How to Use AI Frameworks to Increase ROI

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!

The AI hype cycle is over. Now it’s time for real business value. Organizations spent the last year experimenting with AI tools, often with mixed results. Those who succeeded found that strategic integration is what drives ROI. Here's 11 ways top performers are achieving measurable ROI on their AI investment: 1. Process Automation Integration → Embed AI in existing workflows → 40-60% reduction in manual tasks → Focus on high-volume, repetitive processes Pro tip: Start with processes that have clear metrics and high error rates. 2. Customer Service Enhancement → AI-powered ticket routing and resolution → 30% reduction in response time → Improved customer satisfaction scores Pro tip: Train AI on your top performers' responses to maintain brand voice and solution quality. 3. Data Analytics Acceleration → Automated insight generation → Predictive modeling at scale → 50% faster decision-making cycles Pro tip: Build dashboards that translate AI insights into actionable recommendations for non-technical teams. 4. Revenue Generation → AI-enhanced lead scoring → Personalized customer journeys → 25% increase in conversion rates Pro tip: Use A/B testing to continuously refine AI models against actual sales outcomes. 5. Cost Optimization → Smart resource allocation → Predictive maintenance → 20-30% reduction in operational costs Pro tip: Create an AI savings tracker to document and communicate wins to stakeholders. 6. Product Development → AI-driven feature prioritization → Automated testing and QA → 40% faster time-to-market Pro tip: Implement AI feedback loops between customer support and product teams for continuous improvement. 7. Risk Management → Real-time fraud detection → Compliance monitoring → 65% reduction in false positives Pro tip: Regular model retraining with new fraud patterns keeps detection rates high. 8. Employee Productivity → AI-powered knowledge management → Automated routine tasks → 3-4 hours saved per employee weekly Pro tip: Create AI champions in each department to drive adoption and share best practices. 9. Supply Chain Optimization → Demand forecasting → Inventory management → 30% reduction in stockouts Pro tip: Combine internal data with external factors (weather, events, trends) for better predictions. 10. Content Creation → Automated first drafts → Multichannel optimization → 60% faster content production Pro tip: Build a prompt library of your best-performing content formats and styles. 11. Quality Control → Computer vision inspection → Defect prediction → 45% reduction in quality issues Pro tip: Start with human-in-the-loop systems before moving to full automation. The key? Integration. Success comes from embedding AI into core business processes, not treating it as a standalone solution. What's your organization's biggest AI ROI win? Share below 👇 ♻️ Repost if your network needs this AI implementation blueprint. Follow Carolyn Healey for more content like this.

8 out of 10 businesses are missing out on Ai. I see this everyday in my calls. They jump straight to AI tools without understanding their processes first. Then wonder why their "automations" create more problems than they solve. Here's the proven framework that actually works: STEP 1: MAP YOUR PROCESSES FIRST Never automate a broken process. → List every touchpoint in your workflow → Identify bottlenecks and time-wasters → Note who handles each step → Find communication gaps Remember: You can only automate what you understand. STEP 2: START WITH HIGH-ROI TASKS Don't automate because it's trendy. Focus on what saves the most time: → Data entry between systems → Client onboarding workflows → Report generation → Follow-up sequences One good automation beats 10 fancy tools that don't work together. STEP 3: BUILD YOUR TECH FOUNDATION Most companies use 10+ disconnected tools. AI can't help if your data is scattered everywhere. → Centralize data in one source (Airtable works great) → Connect your core systems first → Then layer AI on top STEP 4: DESIGN AI AGENTS FOR SPECIFIC PROBLEMS Generic AI = Generic results. Build precise agents for precise problems: → Research and data analysis → Customer support responses → Content creation workflows → Internal process optimization Each agent needs specific inputs and defined outputs. STEP 5: TEST SMALL, SCALE SMART Don't automate your entire business at once. → Start with one small process → Get team feedback → Fix bottlenecks as you go → Scale what works Build WITH your team, not without them. The biggest mistake I see? Companies hire someone to build exactly what they ask for. Instead of finding someone who challenges their thinking and reveals what they're missing. Good automation is just process optimization. Nothing more. The result? → 30+ hours saved per month on onboarding → Delivery time cut in half → Capacity increased by 30% → Revenue multiplied without adding team members Your competitors are stuck switching between apps. You'll be dominating with seamless systems. Follow me Luke Pierce for more content on AI systems that actually work.

I've put my last 6 months building and selling AI Agents I've finally have "What to Use Framework" LLMs → You need fast, simple text generation or basic Q&A → Content doesn't require real-time or specialized data → Budget and complexity need to stay minimal → Use case: Customer FAQs, email templates, basic content creation RAG: → You need accurate answers from your company's knowledge base → Information changes frequently and must stay current → Domain expertise is critical but scope is well-defined → Use case: Employee handbooks, product documentation, compliance queries AI Agents → Tasks require multiple steps and decision-making → You need integration with existing tools and databases → Workflows involve reasoning, planning, and memory → Use case: Sales pipeline management, IT support tickets, data analysis Agentic AI → Multiple specialized functions must work together → Scale demands coordination across different systems → Real-time collaboration between AI capabilities is essential → Use case: Supply chain optimization, smart factory operations, financial trading My Take: Most companies jump straight to complex agentic systems when a simple RAG setup would solve 80% of their problems. Start simple, prove value, then scale complexity. Take a Crawl, Walk, Run approach with AI I've seen more AI projects fail from over-engineering than under-engineering. Match your architecture to your actual business complexity, not your ambitions. P.S. If you're looking for right solutions, DM me - I answer all valid DMs 👋 .