Understanding ROI from AI Investments

Kat Shoa - great question - how do you measure the horizontal ROI (ex of AI in email, call transcription, etc)? This is such a smart distinction - and you're right that horizontal ROI is trickier to measure precisely because it's so distributed. Here's how I think about it but Brice Challamel, Greg Shove, Shruthi Shetty, Tony Gentilcore, Section or Tony Hoang may have more to add ... 𝗧𝗵𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗥𝗢𝗜 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 When AI touches everyone's email, transcription, or document creation, the impact gets diffused across every workflow. You can't easily isolate "the AI effect" because it becomes infrastructure - like trying to measure the ROI of electricity or internet connectivity. 𝗧𝗵𝗲 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗧𝗿𝗶𝗰𝗸 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: Create control groups. Roll out horizontal AI to Department A but not B for 90 days. Measure productivity, employee satisfaction, and output quality differences. The delta is your horizontal ROI. 𝗧𝗵𝗿𝗲𝗲-𝗟𝗮𝘆𝗲𝗿 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗧𝗶𝗺𝗲 𝗘𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀 Start with the math everyone can understand: If 500 employees save 30 minutes daily on email/transcription, that's 250 hours per day. At an average wage of $50/hour, that's $12,500 daily or $3.25M annually. Simple, defensible baseline. 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿𝘀 But the real value isn't just time saved - it's what people do with freed cognitive capacity. Track: - Meeting quality scores (when transcription handles notes, do people participate more?) - Email response rates and customer satisfaction - Cross-functional collaboration frequency 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗘𝗳𝗳𝗲𝗰𝘁𝘀 This is where horizontal AI gets interesting. When everyone has better email/transcription, the entire communication system improves. Measure: - Decision speed (time from question to action) - Information cascade velocity (how fast insights spread) - Coordination overhead reduction 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Horizontal ROI is often your biggest ROI story - but you have to measure it at the system level, not the individual level. Think platform economics, not feature economics. Would love other thoughts on above. And if needed Lanai team is happy to deep w/ folks who are working to get more data-driven on delivering measurable impact with AI tooling.

Here's a Gemini deep research prompt that helps quantify ROI for AI—especially if you're building features that save users time. We're using this at HubSpot to estimate "time saved" across our AI product suite (Agents, Copilot, and 100s of embedded features). It's already helped us calculate how much effort is being offset by tools like Breeze Content Agent or Customer Agent. This prompt will: - Analyze any AI feature - Identify its job-to-be-done - Estimate the manual time that job would take - Estimate how much of that time the AI saves - Justify the estimates with clear reasoning Our customers don't want AI that's novel—but necessary. This is a powerful way to show what they're getting when they choose Breeze. Here's the prompt: "You are a deep research model tasked with helping a product manager at [Insert company name] quantify ROI for AI features." Context: [Add context on the AI products you offer] We are building an out-of-the-box analytics product that helps customers understand their AI usage and ROI. The core ROI metric is "time saved." We define time saved as: Estimated time (in hours) that would have been spent doing the task manually × % of the task completed by the AI. Approach: We've already modeled this metric for a few AI features by combining SME interviews and LLM-based research. See examples below: ✅ Prior Examples: [Insert examples of feature job to be done manual hours % time offset] ❓Your Task:  Given a catalog of additional AI features (attached separately), please: For each feature in the catalog: 1. Identify the likely job-to-be-done (JTBD). 2. Estimate manual hours required to perform the job. 3. Estimate the % of time offset by the AI (i.e., how much of the manual effort the AI completes accurately). 4. Justify your estimates with reasoning (cite analogies or research if possible). Output format: pgsql Copy Edit Feature Name: [Insert feature name] Job To Be Done: [Insert JTBD] Estimated Manual Hours: [X hrs] % Time Offset by AI: [X%] Rationale: [2–4 sentences summarizing assumptions, proxies, or analogies used] If a feature is ambiguous or lacks clarity, make a reasonable assumption about its intended use case and state that assumption clearly in your rationale. Each Feature Name should be analyzed individually.

Riddle me this: If #AI is the leading #techtrend of 2024, why isn't it being implemented at a higher rate? So... what do you think? I'll go first: AI is THE leading trend in #business #technology, underscored by its new ranking as the third top priority for CIO Online in the #2024 "State of the CIO" survey, just behind #digitaltransformation (yay!). Despite its prominence, a CBI Economics survey reveals that only 16% of businesses have #implemented AI in their operations, with a significant 58% not adopting #AI technology at all. Here's what happens:  📢 An #executive aiming to #innovate directs their #IT team to explore AI. 💼 The IT team evaluates options and presents a business case. 📃 Contracts are executed, and the system is implemented. ⚙️ The system may function but with limited capacity. 😵💫 The outputs are nonsensical, other systems remain unintegrated, and the system fails to deliver value. 🆘 Consequently, the business incurs losses. This pattern stems from a lack of practical business use cases for AI and, honestly, is a complete misunderstanding of what #artificialintelligence actually is. To avoid this technical department, businesses must clearly attach #ROI to AI projects. This involves CIOs collaborating closely with business-side project sponsors to validate anticipated ROIs. Here's how to calculate the ROI of an #AIsystem designed to optimize #inventorymanagement in a #retail setting: Step 1: Define Costs Initial Costs: purchasing / developing #AIsoftware, hardware, and necessary integrations. How does it work with core #ERP? What is the total cost of ownership within the tricky subscription (#sass) model?  Operational Costs: #softwareupdates, system maintenance, and additional #training. Implementation Costs: integrating and deploying the system, including any custom #integrations. Step 2: Identify Benefits Reduced Inventory Cost: optimizes stock levels, minimizing overstock and understock. Increased Sales: inventory accuracy enhances product availability, reducing missed opportunities. Efficiency Gains: replaces manual inventory management Step 3: Quantify Benefits Cost Savings: reduction in inventory costs compared to previous years. Increased Revenue: increased sales due to better product availability. Labor Cost Savings: decreased time spent on inventory management Step 4: Calculate ROI The ROI calculation should not be seen as a standalone metric but as a tool to ensure the AI #investment aligns with broader #strategic objectives. This strategic vision is crucial for the successful implementation of AI in #businessoperations. #MachineLearning #DeepLearning #DataScience #Robotics #python #deeplearning #programming #tech #robotics #innovation #bigdata #computerscience #data #dataanalytics #businesst #software #automation #analytics #ml #pythonprogramming #innovation #coding #development #erpsoftware #crm #sap #erpsystem #erpsolutions #erpsolution #cloud #clouderp #saphana #dynamics #pos #hana