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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>

What Practical AI Use Looks Like in Service Businesses

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<h3>**TLDR**<br>NotebookLM is an AI assistant from Google that reads and answers questions about your own documents. It sounds simple, but if you use it without clear purpose or guardrails, you risk wasting time, sharing the wrong material, or drawing the wrong conclusions. This blog explains what NotebookLM does, where it fits, and how to use it safely in a workplace context.</h3><h2>“Just try it”—but you’re not sure what it is</h2><p>If someone on your team or in your network has said “NotebookLM can summarise reports for you,” they’re not wrong. But they’ve skipped a few steps.</p><p>You’re being handed tools that sound useful — but without clarity on:</p><p>What they’re actually for<br>How much you can trust the answers<br>Whether they’re safe to use with work files<br>You’re not overthinking it. You’re being responsible. This is the gap most people fall into: trying tools before understanding them.</p><p>Let’s close that gap.</p><h2>What is NotebookLM actually for?</h2><p>NotebookLM is an AI tool built by Google. It lets you upload your own content — documents, PDFs, notes — and then ask questions about what you’ve uploaded. It answers based only on that material.</p><p>Think of it as a private assistant that reads your documents and responds based on what’s inside.</p><p>People typically use it to:</p><ul><li>Summarise long documents</li><li>Extract key points or quotes</li><li>Ask follow-up questions in plain English</li><li>Generate drafts or FAQs from internal materials</li></ul><p><br>But it has limits. It doesn’t fact-check. It doesn’t know if your file is a final version or a draft. It won’t warn you if the content is outdated or irrelevant.</p><p>That’s where problems start.</p><h2>Before you start using it</h2><p>Before you upload a report or project doc, ask yourself:</p><p>1. Is this the right kind of document?<br>NotebookLM pulls its answers only from the content you provide. If that content is incomplete, out of date, or poorly written, the tool still uses it. It won’t know any better.</p><p>2. Do I know what I want from it?<br>NotebookLM isn’t a general search engine. You won’t get value unless you’ve got a specific task in mind — like checking consistency across policies or pulling themes from a training transcript.</p><p>3. Am I uploading anything sensitive?<br>If you’re logged in through a personal Google account, you might be uploading work material into a tool without proper controls. Most people don’t mean to breach policy — it happens because no one’s explained the risks.</p><p>The tool isn’t dangerous on its own. But using it without thinking? That’s where risk creeps in.</p><h2>Why “just experimenting” isn’t always safe</h2><p>When AI tools feel easy to use, it’s tempting to jump in without a plan. But we’ve seen what happens when people skip structure:</p><ul><li>Uploading the wrong content</li><li>Trusting inaccurate summaries</li><li>Sharing outputs without checking them</li></ul><p>You don’t need formal training. But you do need a baseline understanding:</p><ul><li>What kind of tasks it suits</li><li>When to double-check outputs</li><li>What not to upload<br></li></ul><p>That’s what turns trial and error into capability.</p><h2>What effective use actually looks like</h2><p>Used well, NotebookLM can:</p><ul><li>Save time summarising complex content</li><li>Help you prepare meeting packs or team notes faster</li><li>Reduce back-and-forth on large internal documents<br></li></ul><p>But that only happens when:</p><ul><li>You upload the right material</li><li>You use it for scoped, clear tasks</li><li>You know when human judgement is still required<br></li></ul><p>No tool replaces thinking. But tools like this can help you work smarter — if you treat them with the right level of care.</p><h2>Real example: A mistake that nearly went unnoticed</h2><p>We worked with a team using NotebookLM to help summarise monthly audit materials. It was working well until someone uploaded a report from the wrong month. The tool summarised it accurately. The problem? It was the wrong input. The result nearly made it into a board update.</p><p>After that, the team added a simple file labelling process and set clear guidelines: what to upload, how to check dates, and when human review is non-negotiable. It didn’t slow them down,&nbsp; it just removed the blind spots.</p><p>That’s the kind of adjustment most teams never make. And that’s where small errors turn into real consequences.</p><h2>Want to make AI tools useful — without guessing?</h2><p>If you’re already trying tools like NotebookLM, or you can see them creeping into team workflows, it’s time to give people more than “just try it.”</p><p>We run <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 that help teams:</p><ol><li>Match tools to the right use cases</li><li>Build confidence in how to use them</li><li>Avoid unintentional mistakes or rework</li><li>Create small, structured workflows that actually save time<br></li></ol><p>You don’t need to be technical. You just need to know where to start (and where things usually go wrong).</p><p>Book a call now to explore how we help teams build safe, useful AI workflows.</p>

