When Automation is Not the Right Choice

The other day, I deleted 17 failed Zapier + Make automations. Each represented hours of setup, debugging, and maintenance. Each promised to save me time. None delivered. A non-technical post-mortem on why most automation tools fail, and why I’m so excited about AI agents coming to market. CASE STUDY #1: THE MEETING NOTES PIPELINE Goal: Auto-route meeting notes to relevant project folders Time to build: 15 minutes Time actually spent: 4.5 hours Failure point: Edge cases The automation looked simple: IF meeting.ends THEN extract_notes() IF contains_project_keywords THEN route_to_folder(project_id) ELSE notify_user() Reality: Meeting notes rarely contain perfect project keywords. People use abbreviations. They reference projects indirectly. Notetakers transcribe imperfectly. Result: 60% routing accuracy. Considering rework time – worse than manual filing. CASE STUDY #2: THE EMAIL TRIAGE SYSTEM Goal: Automatically sort and prioritize incoming emails Time to build: 45 minutes Time actually spent: 6 hour Failure point: Context understanding The automation seemed logical... IF from_important_sender OR contains_urgent_keywords THEN flag_as_priority IF matches_project_pattern THEN add_project_label ELSE mark_for_review Reality: Email context is nuanced. A casual check-in from the CEO needs priority. A "URGENT" marketing blast doesn't. The system couldn't distinguish between "next week's deadline changed" and "next week's team lunch changed." Result: Started ignoring the automation's priorities entirely after too many false positives. The fundamental problem is not that automation tools don't work. They work exactly as designed, which is the problem. Instead, we need to flip the model. It’s not: IF specific_trigger THEN specific_action We need: GOAL: desired_outcome CONTEXT: user_environment LEARN: usage_patterns ADAPT: execution_strategy This is why AI agents are so compelling. They operate at the appropriate level of abstraction. The implementation details become their problem, not yours.

I just watched a CEO spend $50,000 on AI tools to "revolutionize" his business. Six months later, his operations were more chaotic than ever. After 20 years of implementing systems, I've seen this story play out hundreds of times. Companies think AI is a magic wand that will solve their operational nightmares. But here's the brutal truth: AI is an amplifier, not a savior. It takes whatever you have and makes it bigger, faster, and more pronounced. Great systems? AI makes them incredible. Broken systems? AI makes them catastrophically broken. Think about it: → If your team does the same task 5 different ways, AI will automate all 5 ways. → If your foundation is weak, AI will build a skyscraper on quicksand. → If your processes are inconsistent, AI will be inconsistently efficient. The companies winning with AI aren't the ones with the fanciest tools. They're the ones who systematized first, then automated. Toyota didn't rush into factory automation with broken assembly lines. McDonald's didn't automate kitchen operations before perfecting their processes. Amazon didn't deploy AI on chaotic fulfillment centers. They built predictable, repeatable systems first. Then they let AI amplify what already worked. So before you spend another dollar on AI: Ask yourself: → Does everyone follow the same proven process? → Do we have one best way to handle each task? → Are our operations predictable without AI? If the answer is no, you're not ready for automation. You're ready for systematization. Because the most expensive mistake in business isn't buying the wrong AI tool. It's automating chaos and calling it innovation. PS: Every Saturday, I share systems that 10X revenue fast. I will break this case study fully in this week’s edition. Join 7,903+ leaders scaling smarter, not harder: https://shorturl.at/iQkQv

#AI: A Strategic Asset or an Expensive Mistake? Artificial Intelligence is everywhere—hailed as the next big thing in business. Yet, while some companies achieve breakthrough success, others waste millions chasing AI trends that don’t align with their goals. According to Gartner, 30% of AI projects fail after the proof-of-concept stage due to unclear business objectives, poor data strategy, and underestimating implementation challenges. 🔹 Where AI Delivers Value: ✅ Complex Decision-Making & Pattern Recognition – Finance, e-commerce, and healthcare use AI for fraud detection, risk assessment, and personalization. ✅ Automation for Efficiency – AI streamlines logistics, optimizes supply chains, and enhances customer service with chatbots. ✅ Real-Time Insights & Predictive Analytics – AI helps manufacturers reduce downtime and financial institutions assess credit risks. 🔹 When AI Becomes a Liability: ❌ If-Then Logic Suffices – Simple rule-based automation is often a faster, cheaper solution. ❌ Poor Data Quality – AI is only as good as the data it’s trained on. Inaccurate or biased data leads to unreliable outcomes. ❌ Lack of Explainability – In regulated industries, AI’s "black-box" nature can be a major risk. ❌ High Costs Without Clear ROI – AI investments require talent, infrastructure, and continuous monitoring. The key? Adopt AI strategically—aligning technology with clear business outcomes, robust data foundations, and ethical considerations. AI isn’t magic; it’s a tool. Used wisely, it unlocks growth. Used recklessly, it drains resources. 💡 What’s your take? Is AI helping your business thrive, or do you see companies struggling with AI hype?

