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<h3>TL;DR<br>AI tools are everywhere, and usage rates are growing fast. But according to the latest AI Proficiency Report, only 10% of workers are actually skilled at using them. Most teams still don’t know how to prompt well, navigate hallucinations, or move beyond basic use cases. If your business is investing in AI without structured training, the ROI you’re expecting will never materialise.</h3><h2>What’s really going on inside your business?</h2><p>You might have signed off on ChatGPT Enterprise. Maybe your team is dabbling with Claude, Microsoft Copilot, or other LLMs. That’s adoption.</p><h2>But that’s not the same as skill.</h2><p>The data from the latest <a data-mce-href="https://www.sectionai.com/ai-academy" href="https://www.sectionai.com/ai-academy" target="_blank" rel="noopener">Section State of AI Adoption Report</a> is clear: 55% of knowledge workers now use AI at least weekly. LLM deployments are up. Excitement is up. Even formal training has grown by 80% in the last six months. This signals a shift from the early adopter crowd to a broader mainstream. But that wider uptake is masking a serious blind spot.</p><p>Most people still don’t know how to use AI well.</p><h2>Just 10% of the workforce is AI-proficient</h2><p>The latest Proficiency Report didn’t just ask if people are using AI. It tested them. Workers had to write prompts, describe their workflows, and apply AI to real business scenarios. </p><p>The result?</p><ul><li>Only 10% met the bar for proficiency.</li><li>54% thought they were proficient.</li><li>44% didn’t know what a hallucination was.</li></ul><p>Most prompts looked like Google queries. Yes, querying like they have been using google for the past two decades.&nbsp;Even in high-tech sectors, the average proficiency score was just 47 out of 100. Customer support scored 33. Marketing barely reached 41.&nbsp;These aren’t just technical gaps they’re commercial and human capital risks.&nbsp; What’s holding people back?<br></p><p>Three core issues stood out:</p><ol><li>People still don’t know what to use AI for. A quarter of workers are stuck at surface-level tasks like meeting summaries or generic copy.</li><li>They’re not accessing the best tools. Most proficient users have paid access to enterprise-grade LLMs. Most others don’t.</li><li>They’re not being trained properly. Only 10% of AI sceptics say they’ve had structured training. Among experts, it’s 71%.</li></ol><p>The result? AI is treated like a toy, not a business tool. And that’s a problem if you’re counting on productivity gains, headcount efficiency, or improved decision-making.</p><h2>Most companies are under-investing in the skill gap<br></h2><p>This isn’t something that fixes itself.</p><p>The speed of LLM releases is outpacing people’s ability to keep up. New models drop monthly. Even seasoned users aren’t exploring advanced use cases like deep research, chain-of-thought prompting, or custom workflows. I can barely keep up with each new tool released, never mind the capability in the models. It all seems a bit spinning plates from time to time - we are all a bit stuck beyond, wow that is a cool video or cool image.</p><p>Meanwhile, companies are assuming adoption equals progress.</p><p>It doesn’t.</p><p>If your managers aren’t encouraging smart AI use, if your policies are unclear, and if your team doesn’t understand how prompting works—you’re setting them (and yourself) up to fail.</p><p>So, what should you do next?</p><p>Give up. Just forget it and ban all AI. It will blow itself out and we well get on with business as usual....</p><p>No! Not really! If you’re responsible for delivery, capability, or team performance, this part’s for you. So listen carefully please:</p><p><strong>You don’t need everyone to become a machine learning engineer or build vector databases, embeddings and chunk data. No need for KNN and Baysian statistics - this is general purpose technology so you need a general purpose plan.</strong></p><p>You do however need them to:</p><ul><li>Understand what generative AI is doing (and what it’s not).</li><li>Prompt with structure and intent.</li><li>Navigate hallucinations and know when to trust vs check.</li><li>Use AI for real business tasks, not just admin.</li></ul><p>That’s why we 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>. It’s designed specifically for non-technical teams. We teach the core mechanics, expose the biggest myths, and help people build confidence fast—without jargon.</p><p>If your organisation is adopting AI, this is how you ensure it delivers.</p><p>Most people aren’t AI-proficient. </p><p>But they can be, if you start with the right foundation.</p><p>Learn more about the <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a></p>

