

A man with a watch knows what time it is. A man with two watches is never sure. ~ Segal’s Law More data doesn’t mean better decisions. In fact, it often leads to paralysis, over-analysis, and slower execution. So ... how do you filter out the signal from the noise? While AI cannot replace your instincts and judgment, nor make a high-stakes leadership call on your behalf, it can be a valuable thought partner in decision-making. Here are AI prompts to challenge your own thinking: CLARIFY THE CONTEXT 💭 What is the core problem we’re solving, and how has it evolved over time? 💭 What data or evidence suggests this is the right priority right now? 💭 What are the second- and third-order consequences of this decision? 💭 What does success look like in 12 months? What about failure? 💭 If we had to explain this decision in one sentence, what would it be? MODEL SCENARIOS 💭 What are the best-case, worst-case, and most likely scenarios if we move forward? 💭 How would this decision play out in different competitive conditions? 💭 What factors would make this decision a game-changer or a massive failure? 💭 What are the opportunity costs of choosing this path over others? 💭 If we succeed beyond expectations, what new risks or constraints will emerge? STRESS TEST ASSUMPTIONS 💭 What assumptions are we making that could be flawed or outdated? 💭 What evidence would immediately prove this decision wrong? 💭 What are the hidden risks or unintended consequences we aren’t considering? 💭 Are we making this decision based on past success, or future relevance? 💭 What is the hidden downside of being right? PRIORITIZE SPEED 💭 What is the ONE critical insight that makes this decision 80% clear right now? 💭 If we had to make this decision within 24 hours, what would we prioritize? 💭 Are we optimizing for certainty, or are we delaying out of fear? 💭 If we delay this decision by 6 months, what are the risks and missed opportunities? 💭 What’s the smallest action we can take to test this decision before fully committing? BUILD FEEDBACK LOOPS 💭 What are the top 3 leading indicators that will signal whether this decision is working? 💭 What biases might cause us to ignore early warning signs of failure? 💭 If this decision needs to be reversed, what’s the fastest and least costly way to do it? 💭 How will we ensure that feedback is acted upon, not just collected? 💭 What questions should we be asking 6 months from now to reassess this decision? #leadership #AI #innovation

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

The last few weeks have been intense. I’ve been deep-diving into how AI and LLMs can transform the way we interact with Shopify data—not just for automation, but for smarter decision-making. So I built something small MVP. A chatbot that pulls real-time product, customer, and order data from Shopify, pushes it to vector DBs like Chroma, Pinecone, Milvus, and makes it searchable with OpenAI embeddings. You ask: “Where is my order?” → It checks login and gives you a contextual reply. You say: “Show me a red t-shirt under $30” → It fetches product data semantically. It’s not just for customer support—imagine CXOs chatting with their business data to get instant answers like: “What’s the best-selling product in California last month?” I wrote a deep-dive blog on how I built it, with all the tech breakdowns: - Shopify API - OpenAI embeddings - Vector DB - LLM orchestration Would love to hear your thoughts on similar use cases or how you’re approaching AI in eCommerce. #Shopify #AI #LLM #OpenAI #eCommerce #CustomerSupport #TechForBusiness #GenerativeAI #CRO #CXO

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.

The rush to implement AI solutions can lead to significant pitfalls. Here's a provocative thought: the greatest risk in AI isn't just inaction. It's implementing without understanding. Let’s unravel why AI implementation demands careful thought and expertise. The promise of AI is undeniable. But when businesses leap without looking, the consequences can be dire. → Mismanaged data leads to flawed predictions. ↳ Garbage in, garbage out—AI doesn't magically fix bad data. → Overreliance can breed complacency. ↳ AI is a tool, not a crutch. → Lack of understanding can result in ethical oversights. ↳ Algorithms must be checked for bias and fairness. → Insufficient expertise can stall projects. ↳ Proper training and a clear strategy are essential. AI implementation isn't just about tech. It's about aligning with business goals and ethics. So, how do we get it right? Prioritize data quality → Clean, accurate data is nonnegotiable. Invest in education → Equip your team with the knowledge to leverage AI effectively. Engage multidisciplinary teams → Combine tech expertise with business acumen. Embed ethical considerations → Regularly audit models for bias and fairness. Iterate and refine → Continuous learning and adaptation are key. Remember, AI isn't a onesizefitsall solution. It's a journey that requires thoughtful planning and execution. Done right, AI can transform businesses, enabling them to act with foresight and agility. Yet, it's the careful, calculated steps that ensure this transformation is both successful and sustainable. What steps have you taken to ensure AI success in your organization? Share your thoughts below.

You don't need more AI tools → You need an AI strategy. Everyone's rushing to "use AI in their business." But randomly testing tools isn't a strategy. Here's how to actually implement AI effectively 👇 First, work backwards: → What tasks consume most of your time? → Where do you need faster output? → What could be improved with automation? Then, audit your workflow: → What requires human creativity? → What's repetitive but necessary? → What needs a human final touch? Now choose your AI tools based on needs: Low-complexity tasks: → Email drafts → Social media captions → Basic research → Meeting summaries High-complexity tasks: → Content strategy → Market analysis → Customer insights → Product development Implementation approach: → Start with one process → Test and measure results → Document what works → Scale gradually Pick 2-3 use cases maximum. Master them before adding more. Remember: AI is a tool, not a solution. The key is knowing where it fits in YOUR business. Success comes from strategy first, tools second. #AIStrategy #BusinessGrowth #Productivity P.S. Want my tested AI workflows? Drop a "+" below.

We just built a commercial grade RCT platform called MindMeld for humans and AI agents to collaborate in integrative workspaces. We then test drove it in a large-scale Marketing Field Experiment with surprising results. Notably, "Personality Pairing" between human and AI personalities improves output quality and Human-AI teams generate 60% greater productivity per worker. In the experiment: 🚩 2310 participants were randomly assigned to human-human and human-AI teams, with randomized AI personality traits. 🚩 The teams exchanged 183,691 messages, and created 63,656 image edits, 1,960,095 ad copy edits, and 10,375 AI-generated images while producing 11,138 ads for a large think tank. 🚩 Analysis of fine-grained communication, collaboration, and workflow logs revealed that collaborating with AI agents increased communication by 137% and allowed humans to focus 23% more on text and image content generation messaging and 20% less on direct text editing. Humans on Human-AI teams sent 23% fewer social messages, creating 60% greater productivity per worker and higher-quality ad copy. 🚩 In contrast, human-human teams produced higher-quality images, suggesting that AI agents require fine-tuning for multimodal workflows. 🚩 AI Personality Pairing Experiments revealed that AI traits can complement human personalities to enhance collaboration. For example, conscientious humans paired with open AI agents improved image quality, while extroverted humans paired with conscientious AI agents reduced the quality of text, images, and clicks. 🚩 In field tests of ad campaigns with ~5M impressions, ads with higher image quality produced by human collaborations and higher text quality produced by AI collaborations performed significantly better on click-through rate and cost per click metrics. As human collaborations produced better image quality and AI collaborations produced better text quality, ads created by human-AI teams performed similarly, overall, to those created by human-human teams. 🚩 Together, these results suggest AI agents can improve teamwork and productivity, especially when tuned to complement human traits. The paper, coauthored with Harang Ju, can be found in the link on the first comment below. We thank the MIT Initiative on the Digital Economy for institutional support! As always, thoughts and comments highly encouraged! Wondering especially what Erik Brynjolfsson Edward McFowland III Iavor Bojinov John Horton Karim Lakhani Azeem Azhar Sendhil Mullainathan Nicole Immorlica Alessandro Acquisti Ethan Mollick Katy Milkman and others think!