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<h3>TLDR: GenAI increases the output of every contributor type in an organisation. It speeds up idea creation, improves problem-solving, reduces implementation time and expands the capacity of support teams. It doesn’t replace the organisation, it multiplies it. For non-technical teams, the key is understanding how each role changes and what to prepare for.</h3><h2>What GenAI Changes Inside a Company</h2>Most organisations talk about AI replacing tasks. The more practical view is this: AI amplifies the speed and reach of the people who already create value.You can describe most contributors in four groups:<ul><li>people who set direction </li><li>people who refine ideas </li><li>people who implement </li><li>people who support operations </li></ul>GenAI changes how each group works and how much they can deliver.This isn’t abstract. It’s operational.<h2>How Direction-Setters Benefit</h2>These are the people who define product, choose priorities, create new opportunities and bring context the model doesn’t have.&nbsp;GenAI helps them remove early friction:<ul><li>quick research </li><li>faster comparisons </li><li>clearer summaries </li><li>immediate alternatives </li><li>better-prepared discussions</li></ul> For non-technical leaders, this is the first visible change. Senior staff move faster because they’re less bogged down by early-stage tasks.<h2>How Problem-Solvers Benefit</h2>Problem-solvers turn broad ideas into workable plans. They break down complexity and choose the right approach.GenAI gives them rapid access to:<ul><li>prior work </li><li>technical options </li><li>risk patterns </li><li>potential failure points </li><li>alternative structures </li></ul>They reach clarity faster, avoid dead ends and choose more suitable tools.The value is accuracy in early decisions. Not speed for show.This matters because poor framing is one of the biggest reasons projects slip. GenAI reduces the chance of starting with the wrong assumptions.<h2>How Implementers Benefit</h2>Implementers carry the load of execution.&nbsp;GenAI reduces the weight of:<ul><li>first drafts </li><li>rewrites </li><li>formatting </li><li>structural edits </li><li>basic checks </li><li>repetitive documentation </li></ul>Work that once took hours now takes minutes. Review becomes the main effort rather than generation.&nbsp; It also changes team size pressure. Implementers support more initiatives without adding headcount.<h2>How Support Teams Benefit</h2>Support functions scale with organisational size. That is where pressure usually builds.&nbsp;GenAI increases throughput on:<ul><li>document checks </li><li>policy queries </li><li>internal responses </li><li>information retrieval </li><li>standard communication </li></ul>These tasks compress and the same team handles more volume.&nbsp;It is a huge multiplier, and a powerful one.<h2>Why This Changes Organisational Shape</h2>When each contributor type works faster, the old layers become less necessary.&nbsp;Bottlenecks shift from “we don’t have enough people” to “we can take on more initiatives”.&nbsp;Organisations often see:<ul><li>fewer delays </li><li>fewer dependencies </li><li>flatter communication lines </li><li>faster handoffs </li><li>more initiatives in flight </li></ul>So, where Organisations Go Wrong?&nbsp;The issues rarely come from the model. They come from how people try to use it.&nbsp;Patterns I see repeatedly:<ul><li>fuzzy instructions </li><li>unclear desired outcomes </li><li>no review step </li><li>inconsistent use </li><li>teams working in isolation </li><li>expecting a tool to replace judgment </li><li>not preparing staff before adoption </li></ul>The limitation is almost always clarity, not capability.<h2>What Organisations Should Prepare For</h2>Just foundations:<ul><li>know which tasks drain time </li><li>keep source documents clean </li><li>choose where AI can help first </li><li>keep outputs reviewed before circulation </li><li>normalise AI as part of the process, not an “AI initiative” </li><li>focus on clarity before capability </li></ul>These steps maintain control and confidence during the shift.AI changes organisational performance because it affects every contributor group.&nbsp; Creators get more time to think. Refiners make better decisions earlier. Implementers accelerate output. Support teams scale without extra headcount.AI multiplies every role, not just one. That is the real organisational shift.&nbsp;If your organisation wants a structured, non-technical way to integrate GenAI, the <a data-mce-href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap">AI Strategy Blueprint</a> gives a clear, practical path.<h2>FAQs</h2>Q: Does this mean we reduce headcount? A: Not necessarily. Many organisations use the efficiency gains to increase the number of initiatives rather than shrink the team.Q: How do we know where to apply GenAI first? A: Start with repetitive, high-volume tasks where the output is consistent and the risk is low. These generate quick wins and confidence.Q: Will roles disappear completely? A: Roles will shift. Execution-heavy roles shrink in volume. Refinement, judgment and coordination increase in value.Q: Do we need new job titles for this? A: No. Start with workflow changes before structural changes. Titles can evolve later.Q: How do we manage quality control? A: Keep a simple review step in place. AI handles the volume, people handle correctness.Q: How does this apply to non-technical teams? A: The pattern is the same. AI reduces repetitive work. Staff spend more time on decisions instead of production

