Skip to content

Features
Pricing
Changelog
Status

Tag: Agentic Commerce

Google UCP and SEO: How I’m Preparing for AI Commerce
For as long as I’ve worked in search marketing, I’ve viewed the path to purchase as a simple sequence: search query → click → buy.

I’ve approached SEO through much the same model, using organic traffic, impressions, and click-through rate (CTR) as the primary measures of success.

Google’s Universal Commerce Protocol (UCP) tells me that this familiar path is changing. Google is evolving from a discovery engine into a transaction layer where searching and buying can happen inside the same experience.

With the rise of “agentic commerce,” I’m seeing Google gain the ability to discover, evaluate, compare, and purchase products on a user’s behalf within AI-powered experiences such as AI Mode, Gemini, YouTube, and Gmail.

I believe the SEO implications are substantial. Instead of optimizing only for clicks, I now need to think about optimizing for AI-assisted transactions. If a brand cannot communicate through UCP and the product data that supports it, it risks becoming invisible to the next generation of shoppers.

Here’s how I understand UCP, why I think it will reshape digital marketing, and what I recommend doing now to prepare an SEO strategy for agentic commerce.

UCP: The infrastructure behind AI transactions

I think of UCP as an open-source, vendor-agnostic standard that supports the entire commerce lifecycle inside an AI interface. That lifecycle can extend from product discovery and cart creation through checkout, fulfillment, and post-purchase tracking.

Google co-developed UCP with Shopify, Walmart, Target, Wayfair, Etsy, and other commerce leaders. From my perspective, it acts as a universal translator between AI shopping agents and the systems merchants use to operate their online stores.

The clearest analogy I can make is that UCP may become the ecommerce equivalent of HTTPS. HTTPS standardizes secure communication between browsers and servers; UCP standardizes how AI agents interact with online stores. Instead of building a custom one-to-one integration for every merchant, an AI agent can use a shared framework to browse inventory securely and complete purchases across many stores.

How I see AI transactions flowing through UCP

Imagine I ask AI Mode to “find and order a replacement water filter for a 2021 Samsung French-door fridge with the fastest shipping.” UCP can coordinate that transaction through a structured workflow.

Capability publication

First, I expect the merchant to publish the capabilities its store supports, including product search, live pricing, fulfillment options, and accepted payment methods. This gives the AI agent a clear picture of what it can request and complete.

From product discovery to payment and confirmation, this mobile shopping sequence shows a Monos suitcase purchase completed with Google Pay through Google’s Universal Commerce Protocol.

Handshake

Next, the AI agent reads the merchant’s profile, compares those capabilities with its own, and establishes a secure path forward. I see this step as the point where the systems can align on details such as loyalty programs and supported digital wallets.

Action execution

Once the systems are aligned, the AI searches for the product, verifies real-time inventory, builds the cart, and uses the Agent Payments Protocol (AP2) to complete a secure, tokenized transaction.

Human escalation

If the transaction needs my input—perhaps to select a delivery window or confirm a shipping address—UCP can pause the process and prompt me. After I respond, control returns to the AI so it can finish the workflow.

Dig deeper: How Google’s Universal Commerce Protocol could reshape search conversions

Why I believe UCP matters for search and SEO

I don’t see UCP as merely a technical update. I see it changing the way AI discovers, evaluates, and purchases products—and that makes it directly relevant to SEO.

1. I’m shifting from click-throughs to buy-throughs

In an agentic search environment, I can no longer treat website traffic as the only measure of business value. Features such as Universal Cart can let shoppers add products from multiple retailers to one Google cart and check out with Google Wallet, dramatically shortening the buying journey.

A shopper may never visit my homepage, category page, or product detail page. That changes my SEO objective: I need to earn product selection within the AI recommendation layer so a search query can become a sale even when it generates no intermediate website visit.

2. I’m planning for hyper-personalized queries

I’m also rethinking keyword research. Shoppers are moving beyond broad searches such as “men’s running shoes” and using detailed, situational prompts like “Best running shoes for flat feet under $150 that can arrive by Friday.”