State of AI Adoption: Most Workers Still Don’t Know How to Use AI

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<h3>TL;DR<br>Even the smartest AI models today don’t really reason. They remember. They summarise. They sound fluent.<br>But when logic is buried in a long, messy story? They fail.</h3><h3>A new paper by Alex Pan (who I worked with at IBM in 2017) and Mary-Anne Williams introduces Verbose ListOps (VLO), a test with a cool name that shows exactly where and how today’s top models break. It explains why bigger models and longer memory aren’t enough, and what businesses need to check before trusting AI with decision making.</h3><h2>What’s the real problem with LLMs?</h2><p>Large Language Models like GPT, Claude, or Gemini can write fluent emails, summarise PDFs, and answer questions at speed.<br>That fluency is often mistaken for understanding.</p><p>But here’s the catch.<br>Fluent text is not the same as reasoned thinking.<br>And pulling facts from a document doesn’t mean the model understands the argument inside it.</p><h2>How do you know if AI can really think?</h2><p>Benchmarking is how AI labs test model performance.<br>They ask things like:</p><ul><li>Can it solve basic logic puzzles?</li><li>Can it summarise across documents?</li><li>Can it find a fact hidden in long content?<br></li></ul><p>These tests get reported as impressive scores.&nbsp;For example, GPT-4 might score 98 percent on Grade 8 science problems. But these scores come from clean inputs with clean answers.</p><p>Real work isn’t like that.</p><p>Most business logic is buried in the middle of messy, multi-layered information. Or the copy paste you put into Gemini, or the 88 page PDF on how to market to middle class families!&nbsp;<br></p><p>And that’s where current models start to break.</p><h2>What does reasoning look like in real business tasks?</h2><p>Think about the kind of problems your team faces:</p><p>Reviewing a 15-page contract with inconsistent clauses<br>Analysing a series of customer support emails over time<br>Connecting cause and effect across incident reports<br>Synthesising different versions of events in a compliance audit<br></p><p>These aren’t prompt-response tasks. They require narrative reasoning.<br>Understanding what happened, across time, with ambiguity and distraction in play.&nbsp; Today’s models struggle here. And most benchmarks don’t test for it, and most users do not know how to check for it.</p><h2>A new kind of benchmark: Verbose ListOps (VLO)</h2><p>How cool is that name! The legendary Alex Pan and Mary-Anne Williams built VLO to test comprehension inside complex, realistic language.&nbsp; It is a logic benchmark, but with a twist.</p><p>VLO embeds a logic task inside a long, human-sounding story.<br>It simulates the conditions of real-world work:</p><ul><li>Logic buried in multi-paragraph content</li><li>Distracting but plausible noise</li><li>No giveaways or step-by-step prompts</li><li>Semantic complexity that forces actual reasoning<br></li></ul><p>As Alex and Mary-Anne explain:<br>“Our benchmark tests the ability of models to infer, calculate, and reason within semantically complex and verbose contexts... This is key to evaluating comprehension, not just context retention.”</p><h2>What did the benchmark test reveal?<br></h2><p>They tested seven of the most advanced language models available today, including GPT-4.1, Claude 3.7 Sonnet, Gemini 2.5, DeepSeek V3, Qwen 3, LLaMA 4, and Grok 3. When asked direct logic questions, most models performed well.<br>But when the same logic was hidden in a long narrative, results collapsed:</p><ul><li>GPT-4.1 dropped to 32 percent accuracy</li><li>DeepSeek V3 fell from 93 to 25 percent</li><li>Similar drops were seen across Claude and Gemini</li><li>The story format didn’t overwhelm the models with length.</li><li>It tripped them up by requiring actual reasoning.</li></ul><h2>What does this look like in your workflow?</h2><p>Imagine (some of you will not have to as you are doing this) your sales or service team uses GPT to review a CRM thread and asks:</p><p>“Did the customer churn because of pricing?”