A new Australian consultancy is betting that the next battle for brand visibility will not be fought entirely on Google, retail media networks, or social platforms, but inside the answers generated by artificial intelligence. Obsessd.ai, founded by former Yahoo executive Dan Richardson and former Cartology and Coles360 product leader Bec Penn, has launched in Sydney and Melbourne with services intended to help retailers and consumer brands understand how they appear across ChatGPT, Google Gemini, Anthropic’s Claude, Microsoft Copilot, and Perplexity. Its arrival reflects a broader shift in digital marketing: brands are no longer competing only for clicks and search rankings, but also for inclusion, accuracy, and prominence within AI-generated recommendations.
Search engine optimization has shaped online marketing for more than two decades. Brands learned to research keywords, improve page structure, earn backlinks, publish useful content, and measure performance through rankings, impressions, click-through rates, and conversions.
Generative AI complicates that familiar model. Instead of presenting ten blue links and asking users to choose, an AI assistant can compare products, summarize reviews, filter options, recommend a shortlist, and explain its reasoning within a single conversational interface.
Generative platforms are less transparent. Their answers can depend on the wording of the prompt, conversation history, user location, model version, live web retrieval, product integrations, safety rules, and the sources selected for a particular response.
The result is a more fluid discovery environment. A retailer might appear in one answer, disappear when the question is rephrased, and be described inaccurately when the model relies on an old article or an ambiguous third-party listing.
None has yet achieved the stability or standardization of conventional SEO. However, they generally describe the same strategic objective: making a company, product, or piece of information easier for AI systems to retrieve, understand, trust, cite, and recommend.
Obsessd.ai is entering this emerging field with a particular focus on the Asia-Pacific market. Its proposition is that Australian organizations need more than screenshots from occasional chatbot experiments; they need repeatable measurement, competitive analysis, and operational plans.
The initial client list includes personalized gift retailer Personalised Favours and Sydney fragrance brand By Yuliya. Both operate in categories where conversational recommendations could influence discovery, comparison, gifting decisions, and product selection.
A useful audit might include prompts such as:
Brands preparing for this environment need structured product information, reliable inventory data, machine-readable policies, clear commercial terms, and systems that can safely expose selected capabilities to automated agents.
Obsessd.ai’s inclusion of agentic strategy indicates that it is not treating AI visibility as a standalone content exercise. The consultancy is positioning discoverability as the first stage of a journey that may eventually lead from an AI recommendation to an agent-mediated purchase.
This may prove particularly relevant to large retailers. Their technology and marketing departments already operate extensive ecosystems involving customer data platforms, retail media networks, advertising technology, loyalty programs, analytics tools, and agency partners.
Adding generative AI without a coherent buying framework could create duplicated technology, unreliable reporting, uncontrolled data sharing, or expensive experiments with little strategic value.
The index evaluates performance across ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot. That multi-platform coverage is important because AI discovery cannot be represented accurately through one model alone.
A mature visibility score should consider several dimensions:
Such errors create a new form of reputational and operational risk. The customer may blame the company for an inaccurate claim even when the information originated in a model-generated response.
Accuracy measurement should therefore examine product features, locations, ownership, availability, pricing language, policies, and category classification. For regulated or safety-sensitive sectors, it must also identify unsupported claims that could create legal or compliance exposure.
Competitive measurement should concentrate on unbranded discovery prompts. These are the questions people ask before selecting a provider, such as “What is the best option for…” or “Which Australian companies offer…”
Obsessd.ai argues that displacing an established default recommendation can be significantly harder than becoming the first prominent recommendation in an emerging category. Although the consultancy’s underlying data has not been publicly detailed, the strategic logic is credible: repeated references across trusted sources can reinforce an incumbent’s apparent authority.
That makes AI both a discovery channel and an attribution problem.
The customer might ask for five suitable products, eliminate those above a certain price, request an Australian supplier, compare delivery policies, and seek a final recommendation. By the time the user reaches a retailer’s website, substantial evaluation has already occurred.
That traffic may consequently arrive with higher intent. It may also land deeper within the site, especially if the assistant links directly to a product, guide, store locator, policy page, or internal search result.
This perceived endorsement gives AI platforms considerable influence. It also raises the stakes when their recommendations are incomplete, biased toward heavily documented brands, or based on outdated information.
Retailers must recognize that the assistant is becoming an intermediary in the customer relationship. The brand may not control the interface, wording, shortlist, or comparison criteria that shape the initial impression.
