Conducting Productivity Audits

Hospitals don’t buy impact. HealthTech founders learn this too late. Every healthcare founder eventually makes the same claim. We save lives. It sounds unassailable doesn't it? The ultimate value proposition. Yet in practice, this framing rarely converts into adoption. Clinicians, procurement teams, and health systems make decisions based on operational utility, not moral gravity. Many innovations fail because they overestimate how impact is perceived. In theory, clinicians prioritise patient outcomes. In reality, they operate in environments shaped by throughput, staffing constraints, and regulatory pressure. When evaluating technology, they ask. Will this fit into my workflow? Will it reduce documentation time? Will it integrate with the EHR? A device that improves workflow efficiency by 20 percent can have more adoption potential than one that marginally improves survival rates but adds complexity. Functional value translates directly to reduced fatigue, higher patient throughput, and measurable ROI. When founders lead with “saving lives,” they position themselves in an ethical rather than operational frame. It feels noble but vague. Hospitals cannot quantify moral claims. Clinicians are sceptical of moral claims because every vendor makes them. Functional gains, by contrast, show that the founder understands the clinical environment. They suggest empathy not through virtue but through precision. Three facts underline the point: A significant majority of clinicians identify workflow disruption as a primary barrier to adopting new healthcare technologies, according to Deloitte (2023). Time savings and improvements in operational efficiency tend to correlate more strongly with technology adoption than purely clinical benefits, as reported by NEJM Catalyst (2022). HealthTech products that emphasize operational metrics are notably more likely to secure pilot funding, based on insights from Rock Health (2024). Moral impact earns admiration. Operational impact earns adoption. Founders in healthcare should design, measure, and communicate their value in functional terms first. Saving lives may be the outcome, but it is rarely the proposition that moves the market.

The more you sweat in agentic evaluations, the less you bleed in deployments. There's an emerging superpower in enterprise AI and it's not a better model. It's better evaluation and governance. The smartest teams are figuring out that evals aren't the speed bump before deployment; they're the launchpad. Think of agentic evaluations like a flight simulator for your AI workflows. We're watching enterprises rush AI agents into production and then spend months firefighting. Unexpected outputs in live workflows. Edge cases that were obvious in hindsight. Rollbacks that quietly embarrass entire programs. The pattern is painfully predictable: skip the hard work of evaluation, and your deployment becomes the evaluation. Except now the stakes are real. The challenge with agentic workflows isn't just accuracy but it's compounding failure. A single wrong step early in a multi-step process doesn't stay contained. It cascades. With complexity of tasks downstream, this planning becomes even more critical.  And unlike a chatbot response you can shrug off, a broken workflow touches real business processes, real data, and real people. Getting evals right means defining what "right" looks like across the full task lifecycle, not just the output, but the reasoning path, the tool calls, the handoffs. That rigor is where most teams underinvest. For LLMs, it’s a debugger to define what can be changed and at what level. The enterprises that will win at AI aren't the ones who deploy the fastest. They're the ones who built the discipline to evaluate deeply before they deployed broadly. Sweat in the lab so you don't bleed in production. Your future self and your users will thank you. #ExperienceFromTheField #WrittenByHuman

