AI Body of Knowledge

Job titles are often one of the clearest signals that AI adoption is moving from experimentation into everyday work. The important shift is not simply giving people access to AI tools. It is building the capability to use those tools safely, practically and in ways that make sense for the specific work being done. That is where organisations will start to see real value.

The adoption gap is becoming clearer: organisations do not just need access to AI tools. They need practical capability, governance, workflow understanding and safe-use habits.

A strong takeaway from the recent Microsoft AI Tour event in Sydney was that most organisations are now past the awareness stage with AI. The real challenge is helping teams use it safely, practically, and consistently in day-to-day work. That gap between experimentation and operational habit is where a lot of the real work still needs to happen.

Access to AI tools is increasing quickly. There are some fantasic tools readily available now for users, with new products and features launching every week. Capability is now the differentiator. Professionals and teams that learn how to use AI safely, usefully, and consistently will move faster than those still experimenting.

A helpful lens: the biggest value from GenAI is not content creation. It’s helping teams make sense of complexity, faster. Most organisations have a “meaning problem” before they have a “writing problem”: too many documents, too many tools, too many messages, and not enough clarity. The advantage comes when people build the capability to: - ask better questions - check the output - track changes over time - make decisions with evidence Consider this: where would turning noise into signal make the biggest difference in your organisation?

GenerativeAI is not just for producing more content. Even greater value is unlocked when it helps teams check work, reduce risk, and catch issues early. Examples include: • spotting inconsistencies across documents • flagging unusual activity in systems and logs • highlighting where outcomes are drifting over time If GenAI is the new spreadsheet, the edge comes from better decisions, not faster typing.

🎄 Day 12/12: Qwen Vision-Language (Qwen2.5-VL → Qwen3-VL) The 2026 Gift: Vision-language moves from demos to real work 🎁 A “quick” task lands: pull key fields from 30 invoices. Half are PDFs, some are phone photos, one is a screenshot, and the totals do not match. Qwen vision-language models read images, video, and text together and reply in plain language, including extracted fields and the on-page evidence they came from. What makes it different in late 2025: ✅ Evidence-first outputs: it can anchor answers to a specific region of the page, so you can check the source. ✅ Better with awkward shapes: dynamic resolution helps with long receipts and ultra-wide diagrams without squashing the layout. ✅ Clear progression: Qwen3-VL is the flagship today, including “Thinking” variants that take an extra reasoning step before answering, which helps reduce confident-but-wrong numbers. When to use it: 💼 Executives: “Find the risk clause in this scanned 50-page vendor contract and show the evidence.” 💼 Knowledge workers: Turn stacks of PDFs and mobile photos into structured fields and follow-up actions. 💼 IT professionals: Build screen-aware automations for legacy systems that do not have an API. When not to use it: ⚠️ Pure text work: Claude or DeepSeek-V3 are safer defaults. ⚠️ Ultra-high volume extraction: Gemini Flash is often cheaper for simple jobs. ⚠️ Zero-engineering needs: use the multimodal features already included in Copilot or Google Workspace. The capability gap: The gap isn’t knowing multimodal AI exists; it’s knowing when your default text model is flying blind because it cannot see the evidence. Following the AI Body of Knowledge "12 Days of AI Models" series? Full guide will be published in January. Follow AI Body of Knowledge to get updates or DM us for details. 🎯 Building internal capability? DM us. We deliver Generative AI training that focuses on #CapabilityOverHype #12DaysOfAIModels #CapabilityOverHype #AIBoK2025

🎄 Day 11/12, Sora 2 You need a 10-15 second “what we shipped” clip for tomorrow’s board pack. You have the message. You do not have a production crew, voice talent, or a spare day. Sora 2 is OpenAI's model that turns a prompt (or a reference image) into a short video clip with synced audio. What makes it different in 2025 ✅  Video plus audio in one pass ✅  More consistent multi-shot scenes across cuts ✅  Storyboards and reusable “characters” built into the workflow When to use it 💼  Executives, a 15-second strategy snapshot for an all-hands update 💼  Knowledge workers, generate three campaign openers, pick one, then polish for review 💼  IT professionals, internal training clips that show the workflow in context When not to use it ⚠️ You need brand-perfect output, draft your stills in FLUX, then animate in your usual stack ⚠️ You only need the script and shot list, use Claude first ⚠️ If Sora 2 is not available in your country yet, use Sora 1 for silent drafts; add voice with ElevenLabs We’ve seen product marketing teams draft multiple alternate product-video openers in an afternoon, then hand the preferred concept to a designer/editor for final polish. The gap now isn’t knowing Sora exists; it’s knowing when video generation is the right lever, and when it is not. 📥 Following the AI Body of Knowledge "12 Days of AI Models" series? Full guide will be published in January. Follow AI Body of Knowledge to get updates or DM us for details. 🎯 Building internal capability? DM us. We deliver training Generative AI that covers model selection, TCO analysis, and safe deployment. #12DaysOfAIModels #CapabilityOverHype #AIBoK2025

