Jazz Rasool’s Post

I laugh at the examples of imperfect AI given in the first part of this post! I agree with Donald H Taylor that a key skill of managers moving forwards is going to be in augmenting human and machine capabilities. Will managers always want to adapt to working with machines if it means a reduction in team size (so loss of team mates) due to the efficiencies AI brings? Not always. So some individuals will embrace this dual mode of operating and others will resist developing in this way. Effective change management and coaching will be key if organisations want to make the most of the efficiencies that AI can bring across the workforce. #Botshit, #ChangeManagement, #Workforcestrategy

In AI where should the human be? In the loop, at the helm, at the centre or somewhere else? The idea of a 'human-in-the-loop' is good for suggesting that AI can't be left unchecked. But does it still give too much control to AI? 🤖 Joshua Wöhle opts for 'Human at the helm' - stressing that humans have the ultimate responsibility for the actions of AI https://lnkd.in/eHnkGa97 🤖 Matija Hiti's term is 'human at the centre'. Matija agrees with the idea of humans taking responsibility, but also stresses that we are in control https://lnkd.in/e7j5PJRA 🤖 Personally, I think we should think of ourselves as 'machinist-managers', combining the technical ability of a highly-skilled machinist with the interpersonal skills of a manager to get the best out of AI (see below). How do you describe how humans should interact with AI?

AI is powerful but imperfect and always will be. To use it effectively, you can't use it like a simple, reliable tool. You’ll have to work differently. Say hello to the machinist-manager.   AI’s imperfections are clear in Google AI Overview, the summary appearing at the top of Google searches.   Launched to the public on 14 May, its imperfections were soon pointed out. Asked how to make cheese stick to a pizza, it suggested adding glue to your recipe - about 1/8 of a cup, to be exact.    AI Overview also suggested cleaning your washing machine with mustard gas, reported that a dog had played in the NBA (just one) and described the nutritional value of rocks, recommending eating one small rock per day.   For AI detractors, this was another example of its uselessness.   For AI lovers, this was a glitch on the road to the inevitable perfection of Artificial General Intelligence.   It’s neither. It’s a reminder that AI is both very powerful and frustratingly imperfect. We’d better get used to it.   You know what else is imperfect? People. We make mistakes all the time. Yet we somehow manage to work with people. How? Through collaboration and good management.   It’s going to be the same with AI.   Because AI is not perfect, you’ll need to manage it like you manage people. A manager chooses the right person for a task and aims to get the best out of them while staying alert for potential errors. The manager encourages the person, gives them feedback and guides them to perform better in the future.   We’ll do same for AI, as AI managers.   But AI is not a person. It’s a way of powering tools. Increasingly, in the near future, we’ll be using software tools powered by AI that focus on doing one particular job well. Think of them like machines – a power drill, sewing machine or lathe – all powered by electricity, but each specialized. Using them well takes skill and a deep knowledge of the tool. In a factory, you’d call the person with that job a machinist.   AI is a highly effective but imperfect tool, and its imperfections are not predictable. This is key. It’s impossible to know whether it will be accurate 98% of the time, 50% or something else. So the person managing an AI tool has to be hyper-aware of their working environment, know the quality of work required, be able to work with both the skill of a dedicated machinist and the flexibility of an excellent manager. They will have to be a machinist-manager. I'll be interested in your thoughts. Do you agree that AI is powerful but imperfect? If so, how would you recommend handling this? #machinistmanager

AI: Just a Tool Let’s clear this up: AI isn’t the enemy. It’s a tool, like a hammer or a pair of scissors. Worried it’s coming for your job? Maybe it is. But if what you’re doing can be replaced by a machine, maybe it’s time to ask yourself—do you really want to be doing it? AI Doesn’t Think for You AI doesn’t have ideas. It’s not sitting there saying, “Here’s something brilliant!” That’s your job. AI just takes what you give it—your ideas, your questions, your vision—and turns it into something useful. But the direction, the spark of inspiration? That’s all you. If you don’t have the vision, the tool is useless. It’s like handing someone a paintbrush and expecting them to create a masterpiece without knowing what to paint. AI amplifies your creativity—it doesn’t replace it. If It Can Be Automated, Embrace It Yes, AI will automate some jobs. But let’s face it: if your work can be automated, it means it’s repetitive, mechanical—things a machine can handle better and faster. The upside? It frees you to focus on the work that really matters: the creative, strategic, and human aspects machines can’t replicate. Think of it as an opportunity. What could you do with the time AI gives back to you? AI Isn’t the Talent—You Are AI isn’t writing novels or making art on its own. It’s a tool, powered by the ideas and instructions you give it. If you’re not bringing anything to the table, the output will reflect that. But if you do have the ideas—if you’ve got creativity, drive, and purpose—AI becomes your collaborator. It helps you work faster, better, and smarter. The Big Picture AI is a great equaliser. It’s removing barriers, making creative tools accessible to everyone, no matter their background or resources. The key is knowing how to use it to enhance what makes you unique. So instead of worrying about what AI might take away, think about what it gives you: freedom, efficiency, and the chance to refocus on the things that bring value to your life and work. The Takeaway AI isn’t replacing people. It’s replacing tasks. And if a machine can do the work, maybe it’s time to look for something more fulfilling. Because at the end of the day, AI is just a tool. The real innovation, the real creativity—that’s still all you. #ArtificialIntelligence #Innovation #FutureOfWork #Creativity #Automation #CareerDevelopment