A brand can benefit from being named without receiving a measurable click. Conversely, a publisher or retailer can supply the information used in an answer while remaining invisible to the user.
This creates tension between visibility and traffic. Marketing teams must decide whether success means a citation, a recommendation, a site visit, an assisted conversion, or some combination of all four.
Even with that caveat, the claim identifies a plausible weakness in current marketing operations. Most organizations have built dashboards for paid search, organic rankings, social engagement, retail media, and website conversion, while AI visibility monitoring remains comparatively immature.
One-off screenshots fail to control for:
Brands should build prompt libraries from several sources:
No single optimization technique guarantees recommendation. The information environment around the brand matters as much as the company’s own website.
Useful pages generally answer specific questions in plain language. Product details, delivery areas, returns policies, dimensions, materials, compatibility information, ingredients, warranties, and contact details should remain current and internally consistent.
Structured data can help search systems interpret entities, products, prices, availability, reviews, organizations, and frequently asked questions. It is not a magic switch for AI visibility, but machine-readable information reduces ambiguity.
This gives public relations, reputation management, partnerships, and customer advocacy renewed importance. A distinctive brand with thin third-party coverage may be less visible than a competitor whose products are discussed consistently across credible sources.
The objective should not be to flood the web with repetitive promotional material. Low-quality syndication, fabricated reviews, and undisclosed sponsored content can create reputational risk without producing durable authority.
Organizations should maintain a shared source of truth for core brand facts. Marketing, commerce, communications, customer service, and technology teams all need access to the same approved information.
This work resembles entity management as much as content marketing. The aim is to ensure that machines can distinguish the organization, connect it with the correct products and attributes, and avoid confusing it with similarly named entities.
Even when platforms consult overlapping sources, they may select, rank, summarize, and cite those sources in different ways.
A company could therefore perform well for established brand facts but poorly for recent product changes. Conversely, fresh third-party coverage might improve visibility in web-assisted answers before it affects more general model associations.
ChatGPT’s conversational format also encourages iterative filtering. Brands should test not only the opening question but the follow-up sequence through which users narrow their choices.
For retailers, Gemini performance is likely to remain closely connected to product data quality, conventional search accessibility, local information, merchant feeds, and the wider Google entity ecosystem.
That does not mean existing SEO automatically guarantees Gemini visibility. Generative systems can use different selection and presentation logic, producing a shortlist that does not mirror standard web rankings.
A user researching a product in Edge may encounter AI-assisted summaries or comparisons without deliberately visiting a standalone chatbot. Within an organization, Copilot could help employees analyze suppliers, draft procurement documents, or summarize market options.
This makes Copilot visibility relevant beyond web traffic. A brand may enter an enterprise decision through an AI-generated briefing long before anyone visits its homepage.
Perplexity emphasizes answer-oriented search and visible citations. This makes its source selection easier to inspect, although citation does not necessarily mean endorsement and prominence can vary within an answer.
A multi-engine index must preserve these differences rather than collapsing them into one opaque score. A brand that performs strongly on one platform but poorly on another needs diagnosis, not merely an average.
Local brands have an opportunity to win when their regional relevance is explicit and verifiable.
Local media coverage and Australian customer reviews can also strengthen contextual relevance. A brand known within its domestic market may otherwise be overshadowed by an international competitor with a much larger global information footprint.
AI discoverability strategies should therefore include geographically specific prompts. Testing only broad category questions may underestimate the value of local positioning.
The next stage may involve AI assistants influencing which products enter consideration while retail media determines which offers receive commercial support closer to purchase. Brands will need to understand where organic recommendation ends and paid influence begins.
Transparency will be critical. If sponsored placements become embedded within conversational recommendations, platforms must distinguish advertising from independently generated guidance in a way users can understand.
Smaller brands can move quickly and define a niche clearly, yet they may struggle to generate enough trusted coverage to enter AI shortlists.
Governance should establish:
The challenge is proving that relevance through accessible and independent evidence. A compelling product without clear documentation may remain invisible.
Smaller brands should concentrate on precise category definitions and real customer needs rather than attempting to compete for every broad prompt. Owning a defensible niche is more realistic than trying to become the default answer for an entire global category.
However, users may not know why a particular brand was omitted. The assistant might lack current data, misinterpret the request, rely on weak sources, or favor products with a larger online footprint.
