How to Assess the ROI of Productivity Tools

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.

Most enterprises waste millions on tech without seeing real impact. I learned this the hard way. Early in my career, I saw companies invest in cutting edge tools only to struggle with adoption, integration, and ROI. That’s when I developed a smarter, outcome-driven approach. Here’s the exact method I use to maximize ROI from technology investments:  Start with Business Outcomes, Not Features ↳ Define the measurable impact before picking the tech. What problem are you solving? What KPIs will prove success?  Ensure Alignment Across Teams ↳ IT, finance, and business leaders must be on the same page. Misalignment leads to wasted budgets and underutilized tools.  Adopt in Phases, Not All at Once ↳ Test, refine, and scale. A phased rollout prevents disruptions and maximizes adoption.  Measure, Optimize, Repeat ↳ Regularly assess ROI. What’s working? What needs adjustment? Continuous refinement drives long-term value. Tech alone doesn’t drive transformation—strategy does. How do you ensure your technology investments deliver real business impact? Let’s discuss. 👇 🔹 Follow me for more insights on digital transformation. 🔹 Connect with me to explore strategies that drive real impact. ♻️ Repost this to help your network. P.S.: Thinking about how to maximize your tech investments? Let’s chat. I’m happy to share insights on what works (and what to avoid).

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.