Why AI? Duh . . . to Save Hours and Days! AI is revolutionizing the way we work, and for good reason. The most compelling argument for adopting AI? Time savings. Let's explore some easy tasks where AI can make a significant impact. ✅ Comparing documents for version changes, errors, typos, and grammar Imagine spending hours poring over multiple versions of a document, trying to spot every minor change. AI can do this in seconds, highlighting differences with precision. No more eyestrain from staring at screens, trying to catch that elusive typo. AI-powered tools can identify grammatical errors that even the most eagle-eyed editor might miss. Version control becomes a breeze, with AI tracking and summarizing changes across iterations. ✅ Summarizing documents, reports, and meetings into bullets and action items We've all sat through meetings that could have been emails. AI can distill hours of discussion into concise, actionable points. Long reports? AI can extract the key takeaways in moments. Action items are automatically identified and organized, ensuring nothing falls through the cracks. No more struggling to recall important details days after a meeting – AI has it covered. These are just the tip of the iceberg when it comes to AI's time-saving potential. Think about the hours spent on repetitive tasks that could be automated. Consider the mental energy conserved when AI handles the grunt work. Imagine the creative projects you could tackle with all that extra time. AI isn't just about efficiency; it's about unlocking human potential. By offloading time-consuming tasks, we free ourselves to focus on high-value work. Innovation flourishes when we're not bogged down by mundane processes. The question isn't whether to adopt AI, but how quickly can we integrate it into our workflows. As AI continues to evolve, its time-saving capabilities will only grow. Early adopters will gain a significant competitive advantage. The future of work is here, and it's powered by AI. Are you ready to reclaim your time and supercharge your productivity? Embrace AI, and watch as hours and days of work transform into moments. The choice is clear: adapt and thrive, or risk being left behind in a rapidly changing world. Why AI? Because time is our most precious resource, and AI is the key to making the most of it.

A post on Medium earlier in the week highlighted the high failure rate of AI projects. I don't agree with all of it, mostly because it is overly complicated. Here is my attempt at simplification: For an AI project or pilot to be successful, it has to solve a problem that is (1) data driven, (2) well-defined, and (3) repetitive. Visions of what AI could look like in the future are all well and good, but at this stage of AI's evolution the case studies need to be realistic and the value of their output measurable. I will sound like a broken record here to my colleagues and followers, but data is the foundation of AI and underlying machine learning. If you use training and test data that is flawed (e.g., incomplete, repetitive, inconsistent, incorrect), then the AI effort is a waste of time. Get your data in order first. Unlike projects of the past, data standardization, consolidation, and harmonization can be done automatically (and quickly) without operational disruption or fear of a loss of data integrity. Even if the pilot goes well, once AI is inserted into daily operations, there are two additional items to consider. The first is differentiating between prediction and judgement. AI can be a phenomenal tool for predicting customer behavior, and thus could support data-driven decision-making around the launch of product and service lines, etc. However, a data-driven decision might not be the right decision in every instance. The need - nay the requirement - for human judgement will not go away as AI becomes more prevalent. The second item is action. You can have a giant pile of predictions and facts dumped on your desk suggesting actions to take. A leader has to step up and take action, or use their judgement to not take action. Analysis paralysis, cultural quagmires, and egos get in the way of making sound decisions on a daily basis. If you continually let shortcoming get in the way of leadership, you have again wasted lots of money and time on an AI solution that has been effectively neutered.

AI is good . . . but not always. Before you jump in with both feet with using AI for your supply chains, consider how AI will be used. Every tool and methodology has its domain of problems that it’s very good at solving, but there are also domains of problems that it’s not good at solving (and it can even provide erroneous or misleading solutions). Every tool has its purpose. There is never “one tool to rule them all.” AI is typically used in the supply chain world to optimize which decisions and actions are best for a company under various conditions, like which distribution center should hold which products to serve all customers that best. This is a perfect use for AI. We need that. But, this use of AI (and any optimization approach, for that matter) can actually cause problems in supply chains. We have seen it with Covid, the Suez and Panama canals, the Red Sea, etc. From a “system” perspective, when the individual parts of the system are independently optimized, the overall system suffers and is sub-optimized. This means that everyone loses in the end, even with all the best intentions to improve performance. The more a company focuses independently on its own operations to make them most efficient in their own tiny little world, the worse the overall supply chain operates as a whole in the bigger picture. Eventually, we have to pick up our heads and quit looking at just our own little world. AI keeps the focus on only our part of the big picture. We need to consider the full supply chain as a system and try to improve the supply chain at that system-level. Don't just look at Tier 1 (which is the domain of AI). Go beyond to Tier N.

