Today, many business intelligence processes are automated, from data mining to analytics. A study has shown that computer synapses can fire up to one billion times per second- several orders of magnitude faster than human neurons, while taking only one ten-thousandth of the amount of energy used by a biological synapse. Artificial Intelligence (AI) speeds up information processing faster than humans ever could, helping companies to take their performance to the whole new level.

It does not mean, however, that AI has completely taken over the business operations. Human input should by no means, be underestimated. On the contrary, these two elements can produce the best results when combined.

The Two-Fold Nature of AI Technology

Although it may seem counterintuitive, AI is still at its infant stage and it is not completely autonomous. Each AI unit is based on numerous algorithms that makes it possible to run multiple operations simultaneously. If algorithms control business intelligence processes, who controls algorithms? Humans do.

Engineers train machines to recognise textual, visual, and audio content as accurately as human brains. They control the process from the beginning until the end, giving the necessary instructions to ensure flawless AI performance.

It is an ongoing mechanism that requires constant attention and adjustments. As market conditions change, engineers have to learn how to instruct the machines to fulfill precisely targeted objectives. Their job is to fine-tune and profile AI to achieve maximum accuracy and to update it over time in order to keep pace with the latest market developments.

Hence, machine learning makes everyday processes much faster and more efficient, while real people work behind the scenes of AI to make sure that it keeps going in the proper direction.

The Synergy between AI and Human Efforts

The synergy between AI and human efforts is essential because machines still require manual intervention. Engineers in all fields of work need to determine the right algorithms, set parameters and give qualitative inputs to AI. This is the case with many industries, but we selected five examples to demonstrate the two-fold nature of AI.

1.     Market Research

The goal of AI-driven market research is to collect huge volumes of data and generate accurate insights out of it. Machines complete most of the work, but engineers set the primary parameters to ensure precise digital ethnography, sentiment analysis and classify positive, neutral, and negative attitudes.

Consumer market insights experts are then required to analyse these data and make statistically proven conclusions to recommend actionable insights and help clients to optimise business strategies.

2.    Medical AI Imaging

Medical AI imaging is not a novelty anymore. The system is widespread, but only a small portion of medical decision-making is completely automated. Namely, AI cannot completely replace diagnosticians or function autonomously because it serves as the data accumulation and image-processing tool. On the other side, it is the clinician who determines the diagnosis.

However, the synergy between human and machine work is not a fiction as it already plays a critical role in the operating room. An MIT-led research team has crafted a machine learning algorithm that can analyse 3D scans so quickly that it helps surgeons in life-saving decisions real-time.

3.    Fintech

Trading specialists have traditionally controlled the stocks exchange market, but now AI performs most of the deals on its own. The procedure is almost completely automated, but humans still have to play the watchdog role to avoid major crashes.

In early 2018, Dow Jones plunged 800 index points within minutes due to the algorithm vulnerabilities. It was one of the bigger incidents that could have jeopardized the entire financial market, but it taught engineers to keep track of AI algorithms and update the system regularly.

4.     Human Resource

Human resource have gone through changes in the last decade, particularly with the emergence of recruitment softwares such as Zoho or BreatheHR. Tools like these improve candidate selection, compensation agreements, onboarding, and retention, but not without the human touch.

Each company is different, so there can be no off-the-shelf solution. HR software requires man-made instructions to craft the profile of an ideal candidate and design the right onboarding strategy. While it filters the most eligible candidates from hundreds of applicants, HR managers make the final decision.

5.     Legal Profession

The legal industry has always been interpreted as the field where everything is crystal clear, but the truth is that its taxonomies, laws, flowcharts, and real-life implications can be perplexing for the AI algorithms. Many law firms use machine learning to scan and analyze thousands of pages within minutes, but the system can only get them so far.

AI needs the real human input to understand how to recognize behavioral patterns and possible outcomes in legal cases. The two forces must cooperate in order to improve the legal industry both qualitatively and quantitatively.


AI is changing the way we plan and do business. It has the power to automate most of the business intelligence processes, but it doesn’t make human influence obsolete. Humans control machines, so it takes a combined effort to maximize the potential of new technologies.

All industries – from fintech to market research – are now in the development phase where they need to learn how to leverage AI to the fullest extent. Machines can do miracles for the global business, but only with the right type of human input.