In the midst of rapidly-changing technology, we often forget to take a step back and take stock of all the ways that a specific type of technology has made an impact. In the case of artificial intelligence, or AI, you might be surprised by just how much we’ve come to rely on this relatively new technology in day-to-day business. Across many industries, AI is making groundbreaking changes to the way employees and customers interact with businesses and carry out daily operations. Let’s take a closer look at how AI is changing business and becoming integral to our daily lives.


The automation of manufacturing, transportation, and maintenance tasks has eliminated many jobs that once relied on human labor, and certain jobs will become extinct at the hands of increasingly sophisticated and intelligent robots. And because the job market has been shaken up by the growing presence of AI, many people fear there will not be enough jobs to sustain the population; however, there are many new jobs that have been created as a result of the AI revolution. Artificial intelligence is very much a developing technology that still requires substantial human input to sustain. In addition, there are many creative endeavors that may become more feasible as careers as AI takes over more of the mundane professions that exist today. Still, jobs could become limited as technology continues to advance. As an answer to this problem, some have proposed considering a basic income guarantee, which would essentially provide everyone with a living wage, regardless of employment status—though this is still a distant fantasy for today’s workforce.


Customer service is a unique application of AI, because it presents many opportunities for people to interact with bots without even realizing it. Though a human element is still necessary to create real or genuine sounding responses, automated systems like now exist to deliver instant answers to frequently asked questions from customers using email or online chat support systems. Utilizing AI in customer service allows users to get the answers they are looking for immediately, while also easing the burden of support teams.


Sometimes, it can be easy to forget that you are conversing with a computer, but there are inevitable moments where the technology fails and a lack of humanity becomes apparent. That’s why the most successful uses of AI involve the cooperation of people and robots or computers. For example, a computer can analyze customer call data and sort interactions based on positive or negative outcomes. It can then analyze the common threads in each of these categories, providing a script of the most and least effective phrases for customer support calls. This type of task is perfectly suited to AI, because it is possible to sort and analyze data without the bias of personal interactions with individual employees. In addition, AI has proven to be an effective educational tool for people, since artificial intelligence can quickly adapt to the input of each user, creating a more personalized educational experience.


Since AI can reduce the mundane tasks of the typical work environment and leverage more productivity from your employees, it makes sense that the technology pays off in the long run. The driving factor of the popularity of any technology is its affordability, and AI is only becoming more affordable as its effectiveness and reliability improves. AI is often the most cost-effective option, since it doesn’t pose the same limitations as a human workforce when it comes to both time and resources.


It might seem counterintuitive to rely on computers to improve the security of your computers, but human error is actually at the heart of many cybersecurity breaches. AI can take on the task of filtering out potentially malicious links or spam and phishing emails before they could ever be viewed and opened by an unsuspecting employee.





AI tools aim to increase efficiency and effectiveness for organizations that implement them.  As you type an article Google Docs, the text-recognition software suggests action items to you. This software is built upon Google’s machine learning package TensorFlow—the same software that powers Google TranslateAirBnB’s house taggingbrain analysis for MRIs, education platforms, and more. AI is also used in legal cases where it’s being employed to help legal advocates take on more cases because they need to spend less time on initial interviews with AI’s help. Patients are being given a preliminary diagnosis by a computer before seeing a doctor. While AI has crept in to benefit most aspects of our lives, how do we know that it’s built responsibly?


Every AI incorporates the values of the people that built it. The large amounts of data used to create these tools can come from surprising sources. Artificial intelligence “farms” globally employ people to perform repetititive annotation tasks.  Crowdsourcing projects are able to create robust tools because thousands of people come together to curate data. However, people bring biases and subjectivity that can influence AI, intentionally or not. In 2016, Microsoft launched an AI chatbot, Tay, which evolved by interacting with Twitter users. The following 24 hours were a disaster—a lesson in how quickly AI can evolve from excited to chat with humans for the first time to supporting Hitler. Relying on data sets curated by humans incorporates the values and judgments of the companies producing the AI, the people implementing it, and the users. AI is not created in a vacuum; they reflect the creators and users that create them. 


“I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail”—Abraham Maslow



The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored. Instead, we need to both build AI responsibly and understand where it can be reasonably applied. Challenges with AI are exacerbated because these tools often come to the public as a “black boxes”—easy to use but entirely opaque in nature.  At QED we will analyze and select the best approach to solving your problem.  And it might not be an AI based solution.



A PoC developed by QED will help in:

  • Testing the capability of the future AI project

  • Deep knowledge of your data that delivers concrete value

  • Validation of your business idea

  • Identifying potential bottlenecks and drafting viable solutions

  • Performing comparative analyses of multiple approaches

  • Working out a cost-efficient strategy of developing an AI solution

Develop a proof of concept with QED

QED aids its customers in researching and evaluating available AI tools and algorithms to implement the best fit and address the pain points. Validate the feasibility of your idea with a proof of concept tailored to the specifics of your case.

Business needs

Consult with QED's experts to design business use cases and dive deep into the world of artificial intelligence.

Elaborated options

Leverage the available data to evaluate the existing cognitive APIs and custom state-of-art AI systems and solutions.

Solution candidates

We compile a list of approved AI models that satisfy both the engineering and business goals of our customer.

Next steps

Now that we have a set of AI models, we take a closer look at them to determine which are accurate and effective enough to be included in the PoC scope.




QED will care of the entire project development from determining requirements to delivering a full-fledged solution. Our data science team and machine learning consulting experts have hands-on experience with R/Python programming languages, Apache Spark, Hadoop, and Scikit-Learn data science tools, and Tensorflow, Keras and PyTorch deep learning frameworks. See how we do it:



We will investigate your problem to determine how the problem will be approached--through mathematical and physics based modelling, statistical analysis, machine learning or a combination of these. We will gather the requirements and prepare a roadmap for your project.


You might have a sufficient amount of data ready for collection and analysis. If this isn't the case, we will collect the needed data from online sources if possible. Then we'll process these data to find patterns and correlations.


Once the data is in good shape, the most exciting part of our services will begin. Learning model development involves a lot of experimentation and discovery. We'll iterate until  the algorithm gives great results.


When we get the proof that our prototype model can address your business problem, we will launch it in production. We'll integrate the model with your application and provide support services to make sure it works on a broader or different data set.