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<h3>TL;DR: Large Language Models (LLMs) don’t “think.” They split text into pieces (tokens), turn those pieces into numbers (embeddings), track their order (positional encoding), weigh importance (attention), refine through layers (transformers), score probabilities (softmax), and generate new text one token at a time. Think of it like building the perfect playlist: songs become words, playlists become sentences, and the system predicts what fits next.</h3><h2>Why use music to explain AI?</h2>Technical terms like tokenization and vector space can lose most audiences in seconds. Music is different. Everyone understands the flow of a playlist, the fit of a song, the way genres cluster. That makes it the perfect analogy for how LLMs work under the hood.&nbsp; <blockquote>Picture a playlist with Oasis classics like Wonderwall and Don’t Look Back in Anger. The vibe is set. But what song comes next? Too similar feels stale, too different breaks the flow.</blockquote>That’s the exact challenge LLMs face: given the words so far, what’s the most likely continuation that “fits”? The model’s job is playlist-building for text.<h2>How Does AI Predict the Next Word?</h2>Computers can’t read words as we see them. They need to break them into tokens the smallest useful pieces of text.&nbsp;Think of it like chopping a song into beats. Each beat can be processed, compared, and reassembled. Without tokenization, an LLM can’t analyse or generate language.Once broken down, tokens are turned into embeddings: lists of numbers that capture their meaning and context.&nbsp;Imagine a giant festival map. Oasis and Blur sit near each other because they often appear on the same playlists. Dolly Parton ends up closer to Willie Nelson. The model doesn’t know guitars or lyrics, only that some songs appear together more often than others.This “music-space” is the vector space of AI, where proximity equals similarity.<h2>Why does word order matter?</h2>Playlists aren’t random. Starting with Wonderwall feels different from ending with it.&nbsp;LLMs use positional encoding: signals that tell the model where tokens sit in a sequence. Without it, “Today is gonna be the day” could come out jumbled. With it, the system knows order is part of meaning.Some songs define a setlist. Drop Don’t Look Back in Anger and the whole crowd shifts.&nbsp;LLMs use self-attention to assign weight to tokens based on context. In a hospital sentence, “doctor” and “nurse” matter more than “coffee.” In lyrics, “gonna” and “be the day” are tied closely together, so the model treats them as linked.&nbsp;Attention isn’t enough on its own. The system needs refinement.&nbsp;That’s where transformer blocks come in: layers that combine self-attention with feedforward networks to polish meaning. Each pass is like remastering the track until the whole setlist flows.&nbsp;The more blocks stacked, the sharper the model’s grasp of how words fit together whether in an Oasis lyric or a policy email or a marketing blog.<h2>How does the model pick the next word?</h2>At the end, the model faces a choice. Every possible token is scored.&nbsp;The softmax function converts raw scores into probabilities: “guitar” 80%, “percussion” 15%, “saxophone” 0.1%. The most probable is chosen, like picking the song that best fits the playlist. The output you want is actually built one word at a time: LLMs are autoregressive. That means once one token is chosen, it’s added to the prompt, and the cycle repeats: predict, add, predict, add.It’s like building a playlist track by track. After Wonderwall, the model sees Champagne Supernova as likely next. Add it, then look for the next fit.So for example if you prompt your favourite model (ours is Gemini from Google) with:“When I’m away, I will remember you…”From billions of sentences, it predicts the continuation: Ed Sheeran’s Photograph: “I will keep you inside the pocket of my ripped jeans…”It doesn’t “understand” love or memory. It follows statistical patterns: tokenization, embeddings, positional encoding, attention, transformer layers, probability scoring, and autoregressive generation.The result feels human because the system is excellent at capturing flow, not because it grasps meaning.<h2>So Why do LLMs Sometimes Fail? </h2>The playlist app sometimes suggests ABBA after Metallica because of one odd data point. LLMs can do the same.&nbsp;<blockquote>They work with patterns, not comprehension. That’s why they can produce fluent nonsense sentences that sound right but aren’t.</blockquote><h2>The AI learning that matters</h2>Large language models aren’t mystical. They’re playlist builders at scale: splitting text into tokens, mapping meaning into vector space, tracking order, weighing importance, refining through transformers, scoring probabilities, and generating word by word.The value isn’t in treating them as intelligent beings. It’s in knowing their mechanics so you can use them effectively without over-trusting them.That’s why the first step for any professional isn’t blind adoption, but building safe foundations. If you want to go further, our <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a> gives you the grounding to use these systems with confidence, without needing a technical background.<h2>FAQs</h2>Q: Do LLMs understand meaning? No. They recognise and predict patterns in text, but they don’t comprehend concepts the way humans do.Q: Why do LLMs sometimes get facts wrong? They predict the most likely continuation, not the most accurate one. If the training data contained errors or the context is thin, the model can generate plausible but false information.Q: What’s the difference between training and using an LLM? Training builds the map of word relationships (vector space). Using an LLM means giving it a prompt and letting it generate outputs from that pre-trained map.Q: How is this different from search engines? Search retrieves existing information. LLMs generate new text word by word, based on probabilities.&nbsp;

