Evaluating AI Tools For Enterprise Needs

Companies waste millions on AI products that turn out to be vaporware. I have been simmering and seasoning this AI product evaluation framework for 12 years. My clients need innovative AI tools that deliver competitive advantages, so it’s not feasible to reject startups altogether. Here are my assessment points. ✅ The startup knows something about the market or your needs that no one else does. They discuss your problems and desired outcomes like they’ve worked at your company. ✅ They explain how early design partners and limited releases led to improvements and new features. They share early outcomes from both, and the result metrics align with your strategic goals. ✅ The solution makes sense, and demos are focused on functionality, not just technology. They are transparent about the product or platform’s weaknesses and gaps and have plans to address them. ✅ They ask questions during the demo to better understand your needs and showcase the most relevant functionality based on your answers. ✅ They have built competitive advantages with data, and the platform or product delivers functionality that competitors can’t. ✅ They have a platform or product roadmap and admit it isn’t set in stone. However, they can provide a clear vision for the product or platform. ✅ The company has a low burn rate, path to profitability, or strong financials that indicate it will be around for several years. ✅ Their service level agreements, data management practices, contract/pricing structures, etc., are mature and built for enterprises vs. consumers. ✅ They have an implementation/integration roadmap and provide initial support or onboarding. The company doesn’t just drop and run or rely 100% on chatbot support. My book and articles provide more frameworks to help businesses navigate the emerging AI tools landscape. Follow me here or use the link under my name to access my library. #GenerativeAI #AIStrategy

After reviewing dozens of enterprise AI initiatives, I've identified a pattern: the gap between transformational success and expensive disappointment often comes down to how CEOs engage with their technology leadership. Here are five essential questions to ask: 𝟭. 𝗪𝗵𝗮𝘁 𝘂𝗻𝗶𝗾𝘂𝗲 𝗱𝗮𝘁𝗮 𝗮𝘀𝘀𝗲𝘁𝘀 𝗴𝗶𝘃𝗲 𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝗰𝗮𝗻'𝘁 𝗲𝗮𝘀𝗶𝗹𝘆 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲? Strong organizations identify specific proprietary data sets with clear competitive moats. One retail company outperformed competitors 3:1 only because it had systematically captured customer interaction data its competitors couldn't access. 𝟮. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗼𝘂𝗿 𝗰𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝗿𝗮𝘁𝗵𝗲𝗿 𝘁𝗵𝗮𝗻 𝗷𝘂𝘀𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀? Look for specific examples of fundamentally reimagined business processes built for algorithmic scale. Be cautious of responses focusing exclusively on efficiency improvements to existing processes. The market leaders in AI-driven healthcare don't just predict patient outcomes faster, they've architected entirely new care delivery models impossible without AI. 𝟯. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝘂𝗿 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘄𝗵𝗶𝗰𝗵 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗺𝗮𝗶𝗻 𝗵𝘂𝗺𝗮𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝘃𝗲𝗿𝘀𝘂𝘀 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱? Expect a clear decision framework with concrete examples. Be wary of binary "all human" or "all algorithm" approaches, or inability to articulate a coherent model. Organizations with sophisticated human-AI frameworks are achieving 2-3x higher ROI on AI investments compared to those applying technology without this clarity. 𝟰. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗲𝘁𝗿𝗶𝗰𝘀? The best responses link AI initiatives to market-facing metrics like share gain, customer LTV, and price realization. Avoid focusing exclusively on cost reduction or internal efficiency. Competitive separation occurs when organizations measure algorithms' impact on defensive moats and market expansion. 𝟱. 𝗪𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗵𝗮𝘃𝗲 𝘄𝗲 𝗺𝗮𝗱𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗮𝗹𝘂𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀? Look for specific organizational changes designed to accelerate algorithm-enhanced decisions. Be skeptical of AI contained within traditional technology organizations with standard governance. These questions have helped executive teams identify critical gaps and realign their approach before investing millions in the wrong direction. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: V𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 own 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴.

