Evaluating the ROI of Tech Innovations

Need help justifying an AB tool switch/implementation? Use cases: • 𝗧𝗼𝗼𝗹 𝗝𝘂𝘀𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗕𝘂𝗱𝗴𝗲𝘁 𝗔𝗽𝗽𝗿𝗼𝘃𝗮𝗹 • 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 • 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗕𝘂𝘆-𝗜𝗻 • 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗳𝗼𝗿 𝗩𝗲𝗻𝗱𝗼𝗿 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗼𝗿 𝗥𝗙𝗣 Here's my template we're starting to use with clients and vendors (this one was for an edge case, don't use as a template but rather a guiding framework): 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗖𝗮𝘀𝗲 𝗕𝗿𝗶𝗲𝗳: 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗥𝗢𝗜 𝗼𝗳 𝗮𝗻 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗼𝗼𝗹 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 Implement an experimentation analysis platform integrated with the data warehouse to improve test analysis efficiency, ensure data reliability, and support scalable experimentation across teams. 𝗞𝗲𝘆 𝗥𝗢𝗜 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀: 1. 𝗧𝗶𝗺𝗲 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 & 𝗖𝗼𝘀𝘁 𝗦𝗮𝘃𝗶𝗻𝗴𝘀    𝘊𝘶𝘳𝘳𝘦𝘯𝘵 𝘪𝘯𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺: Analysts spending ~4–8 hours/week manually aggregating and formatting test data.    𝘗𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭 𝘨𝘢𝘪𝘯: Automating this process could save ~200–400 hours/year per analyst.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Value of reclaimed time × analyst cost (e.g., $60–$100/hour) = $12K–$40K per analyst/year. 2. 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 & 𝗧𝗿𝘂𝘀𝘁    𝘐𝘴𝘴𝘶𝘦: Sample Ratio Mismatch (SRM) in GA4, attribution discrepancies with current tool.    𝘐𝘮𝘱𝘳𝘰𝘷𝘦𝘮𝘦𝘯𝘵: Direct integration with the warehouse removes reliance on biased or sampled tools, fostering confidence in test outcomes.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Reduced decision risk, improved test quality, fewer invalid tests. 3. 𝗧𝗲𝘀𝘁 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆    𝘊𝘶𝘳𝘳𝘦𝘯𝘵 𝘧𝘳𝘪𝘤𝘵𝘪𝘰𝘯: Manual processes and tool limitations slow down testing cycles.    𝘉𝘦𝘯𝘦𝘧𝘪𝘵: A dedicated tool accelerates experiment cycles through auto-generated reports, and easy-to-share insights.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Increase in tests run/year × average test impact = greater cumulative business impact. 4. 𝗖𝗿𝗼𝘀𝘀-𝗧𝗲𝗮𝗺 𝗘𝗻𝗮𝗯𝗹𝗲𝗺𝗲𝗻𝘁 & 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻    𝘊𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦: Disparate methods, siloed reporting, misalignment across functions.    𝘉𝘦𝘯𝘦𝘧𝘪𝘵: Shared platform = standardized test logging, clear version control, consistent metrics, better governance.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Time saved in coordination, increased collaboration, fewer redundant or conflicting tests. 5. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗜𝗺𝗽𝗮𝗰𝘁    𝘓𝘰𝘯𝘨-𝘵𝘦𝘳𝘮: Empowers decision-making at higher fidelity, underpins a culture of experimentation, and aligns with business OKRs.    𝘙𝘖𝘐 𝘱𝘳𝘰𝘹𝘺: Higher win rate from better experiments + institutional knowledge retained via a centralized source of truth. 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀 • Conduct a pilot with 1–2 teams. • Baseline current effort, accuracy, and velocity metrics. • Define KPI targets: time saved, test throughput, SRM reduction, stakeholder satisfaction.

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

Right now, a lot of leaders are asking: what’s the ROI of our AI investments? Here’s the problem: most teams can’t answer that cleanly. Self-reported time savings can be unreliable. Nobody really knows where the “saved” time is going. And some of the most valuable uses of time—like learning to work in a new way—look unproductive in the short term. People are working differently, but the gains are harder to measure and often show up later. Yes, you’ll see early pockets of impact. But real transformation takes time, energy, and iteration. So, how should you think about ROI today? Instead of expecting neat efficiency numbers, I’d ask four questions: ▶️ Efficiency: Is AI making tasks faster, easier, or more scalable? ▶️ Productivity: Is it helping people produce more of something we already want? ▶️ Quality: Is it raising the bar on output—or making it easier to reach higher quality consistently? ▶️ Innovation: Is it helping us solve problems in a new way—or even do things that weren’t possible before? The last bucket is where the biggest, longest-term returns will come from: better decisions, new insights, new frontiers of work. If you only look for ROI in immediate time savings, you’ll miss the deeper opportunity.