Streamlining Daily Tasks

I’ve worked with A-players for almost two decades. One thing that sets them apart is: Single-tasking Its an almost maniacal focus on one thing, one objective, one goal. Here are 8 reasons why single-tasking beats multitasking: 1. Higher Quality Output This one’s a game-changer. • Focus leads to better results • Attention to detail improves • Less room for errors Multitasking dilutes effort. Single-tasking sharpens it. 2. Reduced Stress Levels Juggling too many tasks creates chaos. Single-tasking brings calm. It allows for deeper concentration. Stress levels drop when you focus on one thing at a time. 3. Improved Efficiency Doing one task well is faster than doing many poorly. Efficiency rises when distractions fall. Single-tasking means: • Clear goals • Direct paths to completion • Fewer interruptions 4. Better Mental Well-being Multitasking can drain your energy. Single-tasking preserves it. When you focus on one task: • Mental fatigue decreases • Satisfaction increases • You feel more accomplished 5. Enhanced Creativity Creativity needs space to grow. Single-tasking provides that space. When your mind isn’t scattered: • New ideas flow • Solutions come easier • Innovation thrives 6. Stronger Memory Switching tasks often hurts memory. Single-tasking strengthens it. Focused attention helps you: • Retain information • Recall details • Build stronger mental connections 7. Greater Job Satisfaction Single-tasking leads to better work. Better work leads to more pride. Employees feel: • More engaged • More valued • More fulfilled 8. Practical Strategies Implementing single-tasking is simple. • Set clear priorities • Use time blocks • Minimize distractions Leaders should: • Encourage focus • Promote mindfulness • Create a supportive environment Single-tasking isn’t just a technique. It’s a mindset. Adopt it, and watch your productivity and well-being soar. --- Considering working with a coach? https://lnkd.in/dC4tYDSS

Picture this: Dave, a modern-day professional, immerses himself in the hustle and bustle of daily tasks, darting from one activity to another, each demanding a slice of his fragmented attention. Hold on, scratch that. Let’s not romanticize the gritty reality of multitasking. It’s not an art, it’s not a skill. It's a scientifically documented pitfall. Let's talk facts. According to a study from the University of London, multitasking can drop your IQ as much as a night without sleep. That's not a badge of honour, that's a red flag waving vehemently, screaming for attention. Here’s another: a report published in the Journal of Experimental Psychology highlights that multitasking can reduce productivity by as much as 40%. That’s not just a dip, it's a cliff, a dangerous drop into the abyss of inefficiency. Think of your brain as a computer processor. When you overload it with too many programs running simultaneously, what happens? It slows down, lags, and sometimes crashes. The human brain, despite its complexity, operates on a similar principle. We are not built for simultaneous processing. We are built for focus, for dedicated engagement with one task at a time. But Dave is relentless, right? He bounces from emails to meetings, from spreadsheets to Slack notifications, a relentless pinball in the arcade of modern business chaos. Wrong move, Dave. Because with each switch, Dave pays a tax, a "switching cost" that drains cognitive resources and time. It’s like driving with a foot on the brake – a surefire recipe for burnout and decreased output. Steve Jobs didn’t rise to the pinnacle by scatter-gunning his focus. His genius lay in the relentless pursuit of perfection, in doing one thing, doing it extraordinarily well, and then moving on to the next. So, here’s the hard-hitting reality: Multitasking is not a skill to be honed; it's a mirage to be avoided. In the realm of business and entrepreneurial excellence, it's time to dismantle the multitasking myth, to discard it like the outdated relic it is. We need a shift, a radical refocusing of our energies. Because the future belongs not to the busiest, but to the focused, to those who can navigate the noise and hone in on what truly matters. Remember Dave? Tomorrow, Dave opts for a change. He decides to embrace unitasking, giving each task his undivided attention, nurturing it to completion without the cacophony of modern-day distractions. And as the day winds down, Dave realizes a profound truth: Multitasking was the greatest con of the modern business world. No more divided focus, no more fractured efforts. Do one thing, do it well, then move to the next. In the quest for excellence, it’s not about juggling tasks but mastering focus. One focused step at a time, onto a path less chaotic and more productive.

