Overview
Artificial Intelligence (AI) is broadly understood as the ability of computer systems to simulate human intelligence and thinking patterns. There are two fundamental types of AI, depending on applicability: general AI and narrow AI (Heath, 2018). Narrow AIs are designed to perform specific tasks, such as replying to customers, interpreting video feeds, and organizing personal or business calendars (Heath, 2018). General AI is a broader system, which is meant to do whatever a human can do. While general AI is a widely discussed topic and a fascinating idea, unfortunately, general AIs do not exist (Heath, 2018). The present report focuses on overviewing the current AI market and provides a case study of Evie.AI, which discusses its strengths, weaknesses, future plans, and ethical questions.
Trends of AI
The history of AI started in science fiction and evolved into a feature that is routinely used by millions of people. According to Lu (2019), the history of AI dates back 1950s, when the idea of developing an independent learning capability in computers appeared in the minds of computer scientists. However, several generations of computer systems had to change before AIs could come to life. The development of AIs struggled due to the lack of government financial aid (Anyoha, 2017). However, in the 1990s, public funding and hype led to the emergence of fully working AIs, such as IBM’s Deep Blue, which defeated world chess champion and grandmaster Gary Kasparov in 1997 (Anyoha, 2017).
Today, AIs are widely used in all spheres of human life, including education, business, marketing, social security, agriculture, and manufacturing (Lu, 2019). From the technology perspective, AIs three breakthroughs are expected in platforms, algorithms, and interfaces (Lu, 2019). At the same time, more ethical and regulatory issues are expected to emerge due to the extensive commercial use and development of AI applications (Lu, 2019). In summary, AI will continue its progress towards creating a general AI.
Definitions and Examples of AI for Business
Artificial intelligence, particularly machine learning, is the most important general-purpose software. Machine learning is the ability of the computer to keep developing its performance without having to tell how to do all the tasks, such as when you enter equations in excel (Marr, 2018). Machine learning has become much more accessible and widely available in the last few years. Businesses are now able to purchase machines that know how to manages tasks themselves. This technology increases buses, ships, chain saws, and lawnmowers, along with big-box supermarkets, shopping centers, new supply chains, and, when you think about it, neighborhoods (Mason, 2017). Businesses as varied as Amazon and Uber all identified ways to leverage technology to create new competitive business models.
Artificial intelligence is used in general for industrialization and branding initiatives, showing new trends in growth (Lu, 2019). The application of artificial intelligence to manufacturing and agriculture is gradually expanding from the commercial and service industries. For example, AI can be used for using robots to grow food without farmers (CNBC, 2018). Moreover, AI can be used for HR purposes to improve the efficiency of employees. According to Chadha (2018), only 4-6 out of 250 resumes will fit the requirements of a company, and only one person will be hired. If a recruiter needs to hire at least eight each month, a scan of about 2,000 low-end resumes needs to be performed to find the qualified applicant.
A recruiter’s average time spent reviewing an application is a maximum of 6 seconds (Chadha, 2018). While some may believe that the approval period is unfair to decide on holding and tossing, recruiters struggle to go through every request and make sure the recruiting is done on time as scheduled. Chandha (2018) suggests that the use of artificial intelligence technology can help to evaluate resumes using the rules on which they decide to hire.
Global Demand for AI
The global demand for AI is high, and it is expected to grow exponentially. In 2018, global artificial intelligence in retail marketing had gained more than $270 million (Research and Markets, 2019). Many factors may be involved in improving market growth, such as spending by merchandising firms on artificial intelligence and e-market industry. Retailers can adjust the working processes using artificial intelligence. AI can help to understand if the customers are satisfied and perceive relevant data by taking developed technologies such as machine learning and computer vision.
When talking about software, the retail market artificial intelligence is divided into computer vision, machine learning, and others. Machine learning has manufactured the highest revenue over the period between 2014 to 2018 and is also looking forward to dominating the market over the forecast period (Research and Markets, 2019). The explanation for this is that technology is being used online for merchandising to promote customer experience by providing satisfaction surveys.
The highest CAGR is projected to be documented by the machine learning technology during the forecast time (Research and Markets, 2019). The artificial intelligence in the retail market is divided into products and services based on the bid.
Thanks to their increasing acceptance by e-commerce firms in North America to provide a better shopping experience for consumers, the class of solutions is expected to generate higher revenue to the industry during the forecast period, the group is further categorized into supply chain management, cost control, management of customer relationships, recommendation system, chatbot, and others (Research and Markets, 2019). In summary, the high demand for narrow AIs is explained by cost-efficiency and improved the customer experience of the solution.