How AI Works: A Simple Musical Guide to Large Language Models

Danger Warning Sign on Black Background Shining Red White Glass Hazard Warning Sign Symbol Among Symbols

<h3>TL;DR: Despite $30–40 billion in enterprise AI spend, 95% of projects deliver no ROI. The reason is not model quality or regulation but failed approaches: systems that don’t learn, pilots that don’t integrate, budgets that chase optics, and builds that never scale. The only proven path out of the GenAI Divide is linking individual value with organisational ROI through training, governance, and adoption frameworks that raise competence, autonomy, and trust.</h3>AI is not the first General Purpose Technology to underwhelm in its early years. Computers and the internet both looked promising but delivered little productivity until workflows were re-engineered around them. Economists call this the Productivity J-curve: a period where costs rise, returns flatten, and intangible investments distort the statistics. Shoutout to my Marcro-economics lecturer for that one!Generative AI is now following that curve. Enterprises have invested tens of billions. Tools like ChatGPT and Copilot are widely used. Yet 95% of firms report no measurable return. The result is the GenAI Divide: a minority of companies extract millions, while the majority stall in pilots, fail to scale, and see no impact on the P&amp;L. To be fair...a lot of companies are still undergoing "Digital Transformations: - another peach sold by larger consutling firms to no avail.The divide is not explained by model quality or regulation. It is explained by approach.<h2>The Four AI Failure Factors</h2>1. Learning Gap Enterprise systems don’t learn. They forget context, reset each session, and fail to improve with feedback. Employees sidestep them with personal subscriptions that provide faster gains. Ninety percent of staff use consumer AI informally, while only 40 percent of companies fund official subscriptions. This shadow AI economy exposes the real issue: productivity is shifting to individuals, while enterprises remain blind to the value.2. Integration Complexity Most enterprise pilots never make it to production. They don’t align with how work is actually done. Eighty percent of companies test AI tools, but only 5 percent of custom builds survive into workflows. Staff embrace consumer tools because they integrate instantly into daily tasks. Leadership, meanwhile, keeps pushing projects that create extra steps rather than removing them.3. Build–Buy Dilemma Internal builds are favoured for control, but they fail at double the rate of vendor solutions. Projects drag, staff turnover wipes knowledge, and budgets evaporate. Vendors that customise by process and tie outcomes to business metrics succeed twice as often. Yet many firms confuse ownership with progress and end up funding initiatives that never clear pilot stage.4. Investment Misdirection Budgets flow to visible functions like marketing because they are easy to justify. But the highest returns are in back-office automation: contract review, reporting, finance, and customer support. Firms that reallocate see measurable savings in BPO and agency spend. Those that chase optics end up with demos and dashboards that never touch productivity.<h2>The Productivity Paradox Repeats</h2>Despite tens of billions spent, only 5% of enterprise pilots scale. Seven of nine major industries show no meaningful structural change. Tech and media are the only sectors showing signs of disruption. For the rest, AI adoption looks busy on paper but delivers little transformation in practice.This is not a temporary quirk. It is a structural failure. Boards are told AI is everywhere, yet the P&amp;L shows nothing. Leaders sign off pilots, watch them stall, and then rationalise it as “early days.” But history is clear: firms that stay trapped in pilot purgatory fall behind those that rewire processes fast (and that is hard, and long).The Shadow AI Economy While official initiatives stall, employees are already crossing the divide. Over 90% of workers report using personal AI tools, but only 40% of companies pay for enterprise subscriptions. Staff are delivering ROI quietly and individually — often without IT’s knowledge or approval.This creates two risks. First, productivity gains are invisible to leadership and unmeasured by the organisation. Second, the lack of governance leaves firms exposed to ethical, security, and compliance failures. The workforce is moving ahead while leadership lags behind, widening the adoption gap inside the enterprise itself.