How to Balance AI Use for Deep Thinking

Harvard Business Review just found that executives using GenAI for stock forecasts made less accurate predictions. The study found that:  • Executives consulting ChatGPT raised their stock price estimates by ~$5.  • Those who discussed with peers lowered their estimates by ~$2.  • Both groups were too optimistic overall, but the AI group performed worse. Why? Because GenAI encourages overconfidence. Executives trusted its confident tone and detail-rich analysis, even though it lacked real-time context or intuition. In contrast, peer discussions injected caution and a healthy fear of being wrong. AI is a powerful resource. It can process massive amounts of data in seconds, spot patterns we’d otherwise miss, and automate manual workflows – freeing up finance teams to focus on strategic work. I don’t think the problem is AI. It’s how we use it. As finance leaders, it’s on us to ensure ourselves, and our teams, use it responsibly. When I was a finance leader, I always asked for the financial model alongside the board slides. It was important to dig in and review the work, understand key drivers and assumptions before sending the slides to the board. My advice is the same for finance leaders integrating AI into their day-to-day: lead with transparency and accountability. 𝟭/ 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿, 𝗻𝗼𝘁 𝗮𝗻 𝗼𝗿𝗮𝗰𝗹𝗲. AI should help you organize your thoughts and analyze data, not replace your reasoning. Ask it why it predicts what it does – and how it might be wrong. 𝟮/ 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗔𝗜 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻. AI is fast and thorough. Peers bring critical thinking, lived experience, and institutional knowledge. Use both to avoid blindspots. 𝟯/ 𝗧𝗿𝘂𝘀𝘁, 𝗯𝘂𝘁 𝘃𝗲𝗿𝗶𝗳𝘆. Treat AI like a member of your team. Have it create a first draft, but always check its work, add your own conclusions, and never delegate final judgment. 𝟰/ 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗿𝗼𝗹𝗲𝘀 - 𝘂𝘀𝗲 𝗶𝘁 𝘁𝗼 𝗰𝗵𝗲𝗰𝗸 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸. Use AI for what it does best: challenging assumptions, spotting patterns, and stress-testing your own conclusions – not dictating them. We provide extensive AI within Campfire – for automations and reporting, and in our conversational interface, Ember. But we believe that AI should amplify human judgment, not override it. That’s why in everything we build, you can see the underlying data and logic behind AI outputs. Trust comes from transparency, and from knowing final judgment always rests with you. How are you integrating AI into your finance workflows? Where has it helped vs where has it fallen short? Would love to hear in the comments 👇

AI poses serious risks when used the wrong way. Our present situation with the emergence of AI reminds me of the early years of my engineering career. Graphing calculators and engineering software were introduced and some thought it was the beginning of the end of quality engineering. In reality, these tools have been a net positive, but only once we put them in capable hands and in a proper workflow. Fast forward 20 years and AI is here in safety, and its here to stay. But, how do we use it well and avoid the traps? I see four potential scenarios: - Effective and Efficient: A knowledgeable person who knows how to use AI to accelerate, enhance, and review their work. - Effective but Inefficient: A knowledgeable and skilled person who does not use AI. - Ineffective and Inefficient: An ignorant or unskilled person who doesn’t use AI. - Dangerous: An ignorant or unskilled person using AI to rapidly produce bad output The risk of the “dangerous” category is very real. That’s why our team is equally focused on two things: (1) enhancing the fidelity of the AI and (2) ensuring the AI is used effectively. --- Here is an example of a good and bad use of ChatSafetyAI: ✅ DO: Use ChatSafetyAI to check your high-energy control assessments (HECA) to see if you missed anything. ❌ DONT: Use ChatSafetyAI to do your HECA for you. Proper workflow: Integrate the ChatSafetyAI API after an initial assessment to provide feedback and recommendations. This additive function helps the assessors to “fill in the gaps” with more intelligence. This workflow leverages both human and artificial intelligence, assuming effort is placed in the initial assessment. Our council, comprised of the licensees of ChatSafetyAI, is working on this. Consider joining us. I would love to hear your ideas on the effective use of AI for safety. 

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗲𝘅𝗽𝗲𝗿𝘁𝘀; 𝗶𝘁 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 𝘁𝗵𝗲𝗶𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲! 👉 It’s about harnessing AI to enhance our human capabilities, not replace them. 🙇♂️ Let me walk you through my realization. As a healthcare practitioner deeply involved in integrating AI into our systems, I've learned it's not about tech for tech's sake. It's about the synergy between human intelligence and artificial intelligence. Here’s how my perspective evolved after deploying Generative AI in various sectors: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞: "I 𝐧𝐞𝐞𝐝 AI to analyze complex patient data for personalized care." - But first, we must understand the unique healthcare challenges and data intricacies. 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧: "I 𝐧𝐞𝐞𝐝 AI to tailor learning to each student's needs." - Yet, identifying those needs requires human insight and empathy that AI alone can't provide. 𝐀𝐫𝐭 & 𝐃𝐞𝐬𝐢𝐠𝐧: "I 𝐧𝐞𝐞𝐝 AI to push creative boundaries." - And yet, the creative spark starts with a human idea. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: "I 𝐧𝐞𝐞𝐝 AI for precise market predictions." - But truly understanding market nuances comes from human experience and intuition. The Jobs-to-be-Done are complex, and time is precious. We must focus on: ✅ Integrating AI into human-led processes. ☑ Using AI to complement, not replace, human expertise. ✅ Combining AI-generated data with human understanding for decision-making. ☑ Ensuring AI tools are user-friendly for non-tech experts. Finding the right balance is key: A. AI tools must be intuitive and supportive. B. They require human expertise to interpret and apply their output effectively. C. They must fit into the existing culture and workflows. For instance, using AI to enhance patient care requires clinicians to interpret data with a human touch. Or in education, where AI informs, but teachers inspire. 𝐌𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐫𝐨𝐥𝐞𝐬 is critical. And that’s where I come in. 👋 I'm Umer kHan, here to help you navigate the integration of Generative AI into your world, ensuring it's done with human insight at the forefront. Let's collaborate to create solutions where technology meets humanity. 👇 Feel free to reach out for a human-AI strategy session. #GenerativeAI #HealthcareInnovation #PersonalizedEducation #CreativeSynergy #BusinessIntelligence

"Just because we can use AI, should we?" I was asked this question yesterday on a call by a thoughtful leader of a team who was thinking about adopting GenAI. We should all be asking this question regularly. In my view, AI should support human judgment, not replace it. Even the best AI-generated output is often mediocre, requiring human expertise and verification to achieve high quality. I joked that AI can be like the dumbest smart person you know. At boodleAI, we designed BoodleBox to enable collaboration between people, AI, and knowledge. Our goal is for AI to enhance human decision-making, not supplant it. Consider these contrasting examples: AI supporting human judgment: - AI assists in research and information gathering - AI offers suggestions and ideas for a human to evaluate - AI helps automate routine tasks, freeing up human time for higher-level work AI substituting for human judgment: - Blindly accepting AI-generated content without review - Relying solely on AI for critical decisions - Replacing human workers with AI without considering the implications Agree or disagree? Please share your thoughts and examples in the comments.