Category: Artificial Intelligence

Google Labs: The emerging content stack

I watched a recent interview between Vaibhav Sisinty, founder of GrowthSchool, and Josh Woodward, VP, Google Labs & Google Gemini. What makes it worth watching is that Woodward walks through a set of public Google AI products and experiments that, taken together, reveal a much bigger shift in how Google wants creative work to happen.

One interview. Seven demos. One much bigger signal.

On the surface, this looks like another executive interview plus product showcase. Underneath, it is a useful snapshot of Google’s current AI surface across content, design, research, image editing, music, immersive world-building, and communication. Google Labs is the home for AI experiments at Google, and the interview makes that portfolio feel less like scattered demos and more like an emerging system.

The setup is simple. One conversation shows how a marketer can move from source material to interface concept to visual asset to soundtrack to presentation layer without switching mental models every five minutes. That is why the interview matters more than the usual AI highlight reel.

Google is no longer just shipping tools. It is sketching a marketing workflow.

A marketing workflow is the connected chain of jobs from understanding a brief to shipping an asset, interface, or experience.

Google’s current AI surface now covers adjacent stages of work that used to require a mess of separate tools. Stitch handles UI design and front-end generation for apps and websites. NotebookLM handles source-grounded understanding. Pomelli handles on-brand marketing content. Nano Banana 2 handles image generation and editing. Lyria 3 handles music creation inside Gemini. Beam extends the stack into communication.

In practical terms, this means more of the work can happen inside one Google-shaped environment instead of bouncing across a pile of disconnected tools.

My view is that Google is not showing isolated AI tricks here. It is sketching the outline of a marketer-friendly workflow it wants to own. The real question is not whether every tool is perfect yet. It is whether Google is making enough of the workflow usable in one place that marketers start changing their habits.

The tools that make the pattern easy to see

Pomelli

Pomelli is the most directly marketer-facing tool in the set. It is built to help businesses generate on-brand content faster. Easy use case: give it your site and product context, then generate campaign-ready visuals and messaging variations for social, ecommerce, or CRM. I unpacked one part of that story in my earlier Pomelli Photoshoot deep dive.

Stitch

Stitch is Google’s answer to fast interface ideation. It turns prompts into UI concepts and front-end output for mobile apps and websites. Easy use case: turn a campaign landing-page idea or app flow into a first working interface before design and dev teams invest heavier production time.

NotebookLM

NotebookLM stands out because it starts from your own source material. It helps turn messy research into usable understanding. Easy use case: upload research docs, interview notes, or previous campaigns and use it to build a grounded strategy summary, FAQ, or narrative draft.

Project Genie

Project Genie is the experimental outlier, but it matters because it points to where interactive creation is heading. It lets users explore generated worlds in real time from simple prompts. Easy use case: prototype a branded world, retail concept, or immersive experience before committing to a more expensive 3D or gaming build.

Nano Banana 2

Nano Banana 2 is Google’s latest image-generation and editing push inside Gemini. It is built for faster visual creation, editing, and iteration. Easy use case: create localized campaign visuals, packaging mockups, or quick ad variants from one approved base asset without opening a traditional creative suite first.

Lyria 3 in Gemini

Lyria 3 brings music creation into Gemini. It lets users generate short custom tracks from prompts and creative inputs. Easy use case: create a first-pass soundtrack or mood bed for a product reel, internal concept film, or social clip before moving into full production.

Google Beam

Google Beam, formerly Project Starline, is the communication layer in this broader picture. It turns standard video streams into a more life-sized and spatial experience. Easy use case: use it for high-stakes remote collaboration, premium client conversations, or executive workshops where trust and presence matter more than standard video calls can deliver.

Why this lands faster than most AI demos

Most AI demos still fail the practical test. They show capability without showing where that capability fits into real work. This one lands because the tools map onto jobs people already understand. Research. Design. Asset creation. Editing. Sound. Presentation. Collaboration.

That is what makes the portfolio more memorable than a long list of model upgrades. People do not buy into AI because a benchmark moved. They buy in when they can picture a job getting easier, faster, or more creatively open.

What Google is really trying to own

Google’s business intent looks bigger than feature adoption. It is trying to make more of the marketer’s daily workflow feel native to its own ecosystem, from idea formation to content generation to communication. That is a stronger strategic position than winning a one-off feature comparison.

This is also why labs.google matters in the story. It is not just a gallery of experiments. It is the clearest public window into which adjacent jobs Google thinks belong together next.

What marketers should take from this now

Do not watch this interview as another AI tool roundup. Watch it as a preview of how Google wants more of the marketer workflow to happen inside one ecosystem.

Extractable takeaway: The strategic signal here is not one impressive Google AI demo. It is that Google is assembling enough connected creative building blocks that marketers can start reducing tool sprawl and shortening the path from brief to output.

The practical move is to start small and test the clearest sequence. NotebookLM for synthesis. Stitch for interface concepts. Pomelli or Nano Banana 2 for visual production. That is already enough to show whether your current bottleneck is research, creative iteration, or production speed.

A few fast answers before you act

Which Google tools in this interview matter most for marketers right now?