</p><p>Here’s what’s in the email thread between the teams:</p><p>“The new pricing tier felt expensive.”<br>“The CFO flagged the cost as a concern.”<br>“We paused onboarding after the quote came in.”<br>“In the end, we chose the cheaper option.”<br></p><p><strong><em>To a human, the logic is simple: pricing caused churn.</em></strong></p><p>But GPT <strong>might</strong> respond with:</p><p>“Uptime was mentioned.”<br>“Karen questioned ROI.”<br>“Pricing was a factor, but not the only one.”<br></p><p>It doesn’t misread the content.<br>It just fails to connect the reasoning chain:</p><p><strong>Price increase → hesitation → quote rejected → cheaper competitor chosen</strong></p><p>This is the kind of failure VLO reveals.And it’s exactly the kind of reasoning most workflows need. And this is a <strong>BIG BIG </strong>problem for decision making or automated workflows with LLM API's.&nbsp;</p><h2>Why does this matter for your business?</h2><p>Because, legally you are in real deep trouble. It is a real operational risk if you're using AI for:</p><ul><li>Churn prediction</li><li>Lead scoring</li><li>Customer support triage</li><li>Legal contract review</li><li>Policy or compliance analysis<br></li></ul><p>Guess what? If the model can’t reason, it will fail to:</p><ol><li>Spot the real cause in client feedback</li><li>Track obligations across contracts</li><li>Interpret context in complaints</li><li>Justify decisions to auditors or regulators<br></li></ol><p>And worse, you may not realise it has failed because the language still sounds confident and badass correct.</p><h2>What VLO proves</h2><p>VLO doesn’t test if a model can hold more data.<br>It tests whether it can work through the logic that connects that data.</p><p>Questions it answers:</p><ul><li>Can the model track multiple steps in a story?</li><li>Can it ignore distractions?</li><li>Can it compute what isn’t explicitly stated?</li><li>Can it reason like a human analyst?<br></li></ul><p>If the answer is no, then you should not trust that model in any decision chain where cause, consequence, or traceability matter.</p><p>Verbose ListOps (what a name!)&nbsp; is the benchmark the business world didn’t know it needed. It proves a critical gap: models don’t fail because they forget, they fail because they don’t reason.</p><p>That changes how we think about AI in business.<br></p><p>It means:</p><ul><li>You need to audit your reasoning workflows</li><li>You need to scaffold logic, not assume it</li><li>You need to simulate messy, multi-step inputs<br></li><li>You need assurance before pushing AI into live decision processes<br></li></ul><p>Alex and Mary Anne didn’t just build a clever test. They issued a warning. And if your business depends on models to think clearly, now is the time to check.</p><h2>Time to audit your AI?</h2><p>If your workflows involve risk, logic, or decision-making, you need more than a prompt test. You need to know whether your model can reason under pressure.</p><p>We help organisations audit their AI systems against real-world complexity using ISO 42001. We are ISO 42001 Certified Lead Auditors. Make&nbsp;sure your AI decisions hold up under scrutiny.</p><p>Explore our<a data-mce-href="https://aistrategyconsulting.com.au/ai-iso-42001-aims-certification" href="https://aistrategyconsulting.com.au/ai-iso-42001-aims-certification"> ISO 42001 audit and AI assurance services</a></p>

AI Models Still Can’t Think. And Now We Can Prove It.

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<h3>TL;DR<br>Despite the hype, some AI tools still fail at basic reasoning. A recent Apple research paper found that even today’s most advanced language models often give wrong answers to simple logic questions—because they don’t really understand what they’re saying.</h3><h2>What’s going on with AI and ‘thinking’?</h2><p>If you’ve used ChatGPT or any similar tool, you’ve likely seen moments of brilliance and others where it confidently makes something up. It might explain physics in plain English, then completely miss the point of a yes/no question. That contradiction is exactly what a group of Apple researchers explored in a recent paper titled The Illusion of Thinking (published May 2025).