Milena Traikovich has built a career at the intersection of data-driven performance and creative lead generation. As a demand generation specialist, she has witnessed a significant evolution in how buyers find experts, shifting away from the noise of the social newsfeed toward the precision of artificial intelligence. Today, she shares her insights on why the era of chasing “virality” is being replaced by the era of “being cited,” and how professionals can optimize their presence for the algorithms that now control the buyer’s journey. In this conversation, we explore the transition from feed-based scrolling to AI-based querying, the specific metrics that large language models prioritize, and the strategic structural changes needed for a profile to stay relevant in a search-centric ecosystem.
With the rise of AI-driven search, we are seeing a shift where massive viral engagement isn’t necessarily the ticket to being discovered. How does an expert’s content reach a wider audience today when traditional metrics like likes and shares seem to matter less to these systems?
The landscape has fundamentally shifted because AI search engines don’t care about the dopamine hit of a viral post; they care about the utility and depth of the information provided. When we look at the data from 89,000 cited URLs, we find that the median post being recommended by AI actually has only 15 to 25 reactions and maybe one single comment at most. This is a game-changer for specialists because it means you don’t need a massive, loud campaign or a stroke of luck to be seen as an authority by tools like ChatGPT or Perplexity. These machines are looking for clean, direct answers that solve a user’s problem, which allows a well-written post to be cited months after the LinkedIn feed has forgotten it. It’s an empowering shift for the quiet expert who provides real value rather than just “reaction bait,” moving the focus from temporary dwell time to long-term authority.
LinkedIn has become a critical repository for these AI engines to pull information from. What does the data tell us about how platforms like ChatGPT Search prioritize LinkedIn content compared to other sources?
LinkedIn is currently the powerhouse of professional citations, appearing in about 11% of responses across the major AI search tools. If you look specifically at ChatGPT Search, that number jumps to 14.3%, making it the highest-ranking source for professional queries. This happens because the AI is performing a credibility check that humans used to do manually by scrolling through endless feeds. Instead of a buyer asking their immediate network for a recommendation, they are asking an AI who to hire or how to solve a specific problem, and the system is scouring 325,000 prompts to find the most reliable names. It’s no longer about who you know in your immediate circle, but about being the “original knowledge source” that the AI can confidently point to when a stranger is in the middle of a high-stakes decision.
There’s a clear distinction between a quick status update and a deep-dive article. Could you explain the structural requirements for content that actually gets indexed and recommended by AI?
The data is very specific about the format: long-form articles are doing the heaviest lifting, accounting for 50% to 66% of all LinkedIn citations. The “sweet spot” for these pieces is between 500 and 2,000 words, which is long enough to demonstrate deep expertise but concise enough for a machine to parse the primary intent. Interestingly, between 54% and 64% of the posts that get cited are focused purely on sharing practical knowledge or advice rather than personal anecdotes. While a 50-word “thought of the day” might get some quick likes from your friends, it’s the substantive 1,000-word piece that builds a permanent bridge to new customers through AI. You want to write with the machine’s ability to read intent in mind, ensuring your expertise is unmissable and easy to categorize.
Many professionals spend time resharing industry news or commenting on others’ posts to stay active. Why is this strategy potentially failing them in the eyes of AI search engines?
It ultimately comes down to who owns the “idea” in the eyes of the algorithm. Analysis shows that 95% of cited posts are original content, while reshares only make up a tiny 5% of total citations. While commenting and resharing are excellent for building social relationships with your peers, they do almost nothing to build your authority with an AI search engine. The AI quotes the person who actually wrote the insight, not the person who hit “repost” with a few sentences of commentary attached. Furthermore, individual members are much more likely to be cited than company pages, taking 59% of citations on ChatGPT Search and Google AI Mode. It is personal branding backed by original, documented data that wins this race, not just being a curator of other people’s thoughts.
If a professional wants to ensure their profile acts as a “credibility check” for these citations, what specific elements should they be optimizing right now?
Your profile needs to be as legible to a machine as it is to a human. This means your headline needs to use the exact keywords a buyer would use when searching for your expertise—vague titles like “Visionary Leader” don’t help an AI label you. You also need an “About” section that includes hard numbers to validate your claims, as these systems index those data points to establish your level of experience. I always recommend putting your two strongest articles in the “Featured” section so the proof of your knowledge is immediately accessible to the crawler. If you have 2,000 or more followers and maintain a steady rhythm—posting at least five times every four weeks—you hit the profile persona that AI tools are most likely to trust and quote repeatedly.
What is your forecast for the future of professional discovery?
I expect the “citations game” to completely overshadow the “reactions game” over the next few years. We are moving toward a world where your LinkedIn profile isn’t just a digital resume, but a verified data node in a global knowledge graph used by AI to solve problems. As AI search becomes the primary way people solve professional challenges, the value of a single cited article will be worth significantly more than a thousand viral posts that disappear in 24 hours. Professionals who focus on original, long-form knowledge today will find themselves being recommended by name in the answers their next big customers are reading. The ultimate prize is no longer a “like”—it’s being the definitive answer provided by the AI when the world asks a question.