NotebookLM, Stitch, Pomelli, Nano Banana 2, and Lyria 3 are the most directly useful because they map to research, interface concepts, asset creation, editing, and soundtrack generation.

Why does this interview matter more than a normal product launch video?

Because it shows multiple Google AI products side by side, which makes the workflow pattern easier to spot than a single product announcement.

Is Google Labs just a showcase site?

No. It is Google’s public home for AI experiments, which makes it the best place to track how Google is connecting adjacent creative and knowledge tasks.

What is the clearest first test for a marketing team?

Use NotebookLM to digest source material, Stitch to mock the experience, and Pomelli or Nano Banana 2 to produce first-pass campaign assets.

What is the strategic takeaway for leaders?

Evaluate these tools as a workflow play, not as isolated demos, because the compounding value comes from reducing friction between connected jobs.

NotCo: AI-Powered Fragrance With Purpose

Back in 2014, Oscar Mayer showed how powerful scent becomes when it stops behaving like a message and starts behaving like a mechanic. Its bacon alarm let people wake up to the sound of sizzling bacon on the stove, while the brand inserted itself into a daily habit instead of a one-off impression.

Fast forward to 2026, and NotCo is pushing scent from playful activation into AI-enabled product development. With Giuseppe AI and its fragrance formulation work with Cramer, a Latin American multinational in flavors and fragrances, NotCo is showing how a sensory cue can become a personalized product proposition. Giuseppe is positioned as an end-to-end product development platform, meaning it helps move from idea to formulation to scalable output within one workflow.

How Aroma Best Friend makes Giuseppe easy to understand

Aroma Best Friend does not try to explain AI through dashboards, technical architecture, or speed claims. It explains the platform through a very human tension point: a dog struggling when its owner leaves home. The story is simple, emotional, and commercially useful at the same time.

The mechanism is easy to retell. The campaign presents a personalized fragrance generated from the owner’s scent profile so a dog is left with an olfactory stand-in for presence. An olfactory profile is the identifiable mix of volatile compounds associated with a person’s scent signature.

In consumer goods, this is the kind of AI story that travels fastest because it links formulation capability to a sensory outcome people can instantly understand.

The film frames the idea around making your dog happier, which keeps the promise focused on an outcome instead of a technology demo.

Why this lands harder than most AI demos

Most AI campaigns still make the same mistake. They tell you the model is powerful and then expect the audience to infer the commercial value. Aroma Best Friend works better because the technology claim is attached to a felt problem and a tangible output, which makes the platform easier to understand and easier to remember.

Extractable takeaway: AI becomes more persuasive when it is shown solving a problem people can emotionally grasp, not when it is described as a capability stack. The sharper the human tension and the clearer the output, the stronger the commercial story.

Scent is not decorative here. It is the proof. That turns Giuseppe from a backstage R&D engine into the source of a new kind of product experience. NotCo is not just advertising AI. It is advertising the kinds of product experiences AI can now help create.

The business play behind the emotion

The real question is whether an AI platform can turn an invisible R&D capability into a story that brand teams, partners, and future buyers instantly understand.

The official waitlist for the product makes clear that joining does not guarantee access to or availability of the product. That suggests this is as much about validating demand and capturing interest as it is about launching a ready-to-scale offer.

That is the smarter move. Aroma Best Friend works as a campaign, a proof-of-capability demo, and a demand signal test at the same time. Instead of saying that Giuseppe enables personalization and creativity, NotCo dramatizes a specific version of personalization that people can picture, repeat, and remember.

What FMCG and CPG teams should borrow now

  • Turn capability into consequence. Do not market the model first. Market the human outcome the model makes possible.
  • Use one emotionally legible use case to explain a broader platform. Aroma Best Friend is about dogs on the surface, but the deeper message is that Giuseppe can work where formulation and personalization matter.
  • Make the demo do double duty. The strongest AI campaigns are not just communications assets. They also test demand, capture leads, and reposition the company.
  • Choose outputs people can feel, not just read about. Text is easy. Fragrance is harder. That is exactly why this idea carries more weight.
  • Prove customization through specificity. Personalized fragrance is stronger than generic AI-powered personalization because it gives the claim an object, a use case, and a memory.

A few fast answers before you act

What is Aroma Best Friend really marketing?

Aroma Best Friend markets a personalized scent concept for pet separation anxiety on the surface, but at a deeper level it markets Giuseppe AI as a product-development engine that can move into formulation-led use cases.

Why does this explain Giuseppe better than a typical AI demo?

It explains Giuseppe better because it connects the technology to a human problem and a sensory output. That makes the platform easier to understand than abstract claims about intelligence, speed, or creativity.

Is Aroma Best Friend already a scaled product launch?

Not yet in any proven commercial sense. The waitlist language makes clear that joining does not guarantee access to or availability of the product, so the initiative still functions as a signal test as much as a launch story.

Why is scent such a strong choice for this idea?

Scent carries memory, comfort, and presence more directly than most brand cues. That gives the campaign emotional force and turns formulation technology into something people can instantly imagine in use.

What should marketers and innovation teams steal from this?

They should steal the structure. Start with a real human tension, let the technology solve it in a tangible way, and make the output specific enough that people can retell the story in one sentence.

