A job for every tool.

My first choice when the job needs a broad operating surface: research, files, images, connected tools, repeatable agents, and work that crosses formats.

Shared marketing agents, research, analysis, image generation, connected workflows.

Your team only needs excellent long-form drafting and document work. One primary model is enough for most people.

The model I trust most for sustained reasoning across long documents, strategy drafts, editing, and work where losing the thread is expensive.

Strategy, long-form writing, synthesis, document-heavy projects, Claude Code.

You already have a primary model that handles your writing well. Do not collect subscriptions for tiny quality differences.

Especially useful when the source material is not text. Video, screen recordings, large mixed-media inputs, and Google-native workflows are where I reach for it.

Video understanding, multimodal analysis, large context, Google ecosystem work.

Your work is mostly writing and web research. You can probably cover that with your primary model.

A fast research layer when I need to find the source landscape before doing the harder analysis. Useful because the citations are part of the interface, not an afterthought.

Market scans, source discovery, competitive research, quick factual orientation.

Your primary AI already gives you the research workflow and citations you need.

The automation layer I would learn if I wanted maximum control over serious AI workflows. Flexible enough to become infrastructure instead of just glue.

Multi-step agents, API workflows, data transformations, internal automations, self-hosted options.

You want the lowest-friction no-code experience and do not need much control. Start with Make.

The easiest place for many marketers to understand what an automation is actually doing. Visual enough to debug without pretending everyone wants to be an engineer.

Marketing ops, lead routing, content workflows, app-to-app automations, quick prototypes.

You already need custom code, versioned workflows, or deeper agent orchestration. Go straight to n8n or code.

Useful less because it is the perfect database and more because teams actually put their working context there. Agents are only as useful as the context they can reach.

SOPs, campaign plans, lightweight CRM, knowledge, briefs, agent-readable context.

Your team already lives happily in another system. Migrating the tracker is rarely the highest-value AI project.

Useful once the workflow matters more than the model. One API surface lets you switch providers without redesigning the whole system.

Model routing, testing, fallbacks, cost control, production workflows with interchangeable models.

You are not building software or automations. The consumer apps are simpler.

The interesting part is not another prospecting database. It is treating enrichment and outbound research as a programmable workflow instead of a static list.

Account research, enrichment, signals, outbound preparation, GTM workflows.

You do not have a clear ICP or outbound motion yet. Automation makes bad targeting faster too.

For marketers who want to stop waiting for every tiny engineering request. AI-native development turns a useful internal tool from “someday” into something you can test this week.

Landing pages, internal tools, prototypes, light product work, editing codebases.

You do not want to touch code at all. Use a higher-level builder first.