To match a request that specific, I know a search engine needs more than polished on-page copy. It needs rich, structured, and queryable product attributes. UCP helps bridge that gap by giving AI agents a way to match merchant inventory with a shopper’s precise requirements.

3. I expect less checkout friction

I continue to see cart abandonment as a major ecommerce challenge, especially when shoppers encounter long forms, broken checkout flows, or unexpected shipping costs. Because UCP can work with secure digital wallets and automatically pass verified user data, I expect it to eliminate many of those friction points.

As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

For high-intent, urgent, or repeat purchases, I believe merchants that support UCP may capture more conversions than competitors that send every shopper to a separate checkout experience.

4. I can retain brand control and customer ownership

One detail I consider especially important is that the merchant remains the Merchant of Record when a transaction takes place through UCP. I can still control pricing, fulfillment, and return policies while retaining the customer relationship and first-party data. UCP provides the transactional infrastructure without replacing the merchant’s role.

Dig deeper: Winning the AI decision layer: From AI discovery to agentic commerce

How I recommend preparing a brand for UCP

If I limit an SEO strategy to blog articles and meta descriptions, I overlook the technical infrastructure that powers AI commerce. To make products eligible for UCP-powered experiences, I recommend focusing on the following priorities.

I would optimize the Merchant Center feed

I no longer view Google Merchant Center (GMC) as a tool used only for Shopping ads. I see it becoming a primary source of product information for AI discovery, which makes feed quality central to both visibility and transaction eligibility.
- Enable the native_commerce attribute: To opt into UCP-powered checkouts, I would add the native_commerce attribute to the product feed. Google recommends using supplemental feeds to apply it at the product level without changing the primary feed.
- Map product identifiers: I would make sure every product ID in the GMC feed maps one-to-one with the corresponding ID in the internal checkout API. If the identifiers differ, I would use the merchant_item_id attribute to align them.
- Complete policy data: I would keep returns, shipping, and customer-support information complete and current. Clear policy data gives an AI agent the details it needs to evaluate a merchant confidently.
I would align structured data with the product feed

Because AI search depends on consistent information, I would keep the Product, Offer, and Review schema on the website synchronized with the Merchant Center feed. If the price, availability, identifiers, or other details conflict, validation problems could make a product ineligible for AI-powered checkout.

I would prepare for conversational attributes

As Google introduces semantic attributes designed for conversational AI search, I would prepare inventory and product-information systems to supply richer answers. In particular, I would prioritize:
- Real-time inventory availability.
- Direct answers to product FAQs, such as “Is this jacket machine washable?”
- Detailed compatibility information, including accessory pairings, sizing guides, and model-specific replacements.
I would treat these details as more than feed enhancements. They are the signals that help an AI agent decide whether a product satisfies a nuanced request involving price, fit, compatibility, delivery speed, or another real-world constraint.

Beyond clicks: The next SEO opportunity I see

To me, the Universal Commerce Protocol reflects a broader transformation in search. It expands the role of SEO beyond generating traffic and brings product data, inventory systems, checkout infrastructure, and conversion readiness into the search conversation.

By prioritizing structured product data, reliable commerce information, and readiness for agentic transactions, I can position a brand to capture demand at the exact moment a shopper expresses intent.

I don’t believe the future of search will be only about getting found. Increasingly, it will be about making sure the products I represent can be evaluated, selected, and bought.

Inspired by this post on Search Engine Land.
July 10, 2026
How Gemini Intelligence Will Reshape Search and Commerce

I see Google’s unveiling of Gemini Intelligence at the May 12 Android Show as a significant step toward an agent-powered future. Announced alongside a new laptop called the Googlebook, Gemini Intelligence is designed as an underlying layer that works across the Android operating system on laptops, phones, watches, and glasses.

The Googlebook makes that vision tangible to me. Built from the ground up around an AI agent, it can understand what is on the screen and act on it. I could point to a date in an email and have the agent schedule a meeting, or select furniture in an app and see how those pieces might look in my living room.

