Brand Control and Governance in the Age of AI

Who speaks for your brand when the shopper never visits your site?

The conversation around AI brand governance and control used to be one of outputs. As your team utilizes more and more generative AI, how can you be sure your branding stays in tact?

Today, the conversation around AI brand governance has shifted to: How do you ensure AI answer engines and shopping agents represent your brand in the right light, especially as you scale?

And the even bigger question: How do you ensure your brand is represented in the right light when you’re not even active in the conversation?

With AI agents acting as a go-between for shoppers and brands, entire purchasing decisions can happen without your brand ever having a chance to fully represent itself. On the surface, this sounds like a complete loss of control. But, with the redefining of AI brand governance, this doesn’t have to be the case.

With proper policies, data practices, and monitoring, you can influence how AI answer engines and shopping agents describe, recommend, and transact on behalf of your brand.

Again, this isn’t a conversation around ethical use of AI within your company and governance in that regard, nor brand standards within AI, but taking control of the conversation where many companies feel they have none. Now, here’s how.

AI answers and agents now sit between you and the shopper

The product discovery journey is nearly unrecognizable when compared to what it was even a year or two ago. Instead of traditional search, many journeys begin with AI. In fact, agentic search as the first stop in a shopping journey grew 200% YoY as of July 2026.

On top of this, general AI traffic is on the rise: traffic from AI sources to U.S. retail sites grew 393% YoY in Q1 2026.

Now, what does this agentic-driven experience look like in action? And, does it translate into tangible revenue?

Answer engines first, agents second.

In the midst of this AI revolution, two layers have formed between you and the customer. Answer engines such as ChatGPT, Google AI Mode, Perplexity, and Gemini describe brands in the shopper’s first step.

Meanwhile, shopping agents such as Google’s UCP checkout inside AI Mode and Gemini (Early Access), Amazon’s Buy for Me (beta), and Perplexity’s shopping features are starting to transact.

In other words: Answer engines influence opinion with query results, whereas agents complete a sale.

Note: Every agentic checkout capability named from here on out is in Early Access or beta, and should be treated as such.

The traffic is real and it converts.

Despite AI traffic being on the rise, the human impact of AI-referred visitors is resulting in real conversions:

• AI traffic to U.S. retail sites converted 42% better than non-AI traffic in March 2026

• 38% increase in discovery through AI assistants, social AI, and delivery apps

All of this to say, your brand strategy needs to consider that shoppers are still buying from you. They’re just not starting their journey with you as often.

What brand control means when a model speaks for you

Brand control used to mean controlling the page and your messaging. Today, it means controlling what models ingest, and in turn, shaping their outputs. Sort of like filling a tumbler with the right words, so that people might pull the right response out. (But also way more complex and nuanced than that.)

Fortunately, with a clear understanding of the shifting definition of “control” in this era, and knowledge on what you can govern, you can keep that tumbler full of the right stuff.

Psst. Need a primer on general ecommerce branding? Make sure to read our guide on how to get your brand in the best spot before AI starts sharing it.

Control shifted from the page to the source data.

For two decades, brand control was design, copy, and merchandising on a surface you owned. The moment an AI model paraphrases you, that surface is moot. The model reads your catalog, your attributes, your pricing and availability, and your policies, then writes its own version for the shopper.

While AI models are getting more accurate all the time, they don’t always get it right: A recent study found that four of the top AI engines ran into a factual conflict 86% of the time when asked 220 of the same questions.

You can’t control an AI engine’s accuracy on the model level, but you can on the data level. This is why it’s critical you have accurate, enriched product data for these tools to gobble up, as it will increase the chances they get it right later. (This will also put you in a better position in general within the agentic commerce space.)

Three things you can still govern.

Much like an unruly child, you can’t control what an AI engine says. But, there are three surfaces that remain under your control, each own playing a role in what an engine repeats:

• The facts the model reads: Your catalog, attributes, pricing, availability, and policies are the raw material behind every AI description of your brand.

• The channels you opt into and the terms you accept: Which protocols you expose your catalog and checkout to decides who can transact for you, and on what terms.

