Evaluating Workflows for Efficiency

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!

🛑 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.

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

The AI workflow produced great results, yet people did not feel safe relying on the output. ⛔ That was the situation I encountered in a client workshop in Brussels last week, and it is far more common than most organisations like to admit. The team had invested time and effort into designing an AI-supported workflow. The use case was clear, the technical setup was sound, the data quality was acceptable, and the people involved had already received training on how to use AI. Despite all of this, the workflow was barely used in practice. People ran the AI step, reviewed the output, and then quietly redid the work themselves. During the workshop, we mapped the real workflow together, step by step, focusing not on how the process was documented but on how the work actually happened on a normal working day. At one point, a participant looked at the whiteboard and said: “I only trust the result after I have checked it myself anyway.” That sentence shifted the entire conversation. As we continued mapping the process, a pattern became visible: Everyone validated AI outputs differently.  Some checked everything, even low-risk drafts.  Others barely checked high-risk decisions. Accountability was assumed but never explicitly defined. Human validation was happening constantly, but it was invisible, inconsistent, and highly personal. We redesigned the workflow and introduced a simple checklist for built-in human validation. 💡 This checklist replaced individual safety habits with a shared, explicit process. ✅ Define the risk level of the output. Clarify whether the AI output is a draft, a recommendation, or a decision with external impact. ✅ Decide if validation is required. Make it explicit which outputs require human review and which can flow through without intervention. ✅ Specify the validation moment. Define when validation happens in the workflow and before which downstream step. ✅ Assign clear responsibility. Name the role that validates the output and the role that makes the final decision. ✅ Separate generation from judgment. Ensure the AI prepares content or options, while humans remain accountable for approval and outcomes. ✅ Remove unnecessary checks. Regularly review the workflow to eliminate validation steps that add friction without reducing risk. Once this checklist was applied, people felt much more confident about the AI output because they knew when human judgment was required. 👉 Is human validation in your AI workflows clearly designed, or is it still improvised? Let’s discuss.

Most businesses do not have an AI idea problem. They have an execution problem. I once stepped into a major AI programme with money behind it, internal attention, and plenty of confidence around it. What it did not have was a clearly defined product. There was already momentum. Senior interest. Big expectations. Plenty of talking. But when you stripped it back, the basics were missing. No clear definition of the problem. No shared understanding of the workflow. No real agreement on what needed to be built first. This is where many teams lose time. They start protecting themselves with meetings. They ask for more alignment. They make the deck better instead of making the work clearer. Meanwhile, the pressure grows. Stakeholders want answers. Expectations harden. Confidence becomes performance. In my case, I went back to first principles. What is the real problem? Where is the workflow breakdown? Who is feeling the pain? What would be useful now, not six months from now? Then we started moving. We made decisions before everything felt neat. We got closer to users. We tested what mattered. We shipped in smaller parts. We learned quickly and adjusted. 3 months later, we had a version 1 It eventually went on to be used by 10 Fortune 500 brands. The lesson was simple. AI does not reward the team with the most excitement. It rewards the team that can turn ambiguity into execution. That usually looks like this: 1. Define the workflow before you talk about the tool - If you cannot explain where the work breaks, - you are not ready to design the AI layer. 2. Make decisions while the picture is still incomplete - Waiting for total certainty usually means delay disguised as rigour. 3. Stay close to the people doing the work - Real use cases come from operational friction, - not brainstorm theatre. 4. Deliver proof early - A small working improvement beats a large future promise. 5. Keep momentum visible - When people can see movement, trust grows. If you lead AI work, this is important for you. The advantage does not go to the team with the biggest ambition. It goes to the team that can make AI useful in real work. 🚨 I’m running a free masterclass on how to create AI agents for real workflows. If you want AI to do more than produce demos and disconnected pilots, this will help you think more clearly about where agents fit, what they should do, and how to apply them to actual business work. Register here - https://lnkd.in/eGtFJ26H 🚨 ♻ Reshare to help someone turn AI into execution ➕ Follow Patrick Giwa, PhD for more like this

