The way we use AI in research and the freedom that we give it to operate outside of human input matters, and it matters far more than so many marketers realize. As AI research platforms grow in scale and quantity, so does the technology’s scope. Before, AI in research was limited to machine learning and natural language processing, and its purpose was to track data, output algorithms or models. Deep research AI, on the other hand, delves far beneath the digital surface, handles in-depth, multi-step processes, and generates detailed reports. These semantic differences matter greatly as we zoom out and look at the broader picture of artificial intelligence.
If your marketing department is beginning or increasing its AI research use to gain a competitive edge, you need to understand just how far this technology can take you, as well as where it can hold you back. No matter why you’re here today, this is a topic you desperately need to be aware of and take steps to mitigate. We’re glad you want to take a closer look at AI research capabilities, and we’re hopeful for the flexibility and deeper insights this technology will bring to the marketing arena. Join us as we peel back the layers of AI ethics, biases, and limitations to determine how and when artificial innovations can best be put to use.
What Is Deep Research AI?
Deep research AI refers to a new type of AI tool that performs in-depth, multi-step research on the internet, synthesizing information from numerous sources and generating detailed reports. It’s crucial to note that deep research AI doesn’t just aggregate information; it actively searches, scans, and analyzes multiple online sources, then synthesizes its findings. This technology goes beyond traditional AI problem-solving by engaging in inductive reasoning that mimics human critical thinking. It can iteratively refine its process as it “learns,” paving the way for more comprehensive, well-cited reports that can occasionally rival those produced by human experts.
AI Limits (And How To Spot Them Before They Cause Research Bias)
At this point, you’re likely wondering why we’re even wasting time discussing AI in research; with undeniably powerful outcomes that can be comparable to human analysis, so what’s the downfall? As any new innovation develops, it’s crucial to note its limitations and challenges, and AI research is no different.
For starters, deep learning AI models are prone to limitations and biases that can easily spur inaccurate or unfair results. These limitations stem from the data used to train the models, the design of the models themselves, and how they are applied in real-world scenarios. Deep research AI limits are also highly noticeable when examining source datasets. Because AI models are only as intelligent as the data that trains them, the chance of measurement, confirmation, and selection biases is high.
Other AI limits to consider when implementing AI in research include:
Ignoring these inherent deep AI research flaws is akin to looking the other way as customers steal your merchandise, but unfortunately, some of today’s most popular machine learning platforms have done just that. We’re sure you remember the ChatGPT scandal, when this AI tool faced scrutiny for generating inaccurate (and at times, dangerous) information, and that wasn’t an isolated incident.
Other AI research platforms have faced similar difficulties, in large part because their benefits were touted openly while their need for human input was brushed aside. The only way to combat AI hallucinations, overfitting, operator-dependent input, and lack of critical thinking is to marry human perspectives and insights with machine-based research tools.
The (human)x Side of AI in Research
Nearly two years ago, the (human)x staff took a hard look at the relationship between AI and humans. We surveyed our agency’s multiple internal departments to glean their insights into AI ethics, and we spent months testing the technology ourselves to identify any research bias these new tools might possess.
Collectively, we came to one conclusion: AI in research can serve as a valuable support to an internal marketing team, but it should never replace the level of critical thinking that only humans can provide. We believe that the most powerful research outcomes come from collaboration between AI and humans. Let AI handle the grunt work like data synthesis, aggregation, and keyword flagging while creatives lead with interpretation, strategy, and empathy.
Unfortunately, not everyone agrees with this assessment. As popular deep research AI platforms like Google’s Gemini Deep Research and OpenAI’s Deep Research within ChatGPT gain traction, some businesses have decided that replacing human creativity with AI input can keep their systems functioning at optimum levels. Instead of probing AI in research to uncover its flaws, they’re asking questions of their employees like “Why shouldn’t we replace you with AI?” and “Why are humans still necessary?” To those businesses, we’d like to present these facts:
Deepen Your AI Research Capabilities With (human)x
AI in research will continue to evolve, and so will all of our marketing tactics, internal processes, and perspectives. This technology makes a fantastic addition to any creative team, one that can suggest new creative pathways, churn out data analysis, and automate tedious tasks, but still needs a human touch. Our hope is that, as new information about AI limits and ethics is revealed, a balance can be struck between machine learning models and human critical thinking. The way we see things at (human)x, the future of AI in research includes a “human plus machine” equation, not an either/or scenario. If you feel similarly but need guidance to push your AI research capabilities forward, (human)x can help.