The Brain Isn’t Actually Multitasking What we perceive as multitasking is, in neurological terms, rapid task-switching — a process that incurs significant cognitive costs. The brain doesn’t truly do two things at once; it simply toggles between tasks quickly, and that toggling has a price. It Costs You Time and Accuracy Research by Rubinstein, Meyer, and Evans found that task-switching can cost up to 40% of a person’s productive time due to the cognitive load of moving between tasks. Studies using brain-imaging technology confirm that performance scores are lower and error rates increase in multitask conditions compared to single-task conditions. It Impairs Memory and Attention Chronic multitaskers show inferior working memory performance and greater difficulty filtering out irrelevant information, leading to increased mental fatigue and stress. Frequent media multitasking is also associated with more self-reported attention lapses, mind-wandering, higher impulsiveness, and more problems with executive functions. It Hurts Academic and Professional Performance Research indicates that media multitasking interferes with attention and working memory, negatively affecting GPA, test performance, recall, reading comprehension, note-taking, self-regulation, and efficiency. Students also tend to underestimate how much it’s hurting them in the moment. The Brain Can “Disengage” Under Overload According to research, brain may “downshift” or limit additional resource allocation when cognitive load becomes excessive, rather than rising to the challenge. The Bottom Line For complex, goal-oriented work, monotasking — focused engagement with a single task — remains the superior strategy for sustainable productivity and cognitive fidelity. The research is fairly consistent: the feeling of being productive while multitasking is largely an illusion.

Humphrey Bogart claimed his first real break in Hollywood came from his ability to multitask: he could smoke a cigarette and deliver his lines at the same time. Multitasking is a daily fact of life for most of us. Heck, you may be reading this post while flipping between windows, sitting in a meeting, on even while on call. I hope you’re not driving! Truth is, though, the brain is crap at multitasking. So bad, in fact, it doesn’t even try. Instead it switches between tasks, doing one for a time and then putting it on hold and swapping to the next. What we think of as “multitasking” is actually task-switching and it comes with a cost. The cost of switching between two tasks is pretty significant too. Though the time cost may be only fractions of a second each time, these costs accumulate with each switch. So even someone who appears to be multitasking efficiently might be incurring significant costs of their cognitive processing time! Joshua Rubenstein and his colleagues conducted four experiments to measure how efficiently people completed tasks either in isolation or when switching between them. For example, in a really simple task where participants saw a letter and a number (e.g. “7H”) and were asked to name only of them, switching between letters and numbers slowed responses by approximately 40%. The true cost of multitasking is arguably not efficiency loss, but how it impairs our mental well-being. In one study which simulated an office environment, the researchers found that participants reported significantly higher stress, frustration, workload, effort, and pressure after only 20 mins of multitasking compared to participants who hadn’t switched between tasks. Focus is the antidote to multitasking. When you spend an extended period of time on a single task, focusing all your attention on it, you are capable of accomplishing far more than trying to do many things at once. There are many great ways to practice focus: reading, exercising, meditating or even going to the cinema. They key is to remain in the moment and not interrupt yourself with distractions. It’s a classic use-it-or-lose-it scenario. If you constantly task switch without giving yourself regular time to focus on a single thing, it becomes harder to focus when you really need it. What are some of your favourite focused activities to engage in? #Neuroscience #Multitasking #Productivity #BrainHealth