</p><p>They tested large language models (LLMs) like GPT-4 and Claude 3 on tasks that look simple to humans but require real reasoning—like tracking what’s true, what’s false, and how the two interact. The models got many of these questions wrong. Not because they’re broken, but because they work differently than most people assume.</p><p>Instead of thinking, they predict. And that difference matters.</p><h2>So what did Apple actually test?</h2><p>The researchers gave the AI questions like:</p><p>“John has a red ball. Mary does not have a red ball. Does Mary have a red ball?”</p><p>And the AI would sometimes say: “Yes.”</p><p>This isn’t a trick question. But because language models are trained to guess the next likely word in a sentence rather than reason through truth and falsehood, they can fumble even when the logic is simple.</p><p>The paper tested hundreds of examples like this. The results were consistent: models that looked fluent often got basic logic questions wrong. And when the questions got a bit more complex (e.g. adding double negatives or increasing volumes and adding quadratic reasoning), the models broke down a bit more.</p><h2>Why does this matter?</h2><p>Because many businesses are betting on these tools to do real work. Tasks like summarising contracts, reviewing regulations, answering policy questions, or helping customers navigate legal or financial documents. Whether used directly, part of a workflow or an internal model.</p><p>If your team assumes these models “understand” what they’re saying, you ARE in trouble.</p><p>The Apple team (a brilliant young PHD student and others) didn’t just criticise thought. They built a new kind of test to evaluate whether a model actually grasps truth and reasoning logic, rather than just sounding right-ish (my dash). Their findings suggest a need for much more caution when deploying AI overall and LLM's in particular into tasks where accuracy matters.</p><h2>Some are calling this a ‘knockout blow’is that fair?</h2><p>A few commentators, including well-known AI critic Gary Marcus, have described the Apple paper as a wake-up call. His point isn’t that LLMs are useless. It’s that we’re using them in ways they’re not built for - and on the same day OpenAI forecast a billion users very soon this year.</p><p>These tools are great at:</p><ul><li>Writing emails</li><li>Drafting creative ideas</li><li>Summarising large chunks of text</li><li>But they’re still unreliable at:</li><li>Logical reasoning</li><li>Truth tracking</li><li>Understanding context across time and space</li></ul><p>As Marcus put it: “They sound smart. But they aren’t.” That disconnect creates risk when people start trusting the output too much.</p><h2>So should you stop using LLMs?</h2><p>Yes. Sort off. No! but you should change how you think about them.</p><p>If you’re using AI to speed up workflows, write content drafts, or help structure thinking, it’s still a powerful tool. But if you’re treating the output as fact, or expecting the model to reason through complex issues reliably, you may be overreaching irrespective of RAG or your prompt engineering. Beware the 'One Prompt to Rule them All' salesmen parading their killer prompts on LinkedIN.</p><p>Here’s a simple principle from this research:<br>If the task needs logic, truth, or judgement, then double check it. The same as you did in uni before you turned in a paper, or these days when you check your kid's homework.</p><h2>What’s next?</h2><p>This paper won’t be the last to challenge assumptions about how “smart” LLMs really are. But it’s an important moment for business leaders, content teams, educators, and anyone trying to adopt AI responsibly.</p><h2>It also reinforces why we need:</h2><ul><li>AI policies inside organisations alined to global standards like ISO</li><li>Human oversight in critical workflows</li><li>Better tools to check accuracy, not just fluency</li></ul><p>It’s easy to be dazzled by how human these models sound. </p><p>But the real question isn’t: “Can it talk?” It’s: “Does it understand?”</p><p>Right now, the answer is: not always.</p><p>Want to build AI into your workflows without getting caught out?</p><p>Join our next <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a><br>Or start with our <a data-mce-href="https://aistrategyconsulting.com.au/ai-readiness-assessment" href="https://aistrategyconsulting.com.au/ai-readiness-assessment" data-mce-selected="inline-boundary">AI Readiness Assessment</a> to see where you stand</p>

Apple Says AI Isn’t Thinking. It's Guessing! You Better Check.

