How to Expand Legacy Brand Positioning from Traditional SEO into AI Visibility – Constance Tan, Ahrefs

Constance Tan’s path into product marketing was forged through deep operational proximity to user challenges. Joining Ahrefs to support customer success and enterprise expansion, she spent years mastering the intricate mechanics of the platform, learning how to resolve complex use cases for global clients. Recognising her ability to translate technical system capabilities into accessible educational material, her leadership transitioned her into product marketing. Today, Constance leads education and positioning initiatives for Ahrefs, a foundational data provider that has expanded from indexing backlinks and keyword search volumes into monitoring real-time brand visibility across traditional search, video networks, community forums, and generative artificial intelligence platforms.

How do you shift a legacy brand perception from traditional backlinks to AI visibility?

Constance outlines the challenge of updating a platform’s public narrative when legacy industry content dominates search results.

“For years, authoritative websites, forums, and industry professionals talked about Ahrefs primarily as a backlink tool, which was our main market differentiator. However, that established reputation created a challenge when we expanded into tracking AI visibility. Because there are years of legacy content positioning us solely around traditional search and backlinks, we have to actively shift that narrative.

To show that we provide tools relevant to modern marketers who care about AI, we must consistently push that message out. It is a challenge to do that cohesively across PR, community engagement, and conversations with industry experts, ensuring people understand we cover AI visibility, not just traditional search.”

Why should brand strategy take priority over short-term demand capture?

Constance explains why distinct problem association prevents active market demand from turning into market noise.

“If I had to choose, brand is still the main element because you need to stand out and be the first name that comes to mind when a specific problem arises. Your positioning needs to be consistent, up to date, and directly relevant to the problems customers face today.

While demand is undoubtedly important because you need an active market, if you are not the primary brand associated with solving a critical problem, that demand simply turns into market noise.”

How can marketers navigate the loss of clear click attribution in AI search?

Addressing fuzzy buyer journeys, Constance shares how unscalable customer touchpoints provide essential market clarity.

“In this space, a major challenge is determining whether your efforts are actually working. With the rise of AI platforms, attribution has become much more fuzzy. Traditional metrics like clicks, while sometimes considered vanity metrics, are still reliable indicators that people visited your website with clear intent. With AI platforms, the customer journey is less transparent. You cannot easily see where users are bouncing back and forth or what specific queries they are running. Being unable to get clear data makes it difficult for marketers to report on project progress to leadership or secure support for new initiatives.

One effective approach is getting into the weeds with unscalable touchpoints, such as speaking directly to customers and inviting active platform users to share their specific challenges. Personally, I have started engaging more with local communities and professional networks like Women in Tech. These spaces host active conversations that do not happen in public search forums or traditional search engines, serving as a reliable proxy and sounding board for how your brand is actually perceived.”

Where lies the greatest enterprise growth opportunity in the application layer?

Constance discusses why building niche AI workflows creates higher value than relying solely on foundational models.

“The greatest value will come from the enterprises that consistently solve problems using AI. While foundational models like Claude and ChatGPT provide the base infrastructure, the real value lies in the application layer built on top.

That means directly building tools that solve niche problems, creating intuitive dashboards, or automating complex workflows. Doing this successfully requires absolute clarity on the exact problem you are solving, which keeps product development focused while keeping the technology controlled.”

Why is full automation a trap, and how can structured AI workflows succeed?

Comparing successful copywriting frameworks with brittle end-to-end automation attempts, Constance highlights human-in-the-loop iteration.

“One experiment that worked exceptionally well involves our copywriting workflow. By establishing clear resource documentation and studying best practices from our content team, we used AI to assist in outlining and drafting content that people actually want to read. Rather than skipping fundamental steps by issuing a simple prompt like ‘write a blog post,’ we use AI to support each stage of the planning and drafting process. That structured approach produces high-quality material that we can easily adapt for social media or email newsletters.