How GenAI Changes the Way Work Gets Done

<h3>TLDR: Perplexity has released Sonar, an API that delivers real-time answers from the live web with citations. Most AI tools reply from outdated training data, which is a problem for organisations that depend on current information. Real-time search matters because it reduces manual research, improves accuracy and gives teams faster, grounded answers inside the tools they already use.</h3><h2>What Sonar Does</h2>Sonar is Perplexity’s new API for fast, grounded search. It connects directly to the live web, runs real-time checks and returns answers with citations.&nbsp;Most AI systems rely on training data that may be months old. Sonar avoids that limitation by retrieving information as it exists today.There are two versions: <ul><li>A base model for quick questions. </li><li>A Pro model for complex, multi-step queries that need deeper checking and more citations.</li></ul>The benefit is simple :&nbsp;You get fast, factual responses you can verify.Why Real-Time Search Matters in AIStatic AI answers are fine for general knowledge. They fail when the information changes daily: regulations, product updates, competitor activity, pricing movements or anything time-sensitive.Sonar gives teams access to up-to-date results without switching tools, tabs or workflows.For non-technical teams, this removes two common issues:<ol><li>slow, manual searching </li><li>uncertainty about whether the information is current </li></ol>Real-time search also reduces reliance on guesswork or outdated assumptions that often appear in general AI assistants.&nbsp;Because Sonar provides citations, staff can check the source directly rather than trusting an unverified summary.<h2>How Sonar Works Inside a Workflow</h2>Sonar runs a live search based on a question, evaluates results and produces a short, sourced answer. The Pro version breaks down harder questions into multiple searches and consolidates the findings.Developers can restrict Sonar to approved sources so it stays inside responsible boundaries. This matters for organisations that can’t rely on the open internet for sensitive queries.Sonar also supports large inputs. Teams can ask about long documents, multi-part topics or a mix of internal notes and external information.It is accessed through an API, which means it can be embedded directly into existing systems. No new interface. No new tools for staff to learn.<h2>Where You Feel the Value&nbsp; </h2>Real-time search removes repetitive tasks that take time away from actual work.Teams get immediate value in areas like:<ul><li>basic fact-checking </li><li>understanding new developments </li><li>checking recent news </li><li>validating claims </li><li>pulling current competitor information </li><li>answering customer questions accurately </li></ul>Internal chatbots also become more useful when answers include citations instead of assumptions. Customer support, sales and operations can all benefit from this shift.The value is pretty practical: fewer manual searches, fewer mistakes and less time spent cross-checking information.<h2>How Sonar Fits Early AI Adoption</h2>Sonar is an example of the kind of AI tool organisations can adopt early without creating complexity.&nbsp;You don’t need a custom model. You don’t need a data science team. You don’t need an AI strategy with dozens of workstreams.You need:<ul><li>a simple integration </li><li>clear internal guidelines </li><li>approved sources </li><li>a basic review process for higher-risk use </li></ul>Real-time search is exactly the type of tool that helps non-technical organisations gain confidence with AI because it solves a familiar problem in a low-risk way.&nbsp;It replaces slow research, not judgment. It provides current information, not assumptions. It strengthens decisions by grounding them in evidence.How Much Is it?The base version uses a lightweight model designed for speed and cost control. Pricing is fixed per thousand searches with low input and output costs.&nbsp;Sonar Pro costs more because it handles deeper queries, runs additional searches and returns more citations.For teams that currently rely on manual research or expensive external tools, predictable usage-based pricing is easier to budget for. This keeps the cost of factual retrieval aligned to operational needs.Real-time AI search is not about sophistication. It is about reliability.&nbsp;When teams can access current information with citations:<ul><li>decisions become more grounded </li><li>work speeds up </li><li>errors reduce </li><li>staff confidence increases </li><li>reliance on outdated sources drops </li></ul>Sonar shows a practical path forward: <ul><li>AI adoption begins with tools that remove friction from everyday tasks, not tools that require advanced knowledge.</li><li>For organisations just starting, this category — real-time factual search — is one of the safest and most useful entry points.</li></ul><h2>FAQs</h2>Q: Can real-time search replace our internal knowledge base? A: No. It complements it. Internal knowledge needs its own system. Sonar handles external information.Q: Does Sonar handle sensitive data? A: Only if you restrict the sources. It is designed to use approved inputs when configured that way.Q: Can we use Sonar for regulatory or legal checking? A: It can help surface current information, but decisions still require the appropriate internal review.Q: How much technical work is required to integrate it? A: Minimal. A developer can connect the API, and teams use it through existing tools.Q: Does real-time search solve hallucination issues? A: It reduces them significantly because answers come from current sources with citations.Q: Is this suitable for small teams? A: Yes. It removes manual searching, which is often where small teams lose the most time.If your organisation wants to explore AI tools like real-time search in a safe, structured way, 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" data-mce-selected="inline-boundary">AI 5-Day Bootcamp</a> gives non-technical teams the foundations to evaluate and adopt tools like Sonar without confusion or risk.

