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<h3>**TLDR** 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>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.You’re being handed tools that sound useful — but without clarity on:What they’re actually for How much you can trust the answers Whether they’re safe to use with work files You’re not overthinking it. You’re being responsible. This is the gap most people fall into: trying tools before understanding them.Let’s close that gap.<h2>What is NotebookLM actually for?</h2>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.Think of it as a private assistant that reads your documents and responds based on what’s inside.People typically use it to:<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> 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 It won’t warn you if the content is outdated or irrelevant.That’s where problems start.<h2>Before you start using it</h2>Before you upload a report or project doc, ask yourself:1. Is this the right kind of document? 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 If that content is incomplete, out of date, or poorly written, the tool still uses it.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.2. Do I know what I want from it? 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.3. Am I uploading anything sensitive? 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.The tool isn’t dangerous on its own. But using it without thinking? That’s where risk creeps in.<h2>Why “just experimenting” isn’t always safe</h2>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:<ul><li>Uploading the wrong content</li><li>Trusting inaccurate summaries</li><li>Sharing outputs without checking them</li></ul>You don’t need formal training. But you do need a baseline understanding:<ul><li>What kind of tasks it suits</li><li>When to double-check outputs</li><li>What not to upload </li></ul>That’s what turns trial and error into capability.<h2>What effective use actually looks like</h2>Used well, NotebookLM can:<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 </li></ul>But that only happens when:<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 </li></ul>No tool replaces thinking. But tools like this can help you work smarter — if you treat them with the right level of care.<h2>Real example: A mistake that nearly went unnoticed</h2>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.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.That’s the kind of adjustment most teams never make. And that’s where small errors turn into real consequences.<h2>Want to make AI tools useful — without guessing?</h2>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.”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:<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 </li></ol>You don’t need to be technical. You just need to know where to start (and where things usually go wrong).Book a call now to explore how we help teams build safe, useful AI workflows.

What You Should Know Before Using Google NotebookLM at Work

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**TL;DR** 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.<h2>Why this keeps coming up</h2>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.Suddenly, you're meant to choose.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.The real problem is not your lack of knowledge. It's that most internal AI conversations start in the wrong place.<h2>How to think about AI models without the tech overload</h2>Start with the work, not the model 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.So don’t start with model names. Start with the task:<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> This frame lets you assess fit for purpose not just model performance. <h2>Real differences, real implications </h2>Now that you’re focused on the task, model differences start to matter but only in context.Here’s what’s surfaced in practice:<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. </li></ul>Each model has different strengths. None is universally best. If you haven’t scoped the task, the model comparison won’t help.<h2>Where leaders go wrong</h2>Many leadership teams try to pick a model without first agreeing on where AI fits.&nbsp;Before choosing a tool, your team needs to:<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? </li></ul>Once that’s clear, the model choice often becomes obvious. Or it becomes a vendor’s concern not yours.<h2>What this changes for your team</h2>Once your team stops comparing AI models in isolation and starts thinking about tasks, workflows, and context, the conversation gets clearer.You no longer need to know the technical differences between Claude and GPT. You just need to know:<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 </li></ol>This shift reduces overwhelm, improves alignment, and gives your leadership team a shared framework to evaluate future tools.&nbsp;This approach isn’t hypothetical. Organisations are using it today.<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. </li></ul>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.<h2>Next steps: How to build your team’s confidence</h2>If your executive team feels like it's stuck comparing terms it doesn’t understand, don’t keep pushing through. Step back.Book a fundamentals session that gives your team a shared foundation. Focus on:<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 </li></ul>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.<h2>Ready to move to clarity?</h2>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.You’ll leave with:A shared understanding of how AI models support real work Clear criteria for evaluating tools and vendors Practical guidance on what to prioritise, where to test, and how to avoid mistakes → Book a <a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">Fundamentals Masterclass</a>