On the other hand, fully automating complex, end-to-end workflows has been hit-and-miss. Requirements in marketing change constantly, whether you need to adapt a process for a new experiment, tailor content for a different market, or collaborate across departments. When you try to build a fully automated system, any change in requirements means you have to break the system down and rebuild it. The idea of sitting back while AI handles everything automatically is not realistic because processes always require human intervention, adjustments, and debugging. However, using these tools thoughtfully reduces friction when taking on ambitious projects.”

Why must global marketing leaders audit what AI models are saying about their brand?

In a final thought, Constance shares guidance on maintaining narrative consistency across global data sources.

“Roles are shifting away from being tied to a single, isolated channel or repetitive execution task. AI enables individual marketers to be more capable across the entire workflow. Instead of managing just one small part of the chain, a marketer might take end-to-end ownership of a product, a sub-brand, or an entire market segment. There is a growing expectation for marketers to work on their job rather than just in their job, constantly assessing whether their daily output builds overall brand equity and delivers tangible business outcomes.

Consistency matters immensely. You need total consistency in how your brand is positioned, down to details like where you are present, the products you offer, your pricing structure, and the specific customer problems you solve. You must audit whether external platforms and AI models are pulling from sources with inaccurate or outdated information. If you launch a rebranding campaign or update your core messaging, you have to ensure that those updates reach the specific data sources that AI platforms currently query. Identifying and closing those information gaps is essential for maintaining brand equity.”

Join the Conversation: The Ortus Club’s Executive Network

Across Constance’s insights on shifting legacy brand perceptions, navigating fuzzy AI search attribution, and building structured application-layer workflows, one pattern is clear: these modern growth hurdles cannot be resolved within isolated marketing silos. They require a peer-level perspective and high-trust dialogue that transcends traditional SEO playbooks.

Her vision of the “End-to-End Product Marketer” reflects a broader operational reality: today’s marketing leaders cannot rely on linear click tracking alone to validate their market impact. The most effective executives, especially those managing data, software, and search visibility platforms across global markets, actively seek out peer dialogue as a strategic necessity to benchmark their brand positioning and AI data auditing models against real-world conditions.

At The Ortus Club, we host curated executive roundtables that bring together senior leaders facing these exact challenges. Step away from the daily noise of deteriorating attribution channels and engage in the kind of open, high-value conversations that align your product strategy with true business value.

Frequently Asked Questions

Q: How does tracking brand visibility across AI platforms differ from traditional SEO?

A: Traditional SEO focuses on monitoring keyword rankings, backlinks, and direct website click-throughs from search engine result pages. Tracking AI visibility involves auditing how conversational models synthesise brand information, verifying whether a company is recommended as a solution in unstructured query summaries, and monitoring the specific citation sources LLMs draw from.

Q: What is the “application layer” in AI product development?

A: The application layer refers to specialised software tools, user interfaces, and automated workflows built on top of foundational AI models (such as GPT-4 or Claude). Rather than creating raw AI models, companies operating in the application layer focus on solving niche, industry-specific user problems using proprietary data.

Q: Why is click attribution deteriorating in modern digital marketing?

A: Because buyers increasingly rely on conversational AI search overviews, closed community channels, social media discussions, and dark social recommendations to gather information. These touchpoints answer buyer queries directly without forcing a click to an external website, making traditional web analytics less effective at tracking intent.

Q: How do unscalable touchpoints help marketers evaluate brand perception?

A: Unscalable touchpoints, such as direct customer interviews, focus groups, and participation in niche professional networks, provide qualitative insights into how buyers perceive a brand. These direct interactions reveal underlying pain points and sentiment that cannot be captured by automated analytics tools.

Q: How can global brands ensure AI platforms serve accurate product information?

A: By maintaining consistent narrative messaging across all digital touchpoints and auditing third-party review sites, press releases, and industry databases where AI crawlers scrape training data. Closing information gaps across external data sources prevents LLMs from serving outdated pricing, feature lists, or brand positioning.