Published in Business Intelligence

Why Proposing AI as a Development Topic is Important

Artificial Intelligence is part of our daily lives, nowadays it’s ingrained in everything we do. From what content is shown to us in our social networks, or asking Siri how to get somewhere, to more complex uses, like developments in the information technology and cybersecurity industry. Not to mention the multiple applications of AI that aren’t usually thought about, like healthcare, transportation or finance, to name a few. AI presents a wide-range of possibilities, it has revolutionized the way we process information, integrate data, and make decisions based upon those findings. 

The interesting aspect is that even though a big part of our day to day activities are supported by AI, most people don’t have a clear grasp of what that really means. As the development of this type of technology increases, the informational gap does so as well. There’s one thing that we can say with certainty, even if we ignore how AI affects our lives, it will still continue to evolve. 

Since it’s a new technology, the implementations and ethics involved are still up for debate, the policymakers have a tough time agreeing upon regulating laws. But it’s possible, recently the European Union made great progress in the field, with their European Commision.

Today we wanted to explore the many positive uses AI has, and why the development of this technology is so important. 

Project Management

 Qualities of Artificial Intelligence

Let’s begin with a possible definition. According to research from Shubhendu and Vijay, what we refer to as AI are machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention. These systems and procedures are able to make decisions that would require a human level of expertise. It requires key technologies to function, such as machine learning, natural language processing, rule-based expert systems, neural networks, deep learning, physical robots, and robotic process automation. They can help us anticipate problems or face issues as they come up. They carry 3 central qualities. 


AI is developed alongside machine learning and data analytics. Machine learning analysis data and searches for trends among it. Once it finds something relevant, software engineers can use that information in order to face a certain problem. The only thing it requires is a strong and voluminous set of data, in order to correctly reach useful and significant patterns. These data inputs don’t have to follow a specific type of media or digital form of information. It can be text, photographic information, or even unstructured data. 


There’s a somewhat clear objective when designing an AI algorithm, it is programmed for decision making, for extremely fast and updated decision making. There are not passive machines, since they reach conclusions that are not predetermined or known by their creators. With information gathered from sensors, remotely inputs, and/or digital data, they can combine multiple layers of information from different sources, analyze it in mere seconds, and reach valuable conclusions from the data. Depending on the type of AI set in place it can even make decisions or act upon the gathered insight. Thanks to the enormous technological advances in the field of IT, mechanical and electronic engineering, we now have massive storage systems, rapid processing capabilities and state of the art analytic techniques. All of these make AI capable of near human sophistication in decision making


One of the most interesting aspects of AI is how it can help us in real time, almost instantly. AI systems have the capability of learning and adapting as they integrate new data, thus changing the outcome of their decisions. Imagine you are in your car, driving with the help of a GPS app. Most of these types of map and commuting apps are able to adapt to real time diving conditions thanks to AI, and the data their systems gather from other drivers up ahead. They can inform on real time congestion due to a car crash, show certain spots with a lot of traffic or potholes, among others. Human interference is not even needed, since the simple action of keeping the app open while driving is enough. Enough and immediate, since the data travels instantly, letting the system know what is going on, and letting drivers know what is up ahead.

Types of AI technologies

There are several possibilities for implementing AI technologies, and many fields are already deploying systems such as the one mentioned. Today we will focus on the business applications AI has.

Process automation

This is the most common type of use general business and companies get out of AI. Automating digital or physical tasks that are mundane and time consuming for employees, like administrative and finance activities.

For example, some state of the art project management softwares have features that automatically help in day to day tasks. With routine data input, such as the amount of billable hours, and type of project carried out, these tools can create profitability estimations, and do so in an automated way, just feeding on the regular information the systems uses to function. Imagine being able to modify whatever a project needs to be profitable, without having to waste any resources.

These sorts of softwares also include finance reporting capabilities that allow employees the possibility of cross referencing data sets in order to analyze certain topics. You could check the historical data of a single client, and compare it with revenue rates. This or any report only a click away, and without having to input new data. 

This is usually carried out by using robotic process automation tools that perform as a human introducing and consuming information from multiple and varying sources or information ecosystems.

Process automation can take the form of data entry tasks, introducing information from call centers and e-mail into the business’s record, or constantly updating the client’s information. It can also process legal and contractual documentation, by using natural language processing. These are tasks that are very easy for human intelligence, but would take a lot of time in order to complete. And that’s the driving force behind AI tools, especially process automation, it saves time, and frees the human brain for more challenging or creative work.

This does create some worry for the loss of jobs. But most of the time tasks that can be automated are already being outsourced. Replacing workers is not the objective here, and it rarely happens.