AI is powerful - your business expertise indispensable - and empathy irreplaceable. How are you integrating these three to secure your position and lead in the future of work? The question isn’t whether AI will change how we work anymore - it’s how you, as a leader, will adapt to lead. Integrating AI, Expertise, and Empathy: A Winning Formula 1️⃣ AI’s Power: Imagine leading a global sales team. AI can analyze market trends, predict customer needs, and optimize pricing strategies in seconds. But while AI provides the what, it’s up to you to determine the why and how. 2️⃣ Your Expertise: Your business acumen turns AI’s data into actionable insights. From deciding which market to prioritize to tailoring customer solutions or navigating tough negotiations, your strategic lens transforms data into impactful decisions. 3️⃣ Empathy’s Irreplaceability: AI might suggest the best time to send an email, but it can’t sense when a team member is disengaged or when a stakeholder needs reassurance. Your ability to connect, inspire, and build trust is the differentiator that transforms AI-driven strategies into real results. 🚀 Client Success Story: Merging AI with Leadership A senior operations leader at a multinational logistics firm sought to integrate AI into her team’s workflows to optimize delivery routes, reduce costs, and improve customer satisfaction. While AI provided precise data, her team resisted the changes, fearing redundancy and feeling overwhelmed. In coaching, we prioritized on 3 areas: 1️⃣ Building Empathy: She led the way to open and vulnerable communication, shared her fears too, addressed concerns, and created psychological safety, transforming resistance into collaboration - as a team. 2️⃣Leveraging AI: Together they reframed AI as a tool to enhance capabilities, expanding the team’s perspective to focus on it as strategic, high-value partner. 3️⃣ Showcasing Expertise: Now that the resistance was being replaced by openness, and even enthusiasm, the team was in flow, blending their deep industry knowledge enhanced with their AI readiness. The results? 🚀 A 25% boost in operational efficiency, 18% fewer delivery delays, and a 30% rise in customer satisfaction. 🚀 Her leadership also secured her and members of her team promotions and broader responsibilities. The future isn’t just about what technology can do - it’s about what you can achieve with it. Let’s ensure you’re not just adapting to the future of work - you’re shaping it. DM me to discuss your game plan.

Dear Stakeholders and Executive Leaders: The easiest way to improve the data and AI team’s productivity is to stop scheduling meetings with them. If technical team members have over 3 internal meetings per week, something’s wrong. That’s not collaboration or communication. It’s overhead. Most meetings can be handled via email or Slack message. We’re moving meetings, emails, and DMs into NotebookLM. Each team member has an “Info I know that you may need to know…” notebook. They drop information into it, thinking, “Vin would want to know about this,” or “This will be important in 3 months.” Each client and project has one with meeting recordings, emails, documents, diagrams, where to find data, and whatever else. We handle most information requests by asking questions there first. Everyone has a status notebook. They add updates at the beginning and end of the day. They can talk it out, write it down, take pictures of a whiteboard…it all works the same. I have a “How to do…” collection. It has processes for invoicing, SOW creation, managing difficult client scenarios, etc. Whenever someone asks me, or I ask them ‘how to,’ it’s recorded for a new notebook. For now, Google’s data-sharing policy works for us. We are evaluating Notebook Llama in case it changes. Get used to doing something once, documenting it in the easiest mode, and adding it to an LLM-supported knowledge base. LLMs can help transform it into a knowledge graph that more efficiently represents the workflows and expertise required to run the business. Businesses will only benefit from AI when they rethink and innovate existing workflows. Start with the ones that add the most overhead, like meetings. #ArtificialIntelligence #GenAI #Productivity

Your reps get handed Sales Nav. And you wonder why they're missing quota. Most teams use it like a phone book. Random searches, generic messages, zero buying signals. That's not prospecting. That's expensive busy work. I've used this exact 5-step system to generate millions in pipeline. Same workflow my team runs every week. Step #1: Build your sequence FIRST Don't touch Sales Nav until you have a complete 21-day multi-channel sequence. I write mine in Google Docs, then build it in Apollo. Step #2: Create trigger based searches → Less than 1 year in current role (they're making changes) → Job opening increases (expansion or performance issues) → Posted on LinkedIn recently → Company headcount growth Step #3: Research before outreach I use ChatGPT to uncover strategic priorities and quota attainment data. Found a company with 35% sales job growth and only 23% quota attainment? Perfect storm. Step #4: Batch your outreach Set calendar blocks. When I was an AE, 8 hours minimum weekly for cold outbound. Step #5: Make it ongoing Monday: Check searches Daily: Work your tasks Weekly: Add qualified prospects By the way… check your messaging before you start. Here’s what I mean: Generic: "Hey, saw you're hiring..." Researched: "Hey Sarah, congrats on 8 months at TechCorp. If you're like most VPs, you're uncovering dead bodies. Team hitting 32% vs 42% industry average..." Which gets the meeting? Start today: Build ONE search, research 10 prospects, block daily outreach time. — Check out my FULL Sales Nav tutorial here: https://lnkd.in/gtE-FWax

