Evie.ai: Artificial Intelligence and Future Work

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Table of Contents

Introduction
The Context (Evie.ai)
Business Problem
Problem Analysis and Challenges for Evie.ai
Conclusion
References

Introduction

In this introductory section, the current trends and global demand for artificial intelligence (AI) are highlighted. In addition, some definitions and examples of AI for business are given together with discussing the development of tech companies around the globe.

Trends of AI

AI keeps on evolving and in every year, novel trend changes are witnessed setting benchmarks for the future. Almost every machine or application that is being created in today’s world embraces AI (Martin, 2019). The top trends in this field of technology include machine learning, facial recognition, cloud computing, and AI chips. Machine learning helps computers to gain experience-based knowledge through complex functions, thus improving their functionality (Hernández-Orallo, Martínez-Plumed, Schmid, Siebers, & Dowec, 2017).

Facial recognition uses digitally formed patterns to identify human images, and it is increasingly being used for security purposes. AI chips are being incorporated into CPUs to ensure that such systems function as intelligent machines. Currently, market leaders, such as Google, Alibaba, Amazon, and Oracle are pursuing cloud computing using AI to support business operations.

Definitions and Examples of AI for Business

AI could be divided into four broad categories including reactive machines, limited memory, the theory of mind, and self-awareness. Reactive machines, as the name suggests, are responsive in nature without the capacity to form memories or use experience to make decisions. Limited memory machines have the ability to look at the past for decision-making with a good example being self-driving cars.

Machines under the theory of mind class can form representations about the environment or cosmos and learn about other agents in the same space (Rajan & Saffiotti, 2017). Finally, under self-awareness, machines are capable of understanding themselves. In business, voice-activated assistants, such as Siri and Alexa are shaping the way companies conduct affairs. The Amazon Web Services platform also uses AI to respond to customer needs. In the music business world, Pandora is leading the way by collaborating with human beings to form memorable experiences.

Global Demand for AI

The demand for AI around the globe is increasing by the day with opportunities arising in almost every sector from healthcare through security to financial technology (Johnson, 2015). In 2018, the AI market was valued at $23.94 billion, and by 2025, it is expected to have grown exponentially to 208.49 billion (MarketWatch, 2019). The key drivers for this market include the ever-growing need for virtual assistants for efficiency and timely handling of customer needs and the rising adoption of cloud computing.

Specifically, the automotive and consumer electronics sectors are the major driving force behind the growth of the global AI market. Globalization plays a central role in driving up the global demand for AI technologies because the world is interconnected. Local businesses in different parts of the world are being forced to adopt AI as a way of surviving and thriving in the highly competitive market environment given that international players are also eyeing the same markets. Therefore, the global demand for AI is expected to continue increasing in the coming years.

Development of Tech Companies Globally

Tech companies have been growing steadily around the globe over the last century. However, market leaders are in the field are concentrated in two countries – the United States and China. In the US, Apple, Microsoft, Alphabet, Intel, IBM, Facebook, and Oracle are the major tech companies shaping the future of the industry (Li, Yu, & He, 2019). For example, IBM has established AI Hardware Center in Albany, New York, which is a global research hub focused on “building AI systems from the ground up – from materials, chips, devices, architecture, and the entire software stack” (IBM, 2019, para. 2).

The center focuses on developing digital AI cores, analog AI cores, AI optimized systems, machine learning, heterogeneous integration, and quantum computing for ML. In the future, within the next 5 years, IBM seeks to create sophisticated conversational capabilities of computers based on relevant human contexts. Tesla is also leveraging AI and machine learning to build an innovative neural network to power its autopilot autonomous driving system (Reisinger, 2019). Moving forward, Tesla is expected to use AI and create fully autonomous autopilot automobiles.

The Context (Evie.ai)

While working at Yahoo, Lee Jin Hian, the founder of Evie, a virtual personal assistant, realized that the nature of his work required scheduling meetings with people in different parts of the world. For instance, his boss was in the US, the majority of engineers were in Singapore, and stakeholders were spread around the globe with different time zones and cultures (Joseph, Lim, & Chun, 2018). Therefore, scheduling meetings to accommodate everyone was always a problem for Jin Hian.

He thus thought of a way to solve this issue by levering technology and this is how the idea of Evie was conceptualized. At the core of this revolutionary technology was the need to save time and increase efficiency with which people can accomplish tasks. In 2013, Jin Hian started working on a virtual personal assistant and named the start-up Evie.ai. This virtual personal assistant was designed primarily to improve productivity by minimizing the time and effort used in scheduling and coordinating meetings involving multiple parties in medium and large-sized companies.