Tired of AI projects that don't deliver? Try this human-centred approach. From my research over the past couple of years, I’ve noticed a recurring pattern. We often treat AI as a technology experiment rather than an upgrade to how people actually work. That mindset can quietly limit a project’s success. To support better decisions, I’ve developed a human-centred AI readiness checklist based on that research. I hope it’s useful for your next initiative. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗻𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗥𝗜𝗦𝗣-𝗗𝗠 𝗺𝗶𝗻𝗱𝘀𝗲𝘁) →Are we clear on the operational outcome and metric we are improving? ↳If we cannot say “this reduces X by Y%”, we are chasing tools, not performance. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗖𝗵𝗲𝗰𝗸 (𝗟𝗲𝗮𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Which real human decisions are we supporting? ↳AI should strengthen judgment points like prioritisation or scheduling, not automate activity without purpose. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗟𝗲𝗮𝗻 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲) → Is the workflow stable enough to augment? ↳Automating instability scales, defects and frustrates the people doing the work. 𝗩𝗮𝗹𝘂𝗲 𝘃𝘀 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗖𝗵𝗲𝗰𝗸 (𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Does the benefit outweigh frontline disruption? ↳Operational AI should improve flow, not create friction for teams. 𝗗𝗮𝘁𝗮 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗥𝗜𝗦𝗣-𝗗𝗠 𝗱𝗮𝘁𝗮 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴) →Does our data reflect lived operational reality? ↳Human trust collapses when AI runs on distorted inputs. 𝗛𝘂𝗺𝗮𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗖𝗵𝗲𝗰𝗸 (𝗛𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗲𝗿𝗲𝗱 𝗔𝗜 𝗱𝗲𝘀𝗶𝗴𝗻) →Where does AI advise, where do humans review, and where does automation act? ↳Clear boundaries protect autonomy and accountability. 𝗥𝗶𝘀𝗸 𝗮𝗻𝗱 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗰𝗸 (𝗡𝗜𝗦𝗧 𝗔𝗜 𝗿𝗶𝘀𝗸 𝗺𝗼𝗱𝗲𝗹) →Have we planned for failure, overrides, and fallback workflows? ↳Operations must remain safe and continuous when systems misfire. 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗵𝗲𝗰𝗸 (𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗰𝗹𝗮𝗿𝗶𝘁𝘆) →Who owns outcomes, model behaviour, and data quality? ↳Human accountability must remain visible after launch. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Will this support how people actually work? ↳Tools that slow teams are quietly abandoned. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗧𝗿𝘂𝘀𝘁 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗵𝗮𝗻𝗴𝗲 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲) →Are we designing for understanding, transparency, and behavioural adoption? ↳Trust grows when teams see AI improving their work, not replacing it. AI is an amplifier. It scales what we already have: good or bad ↳𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐢𝐧. 𝐀𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐠𝐚𝐫𝐛𝐚𝐠𝐞 𝐨𝐮𝐭.⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣The strongest AI initiatives aren’t just technology deployments. They are human-centred operating upgrades that happen to use AI. ♻️ Share if you found this useful. #AIinBusiness #HumanCenteredAI #Operations #Leadership #AIStrategy

Following my last post on technology adoption and value realisation, a few people asked: So how do you actually build capability maturity on site?   From my experience supporting technology rollouts across surface and underground mining operations in Africa, capability maturity does not improve through training alone. It improves when technology becomes part of how the operation plans work, executes work, and reviews performance. Guidance from the Global Mining Guidelines Group (GMG) and the World Economic Forum’s Mining & Metals Digital Transformation Initiative both highlight that performance gains from digital systems are only sustained when they are embedded into daily operational routines ; not deployed as stand-alone tools.   In practice, this means: Planning routines must use system outputs: Short-interval plans and shift targets should be informed by haul cycle times, queue data, and payload variance from optimization platforms. (GMG, Data Integration and Interoperability in Mining, 2020) Supervisory routines must reinforce system decisions: Shift handovers and production meetings should review performance using system-generated KPIs. (McKinsey & Company, How digital innovation can improve mining productivity, 2015) Execution must follow optimization logic: Dispatch and operators must make decisions within system logic rather than reverting to manual allocation or experience-based judgement. (WEF, Digital Transformation Initiative: Mining & Metals, 2017) Where these routines are absent, technology often automates existing inefficiencies. Capability maturity improves when leadership routines, planning workflows, and frontline execution are aligned with the system, turning deployment into sustained performance. Adoption is not achieved at commissioning. It is achieved when the operating model changes. #MiningTechnology #OperationalExcellence #DigitalTransformation #MineIQ

Constant workflow evaluation is crucial to meet business demands. A recent leadership training reshaped my approach, stressing the importance of questioning norms and assessing if traditions still add value. One story that perfectly captures the essence of this training is the parable of the soldier’s barracks and the parade slab. Imagine a military base decades ago where soldiers laid a concrete slab to hold parades. However, before the cement dried, animals would often trample on it, creating an unsightly mess. So, a soldier was assigned to guard the slab at night, preventing any intrusion until it dried completely. But over the years, this nighttime guarding became a routine task, regardless of necessity or even the slab’s condition. The soldiers rotated nightly shifts to guard this parade slab—an unexamined duty passed down through generations. One day, a recruit questioned the reason behind guarding this slab. Strangely, nobody knew why they were guarding it, nor could they remember when the slab was last poured. The original purpose had long since faded, leaving only an empty ritual that served no purpose, other than occupying valuable time and resources. This example resonated with me deeply. How often do we continue tasks and workflows because “that’s just how it’s always been done”? Just like the soldiers in the barracks, we may be blindly guarding proverbial slabs that have long outlived their relevance. In our quest to become more productive and cost-effective, these "slabs" need to be identified and eliminated. The training encouraged steps to dismantle workflows and streamline processes: Map Out the Process Chart each action and person involved to expose redundancies and tasks done out of habit, not purpose. Define Purpose for Each Step Ask, “What’s the intended outcome?” Many tasks are formalities with no impact. Engage Team Members Team feedback reveals inefficiencies leaders may overlook. Front-line employees often see issues we don’t as most leaders. Use a “What if” Mindset Boldly ask, “What if we didn’t do this at all?” Challenge task necessity. Implement and Track Testing changes and measuring outcomes ensures productivity gains are tangible. The results: reduced non-value tasks and measurable cost savings. Outdated workflows can waste up to 20% of productive time. Morale also suffers when employees perform pointless tasks. A lasting lesson was that productivity comes from fostering a culture of inquiry. Leaders aren’t just problem solvers; they’re problem finders, willing to challenge even the most accepted routines. Tradition can be comforting, but in business, clinging to unnecessary tasks is an expense we can’t afford. This experience taught me to always ask, “Why are we doing this?” If the answer doesn’t align with our goals, it’s time to break the mold and let go of practices that don’t serve us and the business. By embracing inquiry and challenging norms, we build agile and resilient organizations