<h2>Blind Spots in Exec Focus</h2>Executives cling to visible investments. Budgets flow to sales dashboards and marketing pilots that show well in board packs. But the evidence is always straight up: the real gains are in back-office automation - workflows - the end. Contracting, compliance, reporting, and some level of customer support generate measurable savings.But, as usual, most companies overspend on flashy front-end tools while their cost bases remain bloated. Internal builds fail at double the rate of vendor solutions. Integration is treated as optional. Training is treated as an afterthought. Governance is left till someone in legal goes on a webinar about AI guardrails and ISO 42001 compliance.And so it goes... Adoption is high, disruption is low, and organisations remain trapped in the same paradox that has defined every major General Purpose Tool in its early phase - I count electricity, the car and if I was around...probably fire too.<h2>So, what can you do?</h2>Escaping the GenAI Divide means recognising that enterprise ROI depends on individual value. Yes...tell the CFO with his CPA...people value.Research across industries confirms the link: organisations are nearly six times more likely to report significant financial returns when employees personally derive value from AI. That value shows up in three dimensions competence, autonomy, and relationships.Most firms underestimate this. They talk about “using AI” as if it were binary, like picking up a stapler. In practice, workers use AI in many invisible ways. CRM platforms, analytics suites, and customer tools already embed machine learning, yet staff may not even realise they’re relying on it. What matters is not whether employees know they are “using AI,” but whether they feel it improves their performance and connects them more strongly to their colleagues and customers.This is why so many official deployments fail. Leaders design systems for business value and ignore whether individuals gain anything from them. Employees then bypass clunky enterprise tools in favour of consumer-grade AI that fits better into daily workflows. The result is measurable productivity that leadership never sees, and official projects that produce nothing but cost.The numbers are blunt:<ul><li>64% of workers already report personal value from AI, and these workers are 3.4 times more likely to be satisfied in their jobs. </li><li>60% view AI as a coworker, not a threat — proof that adoption can build confidence rather than fear if managed well. </li><li>Mandating use triples adoption rates, and even begrudging adoption leads to higher personal value than leaving AI optional. </li><li>85% of organisations that get ROI also report employees getting personal value: showing that enterprise outcomes do not come at the expense of individual benefit.</li></ul>This evidence points to a simple conclusion: organisations only achieve AI value when their people do. Without individual competence, autonomy, and connection, enterprise ROI remains out of reach.The way forward is not another round of pilots or one more vendor platform. It is building adoption frameworks that deliberately connect individual benefits to organisational outcomes. That means:<ul><li>Training workers so AI raises their capability and confidence, not their workload. </li><li>Embedding governance, explainability, and ISO-aligned standards so adoption stays within risk thresholds. </li><li>Recognising hidden AI use across the enterprise and integrating it into official practice rather than ignoring it.</li></ul><blockquote>Treating AI as a coworker in design and communication, so employees see it as augmenting their role rather than threatening it. </blockquote>My view is that AI will not deliver value simply because a company deploys tools. To be honest, almost 90% of the use cases I have seen do not need AI and are pretty much softward automations.&nbsp;AI delivers value when employees themselves get value, and when leadership aligns that personal gain with measurable organisational outcomes. Yet, this is so hard to see...maybe beause IT is being handballed the AI strategy..maybe because AI experts have been unearthed by multiple tech integraton failure points in the 2010's, and are the first feet on the street.<h2>My Advice on Closing this Gap</h2>I learned this lesson early. My first role was in a treasury dealing room for a merchant bank. The project was to digitise a deal-making workflow across back, middle, and front office. We built and deployed one of the first online trading systems of its kind in South Africa and globallly. The technology itself was not the breakthrough, it was the change in how people worked around it on implementation. Fluency came first at the individual level, then collaboration linked functions, and only then did the organisation see systemic benefit.AI adoption follows the same pattern. It starts like a single-player video strategy game (StarCraft anyone?). Each individual learns the controls, tests moves, and builds confidence. At first it is fragmented, inconsistent, and sometimes frustrating. But once fluency spreads, collaboration begins. Teams link their usage, processes adjust, and workflows change shape. At that point, orchestration at the organisational level becomes possible. The productivity J-curve starts to bend upward.This is why firms stuck on the wrong side of the GenAI divide cannot wait for technology alone to deliver returns. The research is clear: organisations only see measurable ROI when employees themselves derive value. Workers who feel more competent, more autonomous, and more connected through AI drive adoption that scales. Companies that ignore this repeat the same errors we saw in IT and the internet’s first wave: pilots without outcomes, systems without