Chatbot ROI Assessment

I just built a custom GPT called "AI Use Case Evaluator" to solve a problem that's been haunting my consulting conversations for months. Every week, I talk to brilliant professionals who are either paralyzed by AI possibilities or rushing toward expensive implementations that are destined to fail. The gap between "AI sounds cool" and "this will actually work for my firm" has become a canyon. So I spent an hour building and testing the AI Use Case Evaluator with help from ChatGPT. It walks you through the real questions that matter before you spend a dime on AI automation. Here's what the framework actually evaluates: - Business & Strategic Fit: Does solving this problem directly advance a measurable goal? - Problem Characteristics: Is this high-volume, repetitive work with acceptable error tolerance? - Data Readiness: This is the number one project killer. Do you actually own sufficient, clean, correctly labeled data? Are your data pipelines reliable and secure? - Technical Feasibility: Can you build a minimal viable model in 90 days or less? - Integration & Workflow Fit: Where does the AI output actually land in your daily work? - Human Factors & Change Management: Who gains or loses tasks, and how do they feel about it? - Risk, Compliance & Ethics: From GDPR to bias detection to security vulnerabilities, the hidden compliance costs can dwarf your automation savings. Are you accidentally baking protected attributes into your model? What happens when it fails? - Economics & ROI: Total cost of ownership includes data acquisition, infrastructure, licenses, ongoing monitoring, retraining, and staff time. Could simpler automation deliver 80% of the benefit at 20% of the cost? - Maintainability & Lifecycle: AI isn't "set and forget." How often will your data drift? Do you have budget for continuous monitoring and retraining? What's the shelf life before major re-architecture? - Availability of Non-AI Alternatives: This is my favorite reality check. Could a simple rule, process change, or staff training solve 70% of your problem faster and cheaper? Always benchmark AI against "good enough" simplicity. The AI Use Case Evaluator includes a practical scoring system and a brutal "red flag shortlist" that can save you from disasters like trying to build AI on tiny datasets or automating workflows that aren't even digitized yet. Here's what I've learned: the best AI projects aren't the most technically impressive ones. They're the ones that solve real problems with clean data, clear workflows, and people who actually want to use them. I invite everyone to try it and let me know what you think. Feedback is always appreciated https://lnkd.in/dyspTyQb

CX leaders are in a tough spot today. On one hand, AI agents have become a necessity for fast customer response. On the other hand, there have been several instances of CX AI agents going wrong, Whether it was Air Canada’s support bot giving incorrect policy advice, Or DPD’s swearing bot. In fact, only 11% of companies say they were effective in implementing AI. Choosing the right CX AI agent is indeed a challenge. From what we’ve seen while working with CX leaders, here’s what you need to look for: Run Smart Tests: Run controlled A/B tests in real environments by deploying to 1% of customers. Measure deflection rates, response time, and CSAT scores. Also, stress test the bot with past customer data to check for hallucinations. ROI Tracking: Choose a vendor that tracks and reports ROI. They should share pre- and post-implementation benchmarks. The burden of ROI should not fall on your team. Its not about the product, but the partnership: Don’t just choose a product—choose the right company to work with. Look for partners who dive deep into your business problems and are committed to long-term AI transformation. #CX #CustomerExperience #AI

I scoped 23 AI projects over the last 6 months. Here is what drives the highest ROI: It’s all about small, repetitive tasks. Ones that are executed thousands of times. These tasks will often look boring. Something you would hire an intern to do. But if they happen thousands of times, across dozens of employees? That’s the gem. Automate just 10% of that, and you’re already saving a few full-time roles. Here are the real use cases I came across: Case 1: B2B Retail – Customer Support Problem: A 15-person team spends their day on the phone, answering the same 10–15 questions all over again. AI Solution: AI assistant for customer support. Handles the top 2 question categories (delivery + invoices) ROI: Reducing 30% of workload In Switzerland: ~ €18K savings annually per employee  Impacted: Customer support employees Case 2: Finance – Processing Subsidiary Reports Problem: Global company with >1,000 subsidiaries. Each sends monthly reports as semi-structured PDFs. Each can use their own format. Team spent ~20 mins/file: parsing, formatting and inputting into ERP. AI Solution: Document parsing + data extraction Extracted key fields: revenue, amortization, actives, passives Flags inconsistencies, sends for review ROI: In Switzerland: ~ €19K savings per employee annually  Impacted: Accounting specialists Case 3: IT Support – Internal Ticket Automation Problem: Simple, recurring IT tickets made up 40% of IT workload (password resets, access requests, laptop replacement inquiries) AI Solution: AI-powered chatbot for first-level IT support, integrated with ITSM tool Handles verification, password resets, basic troubleshooting Triggers internal workflows automatically ROI: Saved 6 minutes per ticket in IT department 73% faster resolution = happier employees In Switzerland: ~ €16K savings per employee annually  Impacted: IT support employees You don’t need to aim for a moonshot. You need small use cases with a clear structure, repeated hundreds of times. If you're looking for AI use cases that deliver real value, not just hype: start small, think boring, scale fast. Want help spotting these in your org? Let’s talk.