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<h3>TL;DR<br>If your team is using ChatGPT, Gemini, or Microsoft Copilot at work—even casually—you’re already exposed to legal, ethical, and reputational risks. Australia is moving fast on AI regulation, and ISO/IEC 42001 is now the clearest way to bring your AI use under control. An internal audit is the first step to prepare.</h3><h2>AI isn’t just coming to your workplace. It’s already there.</h2><p>Wonder how the intern with no financial analysis experience is delivering sector specific deep research on valuation and industry mechanics in a day? No...you're just happy you have saved $200K on analyst! But, what’s happening on your shared drive, in your inbox, and across your browser tabs right now? Do not know do you? Used Copilot to draft a weekly wrap? Used ChatGPT to build your slide back for the weekly management meeting? Nailed out a market assessment with Claude?&nbsp;</p><p>Employees are already using generative AI tools to rewrite emails, summarise PDFs, clean up notes, and prepare documents. </p><p>No formal strategy. No policy. No oversight.</p><p>The problem is, every time one of those tools is used at work, the organisation becomes accountable. Accountable for what data was entered. Accountable for the output. Accountable for any harm, bias, breach, or unintended use that follows.</p><p>That’s not trying to scare you - but it should (my own dash).</p><p> That’s what regulators are now building policy around.</p><h2>Regulation is Starting to Get Codified</h2><p>In 2024, the Australian Government confirmed it will introduce targeted laws to address high-risk AI systems, enforce transparency, and align with global best practice. This includes mandatory protections when AI is used in ways that affect people like hiring decisions, customer service, financial advice, or legal interpretation.</p><p>But you don’t have to wait for the legislation to get moving.</p><p>Clients are already asking what controls you have in place. Risk committees are flagging exposure. Staff are unsure what’s allowed. And once something goes wrong, an offensive answer, a privacy breach, an AI-generated error in a report (a personal report for high risk indviduals authored by an LLM) the question isn’t </p><p>“Who clicked send?”</p><p>It’s: “What do you have in place to stop this?”</p><p>Most companies don’t have one. That’s where ISO/IEC 42001 comes in.<br>This new international standard isn’t for AI builders. It’s for AI users, especially organisations relying on large language models (LLMs) like ChatGPT, Claude, Gemini, and Copilot.</p><p>ISO 42001 sets out how to manage the risks, data handling, ethical safeguards, and governance responsibilities involved in using AI at work. And it does so in a structured, auditable way that gives boards, regulators, and staff confidence that you're not winging it.</p><p>Here’s what it helps uncover:</p><ul><li>Where AI tools are being used without approval, policy, or visibility</li><li>Whether staff are pasting sensitive information into unregulated systems</li><li>How biased or incorrect outputs could affect decision-making or clients</li><li>Whether your AI use is breaching privacy laws—without you realising it<br></li></ul><p>If your business is using these tools—and most are—it’s only a matter of time before something slips through that could have been prevented.</p><h2>Ignorance won’t be a defence.</h2><p>Internal audits are your first line of protection.</p><p>You don’t need a full AI strategy to start fixing the problem. You need a clear understanding of what’s already happening in your organisation.</p><p>That’s what an internal audit aligned to ISO 42001 delivers. </p><p>It shows you:</p><ul><li>Where AI use is happening across teams, systems, and processes</li><li>What risks are currently unmanaged (or unknown)</li><li>How to build clear, defensible policies and controls that meet the standard</li><li>What immediate changes will reduce exposure, protect data, and prepare you for incoming regulation<br></li></ul><p>AI use doesn’t start with policy it often starts with a staff member under deadline. </p><p>But AI compliance does start with policy. And if you don’t know where your risks are, you can’t manage them.</p><p>AI adoption isn’t a tech initiative anymore. It’s a governance issue. It touches privacy, ethics, legal risk, public trust, and operational reputation.</p><p>Regulators are watching. Clients are asking. And ISO 42001 gives you a way to answer with confidence.</p><p>But the first step isn’t a framework. It’s an audit.</p><p><a data-mce-href="https://aistrategyconsulting.com.au/ai-iso-42001-aims-certification" href="https://aistrategyconsulting.com.au/ai-iso-42001-aims-certification">Book your ISO 42001 Internal Audit</a><br></p><p>We’ll help you:</p><ul><li>Uncover hidden AI risks across your business</li><li>Understand what’s needed to align with ISO standards</li><li>Build trust with regulators, boards, and staff before the rules arrive<br><br></li></ul>

AI Use AI at Work? Beware, You Are Risking More Than You Think

