Understanding Workflow Gaps That Slow Down Progress

During a recent client engagement, I asked a finance director to show me the system that generated their monthly management accounts. She pointed at a spreadsheet on her desktop that had been there since 2011. It wasn’t backed up and it wasn’t documented; a complex series of tabs in a tightly integrated workbook, containing formulas that only three people in the organisation understood. We talk endlessly about legacy systems at the back-end - monolithic applications that won’t scale, or technical debt that needs paying down. But the real operational complexity often lives somewhere else entirely - the user’s machine, or a file share. It’s in the spreadsheet that reconciles data between two systems that were never designed to talk to each other. It’s in the Access database that someone built in 2015 because IT said the “proper” solution would take 18 months. It’s in the email thread that constitutes the entire audit trail for a critical approval process. These are shadow workflows. They’re invisible to the architecture diagrams, absent from the integration roadmaps, and completely unknown to the teams building your new AI platform. And they’re everywhere. This is why automation initiatives keep disappointing. Why observability projects discover gaps nobody expected. Why AI implementations stall when they hit the boundary between “system of record” and “thing that actually happens”. You can’t automate what you can’t see. You can’t integrate with a process that exists only in someone’s inbox (reliably, yet). And no amount of machine learning will help you if the training data lives in a spreadsheet that gets manually updated every Tuesday by someone who’s been doing it that way for nine years. The solution isn’t glamorous. It’s not a platform purchase or a transformation programme. It’s the slow, unglamorous work of actually understanding how your organisation operates - not how the org chart says it should, but how it actually does. So where are all your spreadsheets, and what would happen if they disappeared?

I watched a $50M hospital expansion get delayed by 8 months because of one email sitting in someone's inbox. The approval was ready. The budget was approved. The contractors were waiting. But the project manager had no visibility into where things stood. After working with 200+ organizations, I've seen the same manual workflow mistakes destroy project timelines and team morale. Here are the 5 most damaging ones: → Spreadsheet dependency for project tracking Teams lose hours updating multiple versions, and critical details slip through the cracks. One outdated cell can derail an entire milestone. → Chasing approvals through email chains Decision-makers get buried in their inboxes while projects sit idle. What should take 2 days stretches into 2 weeks. → Disconnected systems creating data silos Finance uses one tool, operations uses another, leadership gets reports from a third. Nobody has the complete picture. → Manual status reporting that's outdated before it's sent By the time you compile that weekly report, three new issues have emerged and two "green" items turned red. → Lack of structured accountability When everything is tracked informally, nothing gets tracked consistently. Problems surface too late to fix them effectively. Behind every delayed project are dedicated professionals trying to deliver value to their communities. They deserve better than being trapped in operational chaos. The solution isn't just better software. It's structured workflows that create transparency and accountability from day one. What workflow challenge is slowing down your current projects?

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.

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!

A company doesn’t stall because people are incompetent. It stalls because work is trapped inside individuals. If progress slows down when one person is unavailable, you don’t have a capacity problem. You have a system problem. Here’s the uncomfortable truth: - High performers often become bottlenecks. Not because they want control. But because they’ve never externalised their judgment. Real scale begins when you move from: Personal execution → to institutional logic. A few hard disciplines that change everything: Document judgment, not just steps. If you make the same decision twice, it needs criteria — not memory. Design decision frameworks. Teams don’t need permission for every move. They need clarity on: • What “good” looks like • Boundaries • Trade-offs • Non-negotiables Identify friction points. Where does progress stop when a senior leader is out? That is your next system to build. Convert recurring work into structure. - Templates. - Checklists. - Operating rhythms. - Review cadences. Consistency reduces chaos. Architecture reduces escalation. Train for outcomes, not micro-steps. Teach intent. Let execution evolve. The goal is not to remove leadership. It is to move leadership upward. From operator → to architect. Systems don’t dilute impact. They compound it. And at scale, compounding beats effort — every time. #OrganizationalDesign #LeadershipEvolution #SystemsThinking #ExecutionExcellence #ScalingUp

