Strategies to Enhance AI User Interactions

★ 𝗔𝗗𝗩𝗔𝗡𝗖𝗘𝗗 𝗔𝗜 𝗜𝗦 𝗔 𝗧𝗛𝗢𝗨𝗚𝗛𝗧 𝗣𝗔𝗥𝗧𝗡𝗘𝗥, 𝗡𝗢𝗧 𝗔 𝗖𝗛𝗔𝗧𝗕𝗢𝗧 ★ OpenAI's latest model, o3, again surpasses all prior benchmarks in reasoning, math, and coding. But are you really using these high-powered models to their full potential? Most AI users are stuck in the "ask-and-answer" trap, treating advanced AI like a souped-up search engine or a typical back-and-forth with ChatGPT. That's a fundamental misunderstanding. ➤ 𝗦𝗧𝗢𝗣 𝗔𝗦𝗞𝗜𝗡𝗚 𝗤𝗨𝗘𝗦𝗧𝗜𝗢𝗡𝗦, 𝗦𝗧𝗔𝗥𝗧 𝗦𝗛𝗔𝗥𝗜𝗡𝗚 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 𝗦𝗣𝗔𝗖𝗘𝗦 Advanced reasoning models aren't meant to give us faster chat responses. They're meant to change how we think and expand our own cognitive capabilities. Models like o1 / o3, Thinking Claude, and the latest Gemini experiments can handle complex and nuanced 𝗠𝗘𝗚𝗔𝗣𝗥𝗢𝗠𝗣𝗧𝗦 that are thousands of words long. Give them: ↳ Entire Mental Models: A complete framework for thinking about a specific domain. ↳ Ontologies & Structured Knowledge: Detailed instructions that shape the model's understanding and approach. ↳ Textbooks, even: Massive amounts of information to ground the model in a particular field. Then tell it to address your needs from there. These models give us a superhuman-level capability to: ↳ Deconstruct Complexity: Break down messy problems into core components. ↳ Navigate Uncertainty: Reason through ambiguity and incomplete information. ↳ Generate & Evaluate: Create new frameworks, strategies, and even code, then critically assess them. Here's how to turn advanced AI into a powerful extension of your intellect: 𝗕𝗨𝗜𝗟𝗗 𝗬𝗢𝗨𝗥 𝗢𝗪𝗡 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗕𝗟𝗨𝗘𝗣𝗥𝗜𝗡𝗧 》》𝐼𝑁𝑆𝑇𝐸𝐴𝐷 𝑂𝐹: Treating interactions & your knowledge as isolated. 》》》》𝐶𝑂𝑁𝑆𝐼𝐷𝐸𝑅 𝑇𝐻𝐼𝑆: Develop a Personal Context Blueprint - a living document outlining your goals, constraints, resources, and mental models. Use it as a foundation for your interactions with the AI.       𝗣𝗥𝗢𝗕𝗘 𝗙𝗢𝗥 𝗟𝗘𝗩𝗘𝗥𝗔𝗚𝗘 𝗣𝗢𝗜𝗡𝗧𝗦 》》𝐼𝑁𝑆𝑇𝐸𝐴𝐷 𝑂𝐹: Using direct Q&A format. 》》》》𝐶𝑂𝑁𝑆𝐼𝐷𝐸𝑅 𝑇𝐻𝐼𝑆: Focus on identifying high-leverage points within your problem space. Example: "Based on the provided Contextual Blueprint, identify three areas where a small change could have an outsized impact on my desired outcome of [xyz]." 𝗖𝗢𝗚𝗡𝗜𝗧𝗜𝗩𝗘 𝗟𝗢𝗔𝗗 𝗔𝗥𝗕𝗜𝗧𝗥𝗔𝗚𝗘 》》𝐼𝑁𝑆𝑇𝐸𝐴𝐷 𝑂𝐹: Using AI for everything (or nothing) 》》》》𝐼𝑀𝑃𝐿𝐸𝑀𝐸𝑁𝑇: Strategically offload high-cognitive-load, low-impact tasks to the AI (e.g., data processing, initial research, generating variations). Reserve your own cognitive bandwidth for high-impact, strategic decisions, and judgment calls. ➤ 𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗖𝗛𝗔𝗟𝗟𝗘𝗡𝗚𝗘 We're underutilizing the most powerful tools of our time. Stop thinking of advanced AI as a chatbot, and start thinking with it as a thinking partner. This shift is the key to unlocking the true potential of advanced reasoning models (and our own potential too). #AI

