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It’s easy to look at AI-powered changes in consumer businesses—retail, ecommerce, FMCG and think, “That doesn’t apply to us.”But look closer. The real lesson isn’t in the tech itself. It’s in the way these businesses are applying it: deliberately, commercially, and with one goal—faster, smarter decisions that improve outcomes.The packaging might be different. But the logic behind it applies just as much to healthcare, professional services, logistics, or tourism.The Competitive Advantage Isn’t the Tool. It’s How You Use ItConsumer companies didn’t win by being the first to adopt AI. They won by being the first to apply it with intent.Think about what’s happening behind the scenes:<ul><li>AI is cutting forecasting errors by half in global retail supply chains.</li><li>It's reducing customer churn by flagging silent drop-offs before they’re visible in dashboards.</li><li>It’s powering autonomous decision systems—not just chatbots, but demand prediction, pricing adjustments, and fraud alerts.</li></ul>A large global consumer goods company used AI to reconfigure its supply chain planning. Rather than reacting to orders and historic trends, they introduced a predictive system that could account for marketing campaigns, real-time demand shifts, and transport constraints.This system allowed the business to reduce fulfilment times, increase shelf availability, and reallocate stock more efficiently without expanding warehouse capacity or headcount.&nbsp;The takeaway isn’t “use AI.” It’s: what operational question are you trying to answer better than before? That’s where AI becomes commercially useful.Why Every Sector Should Pay AttentionMost sectors outside of retail are still testing AI in isolation trying out a tool here, automating a workflow there. There’s often no clear link between these experiments and the strategic goals of the business.Consumer industries show what it looks like when AI use is stitched into a company’s commercial engine. Not every experiment worked. But the ones that did were scaled because they:<ul><li>Solved a problem tied to revenue, margin, or cost reduction</li><li>Could be deployed without overhauling core systems</li><li>Had a sponsor who understood both the tech and the business case</li></ul>You don’t need to be Amazon to start. You need clarity on where AI can shift an outcome that matters.The First Move Is Not the Model. It’s the Business Case.You can’t copy what Nike or Unilever did line by line. But you can follow their approach: treat AI like a business decision, not a pilot project.That starts with building a proper case.If your business is serious about moving from curiosity to real value with AI, then this is where we begin: aligning commercial goals with practical AI use cases that can be tested and scaled.That’s exactly what we do in the AI Business Case Workshop.No fluff. No jargon. Just the questions that help you decide where to start, what’s feasible, and what’s worth the investment.<a data-mce-href="https://aistrategyconsulting.com.au/ai-business-case-workshop" href="https://aistrategyconsulting.com.au/ai-business-case-workshop" data-mce-selected="inline-boundary">Join the workshop to get clarity on how AI fits your business.﻿</a>

What AI in Retail and Consumer Goods Can Teach Every Industry

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Not long ago, if you wanted to find something—a product, a service, a how-to—you typed it into Google. You trusted the results. You clicked, you compared, and you bought.Now? People are skipping search altogether.They’re going straight to TikTok. They’re asking ChatGPT. They’re listening to creators they trust on YouTube, Instagram, or even private newsletters.What used to be a linear customer journey of search, click, purchase has turned into something far more fragmented, fast-moving, and AI-influenced.So if you’re still building your marketing strategy around search alone, you’re not just missing out. You’re building for a customer that no longer exists.AI Has Changed the Way People Find and DecideWhen AI tools give answers faster than Google, people use them. Not because they’re more loyal to OpenAI or TikTok, but because they’re getting what they want with less friction.It’s not just younger users either. Buyers across every demographic are now influenced by a combination of personalised feeds, AI-powered recommendations, and social validation from creators or peers.Here’s what that means in practical terms:<ul><li>Your customer’s first question might now be typed into ChatGPT, not a search engine.</li><li>Their validation comes from a 30-second video with 2,000 comments, not your About page.</li><li>Their final decision is shaped by what AI ranks as relevant or what creators say is useful—not what you paid to rank on Google.</li></ul>What Businesses Need to Do About ItIf you’re a founder or marketing lead, this shift can feel overwhelming. But it’s also a huge opportunity—if you know how to respond.It starts with one change in thinking: stop treating AI like a tool, and start treating it like a new customer behaviour channel.That means:<ul><li>Mapping how your customers now discover and decide, not just where they buy.</li><li>Testing how AI tools describe your product or service—and improving how you're represented.</li><li>Understanding where social and AI interactions overlap, and how that shapes trust and urgency.</li></ul>None of this is theory. It’s already happening.If you don’t audit your customer journey through an AI lens, someone else in your category will and they’ll become the default source for discovery.You Don’t Need to Bet on the Future. You Need a Strategy That Works Now.You don’t have to rebuild your marketing from scratch. But you do need a roadmap for how AI is changing the rules so you can still win in a world where fewer people are typing into Google, and more are acting on a feed, a prompt, or a post.That’s exactly what we help businesses do.If you want to understand how this shift affects your channels, content, brand visibility, and bottom line, start with the AI Enterprise Strategy Roadmap. It’s a clear, commercially grounded plan tailored to your business.<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">Get your roadmap here.</a>