Work is happening. Progress isn’t. That’s usually a sign something deeper is broken. People are busy all day. Calendars are full. Meetings stack back to back. Dashboards look “fine.” From the outside, it looks like things are moving. Inside the team, it feels heavy. Everyone is working. No one is sure what actually matters. That usually means one thing. Ownership is missing. When ownership isn’t clear, a pattern forms: → Effort turns into activity → Activity turns into meetings → Meetings turn into escalation So leaders step in. They answer questions. They make the call. They unblock the work. It helps in the moment. But over time, the system learns something dangerous: → “Wait for approval.” → “Don’t decide too fast.” → “Escalate to stay safe.” People stay busy. Progress slows. Not because they don’t care. Because the system trained them that way. Real progress doesn’t come from pushing harder. It comes from clarity. Clarity looks like this: → One decision has one owner → Decision rights are clear → Trade-offs are named early → Information moves fast → Leaders stop catching every ball mid-air When ownership is clear: → Meetings get shorter → Decisions happen faster → Teams act with confidence → Execution becomes predictable People still work hard. But now the work goes somewhere. Progress isn’t magic. It’s designed. And when teams feel stuck, the answer is rarely more effort. It’s usually one question no one wants to ask: → Who actually owns this? If your team is busy but nothing is moving, look for where ownership never lands. That’s often the real block. ♻️ Share this with a leader who feels like the constant escalation point.

𝗠𝗮𝗽 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗣𝗮𝗶𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: 𝗨𝗻𝗰𝗼𝘃𝗲𝗿 𝗛𝗶𝗱𝗱𝗲𝗻 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗔𝗱𝗱𝗶𝗻𝗴 𝗧𝗲𝗰𝗵 Every successful transformation starts by seeing your current state with crystal clarity. Too often, we rush to evaluate software features before understanding how work really flows and where it grinds to a halt. Imagine treating your processes like a road trip: you wouldn’t choose a new vehicle until you know which roads are blocked. The same goes for systems. A mid‑market manufacturer struggled with late shipments. Leadership blamed their ERP’s lack of functionality, but frontline teams knew the truth: manual handoffs and conflicting spreadsheets created bottlenecks. In addition, 40% of delays stemmed from manual cross‑checks between dispatch and finance, a step invisible on org charts but glaring on the shop floor. By facilitating honest, workshop‑style mapping sessions (complete with sticky notes and whiteboards), they uncovered redundant approvals and invisible handoffs that no feature list could solve. Involving the people who do the work isn’t optional; it’s essential. Their day‑to‑day insights highlight subtle delays, workarounds, and “exceptions” that hide in plain sight. An unbiased facilitator ensures every voice is heard and prevents solutions from being biased by existing hierarchies. The result? A process map that reveals root causes, not just symptoms, and creates a shared baseline for improvement. By critically analyzing your current state, you build a precision roadmap: automate the highest‑impact tasks, redesign workflows to remove dead ends, and close compliance gaps before they escalate. This targeted, human‑centric approach avoids wasted investment, earns frontline trust, and lays the groundwork for sustainable process improvement.    Once you’ve charted reality, you can make targeted changes, whether that’s simplifying an approval step, automating a data transfer, or selecting a tool that fits the way your teams operate. This honest approach prevents costly rework and builds trust across the organization. Ready to uncover hidden friction and chart a focused transformation path? With Digital Transformation Strategist, let’s discuss how a structured pain‑point diagnosis can drive your next wave of operational excellence. #digitaltransformation #operationalexcellence #processimprovement #processmapping #changemanagement

L&D isn’t always the solution. When a training request lands in your inbox, it is tempting to jump straight into solution mode. New module. New playlist. New workshop. It feels productive and it shows movement. The challenge is that movement is not the same as progress. In many organisations, L&D is handed the symptom rather than the problem. Productivity issues. Slow adoption of tools. Inconsistent service. These are real challenges, but training is only one piece of the puzzle. If we want to make a genuine impact, we have to build a habit of stepping back and asking better questions. I have always believed that the most powerful skill an L&D professional can develop is root cause analysis. It gives you clarity before commitment. It gives you justification before investment. Most importantly, it stops you from building learning that no one actually needed. Here is a simple flow that I use: • Start with the problem statement. What is actually happening and what is the impact. • Identify the desired behaviour. What does success look like in real life terms. • Look at the environment. Are there blockers in process, tools, or leadership. • Look at attainability. Does an L&D solution actually do what is needed. • Look at skill gaps last. If everything else is in place, then training becomes a strategic lever. When you practice this consistently, you stop being the person who delivers training. You become the person who improves performance. That shift elevates the work and your influence inside the organisation. Here are some resources that can help you strengthen these skills: • The Five Whys method A simple and effective tool to dig below the surface and uncover what is really driving the issue. • Ishikawa or Fishbone diagrams Ideal for mapping out all the possible causes across people, process, tools, and environment. • Cathy Moore’s Action Mapping A brilliant approach that keeps you focused on real business outcomes and strips out unnecessary content. • Performance Consulting by Dana and James Robinson A foundational read for anyone who wants to move from order taker to trusted partner. • Rummler Brache Performance Improvement framework A structured way to analyse performance at organisational, process, and individual levels. • Learning Cluster Design by Crystal Kadakia Great for identifying the right mix of solutions rather than defaulting to a single course. If you want to build credibility in your role, start by slowing the conversation down. Ask questions that others have not considered. Create clarity before committing resources. When you do, you do not just support learning. You support the business in delivering what matters.

