Reducing Cognitive Load in Support Tools

AI adoption without cognitive load management is setting teams up for mental overload. So many organizations are rushing to integrate AI tools across workflows, but ignoring the neuroscience of how much new information and decision-making the brain can handle before performance degrades. Here's what we know from the research: Working memory has hard capacity limits, and every new tool, interface, or decision point draws from the same finite cognitive resources. Studies on cognitive load theory consistently show that when task complexity exceeds available working memory capacity, learning and performance both decline. Introducing AI without structure adds extraneous load, the kind that doesn't contribute to better outcomes but still taxes the prefrontal cortex. Here are 15 ways we can deploy AI while protecting our teams' cognitive bandwidth: - Introduce one AI tool at a time rather than bundling multiple new systems - Automate repetitive low-stakes decisions first, freeing working memory for complex judgment - Use AI to pre-filter information so teams receive curated, not raw, data - Build standardized prompts so people aren't reinventing their approach each session - Let AI handle meeting summaries and action items to reduce encoding burden - Create clear guidelines for when to use AI versus human judgment - Schedule AI training during circadian peaks for better retention - Use AI to reduce context-switching by consolidating communication channels - Pilot tools with small groups before organization-wide rollouts - Provide decision frameworks so AI outputs don't create new ambiguity - Automate status updates and progress tracking to lower monitoring load - Use AI for first-draft generation, letting humans focus on refinement - Designate "tool-free" deep work blocks to allow cognitive recovery - Collect feedback on perceived mental effort, not just productivity metrics - Revisit and retire tools that aren't reducing load as intended When we exceed working memory thresholds, things can go wrong very fast. People's accuracy drops, their errors increase, and burnout, which was already a problem prior to the AI boom, accelerates even faster. AI should reduce the cognitive demands on our teams, not add another layer of complexity they have to manage.

Clinicians don’t want more data. They want fewer decisions. HealthTech keeps confusing complexity with sophistication. We assume that because clinicians are smart, they want more dashboards. More alerts. More choices. In truth, they want something no algorithm can measure: Cognitive relief. Imagine you’re a pilot. Mid-flight, you’re shown 17 new dials. Flashing red. Each says something important. Now make a life-or-death decision. Fast. Would you say thank you? That’s what most clinical decision support looks like in HealthTech today. And it’s killing trust faster than bad data ever could. Why? Because information isn’t value. Clarity is. The problem, IMO, isn’t the number of alerts. It’s the hidden cost of each micro-decision. Every time we ask a clinician to interpret another data stream, we’re not helping them, we’re taxing them. It’s not death by data. It’s death by 1,000 cognitive cuts. We’ve forgotten the difference between data and decision. Between information and insight. Between noise and relevance. And worst of all? We often design for what looks impressive - not what actually works on a ward round. The best HealthTech doesn’t make clinicians feel smarter. It makes them feel safer. Not “empowered.” Not “augmented.” Just calm. Just clear. That’s the gold standard now isn’t it? Tools that remove thinking, not add to it. If you’re building in HealthTech, Don’t ask: “What more can we show?” Ask: “What decisions can we take away?” That’s where trust is built. That’s where burnout is reduced. Build for fewer decisions. What would you add? P.S. Tools that reduce decisions are finally being valued. VCs are rewarding clarity, not complexity. If your AI product calms the chaos - you're building in the right direction - https://lnkd.in/euA2-8a2

𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗳 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗶𝗻 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁? Cognitive overload happens when the mental effort required to use a system or process exceeds the user’s capacity. In Procurement, this happens when tools are overly complex or poorly designed. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗼𝗳 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗮𝗿𝗲 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 and range from a persistent operational inefficiency, more errors, low adoption of complex solutions and ultimately a risk for employee burnout. While some level of complexity is inevitable to support advanced functionality, the way tools and workflows are designed plays a crucial role for their usability, how effectively users can engage with them and the level of mental load they create. The Cognitive Load Theory (CLT), introduced by John Sweller in the 1980s, provides a framework for reducing mental strain by focusing on how users learn, process and retain information. The CLT identifies three types of cognitive load and offers insights into how Procurement Systems can be optimised for usability: 1️⃣ 𝗜𝗻𝘁𝗿𝗶𝗻𝘀𝗶𝗰 𝗟𝗼𝗮𝗱 which arises from the inherent complexity of the task or information. In Procurement, examples include multi-dimensional RFP scoring or the authoring of complex contracts and their SLAs. 𝗛𝗼𝘄 𝘁𝗼 𝗵𝗮𝗻𝗱𝗹𝗲 𝘁𝗵𝗶𝘀? Break down and simplify complex tasks into manageable steps using modular workflows, and provide pre-configured templates for common scenarios. 2️⃣ 𝗘𝘅𝘁𝗿𝗮𝗻𝗲𝗼𝘂𝘀 𝗟𝗼𝗮𝗱 stemming from poor system design, irrelevant information or inefficient processes. For example, clunky interfaces, unnecessary workflow steps or dashboards that hide insights under excessive detail. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗶𝘀? Minimise Extraneous Load with a functional user interface design, using smart visualisations and streamlining workflows. 3️⃣ 𝗚𝗲𝗿𝗺𝗮𝗻𝗲 𝗟𝗼𝗮𝗱 resulting from the cognitive effort that directly supports learning and mastery. Examples include tooltips, clear guidance, and onboarding processes that make systems easier to navigate. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘁𝗵𝗶𝘀? Enhance Germane Load with role-specific training, embedded tool tips & intuitive help features accelerating user learning. All three types can lead to a reduced capacity of employees to be able to operate effectively and potential negative consequences and mental stress. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗰𝗼𝗺𝗲𝘀 𝗮𝘁 𝗮 𝗵𝗶𝗴𝗵 𝗽𝗿𝗶𝗰𝗲. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘄𝗵𝗶𝗰𝗵 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗮 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗱𝗲𝘀𝗶𝗴𝗻 and optimise their cognitive load levels by unveiling tasks step by-step, simplifying design and providing helpful learning features, 𝗵𝗮𝘃𝗲 𝗮 𝗵𝗶𝗴𝗵𝗲𝗿 𝗰𝗵𝗮𝗻𝗰𝗲 𝘁𝗼 𝘁𝘂𝗿𝗻 𝗳𝗿𝗼𝗺 𝗮 𝗵𝗲𝗮𝗱𝗮𝗰𝗵𝗲 𝘁𝗼 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗯𝗼𝗼𝘀𝘁𝗲𝗿. ❓How do you think can solutions be humanised to reduce cognitive load. ❓What else helps to generate a good usability and user experience.

I often say that in an AI world metacognition is the master capability. This applies at all levels, especially in framing work, but also in interacting with AI. Research reveals specific approaches that yield better outcomes in working with GenAI. Very pleased that Microsoft Research has a significant focus on metacognition, with numerous papers on the topic. One of these, "The Metacognitive Demands and Opportunities of Generative AI", has some particularly instructive findings on both system design and usage: 🧩 Make the task explicit before you prompt. Most prompting interfaces expect you to state clear goals and break work into sub-tasks (e.g., “condense to two paragraphs,” “update the tone”). This metacognitive step is not optional—users who specify goals and decompose tasks gain better control over outputs. 🧠 Treat prompting as a metacognitive exercise. Effective use requires two abilities during iteration: calibrating your confidence (“is it my prompt, parameters, or model randomness?”) and flexibly switching strategies (retry, refine, or decompose further). 🛞 Choose the right interaction mode for control vs. ease. Giving explicit instructions is felt to be harder than inline edits, but it gives more control. Users often struggle at “getting started,” especially when many adjustable parameters are exposed. 🧪 Expect heavier evaluation work when AI generates long content. GenAI outputs (full emails, presentations, or code) shift effort from writing to judging, increasing cognitive load compared to simple auto-complete. People also tend to “eyeball” generated code, risking over-confidence in correctness. ⚡ Watch for fluency-driven overconfidence. Fast, fluent answers can inflate your confidence in both the output and your own evaluation, even when accuracy hasn’t improved. Higher felt confidence then reduces the effort you invest in checking, shortening thinking time and lowering willingness to revise. 🗺️ Use planning aids to improve prompts. Built-in planning support (goal setting + task decomposition) helps users craft better prompts; “prompt chaining” (multi-step sub-tasks) made participants “think through the task better” and target edits more precisely. 🧭🛠️ Reduce demand with explainability and customizability. Surface the right controls (e.g., temperature, shortlist size, output length) and adapt complexity to user state. This can improve self-awareness, confidence, and satisfaction. 🕹️ Support self-evaluation and self-management in the UI. Proactive, neutral nudges based on prior behavior (e.g., “you typically add 15 follow-ups after vague summaries”) can guide users to specify goals up front and reduce rework. ⚖️ Manage cognitive load while improving metacognition. Interventions (decomposition steps, reflections, explanations) add information to process, but studies show metacognitive support can improve outcomes without raising overall load; adapt or fade prompts as skills grow.

