AI ROI Metrics for Technology Leaders

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

𝐀𝐈 𝐡𝐲𝐩𝐞 𝐢𝐬 𝐚 𝐜𝐚𝐫𝐞𝐞𝐫 𝐤𝐢𝐥𝐥𝐞𝐫 𝐟𝐨𝐫 𝐦𝐚𝐧𝐲 𝐩𝐞𝐨𝐩𝐥𝐞 Global IT spending will hit $5.6T in 2025, with GenAI spend alone leaping 76%. Your leaders loves these numbers. But they expect a return, and their patience is thin. When the results don't show, the CIO or CTOs are the first to go. If that math doesn’t line up, your seat is the one marked “cost-optimization.” Now want to keep your badge? Or even better accelerate your growth? Stop guessing and start tracking these three metrics: 1️⃣ Revenue per Headcount (RPH): Are you more efficient than your top two competitors? Report this quarterly. ↳ A rising RPH shows AI is a growth engine, not just a cost center. 2️⃣ Market Cap / Headcount (MCH): How does Wall Street value your team's productivity versus the competition? ↳ This is the ultimate accountability metric. 3️⃣ Function-Level Productivity Index (FLPI): Give every team one core metric to own (e.g., tickets solved, features shipped). ↳ A unified dashboard tells you who is performing and who needs to pivot. This isn't just a theory. I wrote an AI bestseller in AI and I've delivered 30 keynotes to executives in the last 4 months: ↳ and the feedback is overwhelming: more than 90% of them confirmed these three metrics are the absolute core of measuring real ROI from AI. ↳ The most successful leaders are already implementing this. So the question is... Are you in the game, or are you staying out of it? What is one other metric you track to prove tech's value? 👇 #AI #AIROI #Leadership #Career #TheInsider

Vendors say, “AI coding tools are writing 50% of Google’s code.” I say, “Autocomplete or IntelliSense was writing about 25% of Google’s code, and AI made it twice as effective.” When it comes to measuring AI’s ROI, real-world benchmarks are critical. Always compare the current state to the future state to calculate value instead of just looking at the future state. Most companies are overjoyed to see that AI coding tools write 30% of their code, but when they realize that vanilla IDEs with basic autocomplete could do 25%, the ROI looks less impressive. 5% rarely justifies the increased licensing and token costs. That’s the reality I have found with about half of the AI tools I pilot with clients. They work, but the improvement over the current state isn’t worth their price. I have used the same method to measure ROI for almost a decade. 1️⃣ Benchmark the current process performance using value outcomes. 2️⃣ Propose a change to the current process that introduces technology/new technology into the workflow. 3️⃣ Quantify the expected change in outcomes and value delivered with the new process/workflow. 4️⃣ Make the update and measure actual outcomes. If there’s a difference between expected vs. actual, find the root cause and fix it if possible. Measuring AI ROI is simple with the right framework. It’s also easier to help business leaders make better decisions about technology purchases, customer-facing features, and internal productivity initiative selection. I would rather see a benchmark like, percentage of code generated from text prompts vs. the percentage of code recommended by autocomplete. That benchmarks the reengineered process against the old one. AI process reengineering (AI tools augmenting people performing an optimized workflow) is where I see the greatest ROI. Shoehorning AI tools into the current process typically delivers a fraction of the potential ROI.

“𝐑𝐚𝐬𝐡𝐢𝐦, if I can’t show ROI, how do I justify investment in AI?” AI ROI isn’t about long-range fantasy. It’s about operational wins today that compound over time. 🔹 1. 𝐐𝐮𝐚𝐧𝐭𝐢𝐟𝐲 𝐭𝐡𝐞 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐝𝐢𝐯𝐢𝐝𝐞𝐧𝐝. Measure cost-per-task reduction, team velocity improvements, and SLA acceleration. Time is money-track both. 🔹 2. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐀𝐈 𝐭𝐨 𝐫𝐞𝐯𝐞𝐧𝐮𝐞 𝐝𝐫𝐢𝐯𝐞𝐫𝐬. Are sellers reaching customers faster? Is marketing personalizing faster? Is CS pre-empting churn? Align usage to business KPIs. 🔹 3. 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐭𝐡𝐚𝐭 𝐞𝐯𝐨𝐥𝐯𝐞𝐬. Start with time savings. Expand to cost savings. Mature into revenue uplift. Create an ROI path that scales. 💡 AI ROI isn’t a one-time report. It’s a continuous improvement curve. 👇 What ROI signals are you tracking from your AI efforts? #BytesfromRashim #AI #AIADOPTION