What to Know About AI Models Before you Choose One

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<h3>TL;DR 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>Here’s the mistake: leaders assume AI readiness means hiring technical talent or signing a pilot deal. It doesn’t.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.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.<h2>&nbsp; Before buying AI, ask: “Can our execs explain where it fits?”</h2>Most teams start with procurement. But the first real move is aligning your leadership team around three questions:<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? </li><li>When these questions are left unanswered, projects drift.</li></ul>Risk teams raise red flags. People leaders get nervous. CIOs are left to make strategic calls in a vacuum.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.<h2>Readiness is measured in workflows, not models</h2>Here’s a practical reframe: AI is not something you “implement.” It’s something you apply to specific tasks.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.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.If your team doesn’t think that way yet, don’t invest in tools. Build this muscle first.<h2>What changes when you have real readiness</h2>When your team has gone through a proper assessment and capability uplift, decision-making gets easier.Instead of debating vague possibilities, you’re working with:<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 </li></ol>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.That’s what readiness looks like: not enthusiasm, but clarity.<h2>The path forward: what to do this year</h2>If you’re not sure where to start, here’s what a good sequence looks like:<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> This sequence creates alignment, reduces internal pushback, and gives you a real basis for investment.→ 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> → 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> → 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>If you’re expected to lead on AI, make sure you’re leading from a place of structure.