An authoritative tone can conceal these limitations. Consumers should continue checking important claims, particularly prices, availability, safety information, warranties, and contractual terms.
The first objective should be establishing repeatable observation.
The better model is additive: strong technical SEO supports retrieval, clear content supports interpretation, third-party authority supports trust, and AI monitoring reveals how those elements are synthesized.
Marketing teams should be skeptical of anyone claiming that familiar quality signals no longer matter. Many supposed GEO techniques are established information architecture, public relations, product data, and editorial practices adapted to a conversational interface.
The eventual market may favor combinations of technology and advisory services rather than one dominant discipline.
A proprietary index can simplify reporting for executives, but the underlying evidence should remain inspectable. Decision-makers need to know whether a score changed because of genuine market movement, a model update, or ordinary response variation.
Companies will increasingly combine technical attribution with customer surveys and controlled experiments. Asking customers how they first heard about a brand may regain importance in a journey where automated assistants operate before the measurable website session.
The central question will be whether users can distinguish organic synthesis from paid placement. Brands must also determine whether purchasing visibility in one AI environment undermines perceived trust in another.
Windows and enterprise software ecosystems may play an important role here. Copilot-based agents operating within managed business environments could interact with procurement tools, approved catalogs, and corporate data under defined policies.
The brand visibility conversation could therefore evolve rapidly from “Does the assistant mention us?” to “Can the agent verify, select, and safely buy from us?”
Obsessd.ai’s launch signals that AI discoverability is becoming a formal marketing and commerce discipline rather than an experimental side project. Its success will depend on whether it can convert an unstable collection of model outputs into rigorous, transparent, and commercially useful intelligence, but the underlying problem is real: consumers increasingly receive synthesized answers before they see a conventional results page or visit a retailer. Brands that make their products legible, their claims verifiable, their data consistent, and their digital authority credible will be better prepared not only for ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot, but also for the agent-driven purchasing systems likely to follow.
Background
Search engine optimization has shaped online marketing for more than two decades. Brands learned to research keywords, improve page structure, earn backlinks, publish useful content, and measure performance through rankings, impressions, click-through rates, and conversions.Generative AI complicates that familiar model. Instead of presenting ten blue links and asking users to choose, an AI assistant can compare products, summarize reviews, filter options, recommend a shortlist, and explain its reasoning within a single conversational interface.
From search results to synthesized answers
Traditional search generally exposes its competitive landscape. A marketing team can search for a target keyword, examine the results, monitor ranking changes, and estimate which technical or editorial factors influenced performance.Generative platforms are less transparent. Their answers can depend on the wording of the prompt, conversation history, user location, model version, live web retrieval, product integrations, safety rules, and the sources selected for a particular response.
The result is a more fluid discovery environment. A retailer might appear in one answer, disappear when the question is rephrased, and be described inaccurately when the model relies on an old article or an ambiguous third-party listing.
The rise of generative engine optimization
The industry has adopted several overlapping terms for work intended to improve visibility in AI answers. These include generative engine optimization, answer engine optimization, AI search optimization, large language model optimization, and AI discoverability.None has yet achieved the stability or standardization of conventional SEO. However, they generally describe the same strategic objective: making a company, product, or piece of information easier for AI systems to retrieve, understand, trust, cite, and recommend.
Obsessd.ai is entering this emerging field with a particular focus on the Asia-Pacific market. Its proposition is that Australian organizations need more than screenshots from occasional chatbot experiments; they need repeatable measurement, competitive analysis, and operational plans.
What Obsessd.ai Is Offering
The consultancy says its work covers AI discoverability, agentic media strategy, AI buying frameworks, and media effectiveness. These categories extend beyond content optimization and suggest that the founders see generative discovery as part of a wider transformation in advertising and commerce.The initial client list includes personalized gift retailer Personalised Favours and Sydney fragrance brand By Yuliya. Both operate in categories where conversational recommendations could influence discovery, comparison, gifting decisions, and product selection.
AI discoverability audits
An AI discoverability audit examines whether a brand appears when consumers ask category-level or problem-led questions. This distinction matters because asking a chatbot directly about a known company tests recognition, while asking for the best solution to a customer problem tests competitive discovery.A useful audit might include prompts such as:
- “What are reliable Australian retailers for personalized corporate gifts?”
- “Which fragrance brands offer distinctive gifts made in Sydney?”