Why Real-Time AI Search Matters

Isolated Google Logo Symbolizing Internet Search and Technology

<h3>TLDR: Google recently outlined seven principles for writing sensible AI regulation. They focus on outputs, practical safeguards, closing gaps in existing laws, and giving regulators the technical support they need. For organisations trying to understand how policy should be shaped, or how to shape their own internal rules: the value lies in the simplicity. Regulation works when it focuses on measurable behaviour, not technical theory, and when agencies are equipped to apply the rules consistently.</h3>Google published an article describing how governments could regulate AI without slowing productive use. They argued for rules that focus on outputs, rely on existing laws where possible, empower agencies with technical capability and keep regulation tied to clear evidence of harm. They also emphasised the need for alignment across jurisdictions so organisations don’t end up navigating a patchwork of conflicting requirements.The article highlighted a point that often gets buried: regulation is most effective when it is built around what AI does, not how AI models are built. This is important for non-technical leaders because it keeps the discussion grounded in behaviour, responsibility and traceability: things organisations can actually manage.<h2>Why the focus on outputs matters</h2>In simple terms, Google suggests that regulation should look at the quality and safety of AI outputs. If a system creates harmful, misleading or unsafe outcomes, regulators can intervene directly because the evidence is visible and measurable. This avoids regulating model internals or algorithms that change too fast for legislation to keep up.For organisations, this mirrors how internal governance should work. You don’t need to audit the architecture of a model. You need to check what it produces, who used it, what controls were applied and what information it accessed. That’s manageable. <blockquote>It’s also the foundation of ISO 42001 and 42011 thinking: traceability, explainability and clear accountability pathways.</blockquote><h2>Where existing laws already cover the risks</h2>Google’s piece also reinforces something often forgotten in public debates: many risks associated with AI are already illegal under existing laws. Fraud remains fraud. Discrimination remains discrimination. Consumer protection still applies.The gap isn’t the law itself. It’s whether those laws are updated to recognise AI as a tool used within the existing behaviours. This is why “new AI-specific regulation” can sometimes cause duplication rather than clarity. The smarter path is identifying which laws already apply, which need updates and which areas currently lack coverage.For organisations setting internal policy, this approach keeps governance practical. So, start by mapping AI use to existing policies. Most of the required controls are already in place, they just need AI-specific language or workflows.<blockquote>One of the clearer points in Google’s article is the need for regulators to have access to real technical capability.</blockquote>A central “hub” of expertise supporting many organisations (the “hub-and-spoke” model) ensures consistency across sectors. <ul><li>Banking risks differ from healthcare risks. </li><li>Transport differs from education. </li><li>But each domain can rely on a shared technical baseline.