Why Most Leaders Aren’t Ready for AI

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If you’ve noticed a drop in website traffic, even when your pages rank well, this might be why.Google has changed. When someone searches now, they don’t just see a list of websites. They see an AI-generated summary at the very top. This feature is called AI Overview (it used to be called Search Generative Experience or SGE). It takes your content—along with others' and creates a short answer to the user’s query. No click required.So the real issue is no longer “Are you ranking?” It’s “Is Google’s AI using your content in its answers?”First, How Does Google Rank Content?Before we get into what to change, let’s ground this in simple terms.When someone types a search into Google, here’s what’s happening:<ul><li>Google scans its index of the internet.</li><li>Think of this like its giant library of websites.</li><li>It evaluates pages based on hundreds of signals. </li></ul>This includes:<ul><li>Keywords in the page and headings</li><li>How quickly the page loads</li><li>Whether people stay on the page or bounce off</li><li>Backlinks (who else links to your page)</li><li>Freshness (how recently it was updated)</li></ul>It uses a quality framework called E-E-A-T. This stands for:<ul><li>Experience: Do you actually know what you’re talking about?</li><li>Expertise: Are you a credible source in this field?</li><li>Authoritativeness: Do others trust or cite your content?</li><li>Trustworthiness: Is the site safe, reliable, and well-maintained?</li></ul>It ranks your page but now also decides if your content should be included in the AI Overview.This is where things shift. The AI system is no longer just showing links. It’s summarising content. And to be part of that summary, your content needs to be easy to extract, clearly structured, and deeply helpful.Example: How This Affects Your Page Let’s say our business: <a data-mce-href="https://aistrategyconsulting.com.au/" href="https://aistrategyconsulting.com.au/">AI Strategy Consulting</a>, wants one of our offers like the <a data-mce-href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap">AI Strategy Roadmap</a> to show up when someone searches:<blockquote>“How do I create an AI strategy for my business?”</blockquote>In the past, our goal would’ve been to rank on the first page with blog posts or landing pages.Now, that’s not enough.Here’s what happens if our page isn’t written or structured in the right way:<ul><li>The AI Overview might pull an answer from a generalist blog (like Forbes) or a competitor with better on-page structure</li><li>Our site might appear lower down, under the fold, never clicked</li><li>We won’t know unless we check our Google Search Console Traffic</li><li>But if our page is clear, structured, and trusted, it can be pulled into the AI summary even if we don’t rank #1 (we do'nt).</li></ul>What to Do and Where&nbsp; To make this real, let’s walk through what to change, and where it needs to live on your site:1. Homepage and Landing Pages Where to focus:<ul><li>Top-level service pages (like your AI Strategy Blueprint page)</li><li>About page (for E-E-A-T signals)</li><li>Homepage intro section</li></ul>What to change:<ul><li>Open with a clear value proposition in the first 100 words</li><li>Use H2 headings that reflect real user questions (e.g. “What’s included in our AI Strategy Roadmap?”)</li><li>Add a “Quick Answer” or TL;DR section at the top for AI to extract</li><li>Include client results, case study links, and testimonials (E-E-A-T)</li></ul>2. Blog Posts Where to focus:<ul><li>Education blogs (e.g. “What is agentic AI?” or “Steps to adopt AI”)</li><li>FAQ-style articles</li></ul>What to change:<ul><li>Use Q&amp;A format with search-style headings (e.g. “How long does it take to build an AI strategy?”)</li><li>Keep answers short and direct (2–3 sentences before deeper context)</li><li>Link to supporting internal pages (like your services) to signal topic depth</li><li>Make sure posts are regularly updated—especially if they mention tools, frameworks, or regulations</li></ul>3. Metadata and Schema Where to focus:<ul><li>Meta title and meta description of every indexed page</li><li>FAQ schema and HowTo schema for blog posts</li></ul>What to change:<ul><li>Include long-tail, natural language phrases in your meta descriptions (e.g. “Learn how we help small businesses create an AI roadmap in weeks.”)</li><li>Use schema markup to help Google identify your FAQ answers&nbsp;</li><li>Run pages through tools like Google’s Structured Data Testing Tool</li></ul>4. Author and Site Credentials Where to focus:<ul><li>Blog authorship</li><li>About section</li><li>Footer or trust indicators</li></ul>What to change:<ul><li>Add bios for blog authors with relevant experience (even short blurbs work)</li><li>Include trust signals: partnerships, media coverage, certifications (like 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 Lead Auditor</a> credential)</li><li>Make sure your site has an SSL certificate and fast mobile performance (both matter for trust)</li></ul>Bring It Together with One Example Let’s take our AI Strategy Roadmap page.<ul><li>A better version for AI search would:</li><li>Start with a short summary like:</li></ul>“Our AI Strategy Roadmap helps non-technical teams plan and implement practical AI use cases. Delivered in 12&nbsp; weeks, it’s tailored to your business goals and team capacity.”Include H2s like:<ul><li>“What is an AI strategy?”</li><li>“Who is this blueprint for?”</li><li>“How long does it take to implement?”</li><li>Include FAQs at the bottom (marked up with schema):</li><li>“Do I need technical staff to start with AI?”</li><li>“What’s the difference between a strategy and a pilot?”</li></ul>Mention our credentials:<ul><li>“Built by ISO 42001 Lead Auditor and AI implementation specialist Ryan Flanagan.”</li><li>Now you’ve built content that answers questions, builds trust, and is extractable for AI Overview without needing a top ranking.</li></ul>Last Word: SEO Is Still Here, But... SEO hasn’t died. People still search. But the way Google shows answers has changed. It’s not about keyword density. It’s about helpfulness, clarity, and trust.If your site is vague, slow, or cluttered it gets ignored. If your site is structured, answer driven, and credible it gets cited.And it’s not just big media sites getting picked up. Pages from niche blogs, industry services, and solo operators are being used in AI Overview every day.Want Your Content to Show Up in AI Search? If you’re not sure whether your site is AI-ready or how to adjust your structure we can help.Our<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> and<a data-mce-href="https://aistrategyconsulting.com.au/ai-business-case-workshop" href="https://aistrategyconsulting.com.au/ai-business-case-workshop"> AI Business Case Workshop </a>were designed exactly for this kind of shift. We don’t just teach theory, we fix the visibility gaps, rebuild structure, and turn outdated SEO content into assets fit for today’s search.Start by checking your <a data-mce-href="https://aistrategyconsulting.com.au/ai-readiness-assessment" href="https://aistrategyconsulting.com.au/ai-readiness-assessment">AI readiness.</a> It’s free, takes five minutes, and gives you a clear map of what to fix.