uptake, and budgets without return (kind of like those million dollar CRM installs today!).The answer is straightforward:<ul><li>Training so individuals build fluency and confidence. </li><li>Explainability to ensure adoption stays within risk thresholds. </li><li>Integration of personal and shadow use into formal workflows (rather than treating it as a problem to stamp out). </li></ul>This is why I run two entry points for organisations serious about crossing the divide. The <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass </a>builds literacy across teams, while the <a data-mce-href="https://aistrategyconsulting.com.au/introduction-to-ai-5-day-bootcamp" href="https://aistrategyconsulting.com.au/introduction-to-ai-5-day-bootcamp">5-Day AI Bootcamp</a> drives practical fluency by embedding use cases directly into daily work. Both are designed to turn individual value into organisational outcomes, the only proven path to measurable ROI.The J-curve is real, but it is not permanent. Companies that invest in their people, align adoption with governance, and treat AI as both a tool and a coworker will be the ones that turn early disruption into lasting advantage.<h2>FAQ</h2>Q: How long does it realistically take for AI adoption to show measurable ROI? Most organisations underestimate the J-curve. Expect 12–24 months before early investments translate into measurable productivity gains, provided adoption frameworks are in place.Q: What role should boards play in ensuring AI adoption succeeds? Boards need to treat AI as both a technology and a people investment. Oversight should cover training budgets, ISO-aligned governance, and measurement of adoption at the individual level — not just vendor contracts.Q: If employees are already using AI personally, why should companies formalise? Shadow use creates risk. Without governance, explainability, and integration into workflows, productivity gains remain invisible and compliance gaps widen. Formal adoption ensures value is captured and controlled.Q: Should AI use be mandated inside organisations? Evidence shows mandating use triples adoption rates and still leads to higher individual value than leaving it optional. The key is to preserve agency — staff must be able to override AI recommendations when needed.Q: Where should organisations prioritise budget if they want early ROI? Focus on back-office and process automation: contracting, compliance, reporting, and customer support. These areas consistently deliver measurable savings faster than front-line marketing pilots.Q: How can you measure whether employees are actually deriving value from AI? Go beyond usage stats. Survey staff for changes in competence, autonomy, and connection in their roles. Organisations where employees report personal value are nearly six times more likely to show ROI.Reference: The Productivity J-Curve: How Intangibles Complement General Purpose Technologies, MIT AI in Business Report 2025, MIT

Why Companies are Really Failing at AI

<h3>TL;DR: This guide shows you what OpenAI Playground is, where to find it, what a “model” and “mode” mean, and the exact steps to test AI on your own work. You’ll set a pass bar, run a small batch, score results, and calculate cost per item and minutes saved. By the end, you’ll know whether to scale a task, park it, or stop.</h3><h2>What is OpenAI Playground?</h2>It’s a website where you type an instruction and the AI replies. You paste real text, try a task like “summarise” or “draft a reply,” and see results in seconds. Find it at openai.com/playground. New accounts get free credits, then you pay per use.Who is it for? Non-technical teams who want to check if AI helps a real task before buying software. Think support, ops, marketing, legal, or finance. If you can use a search bar, you can use Playground.What is a “model” and which one should I pick?A model is the AI engine. Different engines trade quality, speed, and price. Start with GPT-4o for best quality on business writing and reasoning. If that passes your bar and cost matters, test GPT-3.5 to see if quality holds at a lower price.What are “modes” and which one should I use?Modes are ways the site runs the engine. Use Chat for most work, for example summaries, replies, and Q&amp;A. Ignore Assistants and Completions until you have a clear reason.Which settings matter on day one?Temperature controls variety. Set 0.2 for steady, factual answers. Max length caps how long the reply can be. Raise it if answers cut off. Leave other dials at defaults until your prompt is working.What does it cost?You pay for text in and out, measured in tokens. Short tests usually cost cents. Your account shows totals. Cost per item = total spend ÷ items tested. Compare that to minutes saved per item.<h2>A one week plan you can use now</h2><ol><li>Create access Sign in at openai.com/playground. Add a card so trials don’t stop mid-run. Open the Usage page, you’ll need it for costs.