You Don't Need an AI That Creates Ads – You Need One That Decides

01 /The Concept

Pomo is an AI platform that promises to bring Fortune 500-level marketing capabilities to any business.

The startup's core claim: the platform can "find hidden market signals and turn them into action."

At its core, Pomo is a two-layer machine. The analytical layer – what the startup calls the “brain” – continuously scans the market: surfacing emerging trends relevant to the company’s domain, tracking competitor moves, and flagging opportunities that no competitor has acted on yet. The execution layer – the “hands” – takes that intelligence and converts it into action: rewriting product pages, launching new ad campaigns, adjusting overall promotion strategy.

Pricing has two components:

- A fixed subscription based on the number of marketing channels tracked, competitors monitored, analysis depth, and ad creative quality – ranging from $69 to $1,999 per month.

- A percentage of ad spend placed by the platform – from 4% on lower tiers down to 2.5% on higher ones.

Pomo was founded last year and has now raised its first $4.5 million.

02 /Why It Matters

As the startup argues, small and mid-sized businesses suffer from a critical "judgement gap" – the consistent inability to make high-quality decisions.

On the marketing front, Fortune 500 companies differ from small businesses in one key way: they have marketing teams capable of turning market noise into strategic action. That's what drives results.

A ten-person company doesn't have that team. The founder plays the role. As a result, tens of millions of companies make high-stakes decisions without the infrastructure to support them.

Most marketing platforms are just marketing platforms with AI bolted on. Pomo, by contrast, is infrastructure. It analyzes the market first, then tells you what to do – from overall strategy down to individual ad campaigns and SEO moves.

There's a recent Forbes piece with an apt headline: "The new bottleneck for leaders isn't productivity – it's judgement."

The argument: for the first time in history, the ability to get things done has ceased to be a competitive advantage.

AI can now execute a wide range of tasks faster than any human team can keep up with. A leader who still measures productivity by completed actions has already lost.

Measuring productivity by task count made sense when execution was slow and expensive – when the bottleneck was the ability to execute.

The new bottleneck is judgement. Deciding what's worth doing. Making reasonable calls with incomplete information. Freeing mental bandwidth for what actually matters – and building teams that can direct AI within their domains by the same principles.

Research suggests AI will soon automate the vast majority of tasks currently occupying most employees' time. But as the Forbes author argues, AI cannot automate the act of making decisions – even if only 30–40% of those decisions need to turn out right.

Pomo's founders might push back on that last point, since they're explicitly trying to automate decision-making – at least in the marketing domain.

03 /Opportunities

The headline trend: AI platforms that can only execute are already losing value before they've had time to establish themselves. In the AI era, execution capability is table stakes – you can't build a business on it alone.

The direction that matters now is AI platforms that don't just execute – but decide what needs to be executed.

Some still believe this can be solved by opening a chat window, describing what the company does, and asking for strategic advice. The advice will sound reasonable. But it will be advice generated in a vacuum.

Here's the thing: strategy is determined far more by what's happening in the market and what competitors are doing than by what you're doing yourself. The only real opening is finding the gap between market trends and competitor activity – and moving into it before someone else does.

To get genuinely useful strategic advice from a general-purpose AI, you'd need to feed it far more information about the market and competitors than about your own company – current to the second, with granular detail that's easy to overlook from the inside.

Only specialized engines can do this – purpose-built for your specific market and niche, tracking it in real time. Those engines are what the brains of these platforms need to become.

The need for this kind of specialized intelligence isn't limited to marketing. Multiply the number of domains by the number of niches by the number of decision frameworks – and you have an enormous number of platforms still waiting to be built.

Illustrating the point: Rocket ([covered here](/review/cennost-ii-platform-teper-ne-v-ii)) built a vibe-coding platform that opens with the statement: "Most AI tools help you build faster. But none of them advise you on what to build. And none of them tell you how to win with what you've built."

The core of Rocket isn't the coding module – it's an AI advisor you consult first about what's worth building, and a competitor intelligence layer that feeds back into that conversation.

What AI platform that helps people decide – not just execute – could you build?