For decades, multinational companies have worked to make their websites more consistent using centralized platforms and standardized templates. They’ve consolidated content, enforced global brand standards, and created reusable components that allow dozens of country-specific websites to operate more efficiently.

Yet the research behind our twin engines of marketing agility framework shows that these organizations perform best when they combine the efficiencies of global standardization with the ability of local markets to respond to their own customers, competitive environments, and market conditions. That tension has become even more important as search shifts from selecting pages (traditional search) to constructing answers (AI search).

While tools like hreflang still matter for international SEO, they can’t solve the entire market relevance problem in AI search. AI engines typically retrieve and reconcile information from multiple sources rather than depend solely on your market-specific site.

In some cases, this makes the global-versus-local problem more acute. AI systems must determine which content is relevant and which facts, expertise, experience, and evidence apply to the user’s market. For global enterprises, that creates a new version of an old challenge.

Global authority doesn’t automatically create local authority

Experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) don’t automatically travel across borders. A global E-E-A-T gap may emerge if a multinational company has brand recognition and significant authority in its home market while providing little evidence of experience or expertise in another country.

Take a U.S.-based global technology company with operations in Japan, for example. Its corporate website may contain extensive information about its history, executive leadership, products, research, awards, and industry expertise.

Suppose the company also employs highly experienced Japanese engineers and consultants, belongs to Japanese industry associations, serves well-known Japanese customers, and holds certifications relevant to the local market.

The company clearly has local expertise. But can AI engines see it?

Much of the evidence that establishes the company’s Japanese expertise effectively disappears from its public digital presence if those Japanese experts have no profiles, customer examples appear only in sales materials, and local industry participation is absent from the website.

The global consistency paradox

This creates a type of overstandardization that I call the global consistency paradox. The more effectively an enterprise standardizes its global web presence, the easier it becomes to remove the local signals that demonstrate E-E-A-T in individual markets.

A global organization may use the same page templates, product descriptions, author profiles, proof points, and corporate claims across all countries. The language, currency, and contact information may change. But much of the evidence remains the same.

From an operational perspective, this approach is efficient. From a local E-E-A-T perspective, however, it may tell search engines and AI systems very little about why the company should be considered experienced or authoritative in that market.

Enterprises need to distinguish between what should be globally consistent and what should remain locally distinctive. This allows the organization to retain the efficiency of a global platform without eliminating the signals that demonstrate real participation in each market.

Standardize globally:

  • Company identity.
  • Brand standards.
  • Technical infrastructure.
  • Product definitions.
  • Design systems.
  • Governance frameworks.

Keep local:

  • Local expertise.
  • Certifications.
  • Customer experiences.
  • Regulatory requirements.
  • Partnerships.
  • Reviews.
  • Research.
  • Media coverage.
  • Industry participation.

Don’t make AI engines infer your local expertise

One of the biggest opportunities I see for global organizations is making market-specific relationships more explicit. These organizations already know:

  • Which products are available in Japan.
  • Which employees are experts in particular subjects.
  • Which certifications are recognized in Germany.
  • Which customer stories originated in Singapore.
  • Which industry associations recognize the company in Australia.

Yet websites frequently leave these connections implicit, creating unnecessary ambiguity.

Suppose the same product page appears across 20 markets, but a particular certification applies to only six of them. If this information isn’t clear on the website, an AI system must independently determine which markets the certification applies to.

If dozens of localized pages quote a global expert while local experts remain invisible, the global expert may become the clearest signal of the company’s expertise across all pages.

Similarly, if your market-specific sites are essentially translations of an authoritative U.S. page with few local signals, AI engines have limited evidence to distinguish global authority from local market authority.

Local E-E-A-T requires relevant evidence

Enterprises should rethink how they approach E-E-A-T and perform audits. For multinational organizations, useful evidence of local E-E-A-T may include:

  • Subject matter experts (SMEs) and their credentials.
  • Customer stories.
  • Market-specific certifications.
  • Memberships in respected industry organizations.
  • Local research.
  • Media citations.
  • Awards.
  • Reviews.
  • Partnerships.
  • Regulatory qualifications.
  • Participation in local events.

Relevant evidence will vary considerably by industry. For instance, a financial services company may need regulatory credentials and local professional expertise, while a B2B technology provider may benefit more from customer implementations, technical experts, and industry partnerships.

Audit local authority from the outside in

Instead of asking whether the market-specific website has elements like author profiles, schema, and testimonials, start with factors that support local E-E-A-T. For example:

  • Who are our recognized experts in this market? What subjects are they experts in?
  • What experience demonstrates that we understand this market?
  • Which customers, partnerships, or projects support that experience?
  • What certifications or regulatory qualifications apply here?
  • Which independent organizations or sources recognize our expertise?
  • Which products and capabilities are actually available in this market?
  • Where can a search engine or AI system find evidence supporting each of these facts?

