Being able to gather and analyze information is one of the most important skills in government, national security, public health, finance, and the corporate sector. The abundance of data available at all times actually makes this process more difficult than when information itself was scarce.
The introduction of AI has brought about another layer to this problem. AI is already better at analyzing data points than most human researchers, but it also has many faults. Some of these are about technology, and others are about constructing narratives from data.
In this article, we’ll talk about the benefits of using AI for research and how it compares to traditional research done by humans. It’s a process with benefits, but also with downsides that researchers should be aware of.
What we mean by “AI Tools” vs “Traditional Research”
Before getting into the pros and cons of AI research, let us define the terms we’ll be operating with.
AI research tools refers to using machine learning and natural language processing to scan massive databases, summarize long documents, and spot trends and detect anomalies. AI can also be used to translate multiple languages quickly and to provide predictive insights based on available information. All of this can be done at a speed much faster than any human could catch up with.
Traditional research, on the other hand, refers to research conducted by actual people. It’s done by reading deeply, conducting in-person interviews, analyzing data context, verifying sources, and applying expert judgment. It’s a slower process, but richer in nuance and complexities.
Understanding the key differences between the two will help you choose the approach that is better suited to the task. For instance, both types of research can help you choose the best cryptocurrencies to purchase or which employees to cover, and combining the two often provides the best results.
What You Gain With AI Tools
Speed and Efficiency at an Unmatched Scale
AI systems provide speed and efficiency in handling the large data sets that users couldn’t get with any other research tool. While a human can read about 200 pages a day, at their best, AI can analyze hundreds of reports of that length in the same time.
This is especially important for the research in which speed is of the essence, such as public health and prediction markets. According to experts such as those at CryptoManiaks, AI can be used to predict changes in the crypto market as soon as new data becomes available to model.
Even for businesses and industries where speed isn’t so important, AI can cut down on man-hours like no other tool, thereby lowering the costs of doing business and making analysis more cost-effective. This is essential in competitive fields.
Handling Diverse, Multilingual, and Complex Data
Real-time data feeds, data in multiple languages, and unstructured text are standard for analyzing social media and public attitudes on certain topics. All of these are very difficult for human researchers to handle, while AI excels at them.
When trying to understand public attitudes towards a topic, there are plenty of data points to use, but making sense of them can be difficult. This is the case with political attitudes and market sentiment, which often translate into real value and profit. It’s especially so in industries that are truly global and multicultural, and that’s becoming the case with most industries in recent years.
Advanced Pattern Detection and Predictive Insights
AI can detect correlations, anomalies, sentiment shifts, and emerging trends faster and with greater reliability than human researchers. These skills are most useful in investing and marketing because they can inform business decisions.
It can also be used to create early warning systems, detect outbreaks before they happen, and monitor political risk. However, it’s important to note that AI can’t do more than detect patterns; it’s up to human decision-makers to recognize them and respond accordingly. The policy on when to react is still something AI can’t handle, or at least not entirely.
Lower Cost and Higher Scalability
The main advantage from a business standpoint is that using AI for research is less expensive than traditional research and easier to scale as data grows and becomes more complex. This is especially useful for institutions that need to monitor data 24/7, as such efforts are very labor-intensive.
AI is very good at handling repetitive work, allowing institutions to hire people for more complex and nuanced tasks while saving on the least difficult labor. Humans can therefore focus on high-value interpretation, scenario building, and strategic decision-making.
Democratization of Research
The use of AI can also lead to the democratization of research. Small institutions couldn’t access or process data because they lacked the resources. Now that AI tools are widely available and not that expensive, the process is democratized, and almost all actors have access to data.
This levels the playing field and allows even the smallest companies and institutions to use the data to the best of their abilities. It doesn’t, however, guarantee the same outcome for everyone. Managing risk remains an important asset in business, and every actor handles it differently.
What You Lose With AI Alone
Loss of Nuance, Context, and Human Judgment
AI can’t understand the context of the data it’s analyzing. This means it can judge the nuance of its meaning, especially when it comes to cultural context, coded language, sarcasm, historical sensitivities, and political undertones.
All of these require human judgment or at least need to get better results. Experience is also something that AI can’t really bring to the table, even though it does improve its analysis over time.
Risk of Errors, Hallucinations, and Misleading Summaries
Artificial intelligence is far from perfect when it comes to analysis itself. Anyone who’s used it on a regular basis knows that it often hallucinates, meaning it makes up facts in order to provide coherent answers. This can have serious consequences if the AI is used to make financial or public safety decisions.
Hallucinations can include fake statistics, invented quotes, misclassified content, or flawed translations. Over the years, AI has improved in this regard, and it has become more transparent about its potential mistakes, but research shows that AI users haven’t acted on those warnings and aren’t more cautious when engaging with AI.
Lack of Transparency and Explainability
In many ways, AI feels like a black box, since it’s not transparent about how it processes the data it analyzes or how it arrives at its conclusions. This is one of the most common complaints coming from users, but AI hasn’t addressed those issues. It’s especially true for complex data such as market research, financial analysis, and investment predictions.
The inner workings of AI systems are proprietary code owned by the company providing the artificial intelligence tools, and there’s little chance they’ll become more transparent, since the market is very competitive and no company would divulge such information.
Risk of De-Skilling and Over-Dependence
Another long-term risk that’s become obvious in recent years comes from relying on AI too much. Research shows that businesses are becoming overly dependent on AI, and those who use the services regularly lose some of the skills now being automated. Analysts, for instance, may stop validating sources, deep-reading documents, or exercising judgment.
These skills take years to develop and provide value when used alongside AI, and many institutions are making an effort to preserve them, even as some become outdated.
Why a Hybrid Approach Is Peak
Research doesn’t need to be about pitting AI against human research. In fact, the two can work together towards the same goal. A hybrid approach that allows AI to handle data aggregation, text translation, information summarization, and pattern detection, while humans manage interpretation, ethics, strategy, and decision-making, provides the best of both worlds.
In early warning detection systems, AI can scan information flows, but humans determine which signals matter. In public health, AI identifies anomalies in the data, but human doctors put them in context. In corporate strategy, AI identifies patterns, while humans align them with the company’s goals.
This way, the risk of using AI is reduced, while the speed and efficiency it provides are still utilized.
To Sum Up
AI is widely used for data analysis across many industries and public works. It can provide insight into data patterns much faster and at lower cost than traditional research. However, it falls short in providing cultural context and in decision-making.
In this regard, traditional research done by actual human employees is always better. Therefore, the best approach for most industries is to combine AI and traditional research. That way, users can spot mistakes made by AI and retain the skills needed to make decisions, which are often lost when relying too much on AI.