Martech vendors sell context to address gaps in AI agents’ knowledge. But most of what they’re selling is work your team already owns.
Suppose your marketing operations manager types four sentences into the organization’s new campaign agent on Monday morning. By lunch, it’s built a five-email nurture sequence, two landing pages, and a set of paid social ads. Everything launches Wednesday, but the problem doesn’t surface until Friday, when your head of brand notices the third email promises a 60-day money-back guarantee — an offer your company dropped last spring.
Knowledge about the offer was stuck in a legal reviewer’s inbox and in the memory of two people who sat in on a meeting two quarters ago. The agent had never seen the updated information, and nobody had been assigned to check the sequence before it went out.
AI platforms with context can help avoid these problems. But before you invest in one, get clear on your guidelines and approval processes.
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What context means for martech
Scott Brinker, who has tracked the martech industry at chiefmartec.com since 2008, called context martech’s word of the year for 2026. He defines it as “the instructions, curated data, and permissioned tools that we feed to an AI agent.” But he acknowledges that when vendors use the word, they often gloss over what context really involves.
In a marketing department, context comes down to three things you could write on a whiteboard:
- Brand rules: Which claims, offers, and words are approved and which are banned
- Customer facts: Who a person is, what they bought, and what they agreed to receive
- Approval rights: Who signs off on which kinds of work, and on what timeline
Your customer databases hold the customer facts, while brand rules and approval rights mainly exist in brand PDFs, old email threads, and the heads of your longest-tenured people. This arrangement worked when every campaign passed through those people. But it breaks down when a single marketer can launch an entire campaign before lunch.
The slow, labor-intensive process of producing and approving campaigns used to act as a constraint because it gave experienced people time to flag off-brand work. But AI removed the brake. Brinker calls context “a huge opportunity for martech platforms to deliver incredible value via context-as-a-service.”
The work now moves faster than the review process
Context remains crucial for marketing teams, particularly as AI transforms how they create and review campaign assets.
Bynder’s State of Digital Asset Management 2026 report reveals that AI has changed content operations for virtually all (97%) of respondents. About 42% say AI plays a role in creating or processing their content. These trends also raise the risk of off-brand or inconsistent content.
Review hasn’t kept pace with the influence of AI. Pantheon’s State of the Web 2026 report shows that 79% of surveyed marketing leaders say manually checking AI output is an obstacle to using AI more widely.
Martech platforms can store context your team has to create
When AI marketing platforms sell context, some of the value is genuine. A platform can store your approved claims in a central location, feed them to every agent, and flag a draft that breaks them. That beats a PDF no one ever opens or reads.
But leadership must decide what serves as context. Someone has to take ownership of decisions about which product claims legal cleared, which audiences you won’t market to, and which offers ended last quarter. A vendor’s onboarding team can’t make them for you.
Buy the platform before making those decisions, and you’ll pay to store whatever is lying around. That might include your 2022 brand book, a few prompts someone wrote in a hurry, and a spreadsheet of claims nobody reviewed. The agent will follow those rules faithfully, including the outdated ones.
Decide who approves what and on which timeline
Teams often overlook who has the authority to approve what. Brand guidelines are often written for people who fill the gaps with their own judgment. As AI becomes more embedded in marketing campaigns, these responsibilities and judgment calls become more important to define.
Sort your AI-generated work by what it costs you when it goes wrong. Then, assign a person to each level:
- Low-risk work: Social copy variations created only from approved claims go out with a weekly spot check by your content lead.
- Medium-risk work: A new email sequence or landing page needs sign-off from a specific approver within a business day.
- High-risk work: Pricing, guarantees, regulated claims, or a new audience needs brand and legal approval before launch, every time.
Confirming approved claims and setting guidelines around approval rights can take weeks of your most experienced people’s time. The high-risk tier intentionally delays some launches, causing a few campaigns to go out later than the vendor demo promised.
I’d take that trade. A late campaign can cost you a week. But a dead guarantee sent to 40,000 inboxes can cost you countless refunds and a meeting with legal.
How to balance context with skills and headcount
Adding AI or headcount won’t fix a marketing skills gap, so you need to know which problem you have before you buy. Approval rights are the next decision because they settle who spends their hours checking AI output and what that person needs to know. Whoever approves high-risk campaigns has to know your claims and audiences cold.
When nobody on the team has this knowledge, you have a skills gap. A context platform won’t close it.
Before you sign a contract for any product with context, list the claims you’ve approved, the audiences you won’t target, and the name of the person who approves each level of AI-generated output. If your team can’t provide that information in a week, you’ve found the work no platform will do for you. Do that work first.