</li><li>Choose one task Pick a repeatable job with clear outcomes, for example first-pass customer replies, meeting note summaries, or email triage. Strip names and identifiers. Write the goal in one line so everyone agrees. </li><li>Build a tiny dataset Collect 15–20 recent examples that include easy and tricky cases. Put them in one document for quick copy and paste. No cherry-picking. </li><li>Set a pass bar before you start Use three checks: Accuracy, Tone, Edit time. Example pass: Accuracy ≥4/5. Tone ≥4/5. Edit time ≤3 minutes. Add “zero invented facts” as a hard rule. Have the task owner sign off. </li><li>Lock a precise instruction Example: “Summarise the issue in one sentence. Draft a reply under 150 words. State one action we will take and one action the customer can take. Be direct and courteous. Do not invent details.” Keep it short and concrete. Reuse it for every item. </li><li>Lock your settings Model GPT-4o. Temperature 0.2. Max length 220. Mode Chat. Write these in your sheet. Do not change them during the first run. </li><li>Run the batch and log it For each item, paste input, run, copy the output, and time your edits. Log: ID, prompt version, model, settings, output, Accuracy 1–5, Tone 1–5, Edit minutes, Pass Y/N, failure notes. This becomes your evidence. </li><li>Tune one thing only if needed If results are close, change one dial. Lower temperature for steadier wording. Raise max length if answers cut off. Tighten the instruction if it rambles. Re-run three samples. Keep the better setting or roll back. </li></ol><h2>A real world AI example:</h2>Maya runs customer support. She handles about 120 emails a week. Writing the first reply usually takes her 8 minutes. To make this more efficient, we helped Maya and her team with an automation:<ul><li>She goes to openai.com/playground and signs in. She picks GPT-4o, sets Temperature to 0.2, Max length to 220, and keeps Chat mode.</li><li>She pastes a real email with names removed. Her instruction is: “Summarise the issue in one sentence. Write a reply under 150 words. Say one action we will take and one action the customer can take. Be polite and direct. Do not invent details.”</li><li>She clicks Run. Behind the scenes, Playground sends her text to OpenAI’s servers. The GPT-4o engine predicts the next words, one chunk at a time, until it finishes or hits the Max length. The reply comes back to her browser in seconds.</li><li>She edits a few words and sends it. She repeats this on 10 emails and times each edit on her phone. Edits average about 2 to 3 minutes.</li><li>Her Usage page shows the spend. OpenAI charges by tokens, small chunks of text in and out. The batch costs about $0.90 in total, roughly 9 cents per email.</li><li>She compares this to her normal time. From 8 minutes to about 2.5 minutes saves 5.5 minutes per email. At 120 emails a week that frees roughly 11 hours.</li><li>Her fully loaded rate is about $60 per hour. The time back is worth about $660. Running the model for the same 120 emails would cost about $11.</li></ul>She writes the prompt and settings on one page with two good examples and one bad one. She and her manager agree guardrails: no invented facts, and a person must approve before sending. They run a two-week pilot using the same prompt and settings and bed it in if it works.OpenAI Playground is the simplest way to test AI on your own work. Use it to trial one task, learn the dials, and count time saved and cost per item. In our <a data-mce-href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap">AI Strategy Roadmap</a>, we teach your team how to run these tests, turn the winning prompt into an SOP, and run a two week pilot with owners and quality checks. When it pays back, we scale it across the workflow without wasting budget.<h2>FAQ</h2>Q: What is OpenAI Playground in plain English? A: A website where you type an instruction and the AI replies. You can paste real text and test common tasks. No coding needed.Q: Who is it for? A: Non-technical teams who want to see if AI helps a real task before buying software. If you can use a search bar, you can use it.Q: Which model should I start with? A: Start with GPT-5o for quality on business writing and reasoning. If it meets your bar and cost matters, trial GPT-3.5 next.Q: What do “temperature” and “max length” do? A: Temperature controls variety. Use 0.2 for steady, factual wording. Max length caps response size so answers do not cut off.Q: Which mode should I use? A: Use Chat for most work like summaries, replies, and Q&amp;A. Ignore Assistants and Completions until you know why you need them.Q: How much does it cost? A: You pay for text in and out, counted as tokens. Small tests usually cost cents and your account shows exact spend.Q: How do I stop “creative” or fluffy answers? A: Keep temperature at 0.2 and ask for plain, direct tone. Add “Do not invent details. If information is missing, write ‘Not provided.’”Q: Why are answers getting cut off? A: Raise max length. If needed, ask for fewer words or tight bullet points.Q: Can I test long documents? A: Yes, in sections. Summarise each section, then ask for a short management summary using only those section summaries.Q: How many samples do I need for a fair test? A: Use 15 to 20 recent, mixed examples. Include easy and tricky cases so the pass rate means something.Q: Who should own the pass criteria? A: The business team that does the work. They feel the pain and can judge quality.Q: How do I keep the test consistent? A: Fix one instruction, model, and settings for the first run. Change only one thing at a time if you must tune.Q: How do I version prompts without chaos? A: Label changes v1.0, v1.1 and note what changed and why. Roll back if scores drop.Q: What are common fail patterns to watch for? A: Missed facts, hedging language, and formatting drift. Log them so you can set guardrails.Q: Is it safe for sensitive data? A: Treat Playground as public cloud. Redact names and confidential details or use enterprise options.

