First Step to Agentic Brand: Vectorize

In February 2025, Jakob Nielsen, the usability pioneer known for his groundbreaking work in UX design, published an article that should send shivers down the spine of every brand marketer: "Hello AI Agents: Goodbye UI Design, RIP Accessibility" In it, Nielsen makes a bold prediction: humans will increasingly stop using websites and apps, replaced by AI agents that browse, search, compare, and decide on their behalf.

Especially this part of what he said struck me:

Again:

This isn't science fiction. The transition has already begun.

Nielsen cites research showing that 63% of websites had already received visits from AI agents by early 2025. This should be a wake-up call for brands still operating with a human-centric digital strategy.

But amidst this disruption lies opportunity for forward-thinking companies willing to become what I call "agentic brands" – organizations prepared for meaningful interaction with AI agents.

The Canary in the Coal Mine: Hubspot's Traffic Decline

Need proof this shift is already affecting business outcomes? Consider Hubspot's recent revelation that they've lost approximately 80% of their blog traffic. During a candid YouTube discussion between Hubspot's CMO Kipp Bodnar and Kieran Flanagan, they directly addressed this decline and what it means for content strategy in the age of AI.

Bodnar explained:

They specifically pointed to "no-click searches" as a key factor:

But here's the critical insight: while traditional search traffic declined dramatically, Bodnar noted,

They shared a telling anecdote about how Flanagan's YouTube video on the Deep Seek AI model appeared in ChatGPT search results, tripling the video's views.

This is the new reality: content consumption is moving from direct human visits to AI-mediated experiences.

As Bodnar put it,

The question isn't whether this transition will happen, but how quickly, and whether your brand will be prepared.

Why Vectorization Is Your First Critical Step

To understand why vectorization matters, we need to quickly understand how modern AI agents work. Unlike traditional search engines that match keywords, AI agents understand meaning and context. They don't just scan text; they grasp concepts.

Vector databases transform your brand's information (product details, support documentation, pricing, etc.) into a format that AI agents can deeply understand and reason about. Essentially, vectorization converts your content into mathematical representations that capture semantic meaning and relationships.

Nielsen observed that "current AI agents will work with human-targeted design" but might "perform optimally with differently-encoded information." 

This aligns perfectly with Hubspot's realization that success now requires "influencing both humans and robots." As Bodnar noted, "Historically we've just been thinking about even Google as humans, not as a true, really smart intelligent search engine, and that has changed." Vectorization is how you speak directly to these intelligent systems in their native language.

What Is a Vector Database?

At a high level, a vector database stores information as multi-dimensional vectors – mathematical representations where related concepts cluster together in space. When an AI agent queries this database, it can find not just exact matches but conceptually similar information.

For example, a traditional database might struggle to connect "affordable luggage" with "budget travel bags" if those exact words aren't used together. A vector database understands these concepts are related, making your information retrievable even when the query doesn't match your exact wording.

Popular vector database solutions include Pinecone, Weaviate, and Chroma, each offering different capabilities for storing, searching, and managing these vector embeddings.

The agents.json Standard: A Proposal

Just as robots.txt tells search engines what to crawl and sitemap.xml helps them understand your content structure, we can imagine a new standard called agents.json that instructs AI agents how to interact with your brand.

This isn't just theoretical – it's a practical solution to a growing need. As Nielsen noted, "You want to rank highly in the agents — that's the new imperative for business survival."

Here's what an agents.json file might include:

This structure tells AI agents:

• What vector endpoints to use for different types of information

• What capabilities your brand supports

• How to interact with your systems

• What requires authentication

Use Case: Comparison Shopping

Let's see why this matters in practice. Consider a user asking their AI agent: "Find me the most sustainable hiking backpack under $150 that will last for years."

Non-Agentic Brand Response

Without vectorization or agents.json, the AI might:

• Visit your website and struggle to extract structured product information

• Miss nuanced sustainability claims buried in marketing copy

• Fail to understand your warranty details for durability assessment

• Potentially overlook your products entirely, favoring brands with clearer signals

Agentic Brand Response

With proper vectorization and agents.json:

1. The AI queries your vector endpoint directly for "sustainable hiking backpacks"

2. It receives semantically rich data including sustainability certifications, materials, manufacturing processes

3. It compares warranty information across brands, understanding your "lifetime repair" policy equates to durability

4. It presents your product as a top option to the user with accurate information

Nielsen predicted that "smaller brands received more AI traffic than larger brands as a percentage of total traffic" because "an AI agent will usually explore further in its quest to solve a problem." 