Pomelli Photoshoot: Fast studio-quality assets

Start from a single image of your product and easily create high quality, customized product shots to elevate your marketing.

A jar in your hand. A whole shoot in your CMS

Start with the most ordinary thing in e-commerce. A single product photo, shot on a desk, held in a hand, good enough for internal approval but nowhere near “campaign-ready”. Then imagine turning that one image into a set of studio and lifestyle shots that look like you planned the lighting, the surface, the props, and the framing.

That is the pitch behind Photoshoot, a feature inside Pomelli from Google Labs: take a basic product image and generate professional-grade marketing imagery fast, without booking a studio for every new variant. “Studio-grade” here means assets that can sit on a PDP or paid social without instantly looking like “placeholder content”.

How Photoshoot turns one product photo into usable marketing imagery

Photoshoot is not just “generate me a nicer background”. It is a guided flow designed to keep output consistent.

  1. Pick a product photo. The input can be imperfect. The tool is explicitly designed to handle “don’t worry about polish”.
  2. Choose a template. Templates are pre-built shot styles (for example studio or lifestyle) that constrain composition so results do not drift into random aesthetics.
  3. Generate. Pomelli applies your brand aesthetic via its Business DNA, then generates new shots. Business DNA is Pomelli’s saved brand profile derived from your website (voice, fonts, imagery, color palette).
  4. Refine. You iterate with finishing touches, then download assets or store them back into Business DNA for reuse in later campaigns.

Under the hood, Google describes this as combining business context (Business DNA) with Nano Banana image generation to produce the final scenes.

In high-velocity retail and FMCG e-commerce teams shipping new SKUs (Stock Keeping Units) and promos weekly across many markets, this is the shortest path from “we have a product” to “we have compliant, channel-ready variants”.

The real question is whether one approved product shot can produce enough on-brand variants to increase throughput without increasing review drag.

Why it lands. Because it cuts the real friction, not the fun part

Most teams are not blocked on “having ideas”. They are blocked on throughput with consistency: getting enough variants, in enough formats, that still look on-brand, pass review, and do not trigger rework across design, legal, and local markets.

This is why the mechanism matters. Because Photoshoot grounds outputs in Business DNA and constrains composition via templates, the results tend to feel brand-consistent faster, which reduces review churn and makes variant production scalable.

Extractable takeaway: If you want generative creative to survive enterprise review, do not start with infinite freedom. Start with constraints that encode your brand (a reusable brand profile) and your channel rules (shot templates), then let the model fill in the pixels inside that box.

The business intent is blunt. Production leverage for asset variants

“Production leverage” is the multiplier you get when one person-hour produces many more usable assets without multiplying headcount or agency spend. For e-commerce teams, Photoshoot is essentially a variant engine.

  • More PDP (Product Detail Pages) imagery coverage without re-shooting every pack change.
  • More paid social iterations without waiting on design queues.
  • Faster seasonal refreshes when the same SKU needs a new context (spring, gifting, back-to-work).
  • A tighter loop between merchandising and creative because the cost of “try another angle” collapses.

Important reality check: you still need governance. Treat outputs like any other marketing asset. Rights, claims, pack accuracy, and local compliance do not disappear just because generation is fast.

Where to try it?

The Pomelli app on Google Labs is where you can access the experience.

However availability is currently limited. Pomelli has been launched as a public beta experiment in the United States, Canada, Australia, and New Zealand (English).

What to steal for your next asset sprint if the app is available in your region

  • Codify brand constraints first. Build a reusable “brand profile” (fonts, tone, visual rules) before you chase more generations.
  • Template your shots like you template layouts. Decide the 6 to 10 shot types you actually need (hero studio, detail crop, lifestyle context, ingredient cue) and standardize them.
  • Design for review speed. Define what “acceptable” means (pack legibility, logo integrity, claims, background rules), then generate inside those rails.
  • Run a SKU ladder test. Start with 10 SKUs across easy and hard surfaces (glass, reflective, metallic). If it fails there, it will fail at scale.
  • Instrument the pipeline. Track time-to-first-usable, approval rate, and rework causes. That is how you prove leverage, not by “wow, looks nice”.

A few fast answers before you act

What is Pomelli Photoshoot, in one sentence?

Pomelli Photoshoot is a feature inside Google Labs’ Pomelli that turns a single product photo into professional-style studio and lifestyle marketing images using brand context and image generation.

What is the mechanic marketers should care about?

You choose a product image, select a curated template (studio or lifestyle), generate variants grounded in your Business DNA, then refine and download or reuse those assets in future campaigns.

What does “Business DNA” actually mean here?

Business DNA is Pomelli’s saved brand profile derived from your website, such as tone of voice, fonts, imagery, and color palette, which Pomelli uses to keep generated outputs consistent.

Where is Pomelli available right now?

Pomelli is in public beta in English in the United States, Canada, Australia, and New Zealand. It is not currently available in Germany.

What is the first safe way to pilot this in an enterprise team?

Pilot it on a small SKU set with strict shot templates and review criteria, then measure approval rate and rework reasons before scaling variant production.