</li></ul>This aligns with what many organisations already face internally. Different departments use AI differently, but the standards for documentation, access, review and oversight should be common. That central capability, even if small — prevents fragmentation.<h2>What responsible innovation looks like</h2>Google frames innovation and safeguards as complementary. This is often misunderstood as a political line, but the operational reality is straightforward. Organisations adopt AI more confidently when expectations are clear, documentation is simple and oversight doesn’t create unnecessary delays.Responsible AI innovation is not a slogan. It is:<ul><li>clear access rules </li><li>controlled data sources </li><li>explainable outputs </li><li>predictable review steps </li><li>measurable compliance</li></ul>When they exist, AI can be used more broadly because people know where the boundaries sit.<h2>Why alignment matters for organisations</h2>Google’s article points out that hundreds of AI bills exist across U.S. states alone. A fragmented regulatory environment is expensive, slow and confusing. Alignment lowers the operational burden.For organisations, the same principle applies internally. If each department invents AI rules independently, confusion grows. If the finance team has one set of expectations, operations has another and customer service has none, the governance burden becomes unsustainable.The clearest value in Google’s piece is this: <blockquote>AI regulation works when it focuses on behaviour, evidence and accountability instead of technical analysis.</blockquote>This is exactly how internal AI policy should function:<ol><li>focus on what the system produces </li><li>check for identifiable risks </li><li>define who is responsible </li><li>record how the output was generated </li><li>use clear documents as the baseline </li><li>treat AI as a tool inside existing legal frameworks </li></ol>This keeps governance practical for teams who don’t work in technical roles. It also keeps organisations aligned with emerging international approaches, including ISO 42011 AIMS.<h2>FAQs</h2>Q: Do we need our own regulatory framework to use AI safely? A: No. Start by mapping your existing policies to AI use. Most organisations already have the foundations; they just need updates.Q: How do we know if an AI output is “high-quality”? A: Define criteria tied to your context: accuracy, clarity, relevance, source traceability and compliance with internal standards.Q: What if our organisation has no technical capability? A: You don’t need it to start. You need clear documentation, simple access rules and a controlled environment. Technical depth comes later.Q: How do we prevent inconsistent AI adoption across departments? A: Set a single baseline policy. Departments can extend it, but not replace it.Q: Do small organisations need to think about regulation? A: Yes, but only in proportion to your use cases. The principles still apply: clarity, accountability, controlled data and documented decisions.Q: What’s the first practical step? A: Identify where AI is already being used, even informally. Create transparency before creating new rules.If your organisation needs a structured, ISO-aligned way to develop clear AI policies, our <a data-mce-href="https://aistrategyconsulting.com.au/ai-iso-42001-aims-certification" href="https://aistrategyconsulting.com.au/ai-iso-42001-aims-certification">AI Policy Authoring Program (aligned to ISO 42011 AIMS)</a> gives you the tools and templates to do it properly.