What OpenAI Playground is and how to use it

Mental health, brain disorder, World mental health day, Alzheimer, Psychology concept. Tangle of thoughts and clear line on real brown and white speech bubble paper cut with blue background

<h3>TL;DR: Perplexity combines an AI model with live web search, pulls from multiple sources, and shows citations so you can verify facts. This piece explains how the system works, the modes that matter, how to start, and a worked example on “AI training” research. It closes with the simple next step to build this into your team’s workflow.&nbsp;</h3><h2>What makes Perplexity different?</h2>Most AI tools generate answers from training data and do not expose sources, which makes verification slow. Perplexity’s Pro Search runs a targeted crawl, synthesises across many references, and attaches direct links for audit. You can also choose advanced models before a search.&nbsp;Perplexity supports model selection, including its own Sonar plus third-party models such as GPT-5, Claude 4.0 Sonnet, and Gemini 2.5 Pro. Pick depth over speed when accuracy matters.&nbsp;For evidence-heavy work, use Focus options such as Academic to prioritise journals and peer-reviewed sources. This reduces noise from general web content.&nbsp;<h2>Why this matters for work</h2>Research and reporting stall when staff open ten tabs to build one answer. Pro Search increases source depth and keeps a full trail of links, so your team can defend a claim without extra legwork.&nbsp;Threads keep context across follow-ups, and Spaces let teams organise work, set custom instructions, and collaborate in one place. This cuts switching cost across tools.Enterprise Pro adds Internal Knowledge Search so you can combine the web with your organisation’s files in one query, useful for due diligence and board papers.&nbsp;<h2>How to get started, step-by-step</h2><ul><li>Open Perplexity. For quick checks, standard search is fine. For serious work, click Pro Search.&nbsp; </li><li>Choose model. Select Sonar for fast grounded answers, or a third-party model when you want deeper reasoning.&nbsp; </li><li>Set focus. Use Academic for peer-reviewed material, or keep Web for a general scan.&nbsp; </li></ul><blockquote>Ask a precise question. Example: “What training methods are Australian companies using to build AI literacy in 2025?” Then run Pro Search.&nbsp; </blockquote><ul><li>Audit the answer. Open the cited links. Check whether the key claims appear in those sources. If a claim is critical, confirm it in two independent references.&nbsp; </li><li>Organise. Save the thread to a Space, add notes, and share with colleagues for review.&nbsp; </li><li>Enterprise. If you have sensitive material, run Internal Knowledge Search and select Web + Org Files to blend sources.&nbsp; </li></ul><blockquote>Example: deep research on “AI training” Task: prepare a board briefing on staff AI training options for the next quarter. The brief needs current methods, cost ranges, time to competency, and risks.</blockquote><h2>Action in Perplexity:</h2><ul><li>Run Pro Search with the query above. </li><li>Switch to Academic focus for peer-reviewed programmes and learning outcomes. </li><li>Add a second query for “enterprise AI training cost benchmarks ANZ 2025.” </li><li>Save both threads into a Space called “AI Training Q2 Briefing.” Perplexity&nbsp; </li></ul>What you get: a synthesised answer with linked sources across policy pages, HR case studies, and education journals. You keep the citations, so each claim in your slide or memo points back to a source your board can check. If you need internal policy context, run the same query with Internal Knowledge Search to include your own training catalogues or LMS exports.&nbsp;<h2>Results you should expect</h2><ol><li>Faster briefing prep, because the links are attached to the summary. </li><li>More defensible decisions, because assertions are traceable to sources. </li><li>Less time spent chasing facts, more time spent applying them.&nbsp; </li><li>Known limits and how to handle them </li><li>Perplexity improves transparency, not infallibility. Always scan sources for quality, and be aware that some linked pages on the web may themselves be AI-generated.</li><li>Double-check key claims via independent, reputable sites.&nbsp;</li></ol>Finding and verifying information is a core skill. Perplexity makes it practical at pace. If you want your team to use tools like this safely and effectively, enrol in the <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a> for a structured starting point and hands-on workflow practice.<h2>FAQ</h2>Q: Where do I see sources, and how many will Pro Search surface? A: Sources appear next to the answer. Pro Search performs a deeper crawl and can return many more references than standard search.&nbsp;Q: Which model should I pick? A: Start with Sonar for grounded search. Switch to an advanced model such as GPT-5, Claude 4.0 Sonnet, or Gemini 2.5 Pro when the task needs more reasoning. Test for your use case.&nbsp;Q: How do I restrict to peer-reviewed material? A: Use the Academic focus to prioritise journals, databases, and scholarly publications.&nbsp;Q: Can I organise multi-week research with teammates? A: Yes. Use Spaces to group threads, set instructions, and collaborate. Keep key decisions and citations in one place.&nbsp;Q: Can it search our internal files with the web? A: Yes, with Internal Knowledge Search in Pro and Enterprise Pro. Choose Web, Org Files, or Web + Org Files. Check your data policy first.&nbsp;Q: Is there a mobile or browser option? A: Yes. Perplexity is available on web, mobile apps, and extensions, with threads for context across sessions.&nbsp;Q: How do I avoid low-quality or AI-generated sources? A: Prefer official docs, reputable news, and journals. If a linked page looks synthetic, verify the claim via a second, established source before you use it.&nbsp;Q: When should I not use Perplexity? A: For confidential material you cannot upload, or topics where authoritative databases are paywalled and unavailable. In those cases, collect citations manually from primary sources.