The future is AI* - but is it our present? "The easiest way to solve a problem, is to deny it exists" - Isaac Asimov There is no question AI has a big role to play in the here and now; but where, how, when and even does AI fit into your business strategy or execution plan? There are already many things that AI does extremely well, take this image for example that took me 3.4 seconds to create (+10 seconds typing time for the prompts) via OpenArt.ai - it can already create pretty good content (as long as the input data and materials are already prepared, and the prompts and configuration are created appropriately). Machine Learning as a sub-division of AI is an incredible tool for data interpretation (as long as you devise the right criteria and output parameters for said data analysis). The issue is that even within the above examples where AI is already a proven valuable asset, there are caveats, setup requirements, and limitations. The tool is only as good as the person or team compiling the inputs and configuring the rules that will generate the desired outputs. Depending on what you consider to be AI, or how you define it, it is already everywhere and touches most things we do. *Hence, the future is now. But unless you have the basics in place (input materials, definable rules, valid use case, integrated strategy, desired outcomes that match customer needs and market dynamics), shoehorning AI into your plans to keep up with the Jones's or tick a box will only make your life more complex. If you want to discuss the above, or what elements of AI truly can add value to your business, please get in touch.

🎨 𝗨𝗻𝗹𝗲𝗮𝘀𝗵𝗶𝗻𝗴 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝗻 𝗔𝗜: 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘃𝘀. 𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 🧠 Ever wondered how AI learns to create amazing things like realistic drawings and understand stories better? Let's dive into two cool ways AI does it! 🔍 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 - 𝗣𝗹𝗮𝘆𝗶𝗻𝗴 𝘁𝗵𝗲 𝗚𝘂𝗲𝘀𝘀𝗶𝗻𝗴 𝗚𝗮𝗺𝗲 Imagine you have two teams of artists. One team tries to draw a picture, while the other team tries to guess if it's a real photo or just a drawing. The artists keep getting better because they want to fool the guessers. This game makes the drawings look more and more real! 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Team A (Artists): Draws a picture of a cat. Team B (Guessers): Tries to figure out if the picture is a real photo or a drawing. Result: Team A improves their drawing skills to make the picture look just like a real cat! 🧠 𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 - 𝗙𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗪𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Now, imagine you're reading a story about a friendly robot exploring space. As you read, you remember important details like the robot's name and its mission. Self-attention helps the computer focus on these important parts of the story to understand it better. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Story: "The robot named Astro flies to Mars." Self-Attention: The computer pays extra attention to "Astro" and "Mars" because they are key details. 🚀 𝗛𝗼𝘄 𝗦𝗲𝗻𝗶𝗼𝗿 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 𝗖𝗮𝗻 𝗨𝘀𝗲 𝗧𝗵𝗲𝘀𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Using AI to improve product quality. The AI can learn to distinguish between perfect and defective products, improving manufacturing processes. 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Detecting fake transactions by training AI to spot irregularities. 𝗦𝗲𝗹𝗳-𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝗻 𝗦𝘂𝗽𝗽𝗹𝘆 𝗖𝗵𝗮𝗶𝗻 𝗗𝗲𝗺𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: Analyzing past sales data to predict future demand, helping in inventory management. 𝗦𝘂𝗽𝗽𝗹𝗶𝗲𝗿 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀: Focusing on key suppliers and understanding their impact on the supply chain for better collaboration and efficiency. By combining these AI techniques, senior professionals can create smarter, more efficient supply chains, ensuring products are of the highest quality and operations run smoothly. 👩💼👨💼 Let's keep exploring and learning how AI can transform our world, one innovation at a time! #AI #AdversarialLearning #SelfAttention #SupplyChain #Innovation #AIinSupplyChain #LearningMadeFun

Why Your AI Strategy Might Be Built on a Faulty Foundation Integrating AI is not just installing a new app; it’s adopting a new mindset. Are you trying to fit a square peg into a round hole by treating AI like old software? One key concept every decision-maker must grasp is the distinction between probabilistic and deterministic processes. Understanding this difference is essential to avoid any pitfalls. Deterministic Processes: Think of these as the reliable workhorses of your systems. They’re predictable and consistent. Like when you click “submit” on a form, it processes the same way every time. This reliability is crucial for many business functions. Probabilistic Processes: Probabilistic processes, on the other hand, involve a degree of uncertainty. This is where AI, particularly in its current form, operates. It's like having a creative problem-solver on your team who might approach tasks differently each time, sometimes leading to innovative solutions but also introducing variability. The Challenge: Integrating AI directly into processes that demand absolute certainty can lead to unexpected—and costly—outcomes. For tasks where precision is non-negotiable, relying solely on AI’s probabilistic nature isn’t advisable. Think of It This Way: Imagine AI as a savvy intern who’s incredibly skilled at using Google. They’re quick, process vast amounts of information, and often provide valuable insights. But like any intern, they need guidance and oversight from experienced professionals. The Solution: Embrace a Human-in-the-Loop Approach Combine AI’s creative intelligence with human judgment. Let AI handle initial analyses, while your team reviews outputs, makes decisions, and provides context. This strategy is especially beneficial for departments heavily involved in research and data analysis. The Bottom Line: Don’t let a misunderstanding of AI’s nature cost your organization. Integrate wisely, stay competitive, and maintain the reliability your stakeholders expect.