A Practical Guide to Google’s AI Regulation Principles

AI Agents Analyze business concepts, Businesswomen use AI technology Assistants to Perform Decisions, Automate daily Tasks Smart AI solutions, Automate repetitive work, Education, Data Analysis, Sales

<h3>TLDR: Goldman Sachs has given around 10,000 employees access to an internal AI assistant. It helps with everyday tasks like summarising content, drafting emails and translating code, and it works inside Goldman’s own systems using Goldman’s own data. The point for non-technical teams isn’t the technology. It’s the model: large organisations are now building AI that fits their workflows, standards and internal language instead of forcing staff to adapt to public tools. This is the simplest example of what enterprise AI adoption actually looks like.</h3><h2>What did Goldman Sachs build?</h2>Goldman created an internal AI assistant that sits behind its firewall. Employees use it the same way they use any chat tool: type a question, get a response, ask it to summarise something, ask it to explain something or ask it to draft something.The assistant handles simple, repeatable tasks found in every organisation. <ul><li>It summarises emails.</li><li>It drafts quick notes. It explains code.</li><li>It helps with research.</li><li>It rewrites content into shorter formats.</li><li>It checks small details before something is sent.</li></ul>The important part is that the assistant doesn’t rely on public data. It draws from Goldman’s own materials, terminology and internal knowledge. It reflects how work is done at the firm because it has been shaped by that environment.<blockquote>For non-technical readers, the key idea is simple: this is an internal tool that behaves like a knowledgeable colleague because it uses the organisation’s own information, not the general internet.</blockquote><h2>Why build an internal AI Agent?</h2>Goldman’s reasoning is straightforward. Public AI models don’t understand the organisation. They don’t know its processes, tone, quality expectations or the way staff communicate. They also don’t meet the security and compliance requirements of a regulated industry.By keeping the assistant in-house, Goldman controls:<ul><li>the data used </li><li>the rules the assistant must follow </li><li>the behaviour it should learn </li><li>the information it’s allowed to access </li><li>the outputs it produces</li></ul>When an assistant sits inside the company’s systems, it adopts the organisation’s expectations. It becomes predictable. It becomes familiar. It becomes safe to use in day-to-day work.Smaller organisations don’t need the same infrastructure, but the logic applies everywhere: AI works best when it works inside your environment, not outside it.<h2>What can smaller organisations learn from this?</h2>You don’t need Goldman’s scale to take the right lesson. The assistant started with the tasks that slow staff down the most.<ul><li>Summaries. </li><li>Drafts. </li><li>Explanations. </li><li>Conversions. </li><li>Small checks.</li></ul>These tasks are universal. They exist in every organisation regardless of size or sector.The second lesson is context. Goldman’s assistant works because it is fed structured internal documents, clear examples and consistent processes. It mirrors the way employees communicate because it is trained on materials that already reflect those standards.Most organisations underestimate how much clarity they gain simply by cleaning and organising their internal information before introducing AI. When the inputs are coherent, the assistant produces coherent outputs. When they are not, the tool becomes unpredictable.The third lesson is adoption. Goldman didn’t start with complex agents that automate entire workflows. They started with simple assistants that remove the overhead around the work. That pattern is reachable for any organisation.What changes when AI becomes an internal assistant?The work doesn’t disappear, but the overhead does.People who used to spend time rephrasing content or producing shorter versions of something rely on the assistant to handle the first draft. Instead of rewriting, they review. Instead of formatting, they check. Instead of producing variations by hand, they start with a version that already fits the shape they need.<blockquote>It’s a change from manual reconstruction to supervised refinement.</blockquote>This is what mature enterprise AI adoption looks like. Not flashy. Not dramatic. Just practical really, but not earth shattering, just like rolling out a new interface in the old days.It’s also a realistic preview of how AI will show up in other organisations: quietly, inside existing systems, changing the friction around everyday tasks long before it changes the tasks themselves.<h2>FAQs</h2>Q: Do we need technical staff to build something like this? A: No. The early value Goldman showed comes from simple tasks applied to clean, structured information. Most organisations can start with off-the-shelf tools configured to their environment.Q: How do we stop an internal assistant from giving confident wrong answers? A: By controlling the inputs. When the assistant is fed consistent internal documents and approved sources, it stays within boundaries. Uncontrolled data is where mistakes begin.Q: Can this work without a huge internal dataset? A: Yes. What matters is not the volume of data but the clarity. A small set of high-quality internal materials produces safer and more accurate outputs than large amounts of noisy information.Q: How do we manage employees relying on the assistant too heavily? A: Set a simple rule: the assistant handles the first draft, humans handle the final version. This keeps quality intact without slowing the work down.Q: Does an internal assistant replace roles? A: It replaces repetitive formatting and summarising, not expertise. People still make decisions, give context, and check what the assistant produces.Q: What’s the first step for a smaller business wanting to try something like this? A: Standardise your documents before you introduce any tools. When the information is consistent, the assistant behaves consistently.AI compresses production work. Goldman’s assistant shows that the future of enterprise AI is not dramatic change but practical compression. When the overhead reduces, people focus on decisions instead of rework.&nbsp;If your organisation wants a clear, practical path to introducing AI safely and effectively, the<a data-mce-href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" data-mce-selected="inline-boundary"> AI Strategy Blueprint </a>gives you the structure and steps to start well.