• The monitoring loop: A fixed set of prompts, checked on a schedule, is the only way to catch misrepresentation before a shopper does.

Everything else, including tone, ranking, and summarization, is influenced, not controlled, and belongs to the model.

For a clearer look at this and a better understanding of on-brand messaging and auditing at the storefront level, make sure to check out our guide on AI shopping assistants.

Product data decides who gets found, and how you’re described

When the discussion around brand control and AI centered on generative outputs, your branding and messaging were at the heart of it all. Today, how your product data is presented and published is what matters most where representative and discovery are concerned.

Now, how exactly does product data influence this conversation happening behind your back, and what factors can you shape?

Machine-readable or invisible.

The best product data means little if it’s not machine-readable, as AI won’t be able to find it. Unfortunately, as of April 2026, 34% of U.S. retail product pages are invisible to AI. Home pages fared little better, with 25% being invisible.

Unlike human-facing matters and generative AI outputs, this is a data governance problem. Marketing and content polishing and so on won’t help if the data’s invisible.

Completeness, consistency, and freshness are the new brand guidelines.

Brand guidelines once specified typeface and voice. For a model, three properties do the same job:

• Completeness: Missing attributes are more than blanks. They’re gaps the model fills with a guess or a competitor suggestion instead.

• Consistency: One product named three ways across three channels reads to a model as three products, or as none. Brand consistency across AI agents starts in the attribute table.

• Freshness: Stale price and stock data is the fastest route to a description the model gets confidently wrong.

None of this is to say you still don’t need traditional branding guidelines in place, as mentioned earlier. But, when thinking about AI visibility and how you can stay both relevant and in control during the agentic conversation, the above ARE your branding guidelines.

“Building out the content on your website, optimizing it to a schema, and optimizing it for agents going through your website is going to be crucial to help you be presented — with your products, with your tone and voice — as you want it to be from a brand perspective.”

—Michael Scholz, VP of Marketing, Commerce

One source of truth across every channel.

A single source of truth is always a good idea, whether you’re talking about AI or not. When you have a single source of truth, you leave little to interpretation and promote consistency. But, in the age of AI, this is even more important.

A model that reads one description on your site, another on a marketplace, and a third in a reseller’s feed will not reconcile them. It will pick one, and you won’t get to choose which one that is, either.

A product information management system (PIM) or a feed layer such as Feedonomics exists to make every channel say the same thing. This allows you to easily update how you describe a product across every channel, price changes, and whether you use a different term in one region versus another.

For example, if you’re selling sweaters in both the U.S. and the U.K., you wouldn’t call it a “jumper” in both, only the U.K. With the right PIM or feed management in place, you can make sure AI understands this across both regions.

For a more granular look at how you can get your products into ChatGPT, Google AI Mode, and Perplexity, be sure to read our full guide.

The ecosystem is splitting: open vs. closed models

In the age of AI, many tools and platforms are seemingly similar on the surface. But, in the commerce space, they diverge at the model level: open versus closed.

Understanding the difference plays a large role in who owns the conversation and what level of control you have.

The open model: your data, your checkout, their interface.

An open model is likely what you think of when envisioning AI, led by platforms like ChatGPT. An open model provides easier access to ecommerce brands, requiring the kind of data optimizations and machine-readability mentioned earlier.

An open model gives customers the greatest level of choice, as it can pull from any number of sites and brands. This means your products can get surfaced at the lowest price available, and in the greatest number of markets.

On the other hand, this model can be more competitive. It also injects more unknowns into the conversation, with you having less narrative control and less insights around tracking.

The closed model: their data, their checkout, their customer.

A closed model sits opposite an open model, operating within a closed ecosystem that’s often tied to a specific retailer or brand. This guarantees a certain level of platform control that open models don’t, allowing you to have fewer security and data concerns, and more control over which products get surfaced.

This control comes with a cost however, as a closed model also won’t pull competitive product pricing from other marketplaces, users have less choice, and you’re locked into whatever innovation the AI vendor supplies.

