The missing data layer behind effective brand marketing

As AI makes creative production faster and cheaper, Lindsay King explains why the real marketing bottleneck is shifting from making more content to knowing what will actually work.

I recently recorded an episode of Building with AI: Promises and Heartbreaks with Lindsay King, a consumer-facing marketer with about 15 years of experience across agencies and major consumer tech brands, including Uber and Instacart. (listen to it on YouTube, Spotify, Apple Podcasts)

Lindsay is not the typical guest we bring onto the podcast. Most of our conversations are with data leaders, AI builders, analytics practitioners, and people working deep inside the modern data stack.

That is exactly what made this one interesting.

Lindsay has spent years in brand marketing, but she also spent time in data observability and became fascinated by the modern data stack. So she sits in a very useful place: close enough to marketing to understand the creative and emotional side of the work, and close enough to data to see how much of the system still runs without the infrastructure it needs.

The core question we explored was simple:

What happens to brand marketing when AI makes creative production almost free?

Production is no longer the hard part

Marketing has always had two broad buckets.

Performance marketing is easier to measure. You put messages into channels, test variants, track clicks or conversions, and optimize quickly.

Brand marketing works higher up the funnel. It is about awareness, perception, favorability, trust, and eventually preference. It is the reason you choose one app, product, or service over another before you can fully explain why.

Historically, brand campaigns moved slowly. A team would define the audience, write the brief, align stakeholders, bring in an agency, develop the creative, buy media, launch the campaign, and wait for results.

For a big campaign, this can take months. It can also cost millions.

AI changes the production side of that equation. It is now much easier to create more campaign ideas, more images, more videos, more variants, and more personalized messages for different audiences.

But Lindsay made an important point: that is only the first-order effect.

If everyone can produce more content faster, production efficiency stops being the moat.

The real advantage shifts to effectiveness.

In other words: who can figure out what will actually work?

Brand marketing has a memory problem

This is where the conversation started sounding very familiar to anyone who works in data.

Brand marketing has a lot of information, but much of it is fragmented.

There are campaign briefs, creative assets, media plans, audience segments, brand lift studies, agency learnings, performance reports, research decks, and post-campaign analysis. Some of it is structured. Much of it is unstructured. Some of it sits with agencies. Some of it sits in dashboards. A lot of it lives in people’s heads.

The problem is not that there is no data.

The problem is that the data is not connected into a reusable system of context.

A campaign launches. The team gets results. Maybe it drove lift. Maybe it changed perception. Maybe it taught the team something important about an audience, a message, a channel, or a creative direction.

But does that learning get structured and fed back into the next strategic brief?

Usually, not really.

Lindsay described brand as “the dark matter of the universe.” You know it is powerful. You can see the effect. But it is hard to measure directly.

That may have worked when campaigns were slower and creative production was expensive. It will not work as well in a world where AI lets teams produce many more options, much faster.

More content without better memory just creates more noise.

The next step is campaign simulation

One of the most interesting ideas Lindsay raised was campaign simulation.

Today, some companies already test concepts against synthetic audiences. But Lindsay’s point was that the next step is bigger than synthetic audiences.

It is synthetic channels.

Imagine taking a campaign concept, audience data, media plan, creative assets, past campaign performance, and external market context, then simulating how different options might perform before spending the media budget.

Not perfectly. Not as a replacement for judgment.

But directionally.

Which creative concept is likely to create the most lift? Which audience may respond best? Which channel mix looks weak? Which message is bold but risky? Which campaign is safe but probably forgettable?

That would change the role of the brand marketer.

The marketer would not just be managing a slow, linear campaign process. They would become more of a systems designer: defining the audience, business objective, constraints, message, context, and evaluation criteria, then using AI systems to generate and test more options.

The human still matters. Taste still matters. Judgment still matters.

But the human gets better infrastructure.

This is a semantic context problem

The connection to Solid’s world is pretty direct.

In AI analytics, we see the same pattern all the time. Giving an AI model access to data is not enough. It needs to understand the business context around the data.

What does this metric mean? Which tables are trusted? Which joins are valid? Which definitions are outdated? Which dashboard reflects accepted business logic? Which examples should the AI learn from?

Without that context, Text2SQL breaks. Data agents hallucinate. Business users lose trust.

Marketing is heading toward a similar wall.

If AI is going to help marketers make better decisions, it cannot only generate more assets. It needs to understand the context behind those assets.

What was the brief? Who was the audience? What was the message? What emotional response were we trying to create? Where did it run? What did we expect? What happened? What did we learn? Which assumptions should change next time?

That is not just a content-generation problem.

It is a semantic engineering problem.

Marketing teams need a structured memory of their own business logic. They need to turn briefs, assets, channels, results, and learnings into context that future systems can use.

Otherwise, AI will help them make more things without helping them make better decisions.

The future is not just more AI-generated ads

I do not think brand marketing will become purely scientific.

It should not.

Culture, emotion, timing, humor, and taste will always matter. A great brand campaign will always have something inside it that is hard to fully explain.

But “hard to explain” should not mean “impossible to learn from.”

The future of brand marketing is not just more AI-generated creative. It is a marketing system with memory.

A system that connects strategy, creative, audience, media, results, and business context. A system that helps teams move faster without becoming reckless. A system that helps marketers defend bold ideas with better evidence.

In AI analytics, trustworthy answers require semantic context.

In brand marketing, trustworthy creative decisions may require the same thing.

Thanks to Lindsay for joining me on the podcast. You can listen to the full conversation here.

If you would like to learn more about Solid, reach out to us.

If you want to listen to the podcast, find it on YouTube, Spotify, Apple Podcasts