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<h3>TL;DR<br>Enterprise versions of LLM's protect your data by default. Consumer versions make you opt out, if they let you at all.<br>Some models still send your chats to human reviewers, even after you’ve deleted them.</h3><h2>Privacy is a design choice.</h2><p>Most people think:<br>“If I don’t give it sensitive information, it won’t become a problem.”</p><p>But that assumes the tool treats your data as private. Most don’t—unless you’re paying for that promise.</p><p>Privacy with AI isn’t just about whether you can turn something off. It’s about what’s happening before you even know the option exists.</p><h2>If you’re using ChatGPT…</h2><p>It depends on the version.</p><p>If you’re using the free or Plus version, your chats are part of OpenAI’s training data by default. You have to dig into Settings → Data Controls and switch off the “Improve the model for everyone” option to stop that. Even then, your chat history sticks around—unless you toggle on Temporary Chat, which deletes it after 30 days.</p><p>If you're on ChatGPT Enterprise, it's a different story. None of your prompts, files, or outputs are used for training—ever. You don’t need to change a thing. Your data stays your data.</p><p>So: free is convenient, but leaky. Paid enterprise plans are safer by design.</p><h2>If you’re using Claude…</h2><p>You’re in better hands than most.</p><p>By default, Claude doesn’t train on your data—unless you explicitly opt in by submitting feedback. That’s a major positive, especially for casual users or early testers.</p><p>But here’s the tradeoff: there’s no toggle to turn privacy settings on or off, because there are no settings. You’re trusting the default design. For some teams, that’s fine. Others might want more granular control—especially if you’re handling client material or regulated data.</p><h2>If you’re using Gemini…</h2><p>You need to be careful.</p><p>Gemini stores your conversation history by default. And even if you turn off that history, Google may still keep anonymised versions of your chats—for human review, for up to three years. This happens even after you delete them from your account. That’s not just a footnote. That’s a serious flag.</p><p>Enterprise users (on Google Workspace) get stronger boundaries: no training, no human review, no data leaving your org. But if you’re using the regular version, your inputs are likely being used to improve the model—and possibly reviewed by people outside your company.</p><p>In short: Gemini is the tool most likely to surprise you later.</p><h2>If you’re using Perplexity…</h2><p>It gives you options—but you have to use them.</p><p>By default, your questions and answers help train the model. But in Settings → AI Data Usage, you can turn this off. There’s also an Incognito mode that doesn’t store anything, and a Memory section where you can clear or disable personalisation entirely.</p><p>That’s more transparency than most tools offer. But again, the default is opt-in. You have to act to protect your data.</p><h2>If you’re using Microsoft Copilot…</h2><p>Check which version you’re on.</p><p>The enterprise version of Copilot (inside Microsoft 365) is built with clear walls. Your business data stays inside your tenant. It’s not used to train anything. It’s not shared outside your org. That’s a solid privacy posture.</p><p>But the consumer version is different. Chat history is saved for up to 18 months. Training is on by default (though personal identifiers are removed), and your voice inputs are treated like text. You can manage these settings in your account profile, but you need to go looking for them.</p><p>If you're unsure which version you're using you probably don’t have the protected one.</p><h2>So, how secure is your data?<br></h2><p>It depends on:</p><ul><li>Which tool you're using</li><li>What version you're on</li><li>Whether you've changed any settings</li></ul><p>And let’s be blunt: most people haven’t. Most teams are still experimenting with AI tools assuming they're sealed boxes. But many of them are more like public notebooks—with invisible readers and long memories.</p><h2>Final thought</h2><p>There’s no universal privacy standard in generative AI.<br>Some tools default to protection. Some default to training.<br>Some leave the door open for human review—long after you’ve closed the tab.</p><p>If you're experimenting with AI, it's not about banning tools. It's about knowing where the risks are and how to work around them.</p><p>Start by asking:</p><ol><li>What versions are we using?</li><li>Who has access to our prompts and outputs?</li><li>Have we changed the default settings?</li><li>And if the answer to those is “not sure”<br></li></ol><p><a data-mce-href="https://aistrategyconsulting.com.au/ai-readiness-assessment" href="https://aistrategyconsulting.com.au/ai-readiness-assessment">Start with an AI Readiness Assessment.</a><br></p><p>You’ll get a clear view of where your data’s going, and what to fix before it becomes a problem.</p>

Which AI Tools Respect Your Privacy?