You can’t see cognitive overload. That’s why it’s ignored. Most teams treat accessibility as contrast ratios and alt text. But cognitive accessibility is wider than that, and less forgiving when you get it wrong. Here are 5 common cognitive disabilities And what designers can actually do. 1. ADHD Challenges: • Distractibility • Difficulty prioritizing • Overwhelm from dense layouts Design for: • Clear visual hierarchy • One primary action per section • Step-based flows Avoid: • Competing primary CTAs • Auto-rotating carousels • Notification overload 2. Dyslexia Challenges: • Slower decoding • Reading fatigue • Difficulty with dense text blocks Design for: • Plain language • Left-aligned text • Generous line height (1.5+ recommended) • Clear headings and chunking Avoid: • Justified text • Long paragraphs • Low-contrast body text 3. Autism Spectrum Challenges: • Sensory sensitivity • Cognitive overload • Distress from unexpected change Design for: • Predictable layouts • Explicit labels • Warnings before context shifts • User-controlled animation and motion Avoid: • Sudden modals • Autoplay video • Reduced motion off by default • Ambiguous copy like “Try it” or “Explore.” 4. Memory Impairment Challenges: • Forgetting steps • Losing context in multi-step flows Design for: • Persistent instructions • Progress indicators • Auto-save • Clear error recovery Avoid: • Clearing form data on error • Hiding previous answers • Long forms without sectioning 5. Anxiety Disorders Challenges: • Fear of mistakes • Stress from uncertainty • Decision paralysis Design for: • Reassuring microcopy • Undo functionality • Transparent consequences • Calm error messaging Avoid: • Countdown timers • Aggressive urgency language • Vague destructive actions Ask yourself: "Does this screen reduce thinking or increase it?" 👇🏽 Are we over-indexing on visual accessibility while ignoring cognitive overload? Drop your thoughts in the comments. ♻️ Share and save this for your team. --- ✉️ Subscribe to my newsletter for accessibility and design insights here: https://lnkd.in/gZpAzWSu --- Accessibility note: Content in the post is the same as the image attached (except for a few bullets omitted for easy scanability)

System prompts are getting outdated! Here's a counterintuitive lesson from building real-world Agents: Writing giant system prompts doesn't improve an Agent's performance; it often makes it worse. For example, you add a rule about refund policies. Then one about tone. Then another about when to escalate. Before long, you have a 2,000-word instruction manual. But here’s what we’ve learned: LLMs are extremely poor at handling this. Recent research also confirms what many of us experience. There’s a “Curse of Instructions.” The more rules you add to a prompt, the worse the model performs at following any single one. Here’s a better approach: contextually conditional guidelines. Instead of one giant prompt, break your instructions into modular pieces that only load into the LLM when relevant. ``` agent.create_guideline(  condition="Customer asks about refunds",  action="Check order status first to see if eligible",  tools=[check_order_status], ) ``` Each guideline has two parts: - Condition: When does it get loaded? - Action: What should the agent do? The magic happens behind the scenes. When a query arrives, the system evaluates which guidelines are relevant to the current conversation state. Only those guidelines get loaded into the model’s context. This keeps the LLM’s cognitive load minimal because instead of juggling 50 rules, it focuses on just 3-4 that actually matter at that point. This results in dramatically better instruction-following. This approach is called Alignment Modeling. Structuring guidance contextually so agents stay focused, consistent, and compliant. Instead of waiting for an allegedly smaller model, what matters is having an architecture that respects how LLMs fundamentally work. This approach is actually implemented in Parlant - a recently trending open-source framework (13k+ stars). You can see the full implementation and try it yourself. But the core insight applies regardless of what tools you use: Be more methodical about context engineering and actually explaining what you expect the behavior to be in special cases you care about. Then agents can become truly focused and useful. I’ve shared the repo link in the first comment! ___ Share this with your network if you found this insightful ♻️ Follow me (Akshay Pachaar) for more insights and tutorials on AI and Machine Learning!

