Should human analysts really be afraid of having their rice bowl taken from them?
There’s a long ongoing debate about whether Artificial Intelligence (AI) and Machine Learning (ML) will replace the human brain and surpass its capabilities. Many scientists and psychologists are still not convinced by the idea of a machine being capable of replicating the function of the human mind.
How is AI useful?
There’s hardly any industry that’s been left untouched by AI training. The mode of AI applications may vary, but it is now the backbone of almost everything you use today. Usage of AI is evident among marketers and researchers who use it extensively for outreach and data organization. Other industries like healthcare, tourism, aviation, media, e-commerce, agriculture, job search are all powered by AI in some way to increase task efficiency and reduce human intervention for complex functions.
How AI works?
AI uses Machine Learning (ML) to achieve human brain-like capabilities. It gets fed by a set of a pre-decided chunk of data, and this is the learning process, it learns to respond upon training. It is capable of evolving on its own and thus learns to update its responses based on practical experiences.
The algorithms and historical data are the keys to any AI model, allowing them to perform tasks or make predictions in a way they do. The scope is immensely vast as AI makes it possible to remove the human dependency involved in work; it also frees up time letting the person work on more complex tasks.
Role of AI in business analytics
AI is a disruptive technology which is changing the shape of how people interact. Researchers at Accenture predict that by 2035, AI could double the economic growth of developed countries.
Thanks to cognitive computing, companies can use complex algorithms to break down consumer behaviour and gain business insights, as most of the data, is now in unstructured form because of data sources like smartphones and messaging services.
Also Read: Demystifying artificial intelligence: Breaking down common AI myths
The movement towards data being in unstructured form is evident. In 2017, Google acquired Lattice, a then startup that converts unstructured data to structured form powered by AI.
With the introduction of AI, Business Intelligence (BI) software has evolved from reactive analysis to pro-active analysis-
- Descriptive Analytics– This BI system is self-explanatory; it inputs raw data and breaks it down to human-interpretable form and provides descriptive summaries. It influences companies’ future decisions based on their historical data.
- Predictive Analytics– Enabling companies to predict future outcomes through insights. No system can predict with a hundred per cent accuracy, but such a system helps companies to make proactive decisions, helps them in anticipating results, and make forecasts.
- Prescriptive Analytics- This is one notch above predictive analytics; these systems provide actions and solutions for possible outcomes. They not only predict the result but also state the reason for the result.
Why does BI require AI?
AI-powered BI systems transform businesses with their simple data representations, real-time narratives, and reports.
Here are some points on why AI is needed-
- Interactive Dashboards: Normal dashboards are a mess with data coming from all sources in raw form; AI help BI software to convert data into a digestible human form.
- Manage Big Data Overload: Unstructured form of data is getting accumulated at an unprecedented rate, and AI-powered tools can help professionals get insights from such data.
- Shortage of experts: According to McKinsey, there’s a shortage of professionals in analytics and an acute shortage for experts who could make rational and informed decisions from data.
What is the future for business analysts?
AI is expected to herald an upheaval in the global economic and social landscape. These algorithms can self learn patterns and make decisions based on the information that a human prescribed to it.
The work of business analyst, however, does not just only involve reading data and analyzing data. The analysis needs to be applied to the required context to influence decisions. The final phase of analysing and applying the data still requires a human mind. Here’s an interview from founder of Alibaba Group, Jack Ma on how articulately he describes that machines cannot learn the human character of wisdom.
If someone attempted to create an AI-powered system to replace business analysts, they should probably look into the particular set of skills than business analysts’ need to have. Adaptability to changing environment requires real-life experiences and context understanding, which in the foreseeable future looks challenging to achieve.
Also Read: 3 ways banks, fintechs and FIs can harness AI for success
It means that the asset of the human brain is still relevant, though AI and ML are instrumental in achieving much faster and efficient analysis, algorithms cannot choose individual goals as it requires a certain degree of empathy which AI lacks.
Conclusion
With such advancements in technology and machine learning, process-oriented jobs are on the verge of being automated. However, any work requires contextual decision making, and differentiated goal targets such as business analysts will still be helmed by humans, at least for now.
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