Time tracking & estimation

Cognitive insight

Another use of AI is cognitive insight. It’s the ability of reading big data, enormous amounts of information, and deploying a pattern recognition for detecting trends and interpreting their meaning. These machine learning algorithms can help with large scale predicting, like forecasting the next item a customer will buy. Or analyzing data in real time, finding credit fraud and insurance claims fraud almost instantly. They can also check warranty information in order to identify safety or quality issues in manufactured products. 

These are not the regular data analytics employed by traditional data analysis systems. The development of AI such as this is trained, the model in itself learns and gets better overtime. This ability allows the system to improve its predicting and processing abilities as it continues to carry more intensive data and detailed inputs.

Cognitive engagement

Another application of AI, although not as common in business as the last two, is the use of machine learning, chatbots and intelligent agents. These systems can be incredibly helpful, for example they can offer customer service all year round. This means help with an array of things, from password change requests to technical support. Some even carry speech recognition capabilities, and are able to correctly employ problem solving tools for audio inquiries.

Chatbots are very common, you will come across one very often, even in social media and regular business’s websites. Some companies have begun to use these for internal purposes, as well as client related tasks. They can answer on employee related topics, like benefits or HR policies. 

Another form cognitive engagement can take is an array of recommendation systems for retailers. These have increased greatly in their ability to create accurate and personalized engagement with clients. It is also popular in the healthcare field, where it can help create care plans taking into account previous information from a single patient. 

Business benefits of AI systems and Machine Learning

One of the main reasons there’s a growing body of AI research in academia is that there’s a lot of interest in developing the economical and financial opportunities machine intelligence presents. In a 2017 article, PriceWaterhouseCoopers estimated that by 2030, AI technologies could increase global GDP by $15.7 trillion, about 14%.

The financial benefits are very alluring, and the practical applications are uncountable. The current practical uses for AI in business are as follows:

  • Controller of numerical data: This the use of statistical analyses of numeric data via machine learning. This is generally used to optimize prices and to set pricing strategies that are low enough for attracting customers, and high enough to make a profit.


  • Controller of other types of data: Non-numeric data is a more complex set of information than numbers. These systems use speech recognition and image recognition developed thanks to deep learning neural networks. Some practical examples include email marketing for lead generation, some programs can respond to inquiries and differentiate the promising ones, in order to derive it to a sales operative. 


  • Numerical data robot: almost the same as a Controller of numerical data, but this iteration has a physical body. You can find these types of smart robots in retail spaces with an extremely well-structured operation. These robots are capable of doing mechanical tasks, like serving coffee, folding clothes, and taking products from one point in a warehouse to a delivery gate, like they use in Amazon


  • Data robot: Similar to the last one, but these types of robots can actually process all types of data. Imagine a robotic assistant in a large store that can respond to verbal queries, scan products, and roll towards a certain area of the store leading the way for a customer. There are certain robots that can also help in security, incorporating thermal vision in order to aid security guards in their patrols. The objective is to liberate humans to focus on servicing customers and taking care of the more complex tasks. 

Of course the main financial drive does not come in the form of service robots pouring a coffee with latte foam art, or a science fiction robot butler. Maybe there’s a market for autonomous vehicles in the future. But for now it’s in the analysis performed in order to predict market shifts, and the generation of leads and sales, as well as a driving force of competitiveness . There are multiple business capabilities in AI, such as digital marketing, healthcare, financial, agricultural, to name a few. 

The future of AI

The next frontier to access in the field of AI research would be the incorporation of contextual data in order to make better predictions and to manage more complex tasks. Self-driving cars, for example, are still in development. They seem to have trouble performing under non-ideal weather scenarios.

Another possible use in the future is in the medical research field. There’s a very important aspect of researching new cures for diseases that involves understanding how certain proteins behave. Basically, if you understand the complete shape of a protein you know how it will affect the human body, and how to target it, this is especially important for autoimmune diseases. Proteins can also become cures in themselves, and here lies the true value of this practice.  But the problem is there are millions of different ways a protein can behave and take shape. 

The process of understanding this can be extremely costly and time consuming, even for AI. Although it’s hard, human ingenuity is a force to be reckoned with. Just look at the crowdsourcing game FoldIt. They came up with a way of using the capabilities of humans, and their natural gift with puzzles in order to start shaping the way certain amino acids, the basic components of proteins, are shaped. This protein structure prediction is something that can’t be processed by our current technology in an efficient and cheap way.