I believe this ability to complete tasks without requiring someone to open a webpage will fundamentally change how people search, discover information, and conduct commerce. Here is how I expect that shift to affect the search industry.

What the shift to an agentic operating system means

Until now, I have viewed search as a familiar sequence: someone has a question or intent, enters it into a search engine, receives a list of links, and chooses one. Earning a prominent position on that list was the prize, and much of the SEO industry was built around winning that click.

Gemini Intelligence starts from a very different assumption. Search intent still exists, but an AI agent can handle the steps between the request and the outcome. It can read pages, complete forms, and increasingly finish the entire task. Instead of visiting a website myself, I may have an agent visit and use it on my behalf.

When I look for an early example, Chrome Auto Browse stands out. Launched in January and built on Gemini 3, it can manage multistep tasks such as researching flights, filling out forms, scheduling appointments, and managing subscriptions. It then pauses for approval before making a purchase.

That efficiency gives me a clear reason to believe ecommerce will continue moving toward agentic AI.

A 2025 preprint supports this view. Researchers evaluated the declared-tools approach across online shopping, authentication, and content management. They found that giving an agent pre-structured interaction data reduced processing requirements by 67.6% and lowered costs by 34% to 63% compared with parsing a complete HTML document. Task success declined only slightly, from 98.8% with the traditional method to 97.9%.

The architecture behind Gemini Intelligence

To me, the architecture is as important as the interface. AI agents naturally favor websites they can interact with cleanly and efficiently, and Gemini Intelligence can only deliver on its promise if those agents can perform tasks reliably.

I see two protocols as central to making that possible. WebMCP turns a website’s actions into callable tools, while the Universal Commerce Protocol (UCP) allows an agent to complete a sale. Together, they enable an agent to finish a task without requiring a person to load and navigate the underlying webpage.

As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

WebMCP

I think of WebMCP as a labeled menu for AI agents. The API allows a website to declare functions as structured tools an agent can call, including searching inventory, beginning checkout, or submitting a support request.

Google co-developed WebMCP with Microsoft. An origin trial is live in Chrome 149, Firefox has committed to the third quarter of 2026, and Safari is expected to follow in the fourth quarter.

Universal Commerce Protocol (UCP)

I see UCP as the transactional counterpart to WebMCP. It gives AI agents a shared language for discovering products, building a cart, completing checkout, and managing orders without requiring someone to visit the merchant’s website.

Google also offers a consumer-facing layer called Universal Cart. It can collect items as I move across Search, Gemini, YouTube, and Gmail, creating a more connected shopping experience across Google’s products.

The range of companies behind UCP shows me how seriously the industry is taking this shift. Google, Shopify, Walmart, Target, Etsy, Wayfair, PayPal, and Stripe co-developed the protocol, which launched in January.

How I would prepare for agentic AI

My main takeaway is that websites are rapidly evolving from destinations into backends—from places people actively visit into systems agents quietly use. As the operating system becomes a search and action layer, I no longer think ranking is the only question that matters. I also need to ask whether an agent can actually use the site.

To prepare, I would begin by auditing the site’s most valuable actions, whether that means submitting a lead form, completing a booking flow, or reaching checkout. I would determine whether an agent could complete each action reliably and check the site’s Lighthouse Agentic Browsing score much as I would review Core Web Vitals. The goal is to understand whether an agent can use the site, not merely read it.

If I ran an ecommerce business, I would confirm whether the checkout process is accessible through UCP or ACP. I would also continue investing in retrieval and visibility because an agent still needs to find and trust the business before it can act on anyone’s behalf.

Dig deeper: Are we ready for the agentic web?

Inspired by this post on Search Engine Land.

July 10, 2026
How I Win the AI Decision Layer in Agentic Commerce

I see the next major battleground for brands being shaped by AI. Every day, AI engines and autonomous agents decide which brands to recommend, compare, cite, and transact with on behalf of consumers. To compete, I have to make my brand the trusted choice AI selects.