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

🔍 The Hidden Cost of Leadership Behaviors: A Real Story from the Boardroom Sometimes, the biggest inefficiencies in an organization aren’t in the processes—they’re in leadership behaviors. I was once invited as a guest speaker for an annual review meeting in Goa, speaking on “Team Building & Personal Effectiveness.” After internal approval, an email was triggered to process my travel and stay. By mistake, I was cc’d in that email chain. What followed was shocking: *Over 25 leaders were marked in CC, leading to nearly 300 email exchanges—all for approving a ₹15,000 ticket.* 🔹 Let’s do the math: 300 touchpoints × distraction × loss of leadership time. A conservative estimate? ₹25–30 Lakh worth of leadership attention wasted on a ₹15K decision. Can we justify spending 100x more in leadership bandwidth than the actual cost of the decision? What did this reveal? 1️⃣ Lack of Trust & Empowerment – Too many approvals for a minor expense indicate deeper issues of micromanagement. 2️⃣ Decision-Making Dysfunction – If a simple approval takes this long, imagine the cost of critical business decisions. 3️⃣ The Real Cost of Inefficiency – It’s not just about delayed processes; it’s about how leadership behaviors create invisible inefficiencies. 👉 Here’s the real question: How many times are organizations bleeding resources—not due to bad processes, but due to ineffective leadership behaviors? What’s your take? Have you witnessed such hidden costs in your workplace?

I recently spoke with a sales leader about a common challenge: how overly complex internal processes slow down sales reps. “Our reps are spending more time navigating internal workflows than selling,” they mentioned. This is a widespread issue—when every step of a deal requires approvals or confusing steps, it keeps reps from engaging with prospects effectively. To fix this, simplifying the sales process goes beyond just removing steps; it’s about empowering your team and creating clear, action-oriented pathways. Here’s how: 1. Cut Down Approval Layers: Allow senior reps to make decisions within defined limits, reducing reliance on time-consuming approvals. This speeds up deal cycles and encourages ownership. 2. Use Clear Playbooks: Ambiguity breeds inefficiency. Standardized, easy-to-follow sales playbooks eliminate confusion and help reps move deals forward confidently, knowing what to do at each stage. 3. Automate Admin Tasks: Manual data entry and updating deal stages take up valuable time. Automation tools handle these low-value tasks, allowing reps to spend more time selling and less on busywork. 4. Streamline Communication: Simplify who’s responsible for what. Clear communication lines and fewer meetings reduce delays, ensuring that when reps need answers, they get them fast. 5. Empower Your Reps: Equip your team with the authority to make pricing decisions or offer discounts without having to escalate every time. Giving them the ability to act quickly builds trust and boosts productivity. By making these changes, you’re not just reducing steps—you’re unlocking the full potential of your sales force, enabling them to focus on what matters most: closing deals and building relationships. Simplified processes mean faster, smoother sales cycles and ultimately better results for your team. #SalesOptimization #SalesEfficiency #SalesLeadership #SalesProductivity #SalesProcess #AutomationInSales #SalesTeam #LeadConversion #RevenueGrowth #BusinessEfficiency

Avoid the “Shiny Tool Trap” – Make Automation Work for You! Imagine pouring six figures into a tool that promises efficiency…  only to realize it amplifies your problems instead of solving them. That’s the Shiny Tool Trap - and it’s costing companies millions. 💸 Automation can be a game-changer, but only if you have the right strategy. Here’s how to avoid the biggest pitfalls: 1. The Shiny Tool Trap Pitfall: Falling for the latest software without understanding your processes. Tools don’t fix broken workflows - they just make them fail faster. Fix: Map your processes first. Audit them ruthlessly. Ask: “Does this step add value?” If not, redesign it. Automation amplifies good processes - it doesn’t fix bad ones. 2. The Human Blind Spot Pitfall: Thinking automation is a “set it and forget it” deal. People resist change, and ignoring their concerns leads to failure. Fix: Work with your team, not just for them. Involve end-users early. Train them well. Celebrate small wins (e.g., “This bot saves us 10 hours/week!”). Change management is crucial. 3. The Feedback Black Hole Pitfall: Believing your automated process is “done.” Markets shift, regulations change, and customer needs evolve.  Static automation becomes obsolete. Fix: Build feedback loops. Monitor KPIs, gather user insights, and iterate. Think of automation as a cycle, not a checkbox. Why this matters: Process automation isn’t just about cutting costs - it’s a growth engine. But only if you avoid these traps. At GBTEC Group, we’ve helped companies turn automation into a strategic advantage. How? By pairing tech with human-centric design and agile adaptation. Which of these automation pitfalls have you seen firsthand?