Evaluating Workflows for Efficiency

HR doesn’t need more dashboards. It needs better listening. Most people teams measure what’s easy…like engagement scores or turnover. But the best teams? They build feedback loops that help them predict problems, not just react to them. This post gives you 11 of the most useful, often-overlooked loops you can implement across the employee lifecycle: 🟢 Week 2 new hire check-ins (capture early impressions) 🟠 Post-interview surveys (from both sides) 🔵 Onboarding reviews (day 90 is your goldmine) 🟡 Skip-level 1:1s (cross-level truth-telling) 🟣 Quarterly team health check-ins (lightweight, manager-led) …and 7 more. 📌 Save this if: • You’re building a modern HR function • You want fewer “We should’ve seen this coming” moments • You believe listening is strategy Which feedback loop is missing in your company?

Task productivity is not organisational productivity. AI can reduce the time needed to draft a report from 40 minutes to 20. That is a genuine task-level efficiency gain. But it becomes an organisational productivity gain only if the saving survives the rest of the process: checking, approval, decision-making and implementation. The distinction matters because task-level results can look impressive. A study of 5,172 customer-service workers found that AI support increased the number of issues resolved per hour by 15%. That measure captured completed work, not merely faster drafting. The wider evidence is more cautious. An OECD review found that better performance on individual tasks does not automatically produce better results across a firm. Processes, skills and responsibilities often need to change as well. A useful test is to measure the whole workflow: Did the end-to-end completion time fall? Did quality improve or rework decline? Did checking and correcting take less time? Was the released capacity used for something valuable? Did the bottleneck disappear, or merely move elsewhere? A team can produce documents faster and still wait days for approval. Developers can generate code faster while creating more review work. Managers can receive twice as many summaries without making better decisions. This is bottleneck migration: one stage accelerates, but the constraint reappears further along the process. A practical rule is to claim productivity only when throughput, quality, cost or decision speed improves without another important measure getting worse. Faster tasks are useful. Organisational productivity begins when the system produces a better result with fewer resources.

Much of the discussion around AI focuses on adoption rates, model capabilities, or productivity gains. The more consequential shift may be how organizations redesign work around it.     Most organizations began their AI journey by applying new tools to existing tasks. Drafting content, conducting research, analyzing information, and automating routine tasks are often among the first use cases. Those applications are valuable, but they represent only the beginning.      The larger opportunity emerges when organizations start redesigning workflows around AI rather than simply inserting AI into existing processes. That affects more than efficiency. It changes how decisions are made, how expertise is distributed, how teams collaborate, and how value is created across the organization.     Organizations that treat AI as a workflow transformation capability rather than a productivity tool are likely to realize far greater long-term value. 

Six months ago, a client almost pulled the plug on an AI implementation we were running. Three weeks in. Leadership was aligned. The use case was clear. The tools were live. And yet adoption had started to stall. Usage dropped. Teams quietly slipped back into old workflows. Moments like this define whether an AI project succeeds or dies. At ALTRD, our instinct isn’t to defend the system we built. Our instinct is to investigate the system we missed. So we paused the rollout and audited what was actually happening inside the workflow. What we found was instructive. The training had landed well. But the implementation had been designed around how leadership thought the team worked. Not how they actually worked. Two things were quietly breaking adoption. First, we had optimized the visible workflow but missed an invisible step. There was a key handoff happening informally between two people over WhatsApp. It wasn’t documented anywhere. It never showed up in process charts. But it was where the real decision-making happened. Our redesigned workflow skipped that moment completely. Second, there was a quiet skeptic in the system. The team lead everyone naturally looked to before trying something new had concerns she hadn’t voiced in any meeting. Not because she was resistant, but because she wasn’t convinced the workflow would hold up under real pressure. Once the team sensed that hesitation, adoption slowed down. So we fixed the system. We remapped the actual workflow, not the documented one. Then we worked directly with the team lead. Not to sell the tool, but to understand the operational concerns and redesign parts of the system around them. The engagement expanded. And that project ended up becoming one of the most valuable learning moments for how we implement AI today. Two lessons we now carry into every engagement at ALTRD: Document the informal workflow, not just the official one. And find the quiet skeptic in the room early. They’re rarely the blocker. They’re usually the signal that something important hasn’t been designed properly yet. AI implementation isn’t just a technical system. It’s a human system. And if you want adoption to stick, you have to understand both.

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

Once, we built a machine learning model that was expected to drive a 15% lift in conversions. The result? A shocking 0.01%. What went wrong? The model worked perfectly, but the business process behind it was too long and complex. By the time the offer reached the clients, most leads were lost. And the kicker? The business case was literally giving money to the clients! This experience taught us a crucial lesson: even the best machine learning model can fail without an aligned, efficient business process. The model had identified high-value leads, but the operational workflow to turn those leads into conversions was cumbersome and slow. It involved multiple handoffs, redundant steps, and delays that made it nearly impossible for the offer to reach the client in time. In this case, the problem wasn’t technical—it was systemic. The gap between predictive insights and actionable outcomes created friction that nullified the model's value. When we revisited the process, we streamlined the journey from the model’s output to client interaction. By reducing the time and steps involved, we saw significant improvements—not just in conversion rates but also in the trust clients placed in the business. This is why aligning AI models with business operations is just as critical as building accurate models. Are your machine learning projects driving real business impact, or are they stuck in the pipeline? Let’s discuss strategies to close the gap and unlock the full potential of your AI investments. Share your thoughts or experiences below!