Why Google Isn’t the First Place People Search Anymore

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Every marketing team I know has tried something with AI by now.Some use it to write copy faster. Others use it to target better or produce more content. A few are experimenting with chatbots and image generators.But after the first round of excitement, you start to hear a different tone: “We tested it… now what?”The reality is simple: getting AI into your marketing workflow is easy. Keeping it useful is where teams stumble.Here’s what usually happens:<ol><li>You trial an AI tool and see instant time savings.</li><li>You produce more content in less time.</li><li>You feel like you’ve ‘done’ AI.</li></ol>But unless someone steps back and asks: What are we actually improving here?—the wins fade fast.<ul><li>More content ≠ better content.</li><li>Faster delivery ≠ smarter strategy.</li><li>And using a tool ≠ adopting a system.</li></ul>The Real Risk: AI Without Purpose A lot of marketing teams are falling into the same trap they did with martech: buying shiny tools without clear use cases.<ul><li>They produce newsletters faster, but open rates haven’t changed.</li><li>They generate blog posts at scale, but traffic’s flat.</li><li>They launch AI-driven ads, but customer value hasn’t shifted.</li></ul>If the only thing AI is helping you do is more of the same, you’ll end up bloated, not better.What Sustained Success Actually Looks Like The teams getting long-term value from AI are doing three things differently:They set commercial goals first.Not “Let’s use AI,” but “Let’s improve conversion from these channels” or “Let’s reduce churn in these segments.” Then they choose the right tools.They design feedback loops.When AI outputs a campaign, it doesn’t stop there. They test, review, adjust prompts, retrain their tools or people, and update strategy. It’s not set-and-forget—it’s evolve-and-refine.They build workflows, not just outputs.They don’t just prompt ChatGPT for one-off posts. They connect tools like ChatGPT, Perplexity, Canva, and HubSpot to design repeatable, auditable, and customisable marketing systems.If you’ve already started using AI in your marketing, great. That’s the first 20%.But the remaining 80%—the part that creates real commercial lift—comes from embedding it in the way you operate:<ol><li>Does your team understand how to brief AI tools clearly?</li><li>Are you using AI to shape campaign direction or just write ads?</li><li>Do your prompts change as you learn what works?</li><li>Is your content performance actually improving?</li><li>And if not—what would need to shift?</li></ol>We help marketing and brand teams build AI systems that do more than just save time.Our AI Strategy and Low-Code Implementation offers can help you:<ul><li>Build out connected workflows across your marketing stack</li><li>Train your team on how to write better prompts and structure AI-led campaigns</li><li>Set performance metrics that actually improve your customer funnel</li></ul>If your AI use has plateaued, it might be time to rethink how you're using it—before the next quarter rolls around with the same results.You don’t need more tools. You need a better system.