Is AI Easing Clinician Workloads—or Adding More? Healthcare is rapidly embracing AI and Large Language Models (LLMs), hoping to reduce clinician workload. But early adoption reveals a more complicated reality: verifying AI outputs, dealing with errors, and struggling with workflow integration can actually increase clinicians’ cognitive load. Here are four key considerations: 1. Verification Overload - LLMs might produce coherent summaries, but “coherent” doesn’t always mean correct. Manually double-checking AI-generated notes or recommendations becomes an extra task on an already packed schedule. 2. Trust Erosion - Even a single AI-driven mistake—like the wrong dosage—can compromise patient safety. Errors that go unnoticed fracture clinicians’ trust and force them to re-verify every recommendation, negating AI’s efficiency. 3. Burnout Concerns - AI is often touted as a remedy for burnout. Yet if it’s poorly integrated or frequently incorrect, clinicians end up verifying and correcting even more, adding mental strain instead of relieving it. 4. Workflow Hurdles LLMs excel in flexible, open-ended tasks, but healthcare requires precision, consistency, and structured data. This mismatch can lead to patchwork solutions and unpredictable performance. Moving Forward - Tailored AI: Healthcare-specific designs that reduce “prompt engineering” and improve accuracy. - Transparent Validation: Clinicians need to understand how AI arrives at its conclusions. - Human-AI Collaboration: AI should empower, not replace, clinicians by streamlining verification. - Continuous Oversight: Monitoring, updates, and ongoing training are crucial for safe, effective adoption. If implemented thoughtfully, LLMs can move from novelty to genuine clinical asset. But we have to address these limitations head-on to ensure AI truly lightens the load. Want a deeper dive? Check out the full article where we explore each of these points in more detail—and share how we can build AI solutions that earn clinicians’ trust instead of eroding it.

Most Support Ops optimize for volume. Tickets closed. First response time. Backlog size. But in B2B SaaS, volume is rarely the real problem. Cognitive load is. Two agents can close 25 tickets each. One finishes energized. The other is mentally drained. Why? Because ticket count hides complexity. In B2B, a single ticket can require: • Understanding a custom workflow • Reviewing logs • Reproducing a bug • Coordinating with engineering • Managing an enterprise stakeholder That is not “one ticket.” That is 45 minutes of deep cognitive work. Yet we measure it the same way as a password reset. This creates three problems: 1. We overload top performers with the hardest work 2. We misread productivity 3. We accelerate burnout without seeing it Volume is easy to track. Cognitive fatigue is invisible. If you lead Support Ops, ask: • Do we segment tickets by complexity? • Do we track time to reproduce, not just time to respond? • Do we rotate high complexity queues intentionally? Because in B2B support, complexity per ticket matters more than ticket count. Optimizing for volume feels efficient. Optimizing for cognitive load is what makes teams sustainable. That is a different level of operations thinking.