AI field note: Reducing the 'mean time to ah-ha' (MTtAh) is critical for driving AI adoption—and unlocking the value. When it comes to AI adoption, there's a crucial milestone: the "ah-ha moment." It's that instant of realization when someone stops seeing AI as just a smarter search tool and starts recognizing it as a reasoning and integration engine—a fundamentally new way of solving problems, driving innovation, and collaborating with technology. For me, that moment came when I saw an AI system not just write code but also deploy it, identify errors, and fix them automatically. In that instant, I realized AI wasn’t just about automation or insights—it was about partnership. A dynamic, reasoning collaborator capable of understanding, iterating, and executing alongside us. But these "ah-ha moments" don’t happen by accident. Systems like ChatGPT or Claude excel at enabling breakthroughs, but it really requires us to ask the right questions. That creates a chicken-and-egg problem: until users see what’s possible, they struggle to imagine what else is possible. So how do we help people get hands-on with AI, especially in enterprise organizations, without relying on traditional training? Here are some approaches we have tried at PwC: 🤖 AI "Hackathons" or Challenges: Host short, low-stakes events where employees can experiment with AI on real problems. For example, marketing teams could test AI for campaign ideas, while operations teams explore process automation. ⚙️ Sandbox Environments: Provide low-friction, risk-aware access to AI tools within a dedicated environment. Let users explore capabilities like text generation, workflow automation, or analytics without worrying about “messing something up.” 🚀 Pre-built Use Cases: Offer ready-to-use templates for specific challenges, such as drafting a client email, summarizing documents, or automating routine reports. Seeing results in action builds confidence and sparks creativity. At PwC we have a community prompt library available to everyone, making it easier to get started. 🧩 Embedded AI Mentors: Assign "AI champions" who can guide teams on applying AI in their work. This informal mentorship encourages experimentation without formal, structured training. We do this at PwC and it's been huge. ⚡️ Integrate AI into Existing Tools: Embed AI into everyday platforms (like email, collaboration tools, or CRM systems) so users can naturally interact with it during routine workflows. Familiarity leads to discovery. Reducing the mean time to ah-ha—the time it takes someone to have that transformative realization—is critical. While starting with familiar use cases lowers the barrier to entry, the real shift happens when users experience AI’s deeper capabilities firsthand.

When I talk to my colleagues and graduate students about how they are using AI tools, I realized that they are missing out on some important use cases that I've found extremely valuable. I wanted to share some of these below and look forward to hearing your thoughts on other unconventional ways you've applied these tools! ✅ Iterative Proposal Refinement – Used ChatGPT to evaluate a revised grant proposal in the context of reviewer comments, identifying gaps, strengthening arguments, and ensuring all weaknesses were addressed. This mimics an outside reviewer’s perspective before submission. ✅ Logic and Flow Checks – AI can analyze argument coherence, detect missing connections, and suggest clearer phrasing in technical documents, making research papers and proposals more compelling and concise. I will prompt to ask for what information is missing to enhance understanding or to identify areas that were unclear and need more explanation. ✅ Cutting the Fluff – Academics love long paragraphs, but reviewers don’t. I ask the LLMs to identify areas of redundancy or areas of varying detail between different parts of a proposal. ✅ Comparative Feedback Analysis – Given multiple drafts, ChatGPT can compare versions, pinpointing what improved and what still needs work—saving time in manual cross-referencing. ✅ Visualization Gaps & Idea Generation – Beyond writing, LLMs can help brainstorm visualization strategies, high priority areas where figures can benefit understanding, or suggest charts or tables to ease understanding. Happy to share prompting strategies I've been using that have been successful - please feel free to leave a comment. 💡 How are you using LLMs in your research? Would love to hear about unconventional ways you've integrated AI tools into your academic workflow!

HOW we work with AI matters. Emerging modes of interaction are reshaping roles. Most people are stuck at method 1 or 2. Here are 4 key AI interaction types—and when to use each: 1️⃣ AI as a Microtasker One-shot problem solver. Ideal for quick, contained tasks: rewriting a sentence, generating a one-off image, answering a data question, or fixing a bit of code. High precision, low overhead. 2️⃣ AI as a Copilot Persistent, live support for extended tasks. It stays with you in pairing mode—watching your screen, listening, coding, brainstorming. A back-and-forth partner for creative or technical work in real time. Human in the loop, always. 3️⃣ AI as a Delegate Assign it a goal and let it work autonomously, for minutes or days. Great for complex, long-form tasks like research—no human in the loop. It self-directs, self-checks, and reports back after/while completing tasks. Think: Manus AI, autonomous agents. 4️⃣ AI as a Teammate A presence across your team or org. It joins meetings, takes notes, surfaces insights, runs simulations, offers opinions. Can even be in a manager role. Not just assisting YOU but enhancing the collective. An ambient, participatory AI system. And roles 3 and 4 mean the AI can work in a completely different way than our human systems. Knowing which role to use—and when—is the new AI literacy.