This shift is already underway. Adobe data shows that AI-referred traffic to U.S. retail websites grew 4,700% year over year through mid-2025. Salesforce reports that AI and autonomous agents influenced one in five online orders globally during Cyber Week, driving an estimated $67 billion in sales.

As AI becomes the interface between consumers and brands across discovery, evaluation, and purchase, I need to think beyond traditional rankings. A new competitive layer is emerging: the AI decision layer. This is where AI systems evaluate trust, relevance, authority, and transaction readiness before deciding which brands make the shortlist.

If I fail to influence this layer, my brand may be excluded before a customer ever sees it. That makes AI visibility, credibility, and actionability core parts of modern search strategy.

How I take a brand from found to actioned

Agentic commerce readiness follows a clear sequence. I start by making sure AI engines can find my brand, then I move through the remaining stages until AI agents can understand, trust, recommend, and transact with it.

Step 1: I get found by enabling AI discovery and access

Machine accessibility is the foundation of AI visibility. If I want AI systems to discover and access my brand, I have to prioritize technical hygiene and token efficiency.

I start by allowing the right crawlers on my website. Google, OpenAI, Anthropic, and Bing need to reach my content without unintended restrictions.

Then I get the basics right. I set up XML sitemaps and robots.txt, fix crawl errors, add canonical tags, and maintain strong Core Web Vitals. I also make sure my website content is rendered server-side so agents can reliably navigate and reason over my pages.

I also pay close attention to token efficiency. Bloated HTML wastes valuable tokens that AI systems could otherwise use to understand my content, products, and brand.

To make my site more AI-ready, I publish assets that help large language model crawlers process my content more efficiently. An llms.txt file can give LLM crawlers a concise map of my website, while Markdown versions of key content can reduce token consumption and improve machine understanding.

Dig deeper: The enterprise blueprint for winning visibility in AI search

Between consumers and brands, AI agents now act as the decision layer, handling discovery, evaluation, trust signals, and transactions before products reach the shortlist.

Step 2: I become understood by building semantic clarity

To be understood by AI engines, I need to build entity authority. This helps AI interpret who I am, what I offer, and why my brand matters.

Structured data turns my web pages into machine-readable knowledge that AI systems can understand, trust, and use. I strengthen my entity graph with comprehensive schema, trusted citations, and linked references.

I also deliver clean, server-rendered HTML that AI can access without friction. Semantic HTML, structured @graph IDs, and consistent naming help AI engines connect the right context to my brand.

Step 3: I get retrieved by structuring content for AI extraction

Traditional search ranks pages, but AI search retrieves and cites passages. That means I win on relevance, clarity, authority, and freshness rather than length alone. Original expertise, proprietary data, and real-world experience give my content a stronger chance of being selected.

To structure my content for retrieval, I use a clear heading hierarchy with H1, H2, and H3 tags. Under each heading, I create descriptive, self-contained sections that can stand on their own.

I build interconnected topic clusters instead of isolated pages because AI needs enough context to assemble complete answers.

I also front-load every section. I put the core answer and the most important metrics in the opening sentence before a model hits its token limit.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Step 4: I build trust with authority and grounding signals

Just because AI engines retrieve my content does not mean they will recommend my brand. Retrieval is only one step. Trust is what moves a brand closer to selection.

AI systems prioritize sources they can trust, so authority and credibility become decisive. Google’s experience, expertise, authoritativeness, and trustworthiness principles, known as E-E-A-T, remain some of the strongest signals influencing whether a brand is cited, referenced, or selected.

A visual roadmap for becoming the brand AI selects: first be found and understood, then retrieved, trusted, chosen and finally actioned by autonomous assistants.

Trust extends far beyond my website. AI evaluates review sentiment, location accuracy, pricing consistency, product availability, and entity alignment across the web. When those signals conflict, AI confidence decreases.

Credibility is now computational. Grounding, the process of validating responses against trusted evidence, is the bridge between visibility and recommendation.

