Claude is having a big hype moment—you see it everywhere, and when you talk with peers you realize pretty much everyone is trying it. We think it’s for a simple reason: for many people (us included) it creates a new feeling for an AI model—it feels different, more usable. Not just to “bounce” ideas around, but to produce deliverables, keep context, and hold long conversations without losing coherence. That’s why you’re seeing it in demos, product teams, and increasingly in marketing.

Part of the noise comes from what drives any AI wave: curiosity + FOMO + comparisons (“is it better than ChatGPT, Gemini, Perplexity?”). But there’s also a more practical reason: Claude is gaining adoption because it fits workflows where the final output isn’t “text”, but decisions, documentation, and assets that someone has to edit, approve, and execute.

The hype around Claude also comes from how well it handles context and its “workspace” approach (like Projects and Artifacts), which makes it easier to go from a conversation to an editable deliverable.

What is Claude (Anthropic) and what problem does it solve?

Claude is a family of language models that turns instructions into text, analysis, and structured content. In a company, the problem it solves isn’t “generating content faster”, but reducing friction in cognitive work and improving consistency: less repetitive manual work and more focus on decisions.

How Claude works in practice

Claude models: Opus, Sonnet and Haiku

Claude is offered as a family of models with different trade-offs across cost, speed and quality (for example, Opus, Sonnet, Haiku). As with other AIs, a practical rule is to use faster, cheaper models for drafts/variants, and save the strongest ones for deep analysis, complex synthesis, or tasks where the cost of being wrong is high.

Projects and Artifacts: why this changes how teams use it

Artifacts are modules that let you generate “standalone” pieces in a separate window: documents, tables, or materials ready to edit and share. Operationally, this moves usage from “thinking in the chat” to “producing deliverables”.

For marketing teams, this is great for standardizing: briefs, experiment matrices, scripts, landing pages, FAQs, and internal templates.

Skills: how to remove inconsistency and turn Claude into a system

The most common issue when a team starts using Claude isn’t output quality—it’s inconsistency. One day the result is excellent, and the next day you have to re-explain context, criteria and format. That’s where Skills come in.

Skills are reusable resources (instruction bundles and sometimes scripts/resources) that give Claude “operational memory” to execute tasks using your standards: workflow, context, best practices and criteria. Unlike a one-off prompt, a Skill is loaded when needed and reduces the need to repeat the same rules in every conversation.

For a CMO or founder, this is the difference between “a tool I use when I remember” and “part of the operating system”: less prompt wrangling (fighting with prompts), more predictable outcomes. Anthropic also positions this as a path for Claude to work with organizational standards (for example, formats and rules when generating documents).

One important thing to keep in mind: Skills can introduce risk if you use untrusted or poorly designed Skills (like any extension that can execute actions). It’s best to treat them like software: review, source control, and permissions.

Claude Code and Claude Cowork: real automation (dev and non-dev)

Claude Code is an assistant/agent for code-centric work: it can understand full projects and help build, fix, and automate technical tasks.

But for Growth, Ops or Marketing teams that don’t want to live in a terminal, the key concept is Claude Cowork: an “agentic” system designed for desktop work and multi-step tasks (documents, files, research, reporting) with a simplified experience for non-technical roles. In practice, Cowork is designed around the outcome, not the prompt.

Examples of how we use it at Boomit

  • Email review and inbox organization with criteria (priority, urgency, owners).
  • Optimization logs from Slack conversations: turning threads into decisions, tasks, and learnings (what changed, why, what we expect to see in metrics). (Cowork shines here when it integrates sources and produces a final document.)
  • Action reminders from meeting minutes/transcripts: turning messy notes into actions, owners and dates; and producing reports or presentations from those inputs.

Where Claude creates the most value in marketing, growth and performance

1) Research and strategy: from “ideas” to defensible angles

Claude performs best when you feed it real inputs: sales notes, objections, allowed claims, competitor captures, and business constraints (very common in fintech). With that input, you can use it to:

  • Map messaging by segment (e.g., credit vs investing vs payments).
  • Turn objections into FAQs and trust messaging.
  • Propose landing page structures by intent (not by “creativity”).

The key difference is that the final output isn’t “nice text”, but a set of testable hypotheses.

2) Performance creative: volume with control

Claude is useful for producing volume without losing consistency if you use it as an editor:

  • Briefs with constraints: what you can promise, what to avoid, what event you’re optimizing for.
  • Angle matrix: pain → mechanism → proof → CTA.
  • Channel variants: Meta, TikTok, Search, UAC (keeping the core idea consistent).
  • Copy QA to avoid absolutes or risky claims (critical in finance).

In practice, this reduces back-and-forth and speeds up creative iteration.

3) Paid + Analytics operations: clarity to decide

As we all know, in performance (and even more in financial services), what’s expensive isn’t only CPC (cost per click). It’s making decisions on bad data.

Claude can help you document criteria and typical checks:

  • Consistent naming across campaigns and ad sets.
  • Rules for UTMs (tracking parameters).
  • Coherence checks between events and reporting.
  • Draft queries or validations that an analyst/dev reviews afterwards.

On top of that, Meta recently launched AI Connectors (open beta), which connect Meta Ads accounts with compatible AI tools (including Claude) using MCP (Model Context Protocol). The promise is strong: ask in natural language about performance, diagnose errors, extract insights, and potentially execute management actions without exporting CSVs or building reports manually.

That creates a lot of excitement, but also uncertainty. Even if it’s an “official” integration, it still runs on systems with anti-abuse controls and automated-behavior detection. In other words: being official doesn’t mean it’s impossible to trigger restrictions if behavior looks risky (for example, too many changes in a short time, bulk edits, repetitive patterns). Our advice is to use it first as an analysis and structuring layer, test heavily, and only then explore execution—with extreme care, tight limits, and frequent reviews to avoid any kind of ban.

The most important point for performance: Claude can accelerate, organize and suggest, but you still set the direction. Inspiration, business judgment and control (budgets, risks, learning) remain human. If you let it operate “autonomously”, it’s very easy for things to get out of control.

Common mistakes / What to avoid

  • Using Claude as a factual source without validation. If you’re citing specs, pricing or capabilities, use official sources.
  • Not setting limits (especially in regulated industries). Without constraints, it can suggest aggressive or ambiguous claims.
  • Optimizing for output, not decisions. More copies won’t improve ROAS (return on ad spend) if you don’t have clear hypotheses and measurement.
  • Sharing sensitive information without a policy. Define what data can be pasted and how to anonymize it.
  • Not documenting learnings. If the team doesn’t record what was tested, why, and what happened, AI only accelerates chaos.
  • Implementing automation (Cowork / connectors) without human control. Integrations and agents are great for efficiency, but without limits (what it can touch, when, at what pace, and who approves) an optimization can become an operational incident.

Conclusion

Claude can be a real advantage if you use it as a production and standardization tool—not as a “chat for ideas”. For CMOs and founders, the right question isn’t “is it better than other tools?”, but: where in the Growth system does it reduce friction and improve execution quality? Once you answer that, the rest is integrating it into your operation alongside the tools you already use day to day.