The question around which this article is built is how can AI help to improve a firm’s performance management in general and business decision-making in particular?

Artificial Intelligence (AI) refers to machine intelligence, or a machine’s ability to replicate the cognitive functions of a human being. Simply put, it is a machine’s ability to learn and solve problems. In computer science, these machines are aptly called “intelligent agents,” or bots.

Different types of Artificial Intelligence

Apprehension Using AI

Despite the recent revolution in both the content and methodology of work in AI, managers have not yet embraced big data and AI to support strategic and operational decision-making. However, this stance is understandable:

1. First, executives’ knowledge of AI is inadequate, and the capabilities of the technology in the strategic business decision-making process has not yet been well-publicized.

2. Second, though most EPM systems and Business Intelligence tools provide powerful data analytics and visualization capabilities supported by intensive computational processes, they fail to incorporate Al in a user-friendly way, designed for executives.

3. Third, as many algorithms suffer from the black box problem, in which there is no explanation for how the algorithm derives its answers, an accountability problem arises for some C-leaders.

4. Fourth, the existence of bias in AI products is inevitable, as they are human creations.

Advantages of AI

Too often, executives have had to operate with uncertain, incomplete, and inconsistent information. Now, advances in Artificial Intelligence have made the construction of data-based, real-world models and simulations a reality. According to Amazon CEO Jeff Bezos, “Basically, there’s no institution in the world that cannot be improved with machine learning.”

In fact, the benefits of using AI in business decision-making processes are the following:

· Faster decision-making process. The business world is becoming more and more fast-paced, showing no sign of slowing down. Consequently, the ability of a firm to speed up the decision-making process is crucial. A major example of AI usage in businesses could be that of oil companies and their AI-powered pricing model, which improves their margins by around 5% by changing the price of gas according to demand.

· Machines process multiple inputs better than humans. Inevitably, machines are better at handling many different factors simultaneously when making complex decisions. They can also process much more data at once and use probability to propose or implement the best possible decision.

· Less decision fatigue. According to multiple psychology studies, when individuals are forced to make multiple decisions over a short period of time, the quality of their decisions deteriorates over time. On the other hand, algorithms do not suffer this weakness, helping managers avoid making poor decisions due to exhaustion. The reverse is true, in fact — the more the algorithm works, the better it becomes.

· More original thinking and non-intuitive predictions. Some patterns are not recognizable by humans. This kind of unique insight can have an immediate impact on a business.

Despite the justified anxiety of managers about the process and the implementation of AI, the identified advantages of the technology outweigh them, providing a more secure, faster, more complete, and easier decision-making process.

Quadrant Protocol and AI

Quadrant is a Blockchain-based protocol that enables the access, creation, and distribution of data products and services with authenticity and provenance at its core. It is a blueprint for mapping decentralized data, making them into innovative new data products that can potentially help businesses and organizations meet their data needs.

The chaotic data ecosystem that terrifies and exhausts managers throughout the decision-making process can be simplified via Quadrant and its simple, predefined, and secure execution of Smart Data Contracts. These contracts ensure that the data they need and the data they receive for their models is indeed authentic, and delivered on time, with an audit trail for provenance. This will remove major issues for companies in the following ways:

· Simplifies acquisition of authentic data from diverse sources

· Increases the volume and speed of required data for models

· Provides data verification tools for data audits, safeguarding the company from fraudulent data

· Gives companies the ability to build upon the data they receive and make it into something better for themselves, or for the entire data economy

To sum up, according to Accenture’s Technology Vision 2017 report,

“Artificial intelligence (AI) is coming of age, tackling problems both big and small by making interactions simple and smart. AI is about to become a company’s digital spokesperson. AI is becoming the new user interface (UI), underpinning the way we transact and interact with systems. Seventy-nine percent of business leaders agree that AI will revolutionize the way they gain information from and interact with customers.” — Accenture Technology Vision 2017

Quadrant envisions itself creating a promising future for the quality, transparency, and authenticity of the data received by AI companies, helping them create insights and services that have far-reaching effects.

You can expect more updates in the coming weeks. In the meantime, join our Telegram channel to receive the latest notifications about our project. https://t.me/quadrantprotocol