To earn computational trust, I create original, expert-driven content that shows real experience and unique value. Then I align every external signal so reviews, listings, maps, and directories all tell one consistent story about my brand.

Dig deeper: Integrating SEO into omnichannel marketing for seamless engagement

Step 5: I get chosen by earning machine and human preference

AI agents parse attributes, verify claims, and score confidence in milliseconds. If I cannot make my value clear to AI, my brand becomes invisible at the decision point.

But emotional preference still matters. Consumers may delegate routine purchases, yet they hold tightly to choices tied to identity. The strongest brands optimize for both machine readability and human resonance.

To earn AI recommendations, I measure AI visibility, citation, and recommendation rates through query fan-out testing. I keep brand, product, and location data consistent across every channel. I also work to earn trusted mentions and references that strengthen AI confidence in my brand.

Dig deeper: How to boost your marketing revenue with personalization, connectivity, and data

Step 6: I enable agentic transactions

Recommendation is no longer the finish line for AI search. Discovery, selection, and checkout can now happen inside an AI assistant without the customer ever visiting my site.

An agentic website is designed for AI agents to discover information, retrieve answers, and perform actions on behalf of users. NLWeb helps make website content conversational and machine-readable, improving how AI systems find and understand the site.

A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.

Web Model Context Protocol, or MCP, extends this capability by giving AI agents a standardized way to interact with website functions. That can include retrieving data, initiating workflows, and submitting forms.

Agentic commerce moves the full transaction inside the assistant. Google’s Universal Commerce Protocol, or UCP, enables chat-based bookings. OpenAI and Stripe’s Agentic Commerce Protocol, or ACP, pushes inventory so AI systems can surface it more easily. Agent Payments Protocol, or AP2, then lets the agent pay.

Underneath these capabilities is MCP, which enables an LLM to read products, content, and live data. This changes my website from a destination into a source of truth. It supplies the inventory, pricing, and signals that drive every agent journey.

Dig deeper: How to select a CMS that powers SEO, personalization, and growth

How I measure performance in the AI decision layer

I still track traditional search metrics like rankings, sessions, and clicks. They remain useful, but they are no longer enough to measure success in AI search and agentic commerce.

For visibility, I track AI presence rate, AI share of voice, citation frequency, and agent recommendation rate.

For commerce, I track AI-influenced revenue, agent conversion rate, autonomous transaction volume, and agentic wallet share.

I also expect traffic patterns to change. Direct visits may decline as agents handle discovery, but AI-influenced transactions through machine-readable layers like WebMCP and schema endpoints can offset that loss and create new revenue paths.

With these changes in place, my website can become the trusted source AI systems rely on for both information and action.

From SEO to decision architecture

SEO remains the foundation for winning search, but a deeper shift became concrete at Google I/O 2026. AI agents now parse raw HTML, distill the browser’s native accessibility tree, and capture visual screenshots through vision models.

Together, these three paths determine whether a site is truly actionable for AI. My page can be technically flawless and still fail if its structure, semantics, or user experience breaks the chain. If I miss any stage, trust and transaction readiness suffer.

When I get these pieces right, my brand becomes discoverable, understandable, trusted, and transactable when AI agents make decisions. The brands that build these capabilities today will be the brands AI surfaces, trusts, and recommends tomorrow.

Inspired by this post on Search Engine Land.

July 9, 2026
Goodie vs. Semrush: A Smarter AEO Platform Comparison

When I compare Goodie and Semrush for AI search visibility, I’m looking beyond traditional SEO dashboards. I want to understand how each platform supports answer engine optimization, from monitoring AI visibility to improving the signals that influence AI-generated answers.

A modern AEO performance dashboard brings AI search visibility, brand mentions, traffic attribution, and revenue signals into one measurement view.

For me, the key difference comes down to focus. Goodie is built around AEO monitoring, optimization, agentic commerce, and revenue attribution, while Semrush brings the depth of a broader SEO and competitive research platform.