Web accessibility & mental health: why we need to talk about it In my years working as a web accessibility expert, I’ve often noticed: we tend to focus on physical and sensory disabilities, but mental-health issues and cognitive differences often sit in the shadows of our accessibility discussions. Here’s what I’ve come to understand: · A recent study found that when accessibility features designed for cognitive support were absent, even users without disabilities showed declining cognitive engagement over time (eye-tracking & heart-rate monitoring used) (link to the study: https://lnkd.in/e5ZQe2i7) · The World Wide Web Consortium has a dedicated page on Cognitive Accessibility, acknowledging that many user needs are still not addressed in current standards (link to the webpage: https://lnkd.in/enTWiJdJ) · The European Commission published a 2022 study on inclusive web-accessibility for persons with cognitive disabilities, noting that improved cognitive accessibility benefits everyone (link to the study: https://lnkd.in/e7Z-XAxW) 🚨 Why mental health & cognitive accessibility matters, but gets overlooked · Many mental-health conditions affect attention, memory, processing speed, anxiety, distraction. Yet accessibility standards like WCAG only indirectly address these via criteria like “Readable” or “Predictable”. · This means a website can be technically WCAG compliant, but still highly stressful or inaccessible for a person experiencing anxiety, depression, PTSD, or cognitive fatigue. · Because mental-health issues are less visible and more variable, teams often don’t plan for them, yet by doing so we exclude a very large group of users. ✏️ Practical tips for designing with mental-health & cognitive needs in mind 1. Simplify tasks & reduce cognitive load Use clear, concise language; break down complex processes into simple steps. Provide “skip this step” or “help” options when tasks require concentration. 2. Manage pace, timing & interruptions Don’t assume users can process content the same as usual - allow more time, allow pauses. Provide options to reduce motion, remove auto-refreshing content. 3. Offer predictable, consistent navigation and UI Avoid surprises, unexpected changes, hidden actions. People with anxiety or executive-function challenges benefit greatly from consistency. 4. Enable personalization & adaptation Allow users to choose simpler mode, reduce visual clutter, choose focus mode, change colours or fonts. 5. Test with real users Too often we test only “visual/motor” disabilities, but persons with cognitive or mental-health-related challenges have unique real-world pain points and involve them early. If you’re working on a project, I invite you to pause and ask: “How would this feel if I were anxious, processing slowly, distracted, or tired?” Because accessibility is empathy translated into design. #Accessibility #MentalHealth #CognitiveAccessibility #InclusiveDesign #WebAccessibility #A11y #UX

AI Fundamentals: Prompt Chains Powerful LLMs like GPT-4 can follow complex instructions, but building applications with less capable LLMs requires breaking a single, detailed instruction into a “chain” of simpler prompts. Here’s an overview of practically useful chaining techniques for LLMs... Some background: Typically, we interact with an LLM by passing the model some textual input (a prompt) and receiving textual output. Prompt chains are not much different from this! We still leverage the text-to-text structure of the LLM, but we prompt the model several times in sequence. Additionally, the exact sequence of prompts that we use might be dynamically adapted based on the language model’s output. Sequential chain: The first type of chain that is useful is just a sequential chain! Here, we just have a pre-defined sequence of prompts that we want to pass to the LLM. This sequence of prompts does not change depending on the model’s output, but we can pass the output of the LLM after each prompt as part of the input for the next prompting step. Router chain: What if we don’t want to always use the same sequence of prompts? In this case, we can use a router chain, which gives the LLM multiple options for the prompt to use next and lets the model choose. Typically, this is done by providing the LLM descriptions of each of the prompts that are available, then prompting the model to generate an output that indicates which of these prompts should be called. Different router chain options: Sometimes, asking the LLM to choose the next prompt that it should use can be difficult, especially if we are using a less capable LLM and have several different prompt options available. In these cases, there are a few different types of router chains we can use that reduce the cognitive load (or difficulty) of the LLM’s task: 1. Embedding router: Instead of asking the LLM to choose the next prompt, generate an embedding for the current prompt and all prompt options in the router chain and select the next prompt based on cosine similarity. 2. Sequential router: We can break the router decision into multiple, smaller steps (i.e., just form a sequential router chain!). In most cases, it is easiest to make each “step” in the sequential router chain a simple yes/no decision for the model (e.g., is this a science question? Is this a math question? etc.) TL;DR: Prompt chains are a huge part of successfully using LLMs in practice! If you can get a good sense for how to break a difficult problem into multiple, smaller steps that can each be solved with a prompt, you can probably build a variety of impressive AI applications. Put simply, prompt chains allow us to break difficult problems into sequences of smaller (dynamically-chosen) steps that the LLM can more reliably solve.