How Google’s AI Search Works and What You Need to Do

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Most teams aren’t trying to build AI. They’re trying to get work done faster, create better content, and stay aligned without adding more meetings or admin.That’s where today’s AI tools are useful. They don’t replace jobs. They support tasks people already do:&nbsp; like writing, editing, planning, responding, and reviewing.This post introduces some simple tools that help teams write, collaborate, organise, and create. You don’t need coding skills. You don’t need to change platforms. And you don’t need a big tech budget to get started.Tools That Make Writing Easier Working on a pitch deck, a client report, or a new campaign?<ul><li>Draft Alpha lets you co-write and edit with teammates while using AI to improve flow and structure.</li><li>Sudowrite is useful for brainstorming, rewriting, or coming up with story-driven content.</li><li>Frase.io is built for SEO writers. It helps plan articles, structure them around search intent, and optimise drafts to rank better.</li></ul>These tools are good for anyone tired of writing from scratch or second-guessing structure and keywords.Smarter Communication and Content Summaries Busy inbox. Long documents. Multiple channels. These tools help teams focus on what matters.<ul><li>SoBrief takes long content and produces short summaries for quick reading and decision-making.</li><li>Shortwave helps manage email, highlight important threads, and clean up clutter.</li><li>Zeda.io assists with creating consistent, aligned messages across the team.</li></ul>They’re a good fit for people who write often and want to spend less time sorting through noise.Helping Teams Stay in Sync Planning and scheduling often gets messy. These tools keep teams moving together.<ul><li>SteerApp supports message prioritisation and meeting coordination using AI.</li><li>Clarity AI turns calendar screenshots into actual events and tasks.</li><li>Lyzr.ai helps manage team projects with simple AI-supported planning and tracking.</li></ul>If your team works across time zones or juggles projects, these can save a lot of back-and-forth.Better Content Without Extra Work Many teams now produce content — video, audio, reports — with limited time or expertise. These tools reduce the pressure.<ul><li>VEED.io helps you create and edit video content, with AI-generated subtitles and formats.</li><li>ElevenLabs converts scripts into high-quality voiceovers for training, campaigns, or podcasts.</li><li>MyAudioJournal allows teams to create personalised audio updates for internal or external use.</li><li>Sakana.ai is built for audio production and voice narration across formats.</li></ul>These tools help get things shipped without needing to outsource every piece of creative work.Build and Test Your Own AI Tools Some teams want to go further. They don’t just want to use tools. They want to build something that fits their exact needs without hiring developers.<ul><li>Pickaxe lets you build AI apps, link them to documents or prompts, and test ideas. No code needed.</li><li>Prisma and Retool are great for teams with some technical support who want to connect databases, dashboards, or internal apps with AI features.</li></ul>These platforms are useful for internal innovation, process automation, or customer-facing tools built by non-technical staff.What to Do NextIf you’re curious but not sure where to begin, we’ve built two support options:<ol><li><a data-mce-href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass" href="https://aistrategyconsulting.com.au/ai-fundamentals-masterclass">AI Fundamentals Masterclass</a> gives your team a shared understanding of what AI can and can’t do, and how to talk about it clearly.</li><li><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 trial tools in your own workflows, test ideas, and build live examples in real time.</li></ol>Both are designed for non-technical professionals. We work with the systems you already use. No pressure to become an AI expert.

AI Tools That Help Teams Work Better Together

AI technology Business people who use AI technology, such as using it to create images, chatbots, coding, writer bots, translation, and video creation. LLm. Creating music and advertising, etc.