A prime example of the closed model would be Amazon and their “Buy for me” feature. When you’re on Amazon’s site, you can utilize this, but third-party AI agents can’t access it.

Why 2026 forced the split into the open.

The discussion around open and closed models wasn’t in the forefront as much as it is today. This is the result of a few major events in 2026, each of which has played a pivotal role in the discourse around a merchant’s role in AI discovery and control.

• OpenAI retired in-chat Instant Checkout in March 2026 in favor of a discovery-first Agentic Commerce Protocol (beta), with checkout handled by merchants.

• Google launched the Universal Commerce Protocol (Early Access) at NRF in January 2026, which places the retailer as the seller of record.

• Amazon retired Rufus and launched Alexa for Shopping with an agentic Buy for Me capability (beta) in May 2026, taking a clear stance on closed rather than open models.

• Perplexity ingests structured product feeds directly from merchants rather than relying on scraped pages.

Two of the above moves are a clear declaration for open, one furthering a closed ecosystem, and a fourth that shows the importance of merchant data in discovery. Despite their differences, all four events point toward the same thing: the biggest point of control rests at the transaction layer, and 2026 shifted that control back to the merchant.

Whether closed or open, no transaction occurs without the merchant’s data first powering a recommendation. This is the redefining of governance in 2026.

Who owns the shopper conversation (and what a closed model costs you)

Ownership is one of the highest stake topics in the conversation around brand control and governance in the age of AI. Ultimately, whoever authorizes an agent and holds the payment credentials owns the relationship. Everyone else is just a supplier.

But, it’s a little more complex than that. (Because of course it is.)

The interface is rented; the relationship is owned.

A brand that only pipes product data into someone else’s interface has become a supplier, not a brand. The shopper’s account, consent, payment credential, and purchase history sit with the platform. You receive an order and a settlement. You do not receive a customer.

That’s a fine outcome for a commodity or one-off purchase. But, it’s not ideal for anything you’ve spent money making people prefer, and does little in the way of driving customer loyalty.

What you lose when the platform holds the shopper.

Closed models offer a trade-off: a potentially more secure, walled garden with some modicum of control around what’s surfaced, in exchange for the customer relationship.

Walmart’s experience is the clearest 2026 evidence that closed in-chat checkout served neither side well.

Daniel Danker, Walmart’s EVP of AI acceleration, product, and design, told WIRED that purchases completed inside ChatGPT converted at roughly one-third the rate of click-outs to Walmart’s own checkout. Walmart’s response was to bring its own assistant into ChatGPT and keep the transaction.

Again, while a closed model can provide certain benefits, keep in mind the costs compound when the platform holds the shopper:

• Repeat purchase accrues to the platform’s account, not to yours.

• First-party data arrives as a report, if it arrives at all.

• Margin is exposed to fees and ranking tied to the platform’s checkout.

Lastly, there’s the unique B2B angle to consider. When the platform holds the shopper, B2B terms do not survive. Net terms, account pricing, contract catalogs, and approval workflows have no place in a generic closed checkout. For a B2B seller, the buyer relationship is the product.

Be sure to read our piece on B2B agentic commerce for a better look at what buyer agents need to succeed.

A governance operating model: five signals and one owner

You may not have complete control over the results AI surfaces, but you still have control over your data. Beyond this, you can build a governance operating model that helps you stay on top of major engines and how your brand’s represented across them all.

Five signals to monitor.

Much like everything else online, you can track all kinds of AI-related signals. There are five to follow, each of which can help you understand how well your brand is being represented.

Track the following across ChatGPT, Google AI Mode, Perplexity, and Gemini:

1. Accuracy. Are the facts right? Price, availability, materials, compatibility, and policies.

2. Visibility. What share of relevant prompts surface your brand at all?

3. Sentiment. How are you characterized when you do appear? Premium, budget, reliable, dated?

4. Citation integrity. Does the model cite your pages, or a reseller’s, a marketplace’s, or a forum’s?

5. Compliance. Are regulated claims, pricing, and availability represented within the bounds you are legally accountable for?