What You Should Know Before Using Google NotebookLM at Work

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<p><strong>**TL;DR**</strong><br><strong>If you’ve heard names like GPT-4o, Claude 3.7, or DeepSeek-R1 and thought, “I don’t know what that means or why it matters,” you’re not alone. Most leaders are being shown AI model comparisons without any context. This blog explains what an AI model is, how it shows up inside tools, and how to think about them in terms of tasks not only features. You don’t need to understand the specs. You need to understand what the model is being used to support.</strong></p><h2>Why this keeps coming up</h2><p>You’re a non-technical executive. Maybe in people and culture, operations, or the public sector. You’ve sat through a vendor demo. You’ve heard someone mention GPT-4o. You’ve been asked whether a particular tool is using Claude or Gemini. And somewhere in that conversation, the expectation shifts.</p><p>Suddenly, you're meant to choose.</p><p>But you were never taught how to evaluate an AI model. You don’t even get to see it directly as it’s embedded inside a product. The language sounds technical, the stakes feel high, and you're expected to have an opinion.</p><p>The real problem is not your lack of knowledge. It's that most internal AI conversations start in the wrong place.</p><h2>How to think about AI models without the tech overload</h2><p><strong>Start with the work, not the model</strong><br>AI models are statistical engines trained on text, data, and rules. They sit behind the scenes of AI-powered tools. You’re not interacting with them directly. You’re using a product — and that product is using a model to generate, summarise, suggest, or respond.</p><p>So don’t start with model names. </p><p>Start with the task:</p><ul><li>What job are we trying to make easier?</li><li>Who is doing that job today?</li><li>What matters most:&nbsp; speed, quality, reliability, explanation?</li><li>What happens if it gets something wrong?</li></ul><p><br>This frame lets you assess fit for purpose not just model performance. </p><h2>Real differences, real implications<br></h2><p>Now that you’re focused on the task, model differences start to matter but only in context.</p><p>Here’s what’s surfaced in practice:</p><ul><li>Claude 3.7 Sonnet is known for careful, structured reasoning. It’s been used in policy, legal, and compliance teams for document interpretation and summarisation.</li><li>GPT-4o is well integrated with business tools and handles fast, fluent responses in both text and visual tasks. It works well inside Microsoft 365.</li><li>DeepSeek-R1 is strong in Mandarin and multilingual scenarios, so it’s being explored in call centre support and translation-heavy operations.<br></li></ul><p>Each model has different strengths. None is universally best. If you haven’t scoped the task, the model comparison won’t help.</p><h2>Where leaders go wrong</h2><p>Many leadership teams try to pick a model without first agreeing on where AI fits.&nbsp;Before choosing a tool, your team needs to:</p><ul><li>Map one or two clear processes where AI might help</li><li>Identify risks, controls, and who will use the output</li><li>Agree what success looks like: saved time, reduced rework, improved response quality?<br></li></ul><p>Once that’s clear, the model choice often becomes obvious. Or it becomes a vendor’s concern not yours.</p><h2>What this changes for your team</h2><p>Once your team stops comparing AI models in isolation and starts thinking about tasks, workflows, and context, the conversation gets clearer.</p><p>You no longer need to know the technical differences between Claude and GPT. You just need to know:</p><ol><li>What function the model is performing</li><li>Whether that matches your operational need</li><li>And if the tool using it meets your safety, privacy, and performance criteria<br></li></ol><p>This shift reduces overwhelm, improves alignment, and gives your leadership team a shared framework to evaluate future tools.</p><p>&nbsp;This approach isn’t hypothetical. Organisations are using it today.</p><ul><li>A policy team in government uses Claude 3.7 to check consistency across long-form documents. The priority is accuracy and transparency.</li><li>A research team uses Perplexity (powered by various models) to source quick, citation-based summaries. The focus is speed and breadth.</li><li>A contact centre uses tools powered by GPT-4o for resolving image-based queries and responding in real time within integrated systems.<br></li></ul><p>Each use case led to the right model for that workflow. The decision wasn’t about specs. It was about the job to be done.</p><h2>Next steps: How to build your team’s confidence</h2><p>If your executive team feels like it's stuck comparing terms it doesn’t understand, don’t keep pushing through. Step back.</p><p>Book a fundamentals session that gives your team a shared foundation. </p><p>Focus on:</p><ul><li>What an AI model is — and what it isn’t</li><li>Where it fits inside real tools</li><li>How to frame adoption using business logic, not technical specs<br></li></ul><p>The goal isn’t to become fluent in AI model architecture. The goal is to make confident, coordinated decisions about where AI fits in your organisation.</p><h2>Ready to move to clarity?</h2><p>Our <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a> is designed for leadership teams who are being asked to make AI decisions without the technical background.</p><p>You’ll leave with:</p><p>A shared understanding of how AI models support real work<br>Clear criteria for evaluating tools and vendors<br>Practical guidance on what to prioritise, where to test, and how to avoid mistakes<br>→ Book a <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">Fundamentals Masterclass</a><br><br></p><p><br></p>