𝐀𝐈 𝐈𝐬𝐧’𝐭 𝐄𝐱𝐩𝐨𝐬𝐢𝐧𝐠 𝐎𝐮𝐫 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐆𝐚𝐩𝐬. 𝐘𝐞𝐭 𝐈𝐭 𝐈𝐬 𝐄𝐱𝐩𝐨𝐬𝐢𝐧𝐠 𝐎𝐮𝐫 𝐂𝐥𝐚𝐫𝐢𝐭𝐲 𝐆𝐚𝐩𝐬. For years, most organizations optimized for speed, output, and efficiency, yet very few optimized for true clarity. Not clarity in documentation, not clarity in process maps, but clarity in how work actually happens, why decisions get made, and where real knowledge lives. In most companies, the real operating system has always been people, experience, and context working together inside the environment the business actually runs on. Now we are entering an environment where systems increasingly expect us to be precise about intent. They do exactly what we ask, yet they cannot compensate for what we never defined in the first place. That is where many transformation efforts quietly stall. Not because the technology is not capable, yet because the organization cannot clearly explain what “good” actually looks like across the full operating system. What is being exposed right now is not a tooling problem. It is a shared understanding problem. Many workflows were built on tribal knowledge, handoffs, and experience that lived inside people’s heads. That worked when work moved slower and complexity stayed local. It breaks when work spans teams, partners, platforms, and constantly changing conditions. This is where system thinking has to come back into the conversation. Not as theory, not as a workshop exercise, but as a leadership discipline. The organizations that move forward fastest will be the ones that shift from isolated task thinking to connected system thinking. 𝐓𝐡𝐚𝐭 𝐦𝐞𝐚𝐧𝐬 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐚𝐧𝐝 𝐭𝐞𝐚𝐦𝐬 𝐦𝐮𝐬𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝: ▫️ Where decisions actually originate ▫️ What upstream signals shape downstream outcomes ▫️ Where knowledge is created, stored, and lost ▫️ How small changes create ripple effects across teams and functions ▫️ Which parts of work are truly repeatable and which require human judgment The real opportunity is not to add more tools. The real opportunity is to get brutally honest about how work actually happens, where decisions really come from, and how knowledge is captured before it walks out the door. The organizations that will move forward fastest will not be the ones with the most platforms. They will be the ones that can align on outcomes, translate experience into repeatable understanding, and turn individual knowledge into shared capability across the system. #AI #GenAI #EnterpriseAI #Leadership #HumanFirst #DigitalTransformation Forbes Technology Council InsightJam.com PEX Network Theia Institute Thinkers360 IgniteGTM IA FORUM Intelligent Automation Community Gurus Direct 𝗡𝗼𝘁𝗶𝗰𝗲: The views within any of my posts, or newsletters are not those of my employer or the employers of any contributing experts. 𝗟𝗶𝗸𝗲 👍 this? Feel free to reshare, repost, and join the conversation!

80% of workflow bottlenecks are hiding in plain sight. But most teams don’t look closely enough to see them. When I design workflows, I don’t add new tools right away or build complex systems. I start by mapping the current process. Without knowing every step, we’re just guessing at what’s slowing us down. Here’s my go-to checklist for spotting the hidden issues: 1 - Map every step Document each click, handoff, and decision. Most teams skip this, but it’s where the real insights are. 2 - Spot repetitive tasks Repeated steps often go unnoticed. They feel like “just part of the job” but usually add no real value. 3 - Measure task times Check how long each step actually takes. When times drag, it’s a sign of inefficiency that needs fixing. 4 - Look for approval delays Every extra approval is a potential bottleneck. Too many checks can slow things down more than they help. 5 - Align skills with tasks Ensure tasks fit the person’s skill level. If experts are doing routine work, it’s time to rethink the setup. 6 - Automate simple tasks Automation isn’t about flashy tools. It’s about freeing up your team’s time for critical work, not admin tasks. It’s surprising how often these basics are ignored. Do this if you want to do more with less. Or skip it if you’re okay with unnecessary delays and wasted resources.