MRMR is a feature selection method used by statisticians in biochemistry and then popularized by Uber to select features for machine learning models. MRMR stands for Minimum Redundancy Maximum Relevance. The idea behind this method is to select features that have maximum relevance with the target, and minimum redundancy to other features in the dataset. In laywoman terms: we want features that are highly related to the target, but not related to other features in the dataset. And by related, we usually mean correlated. The relation with the target is captured by the relevance. The relation to other features is captured by the redundancy. So we need ways to quantify relevance and redundancy. Relevance can be determined with statistical tests like ANOVA, correlation, mutual information, or by the feature importance from Random Forests. The first 2 methods are fast. The others, not so much. Redundancy can be measured with correlation (fast) or mutual information (not so fast). MRMR selects features with high relevance and low redundancy in an iterative process: at each round it ranks the features through the ratio or difference between their relevance and redundancy, and then selects the top ranking feature. It is iterative, because the redundancy is calculated between the features being examined and the ones already selected. As we add new features to the selected pot, we need to re-establish the mean redundancy of the remaining ones. The popularity of MRMR was given by its speed. But to be fast, it uses classical statistical tests that measure linear associations. For non-linear associations, we need to use Random Forests and mutual information, and then the algorithm is not so fast any more. You can apply MRMR with Feature-engine: https://buff.ly/cldRXbA If you’ve used this method, I want to hear about your experience. Other than that, to learn more about ways to assign feature importance for selection check out: 🎓 My course: https://buff.ly/o70wmXm 📘 My book: https://buff.ly/NgAGiEN #machinelearning #datascience #datascientist #MRMR #featureselection #mlmodels #dataengineering #ml #ai

Partner enablement is often thought of as how we are enabling our partners. But sales teams are the frontline of revenue, and their success often hinges on understanding the value partnerships bring. Many organizations fail to equip sales reps with the tools and training they need to make the most of partner-driven opportunities. If you want your partnerships to truly drive impact, you must tailor enablement for your sales team. Here’s how to get started: 1. Sales reps need clarity on how to integrate partnerships into their process. Make sure your training covers: * The Partner Pitch: What’s the unique value of a partner-driven lead, and how should they position it to the customer? * Co-Sell Opportunities: How do they collaborate with partners during the deal cycle? Define roles and responsibilities for seamless execution. * Engagement Process: What’s the step-by-step process for involving a partner? Whether it’s looping them in for a demo or escalating technical questions, clear guidelines prevent delays and confusion. 2. Provide Easy-to-Use Tools: Sales enablement shouldn’t feel like homework. Create resources that are quick to access and easy to use, like * Quick-Reference Guides: Summarize partner value propositions, key metrics, and FAQs in a single document. * Cheat Sheets for Objections: Offer pre-written responses to common challenges when selling partner-driven solutions. * CRM Templates: Use CRM workflows to automate the partner engagement process, keeping it simple and repeatable. 3. Integrate Training into Sales Routines Don’t overwhelm your sales team with one-off workshops. Instead, embed partnership enablement into their day-to-day routines: * Add partner updates to weekly sales meetings. * Offer bite-sized training videos or guides they can review on-demand. * Celebrate wins from partner-driven deals to reinforce the value of collaboration. 4. Pair new sales reps with a “partnership ambassador” on your team to provide hands-on guidance during their first partner-driven deals. When sales teams understand how partnerships drive value, they become powerful advocates for partner-driven growth.

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.