AI Briefing: How Perplexity plans to win over enterprise and regular users with AI search

By improving how companies search documents — and everything else on the internet — companies are using generative AI to conduct market research, find sales prospects, analyze sales, and draft product road maps. Advertising agencies and media companies already use search to research campaigns, companies and people. 

There are a few reasons Perplexity is approaching enterprise as a part of its overall user strategy. In an interview with Digiday, chief business officer Dmitry Shevelenko said it’s not only a good revenue generator it’s also a growth channel by letting people use Perplexity at work while finding value outside of work. However, Perplexity prioritizes not being bogged down by ad-driven SEO.  

“Google’s business model is not getting you answers, it’s getting you to click on as many links as possible,” Shevelenko said. “And the reason they’re able to offer it for free is because they monetize your data and your attention.”

That doesn’t mean Perplexity is avoiding ads altogether. While there’s no set timeline in place, Shevelenko said it likely won’t be for “at least one or two quarters” at the earliest. And when it does add them, he said ads won’t appear above answers and won’t allow advertisers to skew search results by paying for placements. Instead, he said, they might appear in the “related questions,” adding that 40% of all queries have a follow-up.

Shevelenko said follow-up questions could be a “rich surface area for brands to get to point a consumer in their direction,” adding that sometimes the most important thing for brands and users is asking the right question.

“Let’s say you’re researching a product, you get an answer about it,” Shevelenko said. “Then in the follow up questions, obviously be clear that it’s sponsored — but there will be a question about specific brands’ products and its features. And that the answer would be the exact same answer as if you would just organically popped in that question.”

Ad measurement might also look different for Perplexity. Shevelenko said he doesn’t think the company’s early ad products will be focused on traditional metrics like performance value or demand-generation. Instead, they might be focused on driving future rather than immediate actions.

“Asking somebody a question isn’t necessarily driving an immediate action,” he said. “It’s about driving consideration. And so ironically, whenever our ads businesses will first [begin], it won’t be directly competitive with [Google] AdWords because we’re not trying to drive conversions to a specific action.”

As anyone playing in the generative AI space has learned by now, getting consistently accurate answers from large language models is a serious challenge, especially with larger document sets and longer documents. And just like with traditional search, knowing which links and documents are more trustworthy and more valuable than others is a tricky task.

“The reason it’s a non-trivial challenge is it’s very hard to rank the signals on internal data versus web data,” he said. “There are many signals. Like how often a site is visited, how often it gets refreshed, how others index it. When we rank the sources that respond to a query, there are a lot of useful signals. That’s what leads to good answer quality.”

Developing enterprise search using LLMs is a lot harder than just plugging in another platform. Kian Katanforoosh, cofounder of Workera, which helps upskill companies with AI abilities, said companies need three pillars: strategy, technology and an AI-trained workforce. However, he said the third leg of that stool is often where risky failures happen.

“Unknown unknowns are a huge problem,” Katanforoosh said. “People thinking they know but they don’t know, or people thinking that they don’t know a lot.”

Perplexity is just one of several generative AI search companies going after the enterprise market. Last week, Snowflake announced a family of AI models for enterprise customers, while Cohere also announced new features for its platforms. Snowflake’s are based on tech built by Neeva, which it acquired a year ago after Neeva sought to also compete with Google by making a better search product.

SEO experts say competing in search requires having a data moat that most companies don’t have, other than Google, Microsoft and Apple. However, some say Perplexity could succeed by staying focused on enterprise first.

Ethan Smith, co-founder of the SEO agency Graphite, said Perplexity could win by making a veritable application to enterprise research — kind of like ResearchGate or Google Scholar. Another way could be to partner with a company that has a lot of proprietary data — like Google, Apple or Microsoft.

“Everyone is going to have access to the same algorithms,” Smith said. “What will matter is the training data, and the training data would be outcomes. So whoever has more outcome data and a better feedback loop, they’ll win regardless of what the algorithm is.”

Prompts and products — AI news and other announcements 

• Earnings from social and search players signal that AI will be a long-play investment.
• Adobe released its new Firefly 3 image model to power AI tools for Adobe and other platforms.
• Coca-Cola debuted a new app called “Coke Sounds” to make AI-generated sounds.
• Microsoft and Coca-Cola announced a new five-year $1.1 billion deal focused on generative AI and cloud computing.
• Apple reportedly acquired the French AI startup Datakalab to help with on-device processing.
• Hugging Face now has a journalism & AI community for learning and discussing AI and media.
• New research from Reuters/Oxford looks at news and chatbot headlines.
• Thousands of explicit “AI Girlfriend” ads are showing up all over Facebook and Instagram, according to a Wired investigation.
• The UK is increasing its scrutiny of AI startups, according to The Guardian.
• How the BBC sees AI being used in media, Fortune reports.
• Five AI lessons from the International Journalism Festival. 

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