How to Use Perplexity AI for Search

Chat with AI or Artificial Intelligence technology. Man using a laptop computer chatting with an intelligent artificial intelligence asks for the answers he wants. Smart assistant futuristic, Chat AI,

<h3>TL;DR: How do people use ChatGPT at work? They draft emails, summarise data, onboard staff, support customers, and manage projects. These are practical usage stories from real workplaces that show how AI simplifies tasks and frees up time for higher-value work.</h3>Most AI conversations stay at the level of hype or fear. What matters more is how people actually use it in their daily work.HubSpot’s research shows that the real impact of ChatGPT comes from small but repeatable tasks. Teams are not reinventing their jobs, they are cutting out friction from the basics: writing, retrieving information, and answering routine questions.<h2>How professionals are using ChatGPT</h2><ul><li>Writing and communication: Staff use ChatGPT to write and refine emails, reports, and presentations. Instead of rewriting the same client update, they paste it in, ask for clearer language, and move on.</li><li>Knowledge retrieval: Employees treat ChatGPT like a fast search bar. When asked to define compliance terms or summarise market reports, it responds in plain English without forcing users to scan endless documents.</li><li>Onboarding and training: HR teams give new hires access to ChatGPT trained on internal FAQs. Instead of interrupting managers with routine questions, new staff can ask the bot how to submit expenses or find policy documents.</li><li>Customer support: Service desks use ChatGPT to handle FAQs and draft initial replies. This clears tickets faster and leaves agents with the complex problems that need human judgment.</li><li>Project management: Managers use ChatGPT to generate draft project plans, task lists, and reminders. It helps teams start faster and reduces time wasted building templates.</li></ul><h2>What this means for business</h2>The common outcome across these usage stories is consistency:<ul><li>Less time lost to rewriting or reformatting. </li><li>Faster onboarding and fewer interruptions. </li><li>Quicker ticket resolution for customers. </li><li>Easier access to information when it is needed. </li></ul>They are visible changes in how teams manage their time.<h2>A day in practice</h2>Take a marketing manager balancing clients and team deadlines:<ul><li>At 9:00 am, they ask ChatGPT to condense a messy client brief into a one-paragraph summary. </li><li>At 11:00 am, they get a draft for a polite reminder email on overdue assets. </li><li>At 1:00 pm, they generate three campaign headline options. </li><li>At 3:00 pm, they review a draft meeting agenda built from last week’s notes.</li></ul>ChatGPT at work is not about radical reinvention. It is really more about making daily tasks easier and faster, and being comfortable with it.&nbsp;The quickest path forward is to pilot it on a single workflow and measure the results. From there, you can decide where scaling makes sense.If you want to identify the best-fit usage stories for your team, our <a data-mce-href="https://aistrategyconsulting.com.au/ai-readiness-assessment" href="https://aistrategyconsulting.com.au/ai-readiness-assessment">AI Readiness Assessment</a> benchmarks current workflows. From there, you can move to an <a data-mce-href="https://aistrategyconsulting.com.au/introduction-to-ai-5-day-bootcamp" href="https://aistrategyconsulting.com.au/introduction-to-ai-5-day-bootcamp">AI Bootcamp </a>or an <a data-mce-href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap">AI Strategy Roadmap</a> to turn individual wins into organisation-wide practice.FAQQ: Will ChatGPT replace roles? A: No. These stories show it removes repetition, not judgment or decision-making.Q: How secure is ChatGPT for business use? A: Start with non-sensitive data. For confidential work, use enterprise deployments with governance controls.Q: Do I need training to use it? A: No coding is needed, but staff see better results with basic prompt training.Q: Where should a team begin? A: Start with one friction-heavy process such as reporting, email drafting, or FAQs.Q: What about wrong answers? A: Keep a human review step. Treat outputs as drafts, not final authority.

How People Use ChatGPT at Work Every Day

<h3>TL;DR: AI didn’t peak with ChatGPT. It has crossed into non-human intelligence, where systems plan, reason, and execute processes on their own. Eric Schmidt and the San Francisco consensus warn of runaway risks, geopolitical conflict, and economic shocks. For white-collar workers, this plays out as jobs unbundling into tasks with AI consuming the repeatable parts and leaving humans to prove value in what remains.</h3><h2>&nbsp; The Moment We Missed</h2>In 2016, an AI program called AlphaGo made a move no human had ever seen in 2,500 years of the game of Go. It wasn’t just clever. It was alien — and it won.For Eric Schmidt, then Google’s CEO, this was the quiet moment the ground shifted. A machine had not only learned from humans but invented something new. That was the first crack in the door to non-human intelligence.Most people missed it. They still think ChatGPT is the big story. It wasn’t.