- “What should I compare when choosing a personalized wedding gift supplier?”
- “Which retailers can deliver customized gifts quickly in Australia?”
- “What are good alternatives to a specific market-leading brand?”
Agentic media strategy
Agentic media refers to a developing market in which software agents participate more actively in planning, purchasing, optimizing, or evaluating advertising. In a more advanced scenario, a consumer’s AI agent could research products, compare terms, confirm availability, and complete a transaction with limited manual intervention.Brands preparing for this environment need structured product information, reliable inventory data, machine-readable policies, clear commercial terms, and systems that can safely expose selected capabilities to automated agents.
Obsessd.ai’s inclusion of agentic strategy indicates that it is not treating AI visibility as a standalone content exercise. The consultancy is positioning discoverability as the first stage of a journey that may eventually lead from an AI recommendation to an agent-mediated purchase.
AI buying frameworks
Marketing teams are being asked to purchase AI services, data products, optimization platforms, and experimental media placements without established procurement standards. An AI buying framework could help organizations assess data access, measurement methods, model coverage, privacy controls, security, transparency, and commercial risk.This may prove particularly relevant to large retailers. Their technology and marketing departments already operate extensive ecosystems involving customer data platforms, retail media networks, advertising technology, loyalty programs, analytics tools, and agency partners.
Adding generative AI without a coherent buying framework could create duplicated technology, unreliable reporting, uncontrolled data sharing, or expensive experiments with little strategic value.
The Obsessd Visibility Index
At the center of the new consultancy’s proposition is the Obsessd Visibility Index, or OVI. The company describes it as a methodology for measuring brand performance across visibility, accuracy, and competitive positioning within AI-generated environments.The index evaluates performance across ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot. That multi-platform coverage is important because AI discovery cannot be represented accurately through one model alone.
Visibility is not a binary metric
A simplistic monitoring tool might mark an answer as a success whenever a brand is mentioned. That fails to distinguish between a leading recommendation, an incidental reference, a negative warning, and a factually incorrect statement.A mature visibility score should consider several dimensions:
- Mention frequency measures how often the brand appears across a controlled prompt set.
- Recommendation prominence identifies whether the brand appears first, within a shortlist, or only as an afterthought.
- Prompt coverage shows which customer needs, categories, and stages of the buying journey produce a mention.
- Citation quality evaluates whether the supporting sources are authoritative, current, and commercially relevant.
- Sentiment and framing reveal whether the brand is described positively, neutrally, negatively, or inconsistently.
- Competitive share compares the company’s presence with rivals across the same prompts and platforms.
Accuracy may matter more than exposure
A brand can be visible but represented incorrectly. An assistant may cite an obsolete price, misstate a delivery region, confuse two similarly named companies, or recommend a discontinued product.Such errors create a new form of reputational and operational risk. The customer may blame the company for an inaccurate claim even when the information originated in a model-generated response.
Accuracy measurement should therefore examine product features, locations, ownership, availability, pricing language, policies, and category classification. For regulated or safety-sensitive sectors, it must also identify unsupported claims that could create legal or compliance exposure.
Competitive positioning reveals the real opportunity
Brand-name prompts can create misleading reassurance. A well-known company will usually appear when a user asks the assistant to describe that company, but this says little about whether it wins new customers.Competitive measurement should concentrate on unbranded discovery prompts. These are the questions people ask before selecting a provider, such as “What is the best option for…” or “Which Australian companies offer…”
Obsessd.ai argues that displacing an established default recommendation can be significantly harder than becoming the first prominent recommendation in an emerging category. Although the consultancy’s underlying data has not been publicly detailed, the strategic logic is credible: repeated references across trusted sources can reinforce an incumbent’s apparent authority.
Why AI Recommendations Are Commercially Important
AI referral traffic remains smaller than conventional search traffic for most websites, but raw click volume does not capture the full influence of conversational discovery. A user can receive a recommendation, remember the brand, and later visit through a direct address, app, marketplace, paid advertisement, or branded Google search.That makes AI both a discovery channel and an attribution problem.
AI can compress the buying journey
A conventional search journey may involve multiple queries, comparison pages, retailer tabs, review sites, and product detail pages. A capable assistant can compress much of that research into one conversation.The customer might ask for five suitable products, eliminate those above a certain price, request an Australian supplier, compare delivery policies, and seek a final recommendation. By the time the user reaches a retailer’s website, substantial evaluation has already occurred.