AI’s Already Changed Marketing—Now Comes the Hard Part

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A few months ago, a client told me they were still setting calendar reminders to copy notes from ChatGPT into their CRM. Every morning.Same client. Same request. Same copy-paste job. Over and over again.This kind of repetitive, manual friction isn’t just a nuisance—it’s where most businesses lose time, forget steps, and kill momentum.That’s why a small but important update to ChatGPT is worth paying attention to: Scheduled Tasks.Here’s what it does, why it matters, and what it unlocks for small teams and service businesses.What Are Scheduled Tasks? Put simply: you can now ask ChatGPT to do something later—and it will.Think of it like setting up a calendar entry, but instead of a reminder for you, it’s a job for ChatGPT.For example:<ol><li>Send a summary of today’s sales calls every day at 5pm</li><li>Create tomorrow’s LinkedIn post every morning at 7am</li><li>Re-run a report every Monday using updated data</li><li>Email a reminder to staff before your Monday stand-up</li></ol>These tasks are set in advance, triggered on a schedule, and happen automatically. You don’t have to log in and prompt ChatGPT manually.Right now, this feature is available to ChatGPT Plus and Team users with GPT-4o and works best when paired with custom instructions or memory settings already in place.Why It Matters The practical benefit? Less busywork. Fewer missed steps. More consistency.If you're running a small firm, a startup team, or a professional service business—this matters. You don’t have time to babysit another app.Most people use ChatGPT reactively: “Hey, write me an email.” “Help me draft a response.” “Summarise this meeting.”But with scheduling, it flips the model. You shift from reactive prompting to proactive systems.That’s a big mental shift. It turns ChatGPT from a helpful assistant into a lightweight operations layer—especially when paired with tools like Zapier, Google Sheets, or Notion.What to Watch Out For This isn’t fully hands-off just yet. A few things to keep in mind:<ul><li>You’ll need to write clear prompts and set up task logic in advance.</li><li>It works best when ChatGPT has memory of your preferences, tone, brand, and task formatting.</li><li>If you haven’t yet enabled memory or custom instructions, you’ll want to do that first.</li><li>Tasks are created within the Explore GPTs area—so it’s still a bit hidden if you don’t know where to look.</li></ul>OpenAI’s guide here breaks down the steps clearly: https://help.openai.com/en/articles/10291617-scheduled-tasks-in-chatgptOpenAI is quietly turning ChatGPT into a workflow engine. Not just something you chat with, but something you rely on.If you’re in a service business—legal, finance, travel, consulting, education—this unlocks a new type of automation without needing to write code or build custom software.And if you're still doing things manually—note summaries, weekly planning, status updates—it’s time to ask: Could this be scheduled?Need help setting this up properly? We work with service businesses to build out low-code AI workflows using tools like ChatGPT, Zapier, Make, Notion, and your existing tech stack.If you’re ready to stop copying and pasting and start building something more consistent, get in touch.PS – A few signs your business is ready to use Scheduled Tasks:<ul><li>You do the same admin tasks at the same time each week</li><li>You manually prompt ChatGPT to do repetitive jobs</li><li>Your team forgets to send follow-ups, recaps, or routine updates</li><li>You want to build automation without hiring developers</li></ul>You don’t need to overhaul your systems. You just need to start small, with the tasks you already repeat.

Stop Copying and Pasting, ChatGPT Can Now Schedule Your Tasks

Every organisation is buried in documents.Meeting notes, internal reports, strategy decks, policies, training guides, compliance logs—the information is there, but finding it when you need it? That’s where the real problem lies.Google has quietly released a tool that solves this: NotebookLM.What is NotebookLM?NotebookLM is an AI-powered research assistant that works directly with your documents. You upload files—Word, PDF, Slides, even notes—and it creates a private AI model trained on your content.Then you talk to it in plain English. You can ask:<ul><li>“What are the main actions from last quarter’s leadership meeting?”</li><li>“Summarise the key risks from all our vendor contracts”</li><li>“What’s changed in our onboarding policy over the last two years?”</li></ul>It reads your files, understands the content, and gives you answers that are specific to your business—not general internet knowledge.There’s no coding, no setup, and no need to integrate anything. It works out of the box.Why does it matter?Most teams spend a staggering amount of time finding, re-reading, or summarising internal material—especially across:<ul><li>HR</li><li>Legal and compliance</li><li>Project management</li><li>Council and government operations</li><li>Education and research admin</li></ul>That’s not work. That’s waste.NotebookLM turns that waste into value.Because when your staff can ask a question and get the right answer—without digging through folders or rereading old documents—you free up time, improve decision-making, and cut admin.Here’s how different sectors are already using this tool:<ul><li>Legal teams upload 20 contracts and ask the model to highlight key differences in termination clauses</li><li>Project managers load meeting decks and task logs, then ask “What decisions still haven’t been actioned?”</li><li>Policy officers feed in legislation and internal strategy documents to generate summaries and community comms</li><li>Educators upload previous curriculum reviews and ask for a synthesis of outcomes and recommendations</li></ul>In all of these cases, the process takes minutes not hours.How to use it safely and effectively It’s tempting to throw everything at a new tool. But the right approach is simple and staged:<ol><li>Start with non-sensitive documents:&nbsp; Upload files like training guides, planning docs, policy manuals—anything publicly shareable or internally approved.</li><li>Set a clear task: Don’t just test the tool. Use it for a real workflow: building a summary pack, preparing a Q&amp;A brief, consolidating past decisions.</li><li>Check the output: AI tools are fast, but they’re not perfect. Always validate outputs against the source material, especially early on.</li><li>Use version control: Keep your original documents untouched. NotebookLM works with copies, so there’s no risk of content loss.</li><li>Trial in teams with high information load: Focus on functions with long documents, repeat queries, or overlapping responsibilities. That’s where ROI appears fastest.</li></ol>NotebookLM is part of a wave of AI tools that don’t need developers or engineers. They’re no-code tools that give everyday staff the ability to work faster, search smarter, and focus on what matters.But tool selection is only half the work. The rest is:<ul><li>Knowing which use cases actually save time</li><li>Testing them securely</li><li>Measuring impact before scaling</li></ul>That’s where our No-Code AI Implementation service comes in. It’s designed for service businesses like legal, government, education, and beyond—to trial tools like NotebookLM in a controlled, practical way.If you’ve got more documents than time, AI can now handle the reading. You just need to know where to start.

