Finding the Right AI Business Intelligence Tool for My Team

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 ❤️

As a CS leader, I don't know where to spend my "AI budget." There's insufficient evidence to help CS leaders decide which AI tools are worth paying for. The marketing sounds the same, the capabilities overlap, and use cases specific to customer success are rarely discussed. So, I spent several months forming some opinions. (I used some dummy data and created scenarios based on my past experiences, etc.). Here's my early take on where each might fit: ChatGPT: Seems strongest for numeric analysis, code, and training scenarios. Enterprise plan costs $30/user/month. Claude: Appears good at human-like communication and summarization. Free tier available, business is $20/user/month. Perplexity: Stands out for real-time product knowledge and documentation. Pro version at $20/month with higher query limits. Here are some initial observations from my testing: For analyzing customer health data: -> ChatGPT (4o) impressed me with customer health score analysis capabilities. Its numeric reasoning seemed strong when I tested it with sample usage metrics across different segments. -> Claude didn't handle complex data visualization as well in my tests, but showed promise for identifying patterns in qualitative feedback like NPS comments and support tickets. For creating personalized communication: -> Claude produced the most natural-sounding email drafts, and QBR outlines in my testing. I tried inputting essential account context and business goals to see what it would generate. For technical documentation and knowledge base work: -> Perplexity seemed helpful when I tested updating technical documentation. The real-time search provided more accurate product information than the other tools I tried. For customer research and market intelligence: -> Perplexity performed well in my tests for gathering competitive intel and industry trends. I tried researching a few customer companies to see what kind of market context it could provide before a hypothetical QBR. -> ChatGPT showed promise when I experimented with analyzing sample customer interview transcripts. It seemed capable of identifying themes that might be useful for product teams. These are just my initial impressions. And I'm no expert. I haven't implemented any of these at scale yet, and your mileage may vary. But I'm curious to see how AI will start to be adopted more and more directly into the workflow of CS teams. How are you thinking about allocating your CS team's AI budget? Have you tested any of these tools for CS workflows?

Navigating the AI landscape can feel like stepping into a bike shop — exciting, but overwhelming. Every tool glistens with promise, claiming to transform how you ride...or, in this case, how you do business. But, just like picking the right bike, selecting the right AI isn’t about getting swept up in a flashy demo or test ride. It’s about finding what will bring real results for you and your business. 🚴♂️ In my first Forbes Technology Council article, I cover four tips to cut through the AI noise and find solutions that actually deliver. Here’s a quick preview, with a 🔗 below to the full article. 1. Efficiency isn’t enough Sure, AI tools love to boast about saving time. But efficiency alone doesn’t win the race. Look for AI that directly impacts critical business outcomes — revenue, customer satisfaction, and brand visibility. Don’t be swayed by tools that look like that fancy bike trainer collecting dust in your garage. It’s not about the “wow,” it’s about the *how.* 2. Real-world stories > demos Seek out real stories from people who’ve implemented this AI tech. What challenges did they overcome? How does the tool handle privacy and data security? And most importantly, does it deliver consistent results or just look good under the showroom lights? 3. AI and human expertise should work together AI isn’t about replacing the human touch — it’s about enhancing it. The best tools provide transparency and empower your team’s creativity. If the AI can’t explain how it got from A to B or why it made a decision, it’s not a partner. It’s a black box. 4. Read through the jargon Beware the buzzword parade. “Paradigm-shifting,” “synergistic” — we’ve heard them all. Great tech doesn’t hide behind complex terms. Look for tools that offer plain-language explanations of how they work and what they’ll do for your business. If you’re left scratching your head, keep moving. Choosing the right AI takes time, effort, and a willingness to dig deeper. But when you find that perfect fit, your business will reach new heights. And just like in cycling, the view is worth the climb. 🌄 🔗https://lnkd.in/gpgQPXeb #TechLeadership #AI