This is your opportunity to compete against larger players by being agent-ready.

Beyond Product Information: Operations and Support

The benefits of becoming an agentic brand extend beyond sales. Support requests that once required human agents can be handled autonomously when your knowledge base is properly vectorized. Internal documents can power AI assistants that supplement or replace expensive SaaS solutions.

For instance, customer support can be transformed through comprehensive vectorization. Unlike the limited AI capabilities of current customer support platforms—which typically restrict you to uploading a single knowledge base document and struggle with nuanced questions—a fully vectorized support system can draw connections across your entire knowledge ecosystem.

This means when a customer asks a complex question that spans multiple products, policies, and technical details, your AI support agent delivers accurate, comprehensive answers immediately without escalation to human agents. The result? Resolution times drop from days to seconds, customer satisfaction soars, and support costs plummet as expensive ticketing systems and large support teams become increasingly unnecessary.

Vectorizing Media Assets: Ad Maker Agents

Perhaps one of the most exciting and cost-saving applications of vectorization is in advertising and creative content production. By vectorizing your brand's entire media asset library – images, videos, logos, past campaigns, brand guidelines, and creative briefs – you unlock a powerful new capability: AI-generated advertising.

Here's how it works:

Rather than paying expensive creative agencies or maintaining large in-house design teams, an AI agent with access to your vectorized media assets can:

1. Generate targeted ad creative based on specific campaign objectives

2. Maintain perfect brand consistency by understanding your visual language

3. Adapt existing assets into new formats

4. Create dozens of variations for A/B testing in minutes, not days

5. Personalize creative for different audience segments at scale

The cost savings are substantial. A mid-sized brand typically spends $250,000-500,000 annually on creative production. With vectorized assets and AI agents, these costs can be reduced by 60-80% while simultaneously increasing creative output tenfold.

For example, when a seasonal campaign needs to launch, instead of briefing an agency and waiting weeks, your marketing team simply instructs an AI agent: "Create summer promotion assets for our eco-friendly backpack line highlighting the recycled materials and targeting college students."

The agent then:

• Retrieves brand guidelines from your vector database

• Analyzes successful past campaigns for this demographic

• Pulls appropriate videos, images, and brand elements

• Generates a complete set of ads across multiple platforms

• Prepares variations for testing different value propositions

All in minutes, not weeks, and at a fraction of the cost.

Getting Started: Practical Next Steps

1. Audit your content: What information should be AI-accessible? Product details, support documentation, FAQs, and pricing are obvious starting points.

2. Select a vector database: Solutions like Pinecone offer robust APIs and scalable infrastructure for vector storage and retrieval. (We use Pinecone for the matchmaking portion of aigencia's influencer aigents.)

3. Develop your embedding strategy: Decide how to convert your content into vectors. This typically involves using large language model APIs to create embeddings.

4. Create your agents.json file: Start simple, focusing on the most important interaction types for your business.

5. Implement and test: Set up your vector endpoints and test with current AI agents to ensure they can effectively interact with your data.

The Time to Act Is Now

Nielsen's article makes it clear: we're not merely facing a technological shift but a fundamental change in how consumers interact with brands. The Hubspot example shows this isn't theoretical – it's happening now, affecting real businesses, even industry leaders.

As Flanagan and Bodnar put it, "If you're going to win, you're going to win on depth and quality and community and real human insights versus breadth of coverage, because AI really solves that breadth of coverage in a way that wasn't true 5 years ago."

Vectorization is the foundation that enables this depth-focused strategy, making your unique insights and expertise discoverable by AI agents while preserving the human value that differentiates your brand.

Early adopters of vectorization and agentic principles won't just survive this transition; they'll thrive in it. As less prepared competitors struggle to adapt, agentic brands will enjoy preferred placement in AI recommendations, more accurate representation of their offerings, and reduced operational costs.

To again quote Hubspot's CMO: "You have to have this high influence, high AI differentiated playbook to succeed over the next three to five years."

The question isn't whether to become an agentic brand, but how quickly you can make the transition.

Ready to vectorize your brand and prepare for the age of AI agents? DM Bora on LinkedIn to jam on getting started.