Offered as SaaS (software as service), Evie solved this problem with high levels of efficiency. According to the case study, this virtual personal assistant was so professional that the majority of users could not believe that “she” was a machine contrary to the belief that “she” was human (Joseph et al., 2018). Companies could subscribe to the different models offered by the start-up to start enjoying this revolutionary technique of organizing meetings.

The implementation process was straightforward because most applications that Evie needed for functioning, such as Google Calendar and Microsoft 365, were being hosted on the cloud. This aspect partly explains why using Evie was affordable for many clients at a cost of $120 annually (Joseph et al., 2018). Despite the seemingly successful story of Evie, the program has faced several challenges as discussed later in this paper.

Business Problem

Despite the seemingly successful story of Evie in the market, it faces several business challenges. The major issue has been the failure to penetrate the market and gain critical mass subscriptions, which means that revenues have remained low. For instance, after completing its beta testing in 2016, it launched worldwide operations in 48 countries, but only managed to sign around 3,000 users. While the real estimates of revenue have not been provided in the case study, such a number of users is minimal given that Evie has a free subscription package and some of the users might have chosen this option. Another problem is the shortage of skilled labor in Singapore. This problem was so severe that Evie’s founder once compared recruiting an individual with the requisite user experience to finding a rare Pokemon (Joseph et al., 2018).

Yet another business challenge for Evie is the unpreparedness or unwillingness of different sectors to use virtual assistants. This issue contributes significantly to the failure to penetrate the market and convince thousands of users to start using Evie. There is also the issue of security concerns with many potential users concerned with the safety of their data once the software is incorporated into their systems. Similarly, Evie’s architecture had to be rebuilt several times to accommodate the dynamic impacts that cultural differences present to virtual assistants. These challenges converge to create a business problem for Evie, especially related to declining revenue returns.

Problem Analysis and Challenges for Evie.ai

According to Spacey (2018), problem analysis involves investigating the underlying issues to understand a given situation holistically with the aim of recommending practical solutions. This section uses the root cause approach to problem analysis to address some of the issues that Evie faces in the marketplace. Several factors can explain the failure to convince thousands of potential users to sign up for Evie in the 48 countries of operations.

First, as mentioned earlier most sectors in the business environment are not ready to accept virtual assistants. For example, the banking and insurance sectors are yet to embrace the extensive use of cloud-based services (Joseph et al., 2018). In addition, some potential users of Evie are concerned with security issues that could accompany the deployment of such services. Additionally, the concept of culture, especially on the issue of time management, which varies from one set-up to another, is another major challenge for Evie. For instance, some cultures do not value punctuality and this could be a serious problem when scheduling meetings involving individuals from such backgrounds. This aspect has led to the rebuilding of the software’s architecture several times, which takes a huge part of resources and capital available for the company.

Finally, the problem of understaffing arises due to the lack of qualified and experienced individuals in the field of AI in Singapore. Any successful business venture depends on a skilled workforce to meet market demands based on customer needs (Ekwoaba, Ikeije, & Ufoma, 2015). Therefore, the lack of skilled labor in the country is a major business challenge that Evie has to address to remain competitive in the market.

Conclusion

The AI environment continues to evolve and shape how companies conduct their day-to-day operations. Top trends in the industry include machine learning, facial recognition, cloud computing, and AI chip. The global demand for AI has also increased significantly and by 2025, it is expected to have grown to 208.49 billion from the current valuation of $23.94 billion. IBM and Tesla, some of the leading players in the industry, are leveraging AI technology to remain competitive in their different areas of operation. Evie.ai has also tapped into the AI technology to offer virtual assistance services to companies in 48 countries around the world. However, Evie faces several business problems that have contributed to the failure to maximize revenues as discussed in this paper.

Summary of Future Work Design and Jobs

AI will be the single most important factor that will shape future work designs and jobs. People and machines will have to work hand-in-hand to influence how each functions with the aim of improving productivity. While some human job roles, such as personal assistants, will be rendered redundant with virtual PAs taking over, machines and software required human input to function effectively. With time, companies will be compelled to automate the majority of tasks as a way of remaining relevant in the increasingly globalized and competitive marketplace. However, despite the promising prospects associated with machine learning and AI, some ethical concerns continue to arise with the adoption of such technology.

Ethical Use of AI

Without stringent checks and balances on how companies use AI, serious ethical issues are bound to arise in the future. According to Martin (2019), companies play a central role in the development and deployment of AI. Therefore, such entities should make value judgments to ensure that customers’ dignity and right to privacy are protected. Johnson (2015) warns that without proper oversight