Then test whether search and AI systems can correctly answer those questions.

Note that managing E-E-A-T is partly a governance challenge. In large organizations, a single department rarely owns local authority. The evidence may exist within the sales, product, public relations (PR), human resources (HR), or legal team — yet be invisible to the SEO team.

Build local evidence into the global framework

Enterprises don’t need to abandon centralized websites to solve the problem of local E-E-A-T. Instead, they need to ensure that their global frameworks accommodate local evidence.

For example, a standardized product template might include optional modules for local certifications, market-specific customer stories, local experts, regulatory information, industry memberships, or regionally relevant research. A global expert framework might establish consistent profile requirements while allowing markets to identify the people who possess relevant local expertise.

A practical local E-E-A-T audit for global enterprises

Practitioners can start this local E-E-A-T audit with one main product or service and three to five priority markets.

1. Define what applies to each market

First, document the elements that confirm E-E-A-T and the facts the business wants customers and search systems to understand.

For each market, identify the products or services available, relevant experts, and major customers or use cases. Make note of relevant certifications, regulatory qualifications, industry relationships, and other evidence that establishes local experience and expertise.

2. Compare the global and local versions

Next, compare how the global site and each local market site represent that information. A simple worksheet can help with visualization:

| | | | | | | --- | --- | --- | --- | --- | | Signal | Global | Japan | Germany | France | | Product available | Yes | Yes | Yes | No | | Local SME identified | Global only | Yes | No | No | | Local customer evidence | Global | 3 cases | 1 case | N/A | | Local certification | Global claim | Specific | Specific | N/A | | Industry membership | Global | Yes | No | No | | Local third-party evidence | Strong | Moderate | Weak | Weak |

Keep in mind, every market will look different. The objective is to identify where digital representation doesn’t match the business’s reality.

3. Map the evidence behind your priority claims

Take the most important claims and document the available supporting material. For each one, record the market, supporting evidence, location, and owner.

For example, an Australian product team may know that a product meets a local certification requirement, but the compliance team may own the documentation. Or a customer success team may have several strong Australian implementations that have never appeared publicly as case studies.

| | | | | | | | --- | --- | --- | --- | --- | --- | | Business claim | Market | Supporting evidence | Where it exists | Publicly accessible? | Owner | | Product meets certification X | Japan | Certification record | Compliance | No | Compliance | | 10+ years serving local market | Germany | Customer/project history | CRM | No | Sales | | Expertise in subject Y | Korea | SME credentials and conference presentations | HR and event sites | Partially | Local marketing | | Experience in industry Z | France | Customer case studies | Global website | Yes | Marketing |

Once you identify missing items, prioritize them. Some may require new content, while others may only need a mention or a link to the appropriate local page, expert profile, or product.

4. Test what AI engines understand

Next, ask AI assistants the questions a customer might ask. Instead of testing branded prompts like “Tell me about company X in Korea,” use questions that require the system to understand local expertise:

  • Which companies have experience implementing X in Thailand?
  • What certifications should I look for when selecting an X provider in Germany?
  • Does Company X offer Product Y in France?
  • Does Company X have experience with Chinese enterprises?

Record not only whether your company appears in the responses but also whether the answers are correct, locally relevant, and supported by the evidence you expect the system to find.

5. Turn the findings into a remediation plan

The final step is to combine the gaps you’ve identified in the website review, evidence map, and AI testing into a remediation plan. Prioritize these actions based on customer needs and the scale of the missing information. Assign an owner and identify where the change needs to happen.

  • A missing local certification may require the product or compliance team to provide the correct documentation.
  • An expert who’s well known in the market but absent from the website may require a profile and links to existing publications or conference appearances.
  • A strong customer example from a sales presentation may be a candidate for a public case study.
  • If the global site makes a claim that doesn’t apply in every market, the solution may be to clarify market-specific pages rather than create anything new.

The result should be a working list of specific improvements. That gives global and local teams something they can work through rather than another score or list of general E-E-A-T recommendations.

From local pages to local knowledge

International SEO has traditionally focused heavily on helping search engines understand which version of a page belongs to which audience. That remains important, but AI search introduces a more granular challenge. We increasingly need systems to understand which facts, expertise, experience, and evidence belong to which market.

The organizations that manage this well won’t be those that create completely independent websites for every market or those that force every market into an identical global template. Instead, they’ll be the organizations that understand what benefits from consistency and what requires local specificity.

A year ago, I raised the question of whether your global authority travels. Today, I’d add another question:

  • When an AI engine tries to determine your authority in a specific market, have you given it enough local evidence to reach the right conclusion?