What Your Business Can Learn from Goldman Sachs’ AI Assistant

<h3>TLDR: Google is adding a Slides generator to NotebookLM. It lets you upload documents and get a usable presentation without rewriting anything. Most non-technical teams won’t see the significance, but it changes how internal content gets made: reports, briefs, updates, and summaries move from manual formatting to automated output. The value isn’t the feature, it’s the reduction in rework. If you rely on Workspace, this will reshape everyday work the moment it rolls out.</h3><h2>What does NotebookLM Slides actually do?</h2>NotebookLM has been slowly expanding: summaries, audio versions, video overviews, mind maps, reports. Useful, but nothing dramatic.Slides is different.<ul><li>You upload a set of documents. </li><li>NotebookLM reads them. </li><li>You click a button. </li></ul>It generates a deck with structure, visuals, and a narrative that matches the source material.Most tools generate text. This one generates the formats people actually use: decks, infographics, quick visuals, and simple summaries. The workflow changes because the manual part disappears.For non-technical teams, this is the bit that matters.It removes the time spent formatting, adjusting, shortening, reworking, and fiddling with slide layouts. The tool isn’t replacing thinking. It’s replacing the admin that sits around it.<h2>How does this change everyday content work?</h2>Every organisation has the same pattern. Someone writes a long report. Someone else turns it into a deck. Someone else shortens it again. Then a manager asks for an even shorter version. Then comms asks for something “visual”.Different formats. Same content. Most of the work is repetitive.NotebookLM closes the gap between formats. One set of documents becomes multiple outputs. People spend time reviewing instead of rebuilding.Nano Banana’s image generation fits in quietly. Teams won’t think about the model. They’ll just notice they no longer need to search for random visuals to fill space. The images appear automatically. Consistent style, consistent tone.<h2>Why will non-technical teams feel this immediately?</h2>Because the feature sits in familiar tools. No new software. No onboarding. No “AI literacy module”. Just another option in a panel they already use.That’s the part many people underestimate. When a tool appears inside Workspace, people adopt it instantly. No training. No rollout plan. Just curiosity and a click.The first output won’t be perfect.It doesn’t need to be. It needs to remove the initial bulk work — the tedious resizing, trimming, shortening.I’ve seen this shift in client environments before. One enterprise had five teams rebuilding the same monthly content for five audiences. When we replaced the reconstruction cycle with a structured output system, the change was immediate. People stopped rewriting. They started reviewing. NotebookLM is aiming for the same behaviour, just packaged differently.<h2>What should teams prepare for before this feature arrives? </h2>Nothing technical.&nbsp; Just the basics:<ul><li>keep source documents clean </li><li>know which file is the “starting point” </li><li>review the first draft before it circulates </li><li>decide who finalises each version</li></ul><h2>FAQs</h2>Q: What stops the AI from creating decks that misrepresent our content? A: Nothing, if your source documents are unclear. NotebookLM generates from what it’s given. If the material is inconsistent, outdated, or cluttered, the deck will mirror it. This is why “clean inputs” matter more than the feature itself.Q: Will this overwhelm teams with too many versions of the same thing? A: It can. Automation makes it easy to produce multiple formats quickly. Without a simple rule like “one source document, one final deck,” teams can drift into version sprawl.Q: How does this fit into approval flows? A: Teams will generate slides faster than current review processes can handle. You’ll need a light rule, who reviews what before a deck goes out, because speed exposes weaknesses in slow approval loops.Q: What happens to teams who normally own presentation formatting? A: Their workload changes. They stop reformatting and start curating. The work doesn’t disappear. It shifts from execution to quality control.Q: Can we use this for client-facing work? A: Only after a human check. NotebookLM handles structure, not nuance. It won’t know your tone or your relationship with the client. Internal use will be fine immediately. External use needs eyes on it.Q: What if our team isn’t ready for tools like this? A: Readiness isn’t a skill issue. It’s a habits issue. People already know how to work with documents and decks. They just need to treat the output as a draft, not a finished product.AI compresses production work. NotebookLM accelerates that compression by shortening the distance between source material and output. The organisations that adapt early will see smoother workflows and fewer wasted hours. The ones that wait will spend their time chasing inconsistent versions and wondering why their old processes no longer fit.If you want a practical plan to prepare your team for tools like NotebookLM without adding complexity, our <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" data-mce-selected="inline-boundary">AI Bootcamp</a> gives you the structure to do it cleanly.