A Semrush project dashboard brings SEO health into one view, from keyword rankings and site audit trends to optimization ideas and backlink toxicity signals.

In this comparison, I look at how both platforms help brands get discovered, cited, and recommended across AI search experiences, and how each one connects visibility to measurable business impact.

Inspired by this post on HiGoodie Blog.

July 6, 2026
Transforming Ecommerce: Google’s New AI Commerce Strategies
For years, I relied on a straightforward ecommerce model: Google attracted visitors to my site, where transactions were completed. Success was measured through rankings, clicks, and conversion rates. That scenario has drastically changed.

With Google’s Universal Commerce Protocol (UCP) combined with AI Mode, it’s possible for Google to uncover, evaluate, and finalize purchases within its AI framework. The dynamic is shifting from merely directing traffic to facilitating transactions. Now, the visibility of my products hinges on whether Google’s AI includes my data in its algorithm.

When AI can recommend and close sales, the optimization challenge moves even farther upstream. The vital question now isn’t just about my ranking; it’s about whether my products get chosen by AI.

So, let’s explore these changes and what strategies those involved in SEO and AI optimization should adopt next.

On January 11, Google introduced the Universal Commerce Protocol, or UCP. This innovative open standard empowers AI agents to explore, assess, recommend, and purchase products seamlessly across the web within Google’s own AI settings.

What caught my attention was not just UCP itself but the entire ecosystem Google devised around it. UCP was created in collaboration with platforms like Shopify, Etsy, Wayfair, Target, and Walmart, with pre-existing payment networks incorporated. This level of planning signifies a long-term vision, rather than a fleeting experiment.

Simultaneously, Google introduced three platform-level features that make this transformation tangible in everyday shopping experiences:
- Business Agent: Brands now have an AI-powered ambassador in Search and the Gemini app. Shoppers can inquire about products, compare choices, and receive brand-specific advice without the necessity to visit a separate site.
- Direct Offers: This feature allows merchants to incorporate exclusive discounts directly into Google’s AI Mode, embedding promotions within the recommendation engine itself.
- Checkout in AI Mode: Google now facilitates purchases directly within its interface, transitioning from a traffic broker to an integral transaction facilitator.
What’s even more remarkable is how Google transforms routine conversations into commerce. Instead of waiting for users to type product-related queries, Gemini can respond to natural language prompts like “help me plan a camping trip” or “what will get wine out of my couch” by sourcing up-to-date inventory, pricing, and availability from retailers, completing the transaction in the same interaction.
Dig deeper: Are we ready for the agentic web?
In the era where AI navigates the purchasing journey, brands must compete within the AI’s recommendation system, not just in search results.

Throughout my career, ecommerce consistently functioned on a model where search engines, ads, and marketplaces aimed to divert users to my site, so it could handle the sales. UCP reshapes that perception entirely.

Now, AI takes charge of the complete journey. It understands the customer’s needs, assesses different options, and can even finalize the purchase. Under this model, the quality of my website’s homepage or category page matters less if AI doesn’t prioritize my product at the outset.

Inspired by this post on Search Engine Land.
March 2, 2026
AI Revolutionizes Digital Advertising by 2026: What You Need to Know

As I look ahead to 2026, Google’s innovative strides in AI are truly reshaping digital advertising and commerce. Thanks to the leadership of Vidhya Srinivasan, VP/GM of Ads & Commerce, AI is significantly enhancing the shopping and advertising landscape, making it more efficient and personalized for everyone involved.

Key Trends:

Creators to commerce: In my experience, YouTube is increasingly becoming a go-to platform for discovery, largely because creators act as influential tastemakers. AI plays a pivotal role in pairing the right creators with brands, transforming influence into tangible business outcomes.

Search ads evolve: With conversational and visual searches gaining popularity, AI Mode is revolutionizing ads to seamlessly integrate into the user’s discovery process. Innovative formats like sponsored retail listings and Direct Offers are crafted to assist users in their shopping journey while offering brands meaningful conversion opportunities.