What to Know About AI Models Before you Choose One

Artificial Intelligence, AI Learning, Machine learning, Internet technology networking concept

<h3>TL;DR<br>You’re being asked to act on AI before your team has agreed what “AI” means. Readiness isn’t about tools. It’s about whether your organisation can make decisions with confidence, manage risk, and apply AI where it adds value. This blog shows you how to start building that readiness across leadership.</h3><h2>What AI readiness really means</h2><p>Here’s the mistake: leaders assume AI readiness means hiring technical talent or signing a pilot deal. It doesn’t.</p><p>AI readiness is about organisational fitness: can your people make good decisions about AI use cases without adding confusion, reputational risk, or unnecessary cost? The World Economic Forum offers a framework for this: seven domains that must work together from strategy to governance to operations. But the real takeaway isn’t the list. It’s the order of operations.</p><p>Start with the leadership conversation. Then build capability around use cases. Only then should you consider tooling. If your organisation hasn’t had this sequence of steps, then no matter how much experimentation is happening you’re not ready.</p><h2>&nbsp;<br>Before buying AI, ask: “Can our execs explain where it fits?”</h2><p>Most teams start with procurement. But the first real move is aligning your leadership team around three questions:</p><ul><li>Where does AI make sense in our workflow?</li><li>What risks or unintended consequences could surface?</li><li>Who will be accountable for outcomes?<br></li><li>When these questions are left unanswered, projects drift.</li></ul><p>Risk teams raise red flags. </p><p>People leaders get nervous. CIOs are left to make strategic calls in a vacuum.</p><p>You don’t solve this with a better demo. You solve it by building a shared internal vocabulary, grounded in your business context.&nbsp;That’s why AI Fundamentals sessions exist, not to teach tech, but to get your leadership team on the same page before money is spent.</p><h2>Readiness is measured in workflows, not models</h2><p>Here’s a practical reframe: AI is not something you “implement.” It’s something you apply to specific tasks.</p><p>Take one example from the WEF report: an APAC government agency used AI to triage service requests. The success didn’t come from the algorithm. It came from understanding the flow of work — and identifying where time and talent were being wasted on rules-based queries.</p><p>That’s the kind of capability you want to build across your operations, HR, and compliance teams: how to spot the task-level patterns that AI could handle — and how to assess risk in context.</p><p>If your team doesn’t think that way yet, don’t invest in tools. Build this muscle first.</p><h2>What changes when you have real readiness</h2><p>When your team has gone through a proper assessment and capability uplift, decision-making gets easier.</p><p>Instead of debating vague possibilities, you’re working with:</p><ol><li>A baseline understanding of what AI means in your setting</li><li>Agreement on where AI can help, and where it shouldn’t</li><li>A prioritised view of risks, gaps, and opportunities<br></li></ol><p>This doesn’t just speed up AI work. It reduces internal friction. It gives your executive peers permission to move without fear of reputational damage or misaligned priorities.</p><p>That’s what readiness looks like: not enthusiasm, but clarity.</p><h2>The path forward: what to do this year</h2><p>If you’re not sure where to start, here’s what a good sequence looks like:</p><ol><li>Assess your organisational readiness</li><li>Map your team’s capability across the seven dimensions. See what’s strong, and where decisions could break down.</li><li>Run a leadership fundamentals session</li><li>Use it to build shared language, clarify misconceptions, and make space for cross-functional concerns.</li><li>Pick one process to explore with AI</li><li>Don’t start big. Start with a use case that’s safe, operational, and visible.</li><li>Document what you learn.</li></ol><p><br>This sequence creates alignment, reduces internal pushback, and gives you a real basis for investment.</p><p>→ Check your team’s readiness with our <a data-mce-href="https://aistrategyconsulting.com.au/ai-readiness-assessment" href="https://aistrategyconsulting.com.au/ai-readiness-assessment">AI Readiness Assessment</a><br>→ Align executives with our<a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass"> AI Fundamentals Masterclass</a><br>→ Move from concept to consensus with our <a data-mce-href="https://aistrategyconsulting.com.au/ai-business-case-workshop" href="https://aistrategyconsulting.com.au/ai-business-case-workshop">Business Case Workshop</a></p><p>If you’re expected to lead on AI, make sure you’re leading from a place of structure.</p>

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