NotebookLM: Google’s AI Tool That Reads Your Docs So You Don’t Have To

You don’t need to build AI from scratch. But you do need to understand where it's heading—because it's already reshaping how work gets done across law firms, councils, banks, universities, and beyond.At the centre of that shift is DeepMind.What is DeepMind?DeepMind is Google’s advanced AI research lab. Think of it as the place where the foundational breakthroughs happen—years before they show up in the tools your teams use day-to-day.They’re best known for building AI systems that mastered games like Go and StarCraft. But now they’re focused on something far more relevant to you: how to make AI think and plan like a human.They’re designing AI agents that can reason, strategise, and solve problems across domains—not just one narrow task.What are they working on now?Their latest work isn’t just interesting for engineers—it’s commercially relevant for anyone exploring how AI could reduce admin, speed up decisions, or improve service delivery.Recent developments include:<ul><li>AlphaGeometry: Solves Olympiad-level geometry problems by “reading” examples and figuring out logical solutions—like a top high school student.</li><li>SIMA: A general-purpose agent that learns how to play complex 3D games from scratch. It interprets natural language instructions, sets goals, and adapts.</li><li>Generative dashboards: Early prototypes of agents that generate business dashboards from a simple brief. You say “Show me how customer wait times changed after the new policy”—and it builds the report using your data.</li></ul>What does this mean for your organisation?The shift is clear: we’re moving from AI as a helper (e.g. “write this email”) to AI as a co-worker (e.g. “analyse this report and recommend the next three actions”).That unlocks new use cases you couldn’t automate before:<ul><li>A compliance officer prompts AI to find gaps in quarterly audit reports</li><li>A policy team generates summaries across multiple stakeholder submissions</li><li>A customer service leader asks, “What are the top 3 reasons complaints spiked in April?”—and gets a full breakdown pulled from internal tools</li><li>A local government uses AI to review community feedback and recommend changes to project planning documents</li></ul>These use cases are now real options for your team.So what should you do now?This is where most businesses get stuck. They either chase tools too early or wait too long, hoping someone else will figure it out for them.Here’s what a smart move looks like:<ol><li>Understand the building blocks: You don’t need to know how to build an LLM but you do need to understand what they can and can’t do. You need to know where reasoning-based models like DeepMind’s fit into your workflow, versus more basic tools like ChatGPT.</li><li>Start with high-friction work: Look at where your people are spending time pulling reports, rewriting content, summarising data, or checking compliance. These are your quick wins.</li><li>Test agent-style tools in a safe, scoped way: Don’t roll out 10 tools at once. Pick one process (e.g. internal reporting, onboarding) and test how AI can reduce time or improve outcomes. We help clients do this using structured pilots and evaluation plans.</li><li>Build internal confidence: Staff won’t adopt tools they don’t understand. That’s why we run tailored AI education and change programs—focused on commercial leaders, not data scientists.</li></ol>We work with government, education, legal, financial, and travel organisations to help them do one thing: use AI to solve business problems without wasting time on hype or overengineering.We translate AI breakthroughs like those from DeepMind into strategic actions:<ol><li>What should you automate first?</li><li>Which tools are secure, tested, and actually usable by your teams?</li><li>Where can you unlock margin or capacity using AI today, not in five years?</li></ol>Because while DeepMind pushes the science forward, your job is to make the right moves now.Book our AI Strategy Business Case if you need a clear, low-risk roadmap for applying AI inside your organisation.&nbsp;

AI Blog

What AI in Retail and Consumer Goods Can Teach Every Indu...

Why Google Isn’t the First Place People Search Anymore

AI’s Already Changed Marketing—Now Comes the Hard Part

Stop Copying and Pasting, ChatGPT Can Now Schedule Your T...

NotebookLM: Google’s AI Tool That Reads Your Docs So You ...

What is Google's DeepMind’s and What does it Mean for You