🎄 Day 10/12: ElevenLabs (model family) Your team needs a 6-minute training video updated by Friday. The script changes twice, legal wants a new disclaimer, and someone asks “can we ship this in Spanish too?” You can almost hear the studio invoice printing. ElevenLabs is a voice generation platform that turns text into natural-sounding speech (text-to-speech) for voiceovers, in-product features like walkthroughs, and multilingual dubbing. What makes it different in 2025: * Voice cloning is now production-ready; you can create consistent voice from a small sample * Dubbing at scale; translate and re-voice content while keeping speaker identity and timing across dozens of languages * Real-time voice is viable; latency low enough for actual product experiences, not just demos When to use it: ✅ Executives: Record a CEO update once; dub it for global teams without re-recording ✅ Knowledge workers: Turn policy and enablement documents into audio summaries people will actually consume ✅ IT professionals: Add voice to apps, kiosks, or support flows where response time matters When not to use it: ❌ If you just need basic text-to-speech inside your cloud stack; Google or Microsoft text-to-speech can be simpler for procurement ❌ If you need speech-to-text transcription; use a dedicated speech-to-text model (this is text-to-speech) ❌ If your risk posture cannot support cloning or synthetic voices yet; start with human narration and tight governance We’re seeing product and enablement teams use high-quality text-to-speech tools to generate narration for ‘how to’ videos from the same source script as the written documentation. It keeps updates in sync, and makes refreshes easy as product features change. The gap isn’t knowing voice exists; it’s knowing when voice becomes a workflow lever, and how to ship it safely. Following the series? Full guide in January. Follow AI Body of Knowledge for updates. Building internal capability? DM us, we deliver training on model selection, evaluation, and safe use. #12DaysOfAIModels #CapabilityOverHype #AIBoK2025

🎄 Day 9/12: FLUX.1 and FLUX.2 (families) Marketing needs 15 creatives today. You write a precise prompt, hit generate, and get something that is almost right. The “close enough” misses are the time sink, especially when the label text is wrong and you are on revision five. That’s the reputation FLUX has built with power users in side-by-side tests: 1) Prompt adherence: it tends to do what you asked, with fewer near-misses. 2) Text and “hard prompts”: signs, labels, packaging, UI style assets, especially in the newer FLUX.2 tiers. 3) Run it your way: FLUX.1 includes options you can run locally (schnell is Apache 2.0), which matters when Legal asks if images left your environment. FLUX is Black Forest Labs’ text-to-image family. What makes it different in 2025: - Less “close enough”: stronger prompt adherence means fewer redo loops and less prompt wrestling. - Typography you can ship: FLUX.2 [flex] is tuned for small details and text-heavy assets (labels, UI, signage). - Edits that don’t drift: FLUX.2 [max] is positioned for consistent edits and tight prompt following, so “change one thing” is less likely to break three others. - Open-weights path: FLUX.1 tiers let you run locally for cost control and governance. When to use it: 💼 Executives: Setting policy for where creative AI runs and what data is allowed. 💼 Knowledge workers: Drafting campaign concepts, diagrams, and slide visuals fast, with fewer redo loops. 💼 IT professionals and teams: Deploying a self-hosted image pipeline. When not to use it: ⚠️ You need enterprise-safe licensing first: Adobe Firefly. ⚠️ You want zero setup and a slick workflow: Midjourney. ⚠️ You only need a few images occasionally: DALL·E or GPT Image. Text-to-image is an area we’ve spent a lot of time in, from early prototypes through to production pipelines. Lately we’re seeing teams prototype packaging and UI on FLUX locally, so they can iterate fast and keep early drafts off external servers. The gap isn’t knowing FLUX exists; it’s knowing when you actually need a model that’s building a reputation for three things - prompt adherence, usable text, and run-it-your-way deployment - and when open weights, licensing, and governance change the right answer. Following the series? Full guide in January. Follow AI Body of Knowledge for updates. Building internal capability? DM us, we deliver training on model selection, evaluation, and safe use. #12DaysOfAIModels #CapabilityOverHype #AIBoK2025