Earlier this summer, cloud-based data platform giant Snowflake dropped US$250 million to acquire Crunchy Data, a cloud-based data management startup. Snowflake’s purchase came hot on the heels of its competitor, Databricks, acquiring Neon for US$1 billion. These aren’t just M&A headlines. They’re strategic moves in a high-stakes race to own the infrastructure for AI agents—a wave of smart, automated software that’s reshaping how businesses operate.
That said, terms like “AI agent,” “workflow automation,” and “data infrastructure” can sound abstract or even confusing … until they crash into your job description. What does this news actually mean for you, the B2B marketer? Let’s dig in.
First things first: What the heck is an AI agent?
An AI agent is a software program that can make decisions, take actions, and adapt based on data. It’s different from a script or rule-based automation. It’s more like a junior team member that never sleeps. These agents sit on top of your data and tools. They’re not just automating tasks; they’re deciding what to do based on context.
An AI agent can act as …
- A virtual assistant that monitors your top accounts for buying signals and automatically adjusts your campaign bids.
- A chatbot that doesn’t just follow a script but learns from every interaction and fine-tunes how it responds to customer objections.
- A tool that reviews performance across channels, learns what messages work for different buyer personas, and recommends creative tweaks—before your weekly team meeting.
How is an AI agent different from workflow automation?
Let’s start by talking about “automation,” which is an umbrella term that covers email scheduling, CRM data entry, chatbot replies, reporting dashboards, and more.
Workflow automation is task based. It’s the series of if-this-then-that logic flows you build into your CRM or marketing platform. For example, when a lead downloads a white paper, they automatically get a follow-up email, and then the sales team gets a Slack notification. Other examples include email scheduling, CRM data entry, chatbot replies, and reporting dashboards.
It’s all rules based: no judgment, no learning, no context. It just executes a predefined path.
But not all automation is created equal. Some automations are rigid and dumb. Others—like AI agents—are adaptive and data hungry.
That’s why Snowflake and Databricks are investing heavily in infrastructure. They want to be the foundation where these intelligent systems live.
Why this matters for B2B marketers
Here’s the connection: AI agents rely on access to clean, structured, and contextual data. And where do they get that? Your CRM. Your website. Your campaigns. Your customer behavior. Your product usage data.
If your data is siloed, outdated, or poorly integrated, your AI agent won’t just be useless—it could be wrong.
That’s why the infrastructure race matters. Snowflake wants to be the home where all this data lives, gets cleaned, and becomes usable by AI. Crunchy Data gives them more muscle to make that happen.
What you should do next
- Start treating your data like a strategic asset. The first steps toward having a complete view of your customers are knowing where your data is stored and how clean it is. If you haven’t already, begin conversations with your technical team counterparts to understand the status of your data, quality issues, and integration gaps, and to be part of your company’s data strategy planning.
- Engage your marketing ops team as a strategic partner. AI agents aren’t a marketing-only tool. They’ll span ops, sales, service, and product. Your marketing ops team is uniquely positioned to manage the technology stack, data, and workflows that AI agents will rely on. You’ll get the most benefit if you commit to cross-functional collaboration.
- Evaluate vendor road maps with a focus on integration and return. The tools you’re using need to help you deliver tangible business value. Include a vendor evaluation along with your own marketing assessments and roadmap planning. Ask questions like: What’s their AI road map? Are they planning to support autonomous agents? What other AI integrations are they planning? How will their AI capabilities help you achieve your business objectives?
- Begin evaluating your current workflows. AI agents excel at multistep decision-making, which may not be needed for all steps in your workflow. In some cases, rules-based automation may be the better option for a specific task. Before adopting AI agents, take time to document and assess your primary workflows. Where would having an AI agent do initial research and analysis best augment the work of your team? Where are opportunities for a low-risk AI agent pilot in the near-term?
The winners in this next wave won’t be the ones with the fanciest dashboards; they’ll be the ones with the cleanest data, the best integrations, and the smartest systems quietly making thousands of micro decisions a day on their behalf.
Is your marketing data ready for that? If you have concerns, let’s talk about how CMD can help.
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