Step 3 of 7 for AI Enablement: Identify and Prioritize AI Use Cases See full 7-step breakdown here: https://lnkd.in/g3t7MiZb In setting up AI for success, we’ve covered the foundations: Step 1 defined clear business objectives. Step 2 assessed team readiness, revealing gaps to achieve outcomes. Now for Step 3: Identify and Prioritize AI Use Cases. This step isn’t just about knowing where AI could fit; it’s also about evaluating tools to ensure they meet essential requirements—and testing the top choices with trial runs. First: Explore What AI Tools Are Out There Before diving into specific use cases, it’s important to understand the types of AI tools available that could support your goals. If you’re unsure where to start, here are two valuable resources: • Theresanaiforthat.com – A searchable directory of AI tools across industries. • GTM AI Tools Demo Library – A curated list of go-to-market AI tools from the GTM AI Academy (l^nk in comments). Identify AI Opportunities with the PRIME Framework With a better understanding of AI options, use the PRIME Framework to identify use cases that directly address your most critical business gaps: • Predictive: Can AI help forecast outcomes? • Repetitive: Are there time-consuming, repeated tasks? • Interactive: Could AI enhance customer engagement? • Measurable: Can AI provide useful metrics? • Empowering: Can AI support creativity or productivity? Evaluate Tools with a Checklist Once you’ve outlined use cases, evaluate potential tools to ensure they meet critical requirements before trialing them: • Security & Compliance: Does the tool meet company standards? • Governance: Does it support data governance and accountability? • Cost & ROI: Is it cost-effective based on expected value? • Scalability: Can it grow with your team’s needs? • Integration: Will it fit with your current systems? Evaluate Tools: Make sure selected tools meet security, compliance, and integration needs before trial runs. Pilot Testing Once you’ve prioritized and evaluated, move into a pilot phase. Select top tools to trial with a small pilot team. This phase helps test effectiveness, build internal champions, and refine any processes before rolling out to the larger team in Step 4. Your Checklist for Step 3 1. Explore AI Options: Start with Theresanaiforthat.com and GTM AI Tools Demo Library. 2. Identify Use Cases with PRIME: Target high-impact areas. 3. Evaluate Tools with the Checklist: Confirm tools meet security, compliance, and integration needs. 4. Pilot Test: Trial top tools with a small team to validate effectiveness. By following this approach, you’ll set your team up for measurable, AI-driven success with tools that are tested and proven valuable. Ready to PRIME your AI Enablement? Check out free resources in the GTM AI Academy: • PRIME Use Case Guide • Impact-Feasibility Template • AI Critical Requirements Assessment Up next.. Step 4 of 7 for AI Enablement..

Time for your weekend long-read! I just published a piece on something I think every organization using AI needs to understand: how to evaluate whether your AI systems actually work for your specific needs, both for legal tech and generally for business. Why? Generic benchmarks tell you how AI performs in abstract scenarios, but they miss your edge cases, your terminology, your standards. The gap between benchmark scores and real performance isn't just numbers - it's damaged trust and sleepless nights. Therefore, what is needed are systematic evaluations of YOUR specific products, workflows, and other applications of AI.  In the post I make the case that articulating quality AI outputs for in a way that can be objectively evaluated is now a core function of executive leadership and governance. Building custom evaluations isn't as technical as you might think. If you can articulate what good work looks like to a human employee, you can create meaningful AI evaluations. I know this first hand because much of my own consulting business service has transformed from help creating or improving existing applications to evaluations of applications to ensure they are hitting quality thresholds and staying under cost and risk ceilings. Custom evals are essential for successful application of AI and they are the key (frequently missing) method and mechanism for AI governance. I've also released Lake Merritt, an open-source platform that makes this accessible. The quick start guide walks you through simple exercises you can try in just a few minutes to see how this works and to become directly familiar with evals. Lake Merritt is designed for business, legal, and other non-technical leaders to be able to quickly get involved with evals. While the software is still in early public beta, in the context of the blog post you can use Lake Merritt to understand and get started with evals and even to get a start on your own internal evals. It also supports more advanced workflows such as OpenTelemetry and multi-agent system evals. The aspect I like best is how we use "eval packs" so you can version and even share your approaches to evals with other in your organization or swap them with the broader community as we all learn together what the best methods are.  More on Lake Merritt here: https://lnkd.in/g2Q7CA2c Many thanks to Artificial Lawyer for noting the launch of Lake Merritt earlier this week in their fine article, here: https://lnkd.in/g55zPbpt Likewise, in the blog post I took a moment to recognize some of the folks in AI evals who I think you should also be paying attention to, including Vals AI, Arize AI, Galileo, Anna Guo, Darius Emrani, and many others! Would love to hear your thoughts on how evaluation fits into your AI strategy, or if you've wrestled with the challenge of measuring AI quality in your own context. Link: https://lnkd.in/gKqMmjQw