Development of Technology Companies Globally
Among the leading utilizers of AI technology is Amazon. In 2014, the company purchased more than 15,000 robots across 10 United States warehouses, a move that promises to cut operating costs by one-fifth and deliver packages out the door faster in the run-up to Christmas (Seetharaman, 2014). The orange 320-pound robots that scoot on wheels around the ground demonstrate that Amazon embraced technology improved by Kiva Systems, a robotics company that it acquired for $775,000,000 in 2012 (Seetharaman, 2014).
Amazon featured go-ahead of Cyber Monday, the year’s biggest online shopping day. Robots are designed to help the leading online retailer in the United States speed up the time it takes to deliver products to customers and compete better with brick-and-mortar stores, where many Americans are still shopping (Seetharaman, 2014).
Robots can also help Amazon prevent the mishaps when parcels rush overloaded delivery for holiday seasons. In summer 2014, Amazon launched the robots ahead of the main holiday period, when the retailer usually sells about one-third of its yearly revenue (Seetharaman, 2014). The new workers were based in five states, including Texas, California, New Jersey, Florida, and Washington (Seetharaman, 2014). The move is taking place at a cost.
In June 2013, Amazon announced that it would spend approximately $46,000,000 on building Kiva robotics at its factory in Ruskin, Florida, including $26,000,000 for the hardware, according to local government company filings (Seetharaman, 2014). The Kiva robots have enabled Amazon to hold about 0.50 more items and shorten the time it takes to deliver on the same day in different areas (Seetharaman, 2014). Close up, gripping the shelves of goods removes the need for staff to walk aisles and collect products purchased by customers (Seetharaman, 2014). Today, a worker is searching for a specific product, and the machine is directing to their workstation.
Each robot can carry up to 720 pounds (Seetharaman, 2014). The robots allowed Amazon to pick up packages from the pick stations in as little as 13min, compared to about 60min and a half on average in older centers (Seetharaman, 2014). Amazon’s case is an excellent example of adoption AIs to boost performance and improve cost-efficiency.
The Context: Evie.ai
Evie.ai (Evie) is a virtual personal assistant (PA), which uses the AI technology to decipher the information from emails and propose appropriate time and place for a meeting to all stakeholders. The source code was written using Ruby and Python programming languages together with a natural language processing (NLP) library “for tokenization and part of speech tagging” (Joseph, Lim, & Chun, 2018, p. 3). The user only needs to add Evie to his or her email correspondences, and the AI will forward emails to all the stakeholders with the proposition of time and place of the needed meeting. Evie works with Office 360 and Google Calendar to facilitate the scheduling process and improve convenience.
The creators of Evie are a small group of developers and sales personnel led by Lin Jin Hian. The company was founded in 2014 and became one of the most successful startups in 2017 (Joseph et al., 2018). One of the reasons for the company’s success is a well-established business model that can meet the needs of a wide variety of customers. There are three types of subscription to the product: free subscription for a limited number of schedules per month, yearly subscription on per-person payments, and corporate monthly subscription.
Evie is particularly helpful for HR specialists and sales managers, which spend a considerable proportion of their time trying to find appropriate time and place for meetings (Joseph et al., 2018). Evie is popular among technology-savvy companies, which value time efficiency and innovation (Joseph et al., 2018). However, despite there some issues the business faces, which form barriers for further development of the project.
Business Problem
The primary business problem of the company is the inability to grow further for a variety of reasons. As mentioned in Section 1.3, the industry is snowballing, which means that Evie needs to develop continuously in order to stay competitive. The inability to maintain the fast pace of market development may lead to considerable implications, such as the loss of customers due to offering out-of-date services. In order to address the competition problem, the company needs to tackle several challenges in different spheres, including technological issues, ethical considerations, lack of skillful personnel, and funding insufficiency. The mentioned challenges are discussed in Section 4 of the present report.
Problem Analysis and Challenges for Evie.ai
There are several challenges the company faces, which are acknowledged by the company’s management. First, there are specific technical issues that prevent Evie from widespread adoption. The project relies on commercial solutions, such as the NLP library. Therefore, the company depends on the development of the outside technology, together with its availability, and cost. Moreover, Evie presupposes that the customers use cloud services, which may be associated with a variety of security concerns.
