AI Suitability Assessment Guide

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

The UK Department for Science, Innovation and Technology published the guide "Introduction to AI assurance," to provide an overview of assurance mechanisms and global technical standards for industry and #regulators to build and deploy responsible #AISystems. #Artificialintelligence assurance processes can help to build confidence  in #AI systems by measuring and evaluating reliable, standardized, and accessible evidence about their capabilities. It measures whether such systems will work as intended, hold limitations, or pose potential risks; as well as how those #risks are being mitigated to ensure that ethical considerations are built-in throughout the AI development #lifecycle. The guide outlines different AI assurance mechanisms, including: - Risk assessments - Algorithmic impact assessment - Bias and compliance audits - Conformity assessment - Formal verification It also provides some recommendations for organizations interested in developing their understanding of AI assurance: 1. Consider existing regulations relevant for AI systems (#privacylaws, employment laws, etc) 2. Develop necessary internal skills to understand AI assurance and anticipate future requirements. 3. Review internal governance and #riskmanagement practices and ensure effective decision-making at appropriate levels.  4. Keep abreast of sector-specific guidance on how to operationalize and implement proposed principles in each regulatory domain.  5. Consider engaging with global standards development organizations to ensure the development of robust and universally accepted standard protocols. https://lnkd.in/eiwRZRXz

Monday reminder: When choosing an LLM, don’t rely solely on public leaderboard rankings. You should look at: 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐛𝐢𝐥𝐢𝐭𝐲: How easily can the LLM be tailored to your use case? 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: How accurate it is on your tasks? 𝐂𝐨𝐬𝐭: How costly is it to run and maintain, especially as you scale? 𝐒𝐩𝐞𝐞𝐝: How fast is it in your real-world apps? 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧: How easily can it fit into your existing tech stack and cloud infrastructure? 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: Can it be trusted with sensitive data?  𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Can it deliver relatively consistent results over time?  𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Does it meet all regulatory guidelines? 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: How easy is it to maintain the model and overall solution?  𝐒𝐚𝐟𝐞𝐭𝐲: How was the model trained, and what steps were taken to mitigate biases and ensure ethical behavior? Assess which factors can be addressed or mitigated through controls or other capabilities in the overall application, and which cannot. What else would you add to this list? -------- 🔔 If you like this, please repost it and share it with anyone who should know this ♻️ and follow me Heena Purohit, for more AI insights and trends. #artificialintelligence #GenerativeAI #AIforBusiness #AIforEnterprises #EnterpriseAI