𝗝𝘂𝘀𝘁 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘄𝗲 𝗰𝗮𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 𝘄𝗲 𝘀𝗵𝗼𝘂𝗹𝗱. There, I said it out loud. In DevSecOps, we love automation. It speeds up deployments, ensures consistency, and helps us scale. But we need to be crystal clear about where automation shines—and where 𝗵𝘂𝗺𝗮𝗻 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝗶𝗿𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗮𝗯𝗹𝗲. Too often, teams treat task analysis as a step-by-step procedural breakdown—something to be optimized, scripted, and handed off to machines. This works well for the well-defined and repeatable portion of a workflow: - Running security scans - Executing test suites - Deploying infrastructure 𝗕𝘂𝘁 𝗻𝗼𝘁 𝗮𝗹𝗹 𝘄𝗼𝗿𝗸 𝗳𝗶𝘁𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁. When architects and engineers design architectures, assess risk, or troubleshoot failures, we aren’t following a linear script. We are synthesizing information, weighing trade-offs, and making decisions in complex, ever-changing environments. I'm continuing to grow and learn techniques and methods to help us with adoption DevSecOps principles as well as adopting AI-augmentation and even bringing onboard, an AI agent. Patty McDermott at MITRE has been teaching me about Goal Directed Task Analysis (GDTA). Instead of breaking work into predefined steps, GDTA focuses on three elements: - What goals engineers must achieve - What decisions we need to make - What information we need to do so effectively Laying this on DevSeOps and modern software practices, this is a fundamentally different way to think about the human work. We care about the value stream... we care about removing friction and waste... though we need to be focused on the humans in the loop. AI and automation are fantastic at augmenting human efforts, but they are not yet capable of replacing the cognitive heavy lifting of secure software design and decision-making. (Note, I said *yet*. What I talk about today may be dramatically different in 3-6 months!) Not that we ever apply automation indiscriminately... but if we did, we risk removing the critical thinking and adaptability that make DevSecOps work. In my experience, automate what’s repeatable, let humans drive what’s strategic. 𝗙𝗼𝗿 𝗻𝗼𝘄, 𝗵𝘂𝗺𝗮𝗻𝘀 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝗴𝗼-𝘁𝗼 𝗳𝗼𝗿 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗳𝗳𝗼𝗿𝘁𝘀. #ArchAITecture #DevSecOps #SoftwareEngineering #GDTA #HumanMachineTeaming #AI4SDLC

Manufacturing Automation – Misalignment Misaligned automation is worse than no automation! The hodgepodge of AVAILABLE "solutions" for adding automation to the process, makes it easy to take a manual process that may or may not be in control and throw it into DISARRAY by focusing on either automating the WRONG STEP OR deploying automation that is INCONGRUENT with a common objective. Much like tuning the carburetor of a car engine rather than tuning the engine itself! Examples of WRONG automation, most often TECHNOLOGY driven, might include: -         Automating the worker rather than the process. -         Using IoT to instrument irrelevant 2nd or 3rd order inputs that have little to no impact on a controlled or predictable process. -         Deploying complex MES systems before having a full understanding of the process with old fashioned documentation, creating complex training disruptions for the workers. -         Deploying autonomous materials transfer devices without first rationalizing workflows … sometimes as simple as moving a pallet of raw inputs next to the point of use. -         Pursuing complex solutions which violate the dictum: “Man does perception and dexterity functions, while Machine does power and precision functions. -         Artificial, vendor driven STANDARDS for hardware, software and methodologies which burden the process with excessive COSTS and COMPLEXITY in the name of “ease of maintenance”. -         Other permutations of trend driven solutions; not native to or flowing from the process but are presented as panaceas from the automation vendor base. For SMEs, the sole objective of increasing PRODUCTIVITY to improve competitiveness, automation that flows from the process itself as it organically evolves will ALWAYS yield better results. To be certain, these solutions will involve some of the technologies on offer BUT at all times these are nothing more than the tool kit available to the automation designer and not the driver of the automation itself. Results of the RIGHT automation will be recognized when: -         Worker travel distances, materials travel distances, and cycle times are getting shorter. -         Responsiveness of the process to product changes is getting faster. -         A handful of metrics, viewed at a glance, quickly tell us IF the process is in control or not. Misaligned automation is worse than no automation! -- “The road to Industry 4.0 goes through Industry 3.0 …. There are No Short Cuts!” -- Is your automation strategy, technology driven or do you follow your process signals? Your thoughts are appreciated and please share this post if you think your connections will find it of interest. 👉 Comment, follow or connect to collaborate on automation for enhanced productivity in your process. https://lnkd.in/eyKhx5ia #industry40 #automation #productivity #robots