&nbsp;<h2>What’s Really Happening&nbsp;</h2>The shift is that AI no longer predicts and imitates. It now reasons and plans. These systems chain tasks together, set goals, and execute them without waiting for step-by-step input. What began as a tool for writing emails and content now looks more like a machine grad assistant that can run a business process end-to-end. It is a CFO's dream...The limits aren’t technical, they’re physical and political. Training frontier AI already consumes the power of whole cities (500K Kwh is the estimate - so the same as a Boeing 747 from Sydney to Johannesburg in power use and carbon). US officials estimate that keeping pace could require the equivalent of ninety nuclear plants. Meanwhile, the race between the US and China for chips, data centres, and open-source dominance raises the risk of conflict. Schmidt has even suggested that data centres themselves may be treated as strategic targets if one side pulls too far ahead.And then there’s the slope. The San Francisco consensus (yes, all those types that live and run our lives over there) is clear: progress compounds. A slight lead becomes a gulf. A country, a company, or even a single team that learns faster can lock everyone else out. Open-source speeds the cycle, fuelling rapid innovation, but also handing dangerous capability to anyone who picks it up.<h2>What It Means for Your Work</h2>Yes even you, you MBA, CFA, CPA, LLB big shot...Schmidt frames this as a civilisation-scale change, not a product, tech or channel launch. The consensus inside the labs is that AI is moving faster than institutions, companies, or citizens can absorb. And this is true...even for those in or around it...it is moving to fast to build real domain expertise.Systems that self-learn beyond our control. Models that replicate themselves across the internet. AI reaching for tools and weapons without a human gatekeeper. These are scenarios tracked seriously, not dismissed as science fiction.Even if we avoid runaway systems, the economic shock is the big problem. Productivity gains of 30 percent a year would smash every existing economic model. Add in falling birth rates with fewer workers supporting more retirees — and AI becomes possibly the only cheap way to keep welfare and pretty useless white collar societies functioning.That shock lands first on white-collar work. Amazon’s cuts were not on factory floors but on analysts and support roles where AI could replicate the task list. For graduates, the first rung of the ladder has already broken. The stage of “learning by doing” has been replaced by AI outputting in seconds what used to take you years to master. Yet...you are expected to exercise judgement immediately.For those mid-career, the change is quieter but just as brutal. Jobs are being unbundled into their component tasks. The repeatable parts: reporting, tracking, producing slides, are absorbed by machines. What remains is the messy work of making trade-offs, deciding under uncertainty, and carrying accountability when things go wrong.Work itself is coming apart. The danger isn’t only unemployment, but being the professional left holding only the task oversight scraps once bundled into a role.<h2>How to Prepare&nbsp;</h2>Schmidt’s advice is blunt: adopt AI, or become irrelevant. But adopting doesn’t mean handing your role over. It means learning how these systems cut, combine, and accelerate the work around you — and then using them to extend your reach.<ul><li>Teachers can harness AI tutors to personalise learning. </li><li>Doctors can use AI to diagnose faster and translate medical knowledge. </li><li>Businesses can automate repetitive admin, freeing managers to focus or be sacked </li></ul>&nbsp;It’s a continuous skill. You need to understand the basics, test systems in your own work, and keep experimenting as they evolve. The real danger is not bad actors or rogue code. It’s actually 'you' (not in the netflix series way) who stand still until they discover their tasks are being stripped away. And make no mistake - any CFO accountant who sees a 10% decline in quality but a 3000% increase in throughput with that flow through hitting the bottom line...is going to choose AI enhanced over you. No doubt.&nbsp;The arrival of non-human intelligence is dismantling work at the seams. Jobs are no longer bundles of tasks, they’re fragments left behind after machines strip away the predictable pieces. The pressure is simple: prove your value where systems can’t, or watch your role dissolve.Fluency is the only defence. You don’t need to become an engineer, but you do need to understand how AI operates and where it fits. That’s why I run the<a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass"> AI Fundamentals Masterclass</a>, built for professionals who need clarity fast. And for those ready to go deeper, the <a data-mce-href="https://aistrategyconsulting.com.au/introduction-to-ai-5-day-bootcamp" href="https://aistrategyconsulting.com.au/introduction-to-ai-5-day-bootcamp">AI 5-Day Bootcamp</a> gives practical, applied experience so you can work with these systems instead of competing against them.Work will keep unbundling whether you act or not.&nbsp;<h2>FAQ</h2>Q: What does “non-human intelligence” mean? A: It refers to AI inventing new ways of doing things, not just copying human output — like AlphaGo’s move that no human had conceived.Q: Why are white-collar jobs hit first? A: Because AI thrives on information-heavy, repeatable work: analysis, reporting, scheduling, drafting. Physical jobs still rely on presence and hands.Q: What about creative work? A: AI already generates drafts for campaigns, designs, and scripts. The human role is shifting to editing, directing, and deciding.Q: What happens to new graduates if entry-level roles disappear? A: Move to Thailand and party like it is 1999. No. They’re expected to act like decision-makers from the start. The “learn by repetition” phase is gone. How do you think that is going to work out?Q: Why does energy and geopolitics matter to me? A: Because access to AI is shaped by who controls the power, chips, and infrastructure. It decides whether your employer — or a rival abroad — gets the advantage.Q: How can I prepare without being technical? A: Start small: learn what AI can and cannot do, apply it to your own tasks, and build fluency in its use. The skill gap is in adoption, not coding. So you do not need to know Python to know how to speak to the C suite.

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