According to Joseph et al. (2018), the issues are especially relevant for the banking and insurance sectors, which can become valuable customers since they have the need to utilize PAs. There are also cultural differences, which need to be interpreted by the AI. For instance, in some cultures, it may be appropriate to delay a meeting to spend extra hours for discussion, while it may be inappropriate for others. Therefore, PAs need to make cultural adjustments for scheduling, which is a complicated task to accomplish.
Second, there are particular ethical challenges associated with the use of AI. Joseph et al. (2018) state that some people believe that personal scheduling of events helps to build rapport, while the use of a program during email exchanges may be considered inappropriate. Third, there is a problem of finding skillful developers competent in the sphere of AI. As mentioned in Section 1.3, the global demand for AIs is snowballing, while formal education in the matter cannot keep up with the rapid growth. Finally, the problem that every startup deals with is the lack of funding. As reported by Joseph et al. (2018), the funding environment in Singapore and other underdeveloped countries is a considerable bother for further development of business. Even though the challenges of Evie.ai are significant, some measures can be implemented to promote further growth.
5. Conclusion
Summary of Future Work Design and Jobs
In order to address the challenges described in Section 4, the company needs to make changes in the work design and hire more staff. The company needs to focus on addressing the security concerns banking and insurance sectors may have in order to promote the service to the companies in the industry. Evie should be able to work not only with cloud services but also with local applications designed for specific companies. While such compatibility may be challenging to achieve due to the lack of technology, changes in the business model may be proposed to address the needs of the sector. For instance, Evie.ai can offer to develop a PA explicitly designed for large corporations and their local systems to increase the sales outlet.
The company also needs to work on its own system of interpreting speech rather than using outside solutions. The proposed improvement will help the company to acquire a competitive edge and increase the value of the company. Even though it will require considerable investments, the company may acquire additional funds by moving to more funding-friendly environments or use public funding. Moreover, the company will need to hire more developers and sales managers, as mentioned by Joseph et al. (2018). The company may also consider having personal managers for big companies and designated software engineers to adopt Evie to meet the needs of these companies. In short, future work design needs to change considerably, and the number of employees and positions should be increased.
Ethical Use of AI
There several ethical issues that may need to be addressed while developing AI. As mentioned by Floridi et al. (2018), AI may have both positive and a negative impact on the society, which is conceptualized in Appendix A. Therefore, the every AI developer, including Evie.ai should follow the principles listed below (Floridi et al., 2018).
- Beneficence, which is promoting well-being, preserving dignity, and sustaining the planet;
- Non-maleficence, which is respecting privacy and security;
- Autonomy, which is emphasizing the right of people to make decisions;
- Justice, which is promoting prosperity and preserving solidarity;
- Explicability, which is enabling the other principles through intelligibility and accountability.
The Future of Evie AI
There are two aspects of the future of Evie that need to be mentioned. In terms of technology, the company will grow to let the PA perform other functions and use other messaging platforms. For instance, Evie can perform financial operations and provide HR services, such as applying for leave or booking a flight (Joseph et al., 2018). At the same time, Evie will enter other markets, such as Japan, to increase its revenues from sales. Moreover, the company will also try to implement the suggestions for improvement mentioned it Section 5.1. In summary, Evie.ai is a fast-growing company in a snowballing industry that can continue its rapid development if it can effectively address the challenges described in the present report.
References
Anyoha, R. (2017). The history of artificial intelligence. Web.
Chadha, S. (2018). AI and hiring trends. Human Capital, 21(8), 12-15.
CNBC. (2018). Watch robots grow food without farmers. YouTube. Web.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., & Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707. Web.
Heath, N. (2018). What is AI? Everything you need to know about Artificial Intelligence. ZDNet. Web.
Joseph, D., Lim, W.K., & Chun, C.T. (2018). Evie.ai: The rise of Artificial Intelligence, and the future of work.
Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1-29. Web.
Marr, B. (2018). 10 amazing examples of how deep learning AI is used in practice?Forbes. Web.
Mason, H. (2017). How AI fits into your science team. Harvard Business Review. Web.
Research and Markets. (2019). Global AI in retail markets, 2019-2024 – Demand for AI-based chatbots & growing adoption of AI-based image recognition technology. PR Newswire. Web.
Seetharaman, D. (2014). Amazon rolls out Kiva robots for holiday season onslaught. Reuters. Web.
Appendix A: AI Opportunities and Risks
