Artificial Intelligence in Business in Canada

Subject: Tech & Engineering
Pages: 5
Words: 1501
Reading time:
6 min
Study level: Master


Sniper Intel is a platform designed for efficient B2B sales collaboration in the marketplace. With the help of Sniper, any employee has the opportunity to receive up-to-date, and, most importantly, necessary, information in a timely manner, without wasting time searching for it. That is essential personalized information for each user who has a request for it, using a convenient mobile interface. It automates the 1-2 hours per day per person of collecting, processing and consuming information and replaces many internal emails (Sniper Intel 2021). For every company partnering with Sniper AI, there is a constant collation and analysis of millions of different data about their customers, competitors and markets.

All information exists in internal individual applications, collected from external data sources, such as the Internet, social networks, government portals, premium databases, and more. Sniper AI offers user-friendly applications, both web and mobile, optimized to increase sales. Each person spends an average of about two hours a day checking multiple applications and searching the Internet for the information they need (Sniper Intel 2021). What is important is that about 70% of customer and market data is never used by employees and executives.

The idea behind Sniper Intel was originally born during one of the research projects in the field of decision support systems, as well as in several startups in which Hamed Taheri participated in knowledge management. Gradually development focused on the business-to-business sales space. Through many ups and downs, the developers founded Sniper Intel, a platform that collects and then distributes all the information on the go that sales or marketing teams need.

Technology as Environmental Forces Impacting Business

Artificial intelligence technologies are useful for various business areas: retail, construction, IT, education, and others. Each of them needs to manage the behavior of consumers and customers, study future market trends and automate routine processes. Artificial intelligence (AI) helps simplify these activities by becoming a reliable assistant in business and improving performance in all areas. Here are a few tasks that AI can help businesses with (Mishra & Saini, 2018):

  • Pricing. It is easier for artificial intelligence to study statistics and forecasts than for humans. It can quickly process huge amounts of information and offer the most optimal price distribution. That will help the company to increase revenue and even multiply the company’s income several times.
  • Safety. It is especially important, as scammers can also be found among consumers. Self-learning neural networks analyze customer behavior and see suspicious transactions so one can respond to them in time. The result is the absence of financial losses, the security of the system and the trust of users.
  • Marketing. The AI can study the firm’s previous sales and research the market to make a prediction. It analyzes the behavior of competitors to compare right and wrong moves. It will also allow leaders to develop a sound marketing strategy that is more likely to be successful.
  • Speed. Sometimes it is very important to quickly analyze the information and react to it even faster. Unfortunately, conventional algorithms cannot adapt themselves to new conditions and data without prior training. Artificial intelligence provides this opportunity and increases the productivity of the business.
  • Automation. No matter how efficient the employees of the organization are, they can make a mistake at any time. That is influenced by dozens of factors that no one can predict and eliminate. It is not typical of artificial intelligence: it does not have emotions and feelings, there are only data, functions and technologies. Sometimes the stability of work is critical, and in this AI outperforms a person.

The main benefits and results of Sniper Intel are aimed at and serve to simplify the work of an employee of a business organization. An employee can schedule ongoing scans of metadata from a wide range of sources that have been individually selected based on sample areas provided by the client. In addition, if an employee already has some data that needs to be processed or distributed, he or she can add it to the system, that is, work directly with the product. AI in business also helps keep metadata up-to-date with full versioning and change management.

Essential for any company is to correlate data elements from source to target, including data in motion, and to harmonize data integration across platforms, which can be automated with Sniper AI. The platform client easily creates and configures a data asset focused on a business, framework, and more. Additionally, all data is tailored to a specific organization, for consistency and reliable information management. Sniper Intel offers collaboration features such as media sharing, commenting, private messaging, channels, videos, and more. Most business decisions require several people to review a piece of information together in order to develop the best next steps.

By using artificial intelligence and automation to quickly create and maintain your governance structure and asset associations, discovering and working with sensitive data is accelerated. AI helps in mitigating a wide range of risks for regulatory peace of mind. It also removes silos between disparate systems, spreadsheets, reports, and analytics to improve stakeholder understanding and trust in data to generate actionable insights (Sun et al., 2018). Sniper provides data consumers with the ability to define and discover data related to their roles, meaning the entire platform is personalized based on the role and scope of the employee (Sniper Intel 2021). Based on this, the understanding and use of data in a business context are facilitated through the socialization and collaboration of business users, the organization is fluent in the data language.

Ethical Issues in Modern Business

Rapid growth in data volume, storage space, and computing power makes it possible to accelerate digitalization. In addition, the COVID-19 crisis has been another powerful catalyst for the change. Recent studies have shown that the response to the challenges of the pandemic has been to accelerate the adoption of digital technologies many times over, and these are not temporary measures. It is no longer just a matter of creating conditions for remote work. Companies have learned how important it is to rely on analytics and digital services to shape an agile, competitive strategy that will help them get through the tough times.

However, despite these advances, the ethical issues of business automation and artificial intelligence – and who will be affected and how – are not well studied. There is a general discussion about privacy and surveillance in information technology, which is mainly about access to private and personally identifiable data (Müller, 2020). Privacy has several commonly recognized aspects, such as the right to be left alone, the confidentiality of information, confidentiality as an aspect of the individual, control over information about oneself, and the right to secrecy.

The ethical concerns of AI in surveillance go beyond the mere collection of data and directing attention. They include the use of information to manipulate online and offline behavior in ways that undermine autonomous rational choice (Wright & Schultz, 2018). Of course, attempts to manipulate behavior are outdated, but they can take on a new quality when using AI systems. Given the intense interaction of users with data systems and the deep knowledge of people this provides, they are vulnerable to nudges, manipulation, and deceit. Given sufficient prior data, algorithms can be used to target individuals or small groups with those inputs that may affect those specific individuals.

Documents are the main source of valuable information; and their leaks not only can lead to short-term financial losses but also deal a serious blow to the reputation and cause the loss of customers. A company may use additional technical data protection measures. For example, rights management services (Active Directory Rights Management Services), which allow controlling the use of files. With this solution, one can prohibit printing, editing, and sending documents to users who are not authorized on the local network.

The Sniper Intel program also tackles the ethical issues that come with digitization and the development of artificial intelligence. For instance, a company uses only public data, that is, they protect their customers from the dissemination of private or confidential information (Sniper Intel 2021). However, developers cannot control all the incoming data, which can lead to the leakage of forbidden information. On the other hand, this is rather a matter of human ethics, which does not apply to the introduction of artificial intelligence in business. The company also avoids spamming and carefully selects and establishes partnerships with organizations. Moreover, when signing the agreement, customers undertake to comply with applicable laws.


Thus, in connection with the rapid development of technology and the introduction of artificial intelligence into business, new ethical issues arise on the agenda that require detailed study and development of solutions. Privacy practices that can largely obscure the identity of individuals or groups are now a standard product in data science. These include (relative) anonymization, access control (plus encryption), and other models in which computations are performed with fully or partially encrypted input data, which is important in today’s work environment.


Mishra, R., & Saini, A. K. (2018). Technological capabilities impacting business intelligence success in organisations. Quality, IT and Business Operations, 405–419. Web.

Müller, V. C. (2020). Ethics of artificial intelligence and robotics. Stanford Encyclopedia of Philosophy, 1–70. Web.

Sniper Intel. SNIPER INTEL. (2021). Web.

Sun, Z., Sun, L., & Strang, K. (2018). Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162–169. Web.

Wright, S. A., & Schultz, A. E. (2018). The rising tide of artificial intelligence and business automation: Developing an ethical framework. Business Horizons, 61(6), 823–832. Web.