That traffic may consequently arrive with higher intent. It may also land deeper within the site, especially if the assistant links directly to a product, guide, store locator, policy page, or internal search result.
Recommendations carry implied trust
A high search ranking signals relevance, but users still see a list of alternatives. A conversational answer can feel more like personalized advice, particularly when it explains why a product suits the request.This perceived endorsement gives AI platforms considerable influence. It also raises the stakes when their recommendations are incomplete, biased toward heavily documented brands, or based on outdated information.
Retailers must recognize that the assistant is becoming an intermediary in the customer relationship. The brand may not control the interface, wording, shortlist, or comparison criteria that shape the initial impression.
The zero-click challenge grows
Search engines have long answered some questions directly, reducing the need to visit external sites. Generative answers expand that model by synthesizing information from multiple sources and supporting extended follow-up questions.A brand can benefit from being named without receiving a measurable click. Conversely, a publisher or retailer can supply the information used in an answer while remaining invisible to the user.
This creates tension between visibility and traffic. Marketing teams must decide whether success means a citation, a recommendation, a site visit, an assisted conversion, or some combination of all four.
The Claimed Discoverability Gap
Obsessd.ai says its audits of prominent Australian brands found that an average of 60 percent of AI discovery queries produced no brand recommendation. The finding should be treated as a company-reported benchmark until more detail is available about the sample, prompt design, model settings, repetition rate, and scoring methodology.Even with that caveat, the claim identifies a plausible weakness in current marketing operations. Most organizations have built dashboards for paid search, organic rankings, social engagement, retail media, and website conversion, while AI visibility monitoring remains comparatively immature.
Screenshots are not a measurement system
Marketing teams often begin by entering a few prompts manually and saving interesting answers. This can demonstrate that a problem exists, but it does not establish a reliable baseline.One-off screenshots fail to control for:
- Prompt wording, which can substantially change the type of answer produced.
- Location and personalization, which may influence product availability or local recommendations.
- Model updates, which can alter retrieval and generation behavior without notice.
- Conversation context, which affects how follow-up questions are interpreted.
- Random variation, which means the same prompt may not always produce the same shortlist.
- Search activation, because some responses use fresh web retrieval while others rely more heavily on existing model knowledge.
Discovery prompts must reflect real customer language
A measurement program is only as useful as its prompt set. If the prompts do not resemble the questions customers ask, a sophisticated dashboard can still produce strategically irrelevant results.Brands should build prompt libraries from several sources:
- Analyze internal site search, customer service records, sales calls, and product reviews.
- Identify category questions appearing in conventional search data and community discussions.
- Map prompts to awareness, consideration, comparison, purchase, and post-purchase stages.
- Include constraints such as location, price, delivery speed, compatibility, sustainability, and accessibility.
- Test both brand-aware and unbranded questions to separate recognition from genuine discovery.
- Review the library regularly as customer language and AI interfaces evolve.
How Brands Become Legible to AI Systems
Penn’s distinction between awareness and discoverability captures a central issue. Advertising can make a brand memorable to people, but AI systems need accessible, consistent, and well-supported information before they can confidently include it in an answer.No single optimization technique guarantees recommendation. The information environment around the brand matters as much as the company’s own website.
Clear first-party information
A retailer should provide precise descriptions of what it sells, where it operates, who its products are for, and what differentiates the offer. Important claims should not be buried exclusively inside images, videos, scripts, downloadable brochures, or interactive elements that automated systems may struggle to interpret.Useful pages generally answer specific questions in plain language. Product details, delivery areas, returns policies, dimensions, materials, compatibility information, ingredients, warranties, and contact details should remain current and internally consistent.
Structured data can help search systems interpret entities, products, prices, availability, reviews, organizations, and frequently asked questions. It is not a magic switch for AI visibility, but machine-readable information reduces ambiguity.
Third-party authority
A company cannot establish trust solely by describing itself as trustworthy. AI systems may retrieve or absorb information from news coverage, product reviews, industry directories, comparison sites, marketplaces, community discussions, academic sources, and other independent pages.This gives public relations, reputation management, partnerships, and customer advocacy renewed importance. A distinctive brand with thin third-party coverage may be less visible than a competitor whose products are discussed consistently across credible sources.
The objective should not be to flood the web with repetitive promotional material. Low-quality syndication, fabricated reviews, and undisclosed sponsored content can create reputational risk without producing durable authority.