🛑 Show Your Work #5: Take the Order. Own the Problem. 🛑 A few weeks ago, we got an ask: “Teach project management skills.” We said yes. And then we did what L&D doesn’t always do. We paused and asked: What problem are we actually trying to solve? So we ran the analysis. * Interviews across the practice. * Surveys with auditors. * Workflow and system reviews. * Looking at how the work really happens. Here’s what we uncovered: 🔥 Disconnected workflows across budgeting, resourcing, and execution 🔥 Systems built for scheduling, not real project management 🔥 Managers buried in admin instead of managing engagements 🔥 A reactive environment by design 🔥 Performance measures reinforcing utilization over proactive management 🔥 Capability gaps… driven by unclear expectations and lack of structural support So yes… there’s a skill component. But that’s not the story. Here’s the harder truth: We don’t own 5 of the 6 areas we uncovered. Different functions. Different systems. Different leaders. Which gives us a choice. Stay in our lane… and build training. Or step into the problem… and help solve it. We chose the second. Because this is what it means to move beyond being an order taker. Not rejecting the ask. Not overstepping. But taking ownership of the outcome. So now the work looks different. ❤️ Influencing workflow redesign ❤️ Advocating for better tools and visibility ❤️ Pushing on performance alignment ❤️ Clarifying expectations earlier in the pipeline ❤️ And yes… building capability where it actually matters We don’t control all of it. But we’re not waiting either. That’s the shift. From delivering programs to solving problems that matter. Great work Chelsea McCormick, CPA, CA, MBA and Coleen Wafer!!! 👉 Where are you staying in your lane when you should be stepping into the problem? 👉 What would change if you took ownership… even without control? Show your work.

Hyperautomation has emerged as a game-changer in the technological landscape, changing how businesses streamline operations, reduce costs, and enhance efficiency. By combining AI, ML, and robotic process automation (RPA), it transformed industries. Gone are the days when automation was limited to assembly lines or customer service bots. Hyperautomation transforms everything — from crunching financial data to streamlining inventory management — into a unified, efficient digital ecosystem. For instance: ▶️ In warehouses, IoT devices monitor inventory and trigger restocking before shelves go empty ▶️ Financial tools like RPA bots process invoices while AI forecasts cash flow trends ▶️ ML algorithms pinpoint supply chain inefficiencies and suggest actionable fixes The result? A seamless, real-time operational flow that saves time, money, and resources. Gartner projects that by 2026, 30% of enterprises will automate more than half of their network activities- up from under 10% in 2023. In finance, AI algorithms detect fraudulent transactions faster than human analysts, while RPA tools manage expenses and generate reports in seconds. Customer service chatbots powered by natural language processing (NLP) handle routine queries, leaving human agents free to focus on high-stakes issues. In manufacturing, predictive maintenance minimizes costly machine downtime by identifying potential issues before they arise. AI-powered quality control systems catch product defects that human eyes might miss, while workflow automation optimizes resource allocation. In the ever-complex supply chain, hyperautomation ensures real-time responsiveness. AI systems analyze traffic and weather to optimize delivery routes, while IoT devices keep stock levels in check. The result? Faster deliveries, fewer errors, and significant cost savings. While the potential of hyperautomation is undeniable, it raises questions about its impact on human labor. Repetitive, low-skill jobs are at the highest risk of being replaced. But, this shift also opens doors for workers to upskill to manage and optimize these systems, focusing on creative and strategic tasks instead of mundane ones. The narrative shouldn’t be “man versus machine” but “man with machine.”  Valued at $45 billion in 2024, the hyperautomation market is projected to exceed $307 billion by 2037. Its future lies in driving sustainability, enabling hyper-personalized experiences, and achieving seamless end-to-end automation. As businesses continue to embrace this technology, it’s vital to maintain a human-centric approach: prioritizing ethical considerations, data privacy, and workforce training. The real question is: How will we harness its potential? #technology #AI #automation #innovation #business

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

The gap between a project estimate and kick-off can be a killer. (Automation Tip Tuesday 👇) For service-based businesses (any business, really!), friction is the ultimate profit killer. A client agrees to the scope, but then… paperwork, approvals, deposits — it all creates delay and destroys momentum. One of our recent automation projects tackled this head-on. Our client, a high-end home remodeling firm, was using a host of tools to manage their workflows, but the process of moving from an estimate to a signed agreement (with a deposit) was still manual and disjointed. We streamlined it. Now: ✅ Estimates auto-generate in Airtable, pulling project details from a structured pricing database. ✅ Signed agreements trigger deposits automatically — Dubsado sends the contract, collects e-signatures, and instantly generates an invoice in QBO. ✅ Once the deposit is paid, the project kicks off in Google Calendar and updates the team’s task board. The result? Faster approvals, fewer dropped leads, and a smoother experience for homeowners eager to begin their renovations. Software should work for you, not slow you down. If your business has gaps in its process, automation might be the missing piece. What’s killing your momentum? -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday #automation #workflow #efficiency