The next visitor may not be human
For most of the web's history, businesses designed websites for two audiences: people and search crawlers. A third audience is becoming impossible to ignore. AI agents can now research options, compare providers, monitor changing information and help users move toward a decision. The agent may encounter the business before the person ever opens a page.[1]
Google describes AI Search experiences that can reason across information, maintain context and help users take action. This is not a reason to abandon traditional search strategy. It is a reason to make the website's meaning, evidence and useful actions much easier to understand. An agent needs to determine what the company does, who it serves, whether the claims are credible and what a qualified next step looks like.
Brad has been building for machine visitors for decades
Brad Nietfeldt's background makes this transition feel less like a sudden revolution and more like the next stage of a familiar discipline. His early work at PayPal and lead development experience at eBay happened while the commercial web was still teaching businesses how identity, trust, data and transactions had to work together. A polished interface was never enough. The systems beneath it had to communicate reliably with people, software and other systems.[2]
Since founding Omaha Media Group in 2003, Brad has applied that same combined design and engineering perspective to Omaha brands, national companies and private organizations around the world. The tools have changed, but the standard has not: understand the real decision, structure the information, design a clear experience, build dependable application behavior and measure whether the system produces the intended result.
People
Need clarity, confidence and an intuitive path forward.
Search engines
Need crawlable content, authority and dependable technical signals.
AI agents
Need explicit meaning, verifiable evidence and actions they can understand.
The strongest experience gives all three audiences the same coherent business truth.
How OMG and MMG apply that experience
At OMG and Monstrous Media Group, AI readiness is not sold as a separate layer of fashionable copy. It is built into the standards used for custom websites and applications. Discovery identifies the questions customers and decision makers actually ask. Information architecture connects services, leadership, proof, locations and subject expertise. Design makes the path legible. Development keeps the important content available in the document, establishes consistent metadata and gives machines stable technical signals.[3]
Application standards extend that thinking into workflows. Forms describe their purpose clearly, validation produces useful states, integrations exchange predictable data and administrative systems maintain accountable source information. When an AI agent, search engine, CRM or human visitor encounters the experience, each should receive a coherent version of the same business truth.
Six layers of an AI-ready website
First comes entity clarity: a consistent company name, services, leadership, locations and relationships. Second is original expertise, including direct answers, accountable authorship, specific experience and evidence that generic summaries cannot reproduce. Third is semantic structure: meaningful headings, crawlable text, descriptive links and structured data that agrees with the visible page.[4]
The remaining layers are experience and operations. Performance and accessibility help people while reducing technical ambiguity. Clear conversion paths tell a visitor or agent which actions are available without inventing access that does not exist. Finally, measurement and stewardship keep the information current. An AI-ready website is not a collection of schema snippets. It is a maintained source of truth with a useful interface.
- 01
Entity clarity
Who you are, what you do and where you operate
- 02
Original expertise
Accountable insight, experience and proof
- 03
Semantic structure
Headings, links, metadata and accurate schema
- 04
Experience quality
Accessible, responsive and fast interfaces
- 05
Action design
Clear conversion paths and dependable workflows
- 06
Stewardship
Measurement, ownership and current information
Design still matters after the AI introduction
An AI system may make the introduction, but people still evaluate the experience. When they follow a citation or recommendation, the website must deliver confidence quickly. Visual identity, motion, hierarchy, accessibility and interaction detail tell the visitor whether the organization is thoughtful, established and appropriate for the assignment.[1]
This is where OMG's founder-led model matters. Brad approaches design and application development as one connected act. The brand promise, content model, interface and technical implementation should reinforce one another. That close attention is especially important for seasoned organizations whose value cannot be compressed into a template or an automatically generated paragraph.
Start with the business decision
The useful way to approach ai agents are becoming website visitors. is your business ready? is to begin with the decision the organization is trying to improve. For seasoned organizations whose digital presence must communicate real expertise and support valuable decisions, the real assignment is preparing a business website to be understood, evaluated and acted upon by people, search engines and AI agents. That is more specific than asking for more traffic, a fresher look or a new tool. It identifies who needs to gain confidence, what evidence they need and what the business should be able to do after the work is complete. A clear decision gives strategy, content, design and development one shared target.[2]
Write that decision in plain language before selecting tactics. Name the audience, the moment they are in, the obstacle that prevents progress and the action that would represent meaningful movement. This short brief becomes a filter for scope. It also exposes requests that sound urgent but do not contribute to the result. The desired destination here is a credible machine-readable source of truth with distinctive design, dependable application behavior and clear human next steps. Every page, workflow and measurement choice should make that destination more likely.