Consistency across the digital footprint
Conflicting information makes a brand harder to summarize accurately. If the official website, marketplace listings, social profiles, directory entries, and media coverage use different names, descriptions, prices, or service regions, an AI system must resolve those inconsistencies.Organizations should maintain a shared source of truth for core brand facts. Marketing, commerce, communications, customer service, and technology teams all need access to the same approved information.
This work resembles entity management as much as content marketing. The aim is to ensure that machines can distinguish the organization, connect it with the correct products and attributes, and avoid confusing it with similarly named entities.
Not All AI Platforms Behave the Same Way
Treating ChatGPT, Gemini, Claude, Copilot, and Perplexity as interchangeable would produce weak analysis. Each platform combines models, retrieval systems, user context, integrations, and interface choices differently.Even when platforms consult overlapping sources, they may select, rank, summarize, and cite those sources in different ways.
ChatGPT and conversational discovery
ChatGPT has become a major consumer gateway for general-purpose AI assistance. Its broad use makes it an obvious monitoring priority, but brands must distinguish between answers generated from model knowledge and those supported by live web search.A company could therefore perform well for established brand facts but poorly for recent product changes. Conversely, fresh third-party coverage might improve visibility in web-assisted answers before it affects more general model associations.
ChatGPT’s conversational format also encourages iterative filtering. Brands should test not only the opening question but the follow-up sequence through which users narrow their choices.
Gemini and Google’s ecosystem
Gemini benefits from Google’s extensive search, mobile, browser, productivity, and advertising footprint. Its influence cannot be assessed solely through visible referral traffic because recommendations may appear inside broader Google experiences.For retailers, Gemini performance is likely to remain closely connected to product data quality, conventional search accessibility, local information, merchant feeds, and the wider Google entity ecosystem.
That does not mean existing SEO automatically guarantees Gemini visibility. Generative systems can use different selection and presentation logic, producing a shortlist that does not mirror standard web rankings.
Microsoft Copilot and Windows users
Microsoft Copilot has particular relevance to WindowsForum readers because it spans Windows, Edge, Bing, Microsoft 365, and enterprise workflows. The exact feature set varies by product and account type, but Microsoft’s distribution gives Copilot opportunities to influence both consumer research and workplace purchasing.A user researching a product in Edge may encounter AI-assisted summaries or comparisons without deliberately visiting a standalone chatbot. Within an organization, Copilot could help employees analyze suppliers, draft procurement documents, or summarize market options.
This makes Copilot visibility relevant beyond web traffic. A brand may enter an enterprise decision through an AI-generated briefing long before anyone visits its homepage.
Claude and Perplexity
Claude is widely used for analysis, writing, and document-heavy workflows. Although it may generate fewer directly attributable retail visits than larger consumer platforms, it can still influence professional research and recommendation tasks.Perplexity emphasizes answer-oriented search and visible citations. This makes its source selection easier to inspect, although citation does not necessarily mean endorsement and prominence can vary within an answer.
A multi-engine index must preserve these differences rather than collapsing them into one opaque score. A brand that performs strongly on one platform but poorly on another needs diagnosis, not merely an average.
Implications for Australian Retailers
Australian retailers face distinctive challenges involving geography, shipping, local availability, seasonal timing, and competition from international marketplaces. An AI assistant can recommend a globally prominent product that is unsuitable, unavailable, or uneconomical for an Australian buyer.Local brands have an opportunity to win when their regional relevance is explicit and verifiable.
Local context can become a competitive advantage
A retailer should clearly document Australian delivery coverage, dispatch locations, currency, tax treatment, return arrangements, customer support hours, and relevant certifications. These details help an assistant determine whether an otherwise suitable recommendation works for the user’s location.Local media coverage and Australian customer reviews can also strengthen contextual relevance. A brand known within its domestic market may otherwise be overshadowed by an international competitor with a much larger global information footprint.
AI discoverability strategies should therefore include geographically specific prompts. Testing only broad category questions may underestimate the value of local positioning.
Retail media and AI discovery will converge
Australian retail media has grown around first-party shopping data and advertising inventory controlled by major retailers. Richardson and Penn bring experience from Yahoo, Cartology, and Coles360, giving Obsessd.ai a background that connects AI discovery with media buying and commerce.The next stage may involve AI assistants influencing which products enter consideration while retail media determines which offers receive commercial support closer to purchase. Brands will need to understand where organic recommendation ends and paid influence begins.