Google Notebook LLM Slides: What This New Feature Means For You

TLDR: AI doesn’t see images the way humans do. It guesses. It stitches patterns together based on probability, not perception. MIT’s research confirms this, and if your organisation uses AI for anything involving images, diagrams, or reports, you should care. Understanding this difference is how you avoid making a big stuff up.Sorry....We’re going to nerd out for a moment, but stay with me. This isn’t academic pain killer. This is the difference between a system helping your team and a system quietly producing errors that nobody notices until they matter. Like when a fire starts and no one looked at the electricals.<h2>What is “visual knowledge” in AI?</h2>MIT researchers looked at how language models develop “visual understanding”. The short version: they don’t. Not in the way you think.<ul><li>They don’t store mental images. </li><li>They don’t form a picture in their “mind”. </li><li>They don’t recognise objects through perception.</li></ul>Instead, they build statistical expectations based on:<ul><li>text patterns </li><li>labels </li><li>common co-occurrences (“cats sit on laps”) </li><li>millions of multimodal pairings (“apple = red”) </li></ul>That creates something like a fuzzy internal map of what an object probably looks like.&nbsp;This is why models can describe an object confidently even when it’s not actually present.<ul><li>It’s not deceit. </li><li>It’s actually learnt pattern logic.</li></ul><h2>So What did the MIT Computer Vision Study Say?</h2>Here’s a summary without drowning you in maths:1. AI builds visual knowledge indirectly If a model has seen the word “thermometer” next to phrases like “thin cylinder”, “reading”, and “glass”, it develops a loose visual template through repetition.2. Multimodal models add more clues, not true perception Yes, they read images. No, they don’t understand them. They compress images into a high-dimensional representation that serves as another statistical signal.3. AI makes specific, predictable mistakes MIT found that models confidently:<ul><li>hallucinate common objects </li><li>misinterpret diagrams </li><li>confuse spatial arrangements </li><li>“blend” categories </li><li>answer based on likelihood rather than details </li></ul><blockquote>If this sounds familiar, it should.</blockquote><h2>Why should a non-technical leader care?</h2>This research matters because AI “confidence” is not a sign of accuracy. It’s a sign of probability.1. AI visual reasoning is probabilistic, not perceptual:&nbsp; When an AI tool describes an image, it’s not describing what it saw. It’s describing what it expects to see based on training data.That sounds like semantics until the moment you discover the model:<ul><li>added a hazard that wasn’t there </li><li>missed a crack in a wall </li><li>misread a compliance figure </li><li>invented context that fits the pattern </li></ul>This is pretty average and common.2. This has real operational consequences:&nbsp; In government, property, healthcare, engineering, insurance, facilities, and safety work, AI often handles imagery or diagrams.Models can misread:<ul><li>thermal hotspots </li><li>electrical heatmaps </li><li>tenancy inspection photos </li><li>structural diagrams </li><li>environmental compliance photos </li><li>OHS hazards in crowded scenes </li></ul>Not because they’re crap. Because they’re not looking the way your team thinks they are.3. Explainability matters more than ever: If your organisation can’t explain how the model formed a judgement, you can’t govern the outcome.&nbsp;Teams need clarity on:<ul><li>which features the model used </li><li>where its assumptions came from </li><li>what the likely failure modes are </li><li>how the system behaves with unusual images </li></ul>This is part literacy, part governance.A quick personal aside, because we might as well acknowledge we’re nerding out We recently built a multimodal image-recognition system trained on more than 10,000 thermal images in the facilities sector.&nbsp;The goal was simple: identify electrical hotspots automatically instead of having technicians review every heatmap manually.It saved about 2,300 hours of analysis time.But it only worked because everyone understood a basic truth:The model wasn’t “seeing” anything.It was recognising statistical patterns that correlate with hotspots.Once you understand that, you design controls that work. If you don’t, you risk treating probabilistic inference as unquestioned fact.<h2>So what, does this matter?</h2>1. Use AI for visual assistance:&nbsp;AI is good at:<ul><li>summarising images </li><li>extracting obvious features </li><li>flagging potential attention points </li></ul>It is not good at:<ul><li>final assessment </li><li>safety-critical decisions </li><li>ambiguous scenes </li><li>fine-grain technical interpretation </li></ul>2. Treat confident answers with suspicion:Fluent explanations are a veneer. Always ask where the answer came from.3. Build a human-in-the-loop review for all visual workflows:This catches the model’s tendency to fill gaps with probability.4. Train people before tools:You don’t need technical depth. You need teams who know when an answer is trustworthy and when it needs checking.<h2>FAQs</h2>Q: Does this mean AI can’t be trusted for visual work? A: It can be trusted for assistance. Judgement still belongs to a human.Q: Why do models hallucinate objects that aren’t in the image? A: Because they predict likely features rather than perceive actual ones.Q: Should government and regulated sectors care more? A: Yes. Visual misinterpretation creates compliance risks, not just inconvenience.Q: Is this too technical for non-AI staff? A: No. The core idea is simple. AI makes visual guesses. You need to know that.&nbsp; If you want your team to understand this without becoming researchers If your organisation wants clear, practical literacy in how AI works, where it fails, and how to use it safely, the<a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass"> AI Fundamentals Masterclass</a> gives non-technical teams the grounding they need to make good decisions.

How AI “Sees” And Why You Shouldn’t Trust It

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How AI “Sees” And Why You Shouldn’t Trust It