How to Use OpenAI Reasoning Models in Business

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!

This is a gem of a case study about how to apply AI across a business. Singapore Airlines is partnering with OpenAI to apply AI to its business in the following ways, reports A'bidah Zaid Shirbeeni in MARKETING-INTERACTIVE: 1. Personalize the airline’s virtual assistant to intuitively plan personalized travel and offer customers self-service options. Business Benefits:  ✅ Self-service delivers higher revenue impact than the flight recommendation chatbot ✅ Intuition (read: ChatGPT’s new memory) and personalization promote customer engagement 2. Create an internal AI assistant to guide employees on operations and automate routine tasks. Business Benefits:  ✅ Faster decision-making when time is critical ✅ The assistant applies learnings from past issue resolutions and support solves to answer current questions 3. Integrate ChatGPT with operations tools to crunch out complex workflows such as scheduling flight crews while referencing applicable regulatory guidelines. Business Benefits:  ✅ Optimizes planning ✅ Streamlines operations WHY THIS MATTERS: Singapore Airlines’ idea of an “AI-first customer journey” shifts the lens from thinking about AI-first companies toward using LLMs to build better customer experiences. That’s a powerful shift. This is applied AI at its finest - to build better customer experiences. What ideas spring to mind when you think about AI-first customer experiences at your company? ✨ Conversational AI imperatives from Chatbot Europe: https://lnkd.in/edxvM8d3 #ai #cx #ux #chatbot #appliedai #marketing Image credit: MARKETING-INTERACTIVE

OpenAI is about to release the first AI models that feature creative thinking. In essence, the new models o-3 and o-4 Mini are able to come up with their own ideas. The technology might soon come up with new ideas on how to attack problems such as designing or discovering new types of materials or drugs, for example. Let's take a look at how this plays out in supply chain management. OpenAI had shifted towards reasoning-based models last September already when it became clear that the evolution of traditional models was slowing. Reasoning models perform better the more time they spend on processing answers, and they excel in problems with solutions that can be verified objectively, such as mathematical theorems. The two new models are small and cost-efficient, designed to deliver strong reasoning capabilities. The o-3 model was especially designed for complex tasks that includes decision-making in ambiguous or complex scenarios. The model generates a detailed, step-by-step internal analysis through reasoning tokens before producing its answer. Interestingly, OpenAI believes that they can eventually charge $20,000 per month for these capabilities. This is roughly the fully loaded salary of a senior researcher. How does this affect supply chains? More immediate, there are a lot of real-life situations where an operator (or machine) may need to adjust on the fly such as changing routes, consolidating freight, or switching capacity. "Brainstorming" may come in handy when planning complex networks. Models produce counterintuitive results more flexibly and much faster than simulations. The latter are always constrained by a handful of variables, while reasoning models have a lot of latitude in what they consider relevant to decision making. In terms of demand planning, a model such as o-3 would significantly change the game. For example, it can break down soft signals, say a lot of TikTok mentions, into a causal chain and can then make accurate predictions. It can also work with dozens of parallel inventory models for a given set of products or materials to optimize them and adjacent processes from transportation to manufacturing extremely well. o-3 might audit supplier contracts in the future using safety policies, flagging clauses that violate compliance such as forced labor risks and suggest alternative sources in seconds rather than hours. Models already digest news in dozens of languages, can include IoT sensor data, and port congestion patterns to predict delays 14 days in advance with a high accuracy. Given how strong reasoning-based models are in coding tasks, it is conceivable that they may eventually generate much of the supply chain software we use today. In the end, we are still a long way away from these scenarios. However, it is most sensible to think about these things now. #supplychain #truckl #innovation