With the advances in the field of AI, maybe there’s a near future where they can analyze the human factor and program AI algorithms in order to unlock these puzzles faster. This could mean giant steps forward for the discovery of cures for HIV, Cancer and Alzheimer’s.

Another possible breakthrough we could be able to see in our lifetime is AI gaining the ability of understanding the content of human language. This could help translate and share multiple resources around the world, and could empower each individual person to understand other languages with every bit of important context in the form of portable translation devices.

When people translate between languages they understand the content and then reproduce it in a different tonge, with the necessary context and character originally expressed. Machines can’t do this yet. They can’t contextualize or comprehend the meaning behind language, what they achieve now is a certain set of responses, but those do not amount to what they could do in the future.

AI basic components

 In order to gain a better understanding of how AI systems work let’s look at some of its basic components. 

Computer Science and Algorithms

Computer Science is the study of computers and computational systems. This discipline studies software and software systems, which involves the theory behind them, its design, the development, and applications. 

One of the main purposes of this field is the creation of computational systems, which are calculations in arithmetical and non-arithmetical procedures. These systems follow a structured and well-defined model that commands its working procedure; we know these models by the name of “algorithm”. They basically are the set of rules and instructions given to the system that orders how it functions.

Data scientist and the importance of data sets

In machine learning, and other AI sub-fields like neural networks or learning systems, this algorithm is what provides the system the capability of learning on its own and reaching new conclusions.

These systems can be programmed by Data scientists. It’s the study of how to extract meaningful and valuable insights from data. They manage to do this with a combination of domain expertise, programming skills, knowledge of math and statistics. The insights gained by the analysis of data sets can be translated into tangible and operational business value. 

AI Bias

In order to reach AI conclusions and insights we need: 

  1. An AI algorithm, with a set of rules, 
  2. programmed by a Data Scientist,
  3. feeding on a data set.

It looks simple, but there are many ways in which bias can occur without the programmer even knowing. In a truly eye opening article published in Nature, which evaluates the role of AI in reaching Sustainable Development Goals, shows the many ways in which AI can be fallible to racisms, sexisms and low income discrimination. As well as not being able to perform equally for developing countries and wealthy nations. 

There are many reasons for this: the programmer’s own internal biases, since most of the AI development is done by male programmer’s living in wealthy nations there’s a lot of mistakes in the data selected, and the way in which the models behave doesn’t always take into account minorities.  

Another problem surrounding AI is the climate impact it has with the current state of technological hardware we have. The centers for data storage and servers leave behind a terribly high carbon footprint and consume large amounts of electricity, and the frontrunners of these types of computational systems are usually carried out by wealthy nations, but affect everyone. There’s hope yet, with the development of more efficient cooling systems and renewable energy usage in the field. 

The datasets used for calculation are extremely important, a recent example of how bias affects us directly is related to the Covid-Vaccines. There have been numerous testimonies around the world that they may have some effects on women’s menstruation cycles. The reason we don’t know for certain is that no one included that information, that subset in the testing research, and that’s caused by a bias. 

Other ways AI can leave a great amount of population behind, is by not realizing there are no datasets that include people living in extreme poverty. By reaching conclusions that don’t involve the whole population, those conclusions are not universal and lack the power to be used effectively during the implementation of government policies without a high-risk effect. Other examples involve law enforcement and racial profiling, feeding predictive criminal AI systems that use facial recognition. These mistakes lower the validation the field has.

These are not reasons to stop the development of new AI technologies. Whenever a new technology appears, there is an expected adjustment period in which the actual reach is still being tested. But since we, as a global society, keep using the amazing possibilities AI gives us, we do need to be conscious of the unintended biases we could fall into.

This means we need to keep researching and understanding the many ways in which we may make a mistake when designing new algorithms and AI systems. Especially during research projects and for AI researchers. The abilities AI has in order to help businesses and reach insightful conclusions is not diminished.

Why is artificial intelligence important?

The simple amount of information and data generated each moment has reached never before seen numbers. Humans, machines, and current AI, are hard at work creating more data by the minute. It’s only logical that we started to require assistance in the new endeavour of analyzing this information, since a human brain simply cannot process the amount of information out there.

We need help. We are at the height of an ongoing informational revolution, and we should be using all the tools we have at our disposal. The benefits of AI powered software can be inmense for businesses, they can help make better, more informed business decisions. They can bring unexpected problems to the light, and help with solutions for them. They can even help make a sinking operation, a profitable endeavour once again. 

We, at COR, are in the business of helping professional service operations reach their maximum capacity, while integrating better care of the talent, profitability estimates and better communication. Working smarter is the way to a better and more proficient business. Let us know if you want to take a step in the smart direction, and request a demo today.

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