I ask every OpEx professional I meet the same question: "What's your current capacity and where's your bottleneck?" About 70% can't answer. I assure you it's a serious problem when I get different answers from people in the same company: planning says one thing, production another, and maintenance something else. They talk about utilization rates or efficiency percentages or "running at capacity." But they can't tell me actual pieces per shift at each step. This tells me they're managing by feel, not data. Here's what strong OpEx leaders have ready: 1) A Process Capacity Sheet: Every process step listed with time breakdowns and capacity calculations. Head forming: 45.5 seconds total time = 633 pieces/shift Threads: 21.2 seconds total time = 1,358 pieces/shift Deburring: 30.0 seconds total time = 960 pieces/shift Deburring is the bottleneck at 960 pieces. That's where you focus. 2) A Standardized Operation Combination Table: Visual timeline of work elements and their duration. Shows the sequence and timing of every task. Displays where work overlaps and where gaps exist. Helps you redesign work flow based on actual timing, not assumptions. 3) An Operation Analysis Sheet: Physical diagram of equipment, material flow, and operator movement. Shows how far parts travel and where motion happens. Makes waste visible. Rearranging two workstations based on this diagram cut one client's cycle time by 14%. Why this matters: You can't improve what you can't measure. And you can't measure what you haven't documented. These three documents transform opinions into facts. They answer executive questions with numbers instead of estimates. They separate OpEx professionals who talk about improvement from those who deliver it. Build these for your top three processes this month. Update them when processes change. Then watch how differently people respond when you can answer capacity questions with data. 📌 In my Newsletter, I share the OpEx leadership playbooks I wish someone gave me in my 30's, the exact frameworks that get your initiatives funded, your results noticed, and your career accelerated. 👉 To Subscribe: Click "𝗩𝗶𝗲𝘄 𝗺𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿" just above this post. and Join 12,500+ OpEx leaders receiving it weekly. Yours, Mohammad Elshahat

A workflow at 90% accuracy handling 1,000 requests produces 100 exceptions. That same workflow at 95% accuracy handling 10,000 requests produces 500 exceptions. Accuracy went up by 5 percentage points. Exception volume went up by 5x. This is a bottleneck most operations teams do not plan for when a workflow moves from pilot to production volume. Teams can usually report seats, prompt counts, or active users. Fewer can show exception volume, median resolution time, or cost per case for a live workflow. In claims intake or invoice processing, that usually means the team cannot show how many cases required human review yesterday or how long those cases sat before resolution. When production stalls, the usual assumption is that the AI is the problem. More often, the problem is that exception handling was never scoped for production conditions. A pilot with curated inputs and extra attention can absorb exceptions through effort. A production workflow with variable inputs and volume cannot. Exception flow is not an edge case to handle after the workflow ships. It is the architecture the operation should be designed around from the start. #AIOperations #ExceptionManagement #AIWorkflows #OperatingModel #BusinessProcessOperations

Process improvement efforts often fail because of this simple reason: the organization changes the process but keeps the same definition of success. When a workflow is redesigned, it is usually because the previous approach could no longer support the level of scale or control the organization needed. Yet many teams continue evaluating performance through the same legacy KPIs that were designed for the old process. That creates confusion quickly. The team is working in a new way, but success is still being measured using assumptions from the prior system. When I see this happen, I encourage leaders to revisit three principles. 𝐀𝐥𝐢𝐠𝐧 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐩𝐫𝐨𝐜𝐞𝐬𝐬: Every KPI should reflect a specific operating model. As well, when the process changes, the measurement framework should change with it, otherwise the team may optimize for outcomes that no longer matter. 𝐄𝐱𝐩𝐞𝐜𝐭 𝐭𝐞𝐦𝐩𝐨𝐫𝐚𝐫𝐲 𝐝𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧: New workflows often affect historical benchmarks in the short term. That means a KPI moving the wrong direction does not automatically mean the process failed. The underlying causes of performance should be examined before drawing conclusions. 𝐃𝐞𝐟𝐢𝐧𝐞 𝐰𝐡𝐚𝐭 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐥𝐨𝐨𝐤𝐬 𝐥𝐢𝐤𝐞 𝐧𝐨𝐰: If the organization outgrew the old process, it likely outgrew the old goals as well. Leaders should be explicit about which outcomes now matter most When we acknowledge that the old way of operating no longer works, we also have to acknowledge that the old definition of performance may no longer apply.