Agentic commerce arrives: Through Google’s Universal Commerce Protocol (UCP), AI-driven shopping experiences are becoming standardized. This advancement allows users to browse, purchase, and finalize transactions effortlessly. Early adopters like Etsy and Wayfair have already started using this system, with giants like Shopify, Target, and Walmart soon joining the bandwagon.

AI-powered creative and performance: I’m thrilled to see how tools powered by Gemini 3 are enhancing creative production and campaign optimization. Generative platforms like Nano Banana and Veo 3 help advertisers produce high-quality assets swiftly, while AI Max boosts reach and performance.

Trust as a foundation: It’s reassuring to know that each advancement prioritizes privacy and security. Strong data management practices, alongside transparent ad personalization, are founded on Google’s legacy of trust.

Why we care: 2026 is poised to be a groundbreaking year, with AI enhancing every facet of the consumer journey. With cutting-edge tools like Gemini 3, Nano Banana, Veo 3, and AI Mode, brands like mine can efficiently create superior content, target the perfect audience, and seamlessly convert interest into purchases during search and discovery.

The advent of agentic commerce through UCP presents a novel approach, connecting advertisers to consumers at critical purchasing moments, all while preserving trust and transparency.

The big picture: The year 2026 heralds an expansive era for digital commerce and advertising, where the fusion of speed, personalization, and AI-driven insights eliminates barriers, facilitating smoother transitions from discovery to purchase while keeping trust paramount.

Dig Deeper: Discover what’s next in digital advertising and commerce by 2026

Inspired by this post on Search Engine Land.

February 11, 2026
Unveiling Agentic AI: Guiding E-commerce Execs with Clarity
Agentic AI is now a hot topic among executives. I’m here to break down precisely what’s happening, what remains unchanged, and how e-commerce brands should adapt.

As an SEO leader working with e-commerce brands, I’m often in the position of clarifying the realities behind buzzwords like ‘agentic AI’. Executives frequently inquire about its implications for growth, risk, and competition.

Executives crave facts over hype. They seek concise explanations, grounded insights, and actionable advice.

My role as an SEO leader becomes essential here, not in predicting the future, but in enlightening leadership about the changes, the constants, and how to proceed pragmatically. Here’s my roadmap.

Start with Defining ‘Agentic’

First, I focus on demystifying the term. Agentic systems don’t replace customers; they work on their behalf. While the intent and preferences originate from individuals, the execution is taken over by the software.

The working dynamics shift, where tasks like discovery, comparison, and even execution are now managed by software, processing data faster than any human.

In discussions with executive teams, I emphasize simple illustrations:
- “We’re not losing customers; instead, we’re incorporating a new decision-maker, which is the software acting as a customer proxy.”
Understanding this calms the conversation and steers focus away from fear towards preparation.

Manage Expectations to Avoid Hype

Another key role I play is in tempering expectations. Agentic AI won’t sweep over all at once. Its effects will be gradual and varied across different categories.

Some industries, with standardized products and organized data, will adapt faster. Others will face more challenges due to complexities and regulatory hurdles.

I often see leadership teams falling into two detrimental traps:
1. Panic: Hastily altering strategies and budgets without clarity.
2. Dismissal: Ignoring changes until it impacts performance, leading to rushed responses.
I offer a steady perspective, noting that agentic AI merely accelerates existing trends. It’s not about chasing new features but reinforcing strong fundamentals.

Dig deeper: Are we ready for the agentic web?

Shift Focus from Rankings to Eligibility

I encourage conversations to evolve beyond search rankings. When agents lead the journey, the critical question becomes, “Are we eligible to be chosen?”

Eligibility hinges on clear, consistent, and trustworthy data. Agents must grasp your offerings, target audience, pricing, availability, and risk factors associated with choosing your brand.

Raising thoughts about data consistency, pricing reliability, and whether policies add or reduce uncertainty positions SEO as a practical bridge between strategy and execution.