Build from evidence, not assumptions
Good digital work combines internal knowledge with observable behavior. Interview the people who speak with customers, review search queries, inspect analytics, read support questions and examine the materials that already help close decisions. Those sources reveal the language people use and the proof they require. They also prevent a team from mistaking its organizational chart for a customer journey. The cited industry guidance below provides an external baseline, but the strongest content will include experience that only the organization can contribute.[3]
Evidence should remain visible in the finished experience. Specific examples, named methods, accountable authorship, current dates and clear sources make an article more useful to readers and easier for retrieval systems to evaluate. Structured data can describe those elements, but it should mirror the page rather than decorate it with unsupported claims. This article therefore pairs visible authorship, dates, citations and related reading with machine-readable Article, Breadcrumb and FAQ information.
- 01
Discover
Find the real customer questions
- 02
Structure
Connect entities, services and evidence
- 03
Design
Make meaning and next steps legible
- 04
Build
Create stable, accessible application behavior
- 05
Integrate
Exchange predictable data across systems
- 06
Measure
Track qualified discovery and action
Brad Nietfeldt leads the standard across strategy, design and application development.
Design the path, not only the page
A useful article is part of a larger knowledge system. A reader may enter with a broad question, need a concise answer, compare options, inspect evidence and then decide whether to contact a specialist. Headings, summaries and internal links should support that progression without forcing a linear read. The three related OMG links included with this guide connect the subject to a deeper service, a relevant perspective and a direct next step. That is helpful navigation for people and meaningful context for crawlers.[4]
The same principle applies beyond content. Forms, calls to action, menus and follow-up messages should respect the amount of confidence a person has earned at each point. A first-time visitor may need orientation. A returning buyer may need a specific proof point. Someone referred by a trusted peer may only need confirmation and an easy way to start. Designing these states creates a more useful experience than repeating the same sales prompt after every paragraph.
Make the system operational
A strong launch needs ownership. Decide who can update the information, who approves material claims, how frequently sources are reviewed and what happens when a service or policy changes. Create reusable editorial fields for the title, summary, author, publication date, image, citations, related links and structured data. The goal is not more administration. It is to make quality repeatable without depending on one person remembering every technical requirement.[1]
This is also where teams should guard against treating agentic discovery as a schema shortcut or publishing generic AI copy without accountable expertise. A pre-publication checklist can confirm that the page offers original value, cites important factual claims, includes descriptive link text, uses accessible image alternatives and sends a valid canonical signal. After publication, the same owner can review search coverage, referral traffic and user behavior. Small, regular stewardship is far less expensive than letting an entire content library drift out of date.
Measure the signal that matters
Measurement should reflect the purpose of the resource. For this subject, a useful scorecard includes AI search visibility, qualified referrals, cited discovery, completed journeys, lead quality and assisted revenue. Those indicators work together. A single number can be misleading, but a pattern across discovery, engagement, movement and business quality can show whether the page is doing its job. Establish the baseline before major changes, annotate launch dates and allow enough time for search systems and customers to respond.[2]
Review the article with sales, service and leadership teams, not only the analytics dashboard. Ask whether it answers a real question, attracts the right conversations and reduces repeated explanation. Update examples when the market changes and retire claims that can no longer be supported. The best search and LLM inclusion strategy is a trustworthy publishing practice: expert-led information, visible evidence, crawlable technical foundations and a point of view worth citing.
Turn the guidance into a ninety-day plan
In the first thirty days, document the present state and agree on the decision that matters. Gather customer questions, analytics, search data, existing creative materials and the operational constraints that could affect delivery. Use the next thirty days to produce the highest-value improvements, review them with the people closest to customers and test whether the new experience communicates the intended meaning. The final thirty days should focus on publishing, measurement and a short backlog informed by real behavior rather than guesses.[3]
Keep the plan deliberately small enough to finish. Assign an owner, a review date and one observable outcome to every action. Preserve the research and decisions in a shared record so future contributors understand why the system works the way it does. This creates a practical bridge between strategy and stewardship. It also gives leadership a clearer view of investment: what changed, what the team learned, what business signal moved and what deserves attention next.