Last week at the𝐘𝐚𝐫𝐝𝐢 𝐀𝐟𝐟𝐨𝐫𝐝𝐚𝐛𝐥𝐞 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐢𝐧 𝐁𝐨𝐬𝐭𝐨𝐧, I saw a version of compliance that most organizations are still missing. Not more reports. Not more trackers. Not more follow-up emails. Better workflow. Most affordable housing teams are not struggling because they lack effort. They are struggling because the process is designed around friction instead of execution. Recertifications fall behind for predictable reasons: Residents can only complete tasks during limited windows Staff have to chase documents across mismatched schedules Communication depends on manual follow-up 𝐒𝐨 𝐰𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐬? More trackers. More emails. More coordination. More activity. Not more reliability. What stood out with Compliance Manager 8 is not just the dashboard. It is the shift in how the work actually moves. 𝐕𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐭𝐢𝐞𝐝 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐭𝐨 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: What is overdue What is in progress What has not started What is coming next In real time. Inside the system. But the bigger change is around it: Notices automated, on time, with correct legal language Residents completing recerts on their own schedule Communication, documentation, and execution all in one workflow That removes the biggest constraint most teams face: Coordination. Instead of chasing documents, teams are monitoring progress. Instead of managing the process, they are executing it. That is where capacity comes back. 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐦𝐨𝐬𝐭 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐭𝐨𝐝𝐚𝐲 𝐬𝐭𝐢𝐥𝐥 𝐫𝐞𝐥𝐲 𝐨𝐧: Side trackers Manual follow-ups Inconsistent notice delivery Staff-heavy coordination Which is exactly where risk builds. When the workflow lives inside the system: The data reflects reality The process becomes repeatable Visibility becomes reliable That is the difference between thinking you are on track and actually being on track. This is what operators should be pushing for. Not more features. Not more oversight. Alignment between the system and the work. Because when compliance, communication, and execution all live in the same workflow, everything gets simpler. And in this business, simpler is what makes it reliable. #COO #MultifamilyLeadership #Yardi #Compliance #AffordableHousing #OperationalExcellence

I’m excited to introduce the updated Client Journey Workflow Template today as I kick off the next G.U.I.D.E. Cohort. The Client Journey Workflow has been the foundation of my G.U.I.D.E. Framework from day one. It gives companies a lens to see how work actually flows through their business so they can quickly spot the operational gaps slowing them down and keeping them from scaling. For clients outside of Custom Integration, this gets built from scratch and tailored to their business and their process. For Custom Integrators in the CEDIA and AVIXA world, we start with an industry standard framework. That way, teams can immediately identify gaps against a proven model instead of guessing where things are breaking. After building this for 100+ integrators and using it across multiple cohorts, it was time for an update. On the left side is the legend and the supporting documentation. These are the core SOPs and Checklists the business runs on. All listed, all hyperlinked. No hunting through Dropbox, Google Drive, or OneDrive. Click the process, go straight to it. Use them as task templates for your software for maximum efficiency. In the center is the core operations workflow. How a project moves through the company by department, by milestone, by phase. Skip a step and…you already know what happens. Follow it, and you manage exceptions instead of being managed by them. On the right side is a new place to track action items, ownership, and due dates. And here’s where it gets practical. Print this out. Put it on the wall. Hand the team Post-it notes and map every active project to its current phase and owner. In minutes, you can identify project status, what needs to happen next (the map tells you), and which process broke or was skipped to cause the issue. Is it manual? Yes. Would a whiteboard be better than paper? Yes. Would software be better than a whiteboard? Yes. But first, you need a process. The cohort kicking off today will be using this as homework after today's session. Mapping real projects, identifying status, outstanding tasks, and what’s coming next. That’s where the shift starts. For my 1-on-1 Custom Integration clients, we start with this framework and then customize it to match their actual workflow, turning it into a tool the team can actually run on. What's your process?