Assessing the Impact of Workflow Changes on Productivity

Here's what I'm seeing everywhere: AI is making teams faster, but are we making them stronger? AI is making us more productive, but are we becoming more capable? We might be able to do more, but is the ‘more’ translating to ‘more valuable’? And traditional metrics can't tell the difference. That’s why after having done over 200+ deployments, and observing the same issues, combined with recent stats that 88% of all AI projects stall/cancel/pause at POC, and MIT recently stated only 5% of GenAI projects succeed, I invented The Human Amplification Index™ (© 2025 Sol Rashidi. All rights reserved.). We need a way to measure whether AI is making our business and people more valuable or just making them busier. Here's what the product tracks: 𝟭. 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡™ 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝗱 𝗮𝗳𝘁𝗲𝗿 𝗔𝗜 (© 2025 Sol Rashidi. All rights reserved.). It tells you how much of your team's time is spent on what they were actually hired to do? Most teams I assess are operating at 40-60% of their intended function. The rest? Emergency fixes, escalations, triaging, broken process workarounds, administrative busy work that has nothing to do with their core expertise. Before you implement AI, measure this baseline. Then track how AI shifts this equation. 𝟮. 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗙𝗟𝗢𝗪™ 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝗱 𝗮𝗳𝘁𝗲𝗿 𝗔𝗜 (© 2025 Sol Rashidi. All rights reserved.) This isn't about speed, it's about friction. - How many hoops do your people jump through to complete basic tasks? - How many disconnected tools do they toggle between? - How much manual work exists because systems don't talk to each other? AI should remove friction, not just accelerate it. So build a baseline and measure how it improves with AI 𝟯. 𝗪𝗲 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗙𝗢𝗥𝗖𝗘™ 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝗱 𝗮𝗳𝘁𝗲𝗿 𝗔𝗜 (© 2025 Sol Rashidi. All rights reserved.). When you hired each person, you saw the unique value they could bring. How much of that potential are you actually accessing? If AI is handling routine tasks but your people are still stuck in the weeds instead of contributing their highest-value thinking, you've got an amplification problem. The companies that figure this out will separate themselves dramatically from those that don't. While most leaders are asking "Are we more efficient?" The better question is: "Are our people able to contribute more of their unique human value because AI is handling everything else?" When you measure work function strength, workflow efficiency, and workforce amplification, you're measuring your true capacity for sustainable growth. That's the difference between using AI as a tool and using AI to amplify human potential. What's your experience? Are your teams becoming more capable, or just busier?

Yet another reason estimates are ridiculous. One of the silliest things about time estimates is that the vast majority of time it takes for a team to finish something is spent waiting. For the average development team to create something of value, only 10-20% of the total start-to-finish completion time is spent actively working on the item. The majority of the time is spent waiting. 🔵 Waiting for Reviews 🔵 Waiting for team member hand-offs 🔵 Waiting on other teams or departments So much time is spent waiting… instead of asking, “How much time will it take WORKING to complete this?” You’d be better off asking, “How much time will it take WAITING to complete this?” This, of course, is impossible to answer since most teams have zero control (or even awareness) of waiting time. You’re far, far better off ditching time estimates entirely and focusing on reducing wait states instead. But how? 1] Use Flow Efficiency ↳ Few teams are even aware of the most critical flow metric: Flow Efficiency. ↳ Flow Efficiency tells you how much time is spent actively working on increments of value (features, assets, stories, etc.). ↳ Flow Efficiency (%) = Active Time / Total Time X 100 ↳ Any good workflow tool will calculate your Total Time (Cycle Time). 2] Determine Active Time ↳ To figure out Active Time, you need to track your wait states by adding a “Done” state to every existing stage in your workflow. ↳ For Example: Development -> Development Done -> Testing -> Testing Done -> Review -> Review Done -> Released ↳ The “Done” columns are your wait states.  ↳ Now, you can effectively determine Active Time for each item in your flow vs. Wait Time. 3] Improve Flow Efficiency ↳ Once you can visualize and track wait times, you can focus on fixing the worst offenders. ↳ Add team members, reduce work in progress, remove dependencies… there are many ways to minimize wait states. ↳ Any reduction made to any of your wait states will improve Flow Efficiency An average team will have a Flow Efficiency of 20%. Your team should achieve a Flow Efficiency of 40% or greater to be considered high-performing. Will this take some effort? Of course! But far less effort and total team time (and annoyance) than asking for estimates. Plus, the increase in productivity will far outweigh any loss in imagined predictability.

"You can’t manage what you don’t measure." Yet, when it comes to change management, most leaders focus on what was implemented rather than what actually changed. Early in my career, I rolled out a company-wide process improvement initiative. On paper, everything looked great - we met deadlines, trained employees, and ticked every box. But six months later, nothing had actually changed. The old ways crept back, employees reverted to previous habits, and leadership questioned why results didn’t match expectations. The problem? We measured completion, not adoption. 𝗖𝗼𝗻𝗰𝗲𝗿𝗻: Many organizations struggle to gauge whether change efforts truly make an impact because they rely on surface-level indicators: → Completion rates instead of adoption rates → Project timelines instead of performance improvements → Implementation checklists instead of employee sentiment This approach creates a dangerous illusion of progress while real behaviors remain unchanged. 𝗖𝗮𝘂𝘀𝗲: Why does this happen? Because leaders focus on execution instead of outcomes. Common pitfalls include: → Lack of accountability – No one tracks whether new processes are being followed. → Insufficient feedback loops – Employees don’t have a voice in measuring what works. → Over-reliance on compliance – Just because something is mandatory doesn’t mean it’s effective. If we want real, measurable change, we need to rethink what success looks like. 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗺𝗲𝗮𝘀𝘂𝗿𝗲: The solution? Focus on three key change management success metrics: → 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 – How many employees are actively using the new system or process? → 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 – How has efficiency, quality, or productivity changed? → 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 – Do employees feel the change has made their work easier or harder? By shifting from "Did we implement the change?" to "Is the change delivering results?", we turn short-term projects into long-term transformation. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Organizations that measure change effectively see: → Higher engagement – Employees feel heard, leading to stronger buy-in. → Stronger accountability – Leaders track impact, not just completion. → Sustained improvement – Change becomes embedded in the culture, not just a temporary initiative. "Change isn’t a box to check—it’s a shift to sustain. Measure adoption, not just action, and you’ll see the impact last." How does your organization measure the success of change initiatives? If you’ve used adoption rate, performance impact, or user satisfaction, which one made the biggest difference for you? Wishing you a productive, insightful, and rewarding Tuesday! Chris Clevenger #ChangeManagement #Leadership #ContinuousImprovement #Innovation #Accountability