<h3>**TLDR**<br>AI is already embedded in healthcare, finance, retail, and logistics. But many professionals still struggle to picture how it applies to their work. This blog walks through real examples of AI use in different sectors, showing where it adds value, how it’s deployed, and what it means for those curious but unsure how to start.</h3><h2>You’ve heard the stories, but still can’t see how it fits?</h2><p>It’s one thing to read headlines about AI reshaping industries. It’s another to see where it actually delivers results especially if you’re not working in tech.</p><p>This is where many professionals stall:</p><ul><li>You’re interested, but every example feels either too vague or too complex</li><li>The tools seem powerful, but the leap from “proof of concept” to everyday workflow isn’t obvious</li></ul><p>You’re still asking: What would I actually do with this?<br></p><p>That’s a signal that the conversation needs to move from hype to practical, real-world use cases grounded in how people and teams work today.</p><h2>What real AI implementation looks like in practice</h2><p><br>Below are examples of how AI is already embedded in functional operations — not as experimental side projects, but as part of how core tasks are now performed.</p><h2>In Healthcare: Speeding up diagnosis and personalising treatment</h2><p>Hospitals and clinics are using AI to analyse patient scans, triage urgent cases, and identify treatment options more quickly.&nbsp;AI-powered diagnostics help radiologists detect abnormalities faster, with tools flagging anomalies in scans for priority review.&nbsp;</p><p>At Mayo Clinic and Mount Sinai, AI models assist in identifying heart failure risk earlier by combining data from patient histories, imaging, and test results.<br>It’s not replacing doctors. It’s making frontline clinical decisions faster and more consistent — especially in systems under resource pressure.</p><h2>In Finance: Catching fraud and improving customer service</h2><p>Banks and fintech companies have moved beyond chatbots. They’re using AI to monitor transactions, detect suspicious behaviour, and even adapt customer experiences in real-time.&nbsp;</p><p>Fraud detection systems use machine learning to flag unusual transactions before money is lost, learning patterns as they evolve. Credit scoring models use non-traditional data like transaction behaviour or digital footprints to assess risk for customers who might not qualify under legacy systems.</p><p><br>These tools are tuned for accuracy and speed — which matters when you're dealing with financial risk or customer trust.</p><h2>In Retail: Predicting demand and automating decisions</h2><p>Retailers are applying AI across both operations and marketing:</p><ul><li>Inventory forecasting tools predict which products will sell, in which locations, and at what times — helping reduce waste and overstock.</li><li>Personalisation engines analyse customer preferences and behaviour to offer better product recommendations, driving up conversion and retention.<br></li></ul><p>One large fashion retailer, for instance, uses AI to automate daily pricing decisions based on sales velocity, stock levels, and competitor activity decisions that used to take weeks.</p><h2>&nbsp;In Logistics: Managing routes, delays, and supply chains in real time<br></h2><p>Shipping and supply chain businesses use AI to adapt on the fly:</p><ul><li>Route optimisation algorithms adjust deliveries in real time based on traffic, weather, or fleet availability.</li><li>Predictive maintenance tools reduce vehicle downtime by flagging faults before they cause breakdowns.</li><li>In warehouse operations, AI vision systems are sorting, scanning, and managing inventory with minimal human intervention.<br></li></ul><p>This is where AI drives cost savings, lowers error rates, and smooths customer service delivery — not through automation alone, but through better decisions.</p><h2>What you should take away from these examples</h2><p>In every case, the pattern is the same: AI isn’t dropped into a business for novelty. It’s tied to a specific, measurable workflow where speed, accuracy, or personalisation matters. You don’t need to build from scratch. You don’t need to be technical.</p><p> But you do need:</p><ol><li>A clear task or pain point</li><li>A tool that fits your level of readiness</li><li>A safe environment to test and improve how you use it<br>&nbsp;</li></ol><h2>Where most people go wrong:</h2><p>Waiting for someone else to figure it out.&nbsp;Most non-technical professionals wait for someone to hand them a fully-built AI strategy. In reality, the teams who make real progress start small trying one workflow, one task, one experiment.</p><p>What they don’t do is wait for permission. They build confidence through experience.</p><p>&nbsp;Build that confidence with the right kind of support.</p><p>If you want to understand what AI can actually do for your team or business, we’ve built two simple ways to get started:</p><ul><li>Our <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a> helps you understand how AI works, what’s real, and where it fits into non-technical roles.</li><li>Our <a data-mce-href="https://aistrategyconsulting.com.au/ai-no-code-low-code-implementation" href="https://aistrategyconsulting.com.au/ai-no-code-low-code-implementation">No Code AI Implementation</a> sessions help you test real tools — safely — using your own workflows and data.</li></ul><p>Explore our masterclass or implementation support and see how others are moving from interest to impact.</p>

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