As far as the actual tracking process, you can either do it manually and maintain a spreadsheet, or pay for an automated tracking tool. The manual route is feasible and fine if you only have a handful of products to track, but can quickly turn into an unrealistic, time-consuming project.

One owner, three functions.

Many governance initiatives fall apart because of a lack of ownership and accountability. To prevent this, assign a single executive owner that’s accountable for the entirety of three different functions, with each one having a primary stakeholder:

• Brand and marketing: They own the standard and determine what an accurate, on-brand AI description looks like.

• Ecommerce and product data: They own the inputs, including the catalog, attributes, and feeds the model reads.

• IT and data governance: They own the pipeline, focusing on how the record reaches every channel intact.

For each area, have a primary stakeholder who knows the space well. Then, have all three report up to your executive owner, who uses that expertise as a guide when evaluating the five signals.

A monthly cadence, not a one-time audit.

AI models are updating on a weekly cadence in many cases, while your catalogs can update even more frequently than that. A one-time audit might illuminate glaring issues, but much of the information it provides will be stale before your findings are even compiled.

Perform monthly reviews at minimum, aiming for more frequent audits if possible. There are AI visibility tools that can help you automate data pulls, but ultimately the judgment around what to fix and focus on falls on human review.

For more information on the role of agentic AI in ecommerce and how you should frame your thinking, be sure to read our in-depth look.

Questions to ask any vendor before you hand them your brand

There are numerous agentic commerce partners, each offering something different. It’s important any vendor has the right guardrails in place, and that risk management is followed.

To keep both your data and your customer data safe, and to help you with overall brand management and governance, ask any prospective agentic commerce vendor the following before signing on the dotted line:

1. Do we remain the merchant of record on every transaction?

2. Who owns transaction and customer data, and can we export all of it?

3. Which protocols do you support (ACP, UCP, AP2), and what stage is each at?

4. Can we correct a product description the model gets wrong, and how fast?

5. Do you cite our pages, or a marketplace’s?

6. What happens to our catalog and customer records if we leave?

7. Are fees or ranking tied to using your checkout?

8. Do you support B2B pricing, net terms, and approval workflows?

9. What monitoring do you provide across engines, and how often?

10. Which of these capabilities are generally available today, and which are Early Access?

Again, you can have a proper AI governance framework in place, but this does little good if you partner with a vendor that lacks the right systems to help maintain your brand voice across agentic surfaces.

Where BigCommerce stands

BigCommerce is all about empowering businesses to grow in the ways they choose. Because of this, we’re protocol-agnostic, composable, promote you being the merchant of record, and offer an open architecture that’s agentic friendly.

In short: Our native capability covers the baseline, while apps and partners cover the rest.

For proof, look at our recent commitments:

• An integration with Stripe’s Agentic Commerce Suite on ACP (Early Access), announced December 18, 2025.

• A public endorsement of Google’s Universal Commerce Protocol on January 11, 2026. (Note: This is an endorsement, not confirmed early-access enrollment.)

• A host of new agentic commerce features and capabilities that make it easier for merchants to embrace it in a safe and scalable manner.

Again, BigCommerce is here to offer a platform on which you can more easily scale your business. This includes taking advantage of the rapid developments in agentic commerce, while maintaining brand control.

Pssst. For more information on the role of ACP and AI agents, be sure to read our in-depth guide.

The final word

Maintaining brand control and governance amidst the rapid rise of AI is possible, no matter how small your role in the conversation may feel. Don’t feel intimidated, instead, start taking action ASAP.

Start by doing these three things before the next quarter closes:

1. Audit this month: Ask the four major engines to describe your top 20 products, and record what comes back. This is your chance to be the human-in-the-loop.

2. Name one owner: Give AI brand governance an accountable executive and adopt the five signals.

3. Put the checklist to every partner: Include your platform, and get GA versus Early Access answers in writing before you agree to anything.

AI models will speak for your brand no matter what. Whether they speak from your record and your checkout, or from someone else’s, is the only part of the outcome still yours to decide.

BigCommerce can help you grow your business, including into the age of AI, while leaving you in control. See for yourself and explore our platform in full.

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