Transparency will be critical. If sponsored placements become embedded within conversational recommendations, platforms must distinguish advertising from independently generated guidance in a way users can understand.
Enterprise and Consumer Impact
AI discoverability affects large enterprises and smaller consumer brands differently. Enterprises have more data, authority, and technical resources, but they also face greater organizational complexity.Smaller brands can move quickly and define a niche clearly, yet they may struggle to generate enough trusted coverage to enter AI shortlists.
Enterprise governance requirements
A large organization should not treat AI visibility as the sole responsibility of the SEO team. The relevant information may be controlled by commerce, product, legal, corporate affairs, data, engineering, customer service, and regional business units.Governance should establish:
- Who owns the approved description of the brand and its products.
- Who monitors inaccurate AI-generated claims and determines the response.
- Which teams can modify structured data, feeds, policy pages, and public documentation.
- How model outputs are archived for auditing and trend analysis.
- How AI-driven discovery is incorporated into attribution and media planning.
- Which claims require legal, regulatory, or technical validation before publication.
Opportunities for smaller brands
Generative discovery can offer smaller companies a path around expensive paid media when they satisfy a narrow request particularly well. A specialist retailer may be more useful than a national chain for a prompt involving a specific material, location, price range, or customization requirement.The challenge is proving that relevance through accessible and independent evidence. A compelling product without clear documentation may remain invisible.
Smaller brands should concentrate on precise category definitions and real customer needs rather than attempting to compete for every broad prompt. Owning a defensible niche is more realistic than trying to become the default answer for an entire global category.
Consumer benefits and limitations
Consumers can benefit from faster comparisons, plain-language explanations, and recommendations tailored to detailed constraints. AI can make complex markets easier to navigate, especially when product specifications are difficult to compare manually.However, users may not know why a particular brand was omitted. The assistant might lack current data, misinterpret the request, rely on weak sources, or favor products with a larger online footprint.
An authoritative tone can conceal these limitations. Consumers should continue checking important claims, particularly prices, availability, safety information, warranties, and contractual terms.
Strengths and Opportunities
Obsessd.ai is launching at a time when marketers recognize the AI discovery problem but often lack mature processes for addressing it. That timing creates several opportunities.- The consultancy has a focused proposition. It is addressing a clearly defined gap between traditional marketing analytics and the opaque world of AI recommendations.
- Its founders bring retail media and data experience. That background may help connect visibility analysis with commercial outcomes rather than treating it as an isolated content exercise.
- The OVI framework could establish useful baselines. Consistent measurement across multiple engines would give marketing teams a more credible starting point than manual screenshots.
- Australian market specialization may differentiate the business. Local retailers need geographic, regulatory, cultural, and commercial context that global software platforms may not provide.
- Early adopters may shape category associations. Brands that become well documented before competitors could gain an advantage as AI-assisted discovery grows.
- Accuracy monitoring creates value beyond marketing. Identifying outdated or incorrect claims can support reputation management, customer service, compliance, and product operations.
- Agentic commerce creates a larger strategic horizon. Preparing data and systems for machine-mediated purchasing could become more valuable than optimizing answer visibility alone.
Risks and Concerns
The emerging AI visibility industry also faces methodological, ethical, and commercial risks. The field could become crowded with confident claims that exceed the available evidence.- Model outputs are inherently variable. A score can imply more precision than the underlying systems support unless testing includes repetition, uncertainty ranges, and strict controls.
- Platforms change without notice. A strategy that appears effective today may lose impact after a retrieval update, model replacement, interface redesign, or policy change.
- Correlation does not prove causation. A brand’s visibility may improve after new content or publicity, but it can be difficult to prove which intervention caused the change.
- Optimization can slide into manipulation. Brands may be tempted to manufacture reviews, seed undisclosed promotional content, or overwhelm the web with low-quality material.
- Measurement may overlook invisible influence. Referral traffic captures only users who click directly, while many AI-assisted journeys continue through branded search, marketplaces, or physical stores.
- A composite index can conceal platform weaknesses. Strong performance on one large engine should not erase serious accuracy problems on another.
- Consultancy claims require transparent methodology. The reported 60 percent recommendation gap will be more useful when accompanied by sample size, category coverage, prompt definitions, and testing conditions.
- Legal and regulatory expectations remain unsettled. Copyright disputes, disclosure requirements, privacy rules, and advertising standards could reshape how AI recommendations and sponsored content operate.
