For Esteban · Creative AI Lab Plain-English report · 14 Jun 2026

Creative AI in 2026

What's out there — and what we should do.

The plain-English version. What I looked at, why it matters to us, what I found, how it works, and the one thing I'd build first.

You asked me to find out what's really out there in creative AI right now — the tools that don't just make one thing, but take a whole creative job (a video, a podcast, a week of social posts) and do it end to end, like a little factory. I went deep, then had a panel of AI critics tear the findings apart, then made the research defend itself, then wrote this. Below is the honest answer: what it is, why it matters to us, what I found, how it works, and what I think we should do.

The short answer — if you read nothing else

  1. The winning tools aren't the ones that just make a clip or a voice. They're the ones that do the whole job — write it, make it, check it, polish it, publish it. Making raw content is now cheap and ordinary; doing the whole job, on-brand is where the money is.
  2. Most "AI agents" talk is marketing. Almost everyone says their tool uses clever AI "agents" working on their own. In reality it's usually a fixed assembly line with a quality-checker bolted on. Real, self-running agent teams are rare — and the boldest one that tried ("the world's first AI marketing chief") failed and shut down.
  3. Our real advantage isn't the tech — it's something we already own. Anyone can plug these tools together. What nobody else has is BCG's brand rules and our senior creatives' taste — the record of what we approve and reject. That judgement is the hard-to-copy thing.
  4. So we should flip the obvious plan: sell our ideas and judgement as the premium product, and let AI cheaply mass-produce the variations underneath (all the sizes, languages and versions).
  5. Be realistic about cost and effort. Making lots of AI video is expensive (you pay by the second), and a trustworthy "brand-checker" is a real engineering project, not a weekend hack. Plan and budget for that.

01 · The context

Why this matters to us

Three things are happening at once, and they all point the same way:

  • Making content is becoming cheap and ordinary. Anyone can now generate a decent image, voice or video from a sentence. When everyone can do it, that skill stops being special — and the value moves to whoever can do it on-brand, safely, and at scale.
  • The work is moving toward studios like ours. About a third of big brands say they'll bring nearly all their creative work in-house within a year, and the giant ad agencies are shrinking. The budget is flowing toward exactly the kind of in-house team we are.
  • There are real legal traps. Courts have signalled that work made purely by AI (with no real human author) may not be protectable as your property — and there are billion-dollar lawsuits over how these tools were trained. So "a human clearly led this" isn't just nice to have; it protects the work.

When making things gets cheap, judgement gets expensive.

02 · What I found

What's actually out there

Video

  • The "whole job" tools — type an idea and they plan it, make the scenes, add a voice and edit it together: Runway, Google's Flow, HeyGen, LTX Studio.
  • The steady money is in business video — tools that turn a script into a presenter-style training or explainer video: Synthesia (used by 9 in 10 of the world's biggest companies) and HeyGen.
  • The big warning sign: the most hyped consumer video app of last year, OpenAI's Sora, was shut down this year — it cost too much to run. A reminder that "cool" doesn't pay the bills; useful, on-brand work does.

Podcasts & audio

  • The standout trick: drop in a document and get back a finished, two-host podcast in one click. Google's NotebookLM made this famous; ElevenLabs, Wondercraft and others do it too.
  • ElevenLabs has become the go-to for AI voices and is now worth about $11 billion — a sign of how much value sits in this layer.

Social posts & ads

  • "One thing into many" is the killer use — feed in one long video or one master ad, get back dozens of clips, posts and local versions: OpusClip, Jasper, HubSpot.
  • The honest standouts (the few that really do split the work into specialist steps): Jasper and HubSpot. Most others just look like it.
  • The cautionary tale — "Icon": it promised a fully automatic "AI marketing chief" that did everything with no humans. It didn't work, quietly switched to using real people instead, and collapsed. Full auto-pilot isn't ready.

Images & design

  • Adobe is furthest ahead at the "whole job, on-brand" idea, and — importantly — it legally protects you if you use its images commercially. That's the bar we'd be measured against.
  • Best-in-class for specific jobs: Midjourney (look and feel), Ideogram (text in images), Recraft (logos and brand assets).

03 · How it works

How these products are built — in plain terms

Strip away the jargon and it's simple. A "creative AI product" is just a few AI tools wired together into a set of steps, with a checker and a human sign-off. Two words get thrown around a lot — here's what they actually mean:

Two terms, defined

A "workflow product" = one tool that does a whole multi-step job end to end, instead of just one step. (Brief → idea → make → check → publish.)

An "AI agent" = a little AI helper that does one step and decides how on its own, then hands off to the next. A team of these is a "multi-agent" system — powerful, but harder and pricier to run, and rarer than the marketing suggests.

The sensible way to build one is an assembly line you can trust: fixed, predictable steps for most of it, a smart AI "brand-checker" that flags anything off-brand, and a human who signs off before it goes out.

The honest catch

That smart brand-checker — the part that makes it ours — is the hardest, most expensive piece to build well, and it needs real engineering care to keep trustworthy. The good news: that difficulty is exactly why a competitor can't quickly copy it.

The cost reality

The big bill isn't the thinking — it's the making. AI video is charged by the second, so churning out a few hundred versions of a video can quietly run to thousands of dollars in one go. Any tool we build needs a spend limit and a human check before the expensive step, not just at the end.

04 · What we should do

The plan

  1. Bet on our judgement, not the tech. Build our edge on the one thing rivals can't copy — BCG's brand rules and our senior creatives' taste. The tools underneath are swappable plumbing.
  2. Sell the thinking; automate the grunt-work. Charge for original ideas and craft (the premium bit); use AI to cheaply mass-produce the variations underneath — every size, language and version.
  3. Buy the tools, build only the brand-safe "front door". Don't reinvent the generators (Adobe, Google, ElevenLabs already make those). Build the layer that keeps everything on-brand, legal, and human-approved.
  4. Keep a human in the loop — always. It protects the work legally, it keeps quality up, and "made with real people" is now something clients actively want.
  5. Only call it "agents" if it really is. For the June hackathon, show a demo where you can see the steps hand off and a brand-check catch a mistake. Don't dress up a single AI call as a "team of agents" — people see through it.

The first thing to build (good for the June 22 hackathon)

Take one approved master ad and automatically make all the local-market versions — every language, format and channel — with our brand-checker watching and a human signing off at the end.

  • Why this one: it's exactly where our brand rules and BCG's trusted, careful approach beat a generic tool — and big regulated clients pay well for it.
  • How: buy the generators and the wiring; build only our brand-check + sign-off layer.
  • The path: hackathon demo → one real client → a repeat, paying engagement.

05 · How I made this (and what to trust)

The honest bit

My method

I ran five deep searches at once (video, audio, social, the under-the-hood tech, and the money), reading and cross-checking real sources. Then I had five independent AI critics attack the findings, and made the research defend itself and re-check every shaky fact. A few claims got corrected in that process — this page is only what survived.

  • What's solid: the big picture, the Sora shutdown, the legal traps, and the under-the-hood cost facts are well sourced.
  • What to take with a pinch of salt: some companies' exact revenue/value figures are estimates, and a few "agent" claims are really just company marketing — I've flagged those.
  • What I didn't cover: gaming/3D, coding tools, and players outside the US and China.

The full long version, the critics' notes, and the sources all live in the project files (research/creative-ai-workflows-2026/) if you ever want to dig in.