You don’t need a PhD or a six-figure tech budget to get value from AI anymore. But too many business leaders still think AI implementation means hiring machine learning engineers or building proprietary models from scratch. Look at most job ads for AI specialists and you'll see the same requirments: Data Science, Tensorflow, Python, Pipelines and Embeddings...That may have been true five years ago. It’s not true now.Most businesses don’t need “state-of-the-art” — they need “just-right” The real shift in AI right now isn’t about what’s possible. It’s about what’s practical.Smaller language models (SLMs) are gaining traction because they run faster, cost less, and don’t require specialist infrastructure or skills. VentureBeat’s analysis puts it plainly: these models are becoming the default for businesses that want AI tools embedded into their existing systems and not sprawling research projects.That means:<ul><li>No custom GPU clusters.</li><li>No endless tuning and training loops.</li><li>No dependency on one provider.</li></ul>Instead, small models can be deployed on laptops, browsers, or secure on-prem systems ideal for finance teams, legal workflows, marketing ops, and other use cases where privacy, speed, and integration matter more than raw scale.What this means for how you implement AIIf you’re leading a team and trying to bring AI into real workflows, here’s what this shift enables:<ol><li>Faster deployment cycles. SLMs are smaller by design, meaning faster load times, easier testing, and quicker integration into your tools via no-code or low-code platforms.</li><li>More control over your data. You’re not sending sensitive information to a third-party server or waiting on a vendor to fix bugs. These models can be run locally or hosted in your own secure environment.</li><li>Cheaper experimentation. You can trial multiple use cases like customer support, document summarisation, inventory forecasting — without blowing the annual IT budget.</li></ol>Most importantly, you don’t need an internal data science team to do any of this. Platforms like Lamini and OctoAI now offer fine-tuning and hosting tools built specifically for small models, and they’re made to work with no-code interfaces.When you reduce the size of the model, you reduce the barrier to getting started. That unlocks two things for your business:<ol><li>Speed to value. You don’t wait 12 months to launch a prototype — you test it in weeks.</li><li>Confidence to scale. Your team can explore real AI applications on familiar platforms, without needing to become prompt engineers or model trainers.</li></ol>That’s where the real opportunity lies. Not in catching up to OpenAI.But in catching the value sitting right inside your existing stack: customer data, internal reports, PDFs, spreadsheets, meeting notes, emails, transcripts and turning it into insight and then into action.<ul><li>Real-world examples Healthcare teams are using SLMs in local triage tools to draft summaries for patient intake without sending data to external APIs.</li><li>Legal firms are embedding small models into document review tools, using no-code UIs to flag risk without engineering bottlenecks.</li><li>Retail operators are deploying smaller models inside CRM systems to analyse feedback and surface upsell prompts all without writing a line of code.</li></ul>These are everyday businesses.And here’s how they’re doing it:<ol><li>Pick a small model: Tools like Mistral, LLaMA 3, or Gemma are freely available and well-suited for business tasks like document analysis, summarisation, or form filling.</li><li>Use a platform built for teams: Instead of building infrastructure, most teams use tools like OctoAI, Lamini, or Hugging Face that offer a simple dashboard to run and test models. Some even plug straight into Excel, SharePoint, or CRMs.</li><li>Connect your own data: With no-code tools like Flowise, LangChain Templates, or Zapier AI, you can drop in documents, customer queries, or reports — and the model will respond using your data, not just what it was trained on.</li><li>Test in one workflow: Start small. For example, load 50 support tickets and ask the model to group them by issue type. Or summarise 10 supplier contracts and flag payment terms.</li></ol>That’s it. No engineering. No new hires. No black-box vendor lock-in.Once it works in one part of your business, you can expand to others.If you’re exploring how AI fits into your business and you’re weighing the risks of vendor lock-in, bloated tools, or half-baked pilots start with the strategy.Our <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> helps you:<ul><li>Identify where small language models can create value across your workflows</li><li>Assess what architecture and governance model fits your real-world constraints</li><li>Map out a sequence of pilots that are commercially viable — not just technically interesting</li></ul>You don’t need to chase scale. You need the right-size model, in the right place, solving the right problem.That’s what we help you define.Ready to design a focused, value-led AI strategy?Book a session or explore the <a data-mce-href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap" href="https://aistrategyconsulting.com.au/ai-enterprise-strategy-roadmap">Strategy Blueprint</a> now.

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