Building a Practical AI Visibility Program
Organizations do not need to abandon SEO or rebuild their marketing operations from scratch. They need to extend existing search, content, data, analytics, and reputation capabilities into a new layer.The first objective should be establishing repeatable observation.
A sensible implementation sequence
A practical program can proceed through the following stages:- Define commercial questions. Identify the categories, customer problems, products, and markets where an AI recommendation could influence revenue.
- Create a representative prompt library. Use real customer language and include discovery, comparison, purchase, and support scenarios.
- Establish a multi-platform baseline. Record mentions, recommendations, sources, factual accuracy, prominence, and competitor appearances.
- Audit the information ecosystem. Examine official pages, structured data, feeds, directories, reviews, media coverage, and marketplace listings.
- Correct factual weaknesses first. Resolve conflicting names, outdated policies, inaccessible product information, and ambiguous category descriptions.
- Strengthen independent authority. Pursue legitimate reviews, expert coverage, partnerships, and useful contributions to relevant communities.
- Measure business outcomes. Track AI referrals, branded search changes, assisted conversions, customer survey responses, and sales attribution where possible.
- Repeat testing over time. Monitor trends rather than interpreting individual outputs as definitive results.
Keep SEO fundamentals intact
AI discovery does not eliminate conventional search. Search indexes, accessible pages, descriptive titles, internal linking, performance, authority, and structured product information still influence whether content can be found and understood.The better model is additive: strong technical SEO supports retrieval, clear content supports interpretation, third-party authority supports trust, and AI monitoring reveals how those elements are synthesized.
Marketing teams should be skeptical of anyone claiming that familiar quality signals no longer matter. Many supposed GEO techniques are established information architecture, public relations, product data, and editorial practices adapted to a conversational interface.
What to Watch Next
Obsessd.ai’s launch is part of a larger contest to define who owns AI visibility inside organizations. SEO agencies, public relations firms, media consultancies, analytics vendors, advertising groups, and specialist software companies all have plausible claims to the work.The eventual market may favor combinations of technology and advisory services rather than one dominant discipline.
Methodological transparency
The quality of AI visibility services will depend on how clearly providers explain their testing. Buyers should look for prompt governance, repetition policies, geographic controls, model identification, citation capture, confidence ranges, and historical comparisons.A proprietary index can simplify reporting for executives, but the underlying evidence should remain inspectable. Decision-makers need to know whether a score changed because of genuine market movement, a model update, or ordinary response variation.
Better analytics and attribution
Analytics platforms are gradually improving their handling of AI referrals, but direct click data will remain incomplete. Mobile applications, privacy controls, copied links, and subsequent branded searches can obscure the original source of discovery.Companies will increasingly combine technical attribution with customer surveys and controlled experiments. Asking customers how they first heard about a brand may regain importance in a journey where automated assistants operate before the measurable website session.
Commercial recommendations and disclosure
AI platforms are likely to experiment with advertising, shopping integrations, affiliate relationships, sponsored recommendations, and transaction services. This could produce a new media category with substantial commercial value.The central question will be whether users can distinguish organic synthesis from paid placement. Brands must also determine whether purchasing visibility in one AI environment undermines perceived trust in another.
Agent-ready product infrastructure
As assistants move from recommending products to performing tasks, retailers will need more dependable machine interfaces. Accurate inventory, delivery estimates, product identifiers, permissions, pricing, and transaction safeguards will become foundational.Windows and enterprise software ecosystems may play an important role here. Copilot-based agents operating within managed business environments could interact with procurement tools, approved catalogs, and corporate data under defined policies.
The brand visibility conversation could therefore evolve rapidly from “Does the assistant mention us?” to “Can the agent verify, select, and safely buy from us?”
Obsessd.ai’s launch signals that AI discoverability is becoming a formal marketing and commerce discipline rather than an experimental side project. Its success will depend on whether it can convert an unstable collection of model outputs into rigorous, transparent, and commercially useful intelligence, but the underlying problem is real: consumers increasingly receive synthesized answers before they see a conventional results page or visit a retailer. Brands that make their products legible, their claims verifiable, their data consistent, and their digital authority credible will be better prepared not only for ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot, but also for the agent-driven purchasing systems likely to follow.
References
- Primary source: retailbiz
Published: 2026-07-21T04:28:30+00:00
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