Lately, LinkedIn has been flooded with AI influencers hyping the “game changing” capabilities of deep research tools from OpenAI, Google, Perplexity and xAI. But hands-on testing, including a typical marketing research assignment (a Nike competitive analysis), has revealed a far more nuanced reality. Are they valuable? Absolutely. A silver bullet? Not quite.
The Speed-to-Insight Advantage
Let’s give credit where it is due. These tools can identify and process massive amounts of information in minutes, summarizing company backgrounds, financials, market trends and competitive insights. In the Nike analysis, the tools pulled anywhere from five to 35 pages of data, turning what would normally take hours or days into a nearly instant deliverable.
Research isn’t just nice to have – it’s foundational to a data-driven insights approach essential for marketers. McKinsey Global Institute recently reported what we already know: Brands driven by data are 23 times more likely to acquire customers and 19 times more profitable overall. For marketers managing deadlines and time constraints, these deep research tools deliver data at the speed we crave (sometimes, however, at the expense of accuracy).
When AI Research Tools Miss the Mark
Here’s the catch: These tools aren’t flawless. The Nike analysis revealed blind spots across every platform. Who’s Nike’s CEO? OpenAI and Perplexity correctly identified Elliott Hill (appointed October 2024), while Gemini still listed John Donahoe and Grok waffled between answers. On breaking news, only OpenAI caught the recent SKIMS partnership. Meanwhile, Gemini delivered Nike’s AI implementation analysis that no other platform mentioned.
Even more troubling: When reporting Nike’s financials, OpenAI and Gemini correctly cited $51.4 billion in revenue, while Perplexity was off by billions and Grok missed 2024 data entirely. And there were no warning flags for any of this potentially flawed data.
For marketers, overreliance on this deep research output at face value to drive strategy is a major risk. The same tool delivering brilliant insights in one area can completely miss critical developments in another.
A Strategic Approach: Human + AI Partnership
At Luckie, we’ve found that the answer to improving research efforts with tools like this involves crafting the right mindset and partnership with AI. Here’s a high-level framework we use to turn surface-level data dumps into campaign-defining intelligence:
- Establish AI as Your Research Accelerator: Start with AI for comprehensive data collection, remembering that this is the starting point, not the conclusion. Our current preference is OpenAI’s deep research tool, which provides far more depth of analysis and information over the other platforms. However, we frequently run the same prompt in Gemini or Perplexity to augment and compare output.
- Verify Critical Information: As Ronald Reagan once said, “Trust, but verify.” In the Nike example, cross-checking key claims against reports and other reliable sources resolved contradictions within minutes. The good news? These tools all outline their sources so you’re not guessing where to look. This preserves the time-saving advantage from step 1.
- Apply Marketing-Specific Context: AI can assemble facts, but it doesn’t (yet) truly think like a marketer. That’s where we come in to turn those facts into a competitive strategy. While OpenAI did connect the SKIMS partnership to Nike’s women’s market expansion, translating that insight into your brand’s competitive response or market positioning still requires human strategic thinking.
The Bottom Line
Emerging AI capabilities like deep research tools should absolutely be part of your marketing toolkit. Just remember that the tools themselves don’t differentiate you (everyone has access to them). Your strategic integration and utilization of them does. The real competitive advantage isn’t in having these tools. It’s in mastering the AI + human partnership that turns raw information into marketing brilliance.