SEO Beyond Marketing

There’s a misconception that SEO is confined to marketing. Agentic behavior challenges this notion.

Selection by an agent involves variables beyond marketing, like data accuracy, technical integrity, inventory management, and payment reliability.

My explanations revolve around broadening SEO’s scope—it’s about ensuring the business is machines-readable, trustworthy, and consistent.

SEO becomes vital in helping leaders identify system or data gaps that could hinder the brand’s selection, highlighting its connection to both risk management and operational resilience.

Dig deeper: How to integrate SEO into your broader marketing strategy

Discovery’s Evolution

In most e-commerce brands, agentic systems affect the top of the funnel first. Discovery shifts towards more personalized, conversational interactions.

Instead of brief search phrases, users convey needs, constraints, and preferences, which the agent then transforms into actions.

This decreases the significance of owning category head terms. If an agent has comprehensive user data, it acts like a knowledgeable repeat customer.

This presents a new reporting challenge. Not all SEO work will appear as direct demand creation, yet it still impacts outcomes. Leaders need to anticipate this shift.

Rethink Consideration

The consideration phase evolves too. Traditionally, it involves hosting reviews, comparisons, and reassurances.

With agentic intervention, consideration morphs into a filtering process, retaining only the options that align with user preferences.

This necessitates a quality over quantity strategy in content, emphasizing structural trust signals and consistent, verifiable information.

Brands might be selected without user awareness. While this could boost conversions, it also poses a risk to brand recognition if not addressed elsewhere.

Dig deeper: Align your SEO strategy with buyer intent stages

Establish Honest Measurement Expectations

Measurement often concerns executives, and agentic AI complicates this. With more processes happening inside AI, fewer interactions leave traceable or clear data.

I address this early by stressing that while this isn’t a failure of optimization, it merely highlights the analytics limits in a complex digital landscape.

The focus should shift to directional indicators and blended performance over precise attribution, acknowledging the new decision-making landscape.

Advocate Proactive, Low-risk Responses

The crux of leadership dialogue is next steps. Fortunately, most appropriate responses to agentic AI carry low risk.

Enhancing product information, eliminating inconsistencies, strengthening reliability signals, and addressing technical vulnerabilities benefit the business now and pave the way for the future.

Building brand trust outside search also plays a critical role. Trusted brands are more likely to be selected by agents performing comparisons.

This strategy reassures leaders that success doesn’t require radical change but calls for focused improvement.

Agentic AI: Focus Shifts, Fundamentals Persist

For us SEO leaders, agentic AI modifies our focus. Instead of solely optimizing for visibility, we aim to protect eligibility, reduce ambiguities, and illustrate influence.

This demands confidence and clear articulation, challenging hype with grounded perspectives. Agentic AI renders SEO more strategic and no less crucial.

Agentic AI isn’t an imminent threat or foolproof advantage. It’s a transformation in decision-making approaches.

For e-commerce brands, the winners are those who stay composed, communicate effectively, and transition their SEO approach from driving clicks to securing selections.

This transition forms the backbone of the current SEO leadership discussions.

Dig deeper: SEO Predictions for 2026: Insights from Leaders

Inspired by this post on Search Engine Land.
February 10, 2026
Mastering Agentic Commerce: Succeeding with Google UCP & OpenAI ACP

I’m excited to share my comprehensive guide on agentic commerce, where I dive into the powerful dynamics of Google’s UCP and OpenAI’s ACP. This guide is tailored for brands eager to master AI-driven product discovery and boost their revenue.

Agentic commerce is reshaping how we interact with AI in business. By understanding Google’s Unique Commerce Protocol (UCP) and comparing it with OpenAI’s Advanced Commerce Protocol (ACP), I’ve carved out strategies that you can implement to thrive in the evolving landscape.

Through these insights, I aim to empower brands to navigate the complexities of AI product discovery systems effectively. I’m confident that with the right approach, your business can leverage these technologies to gain a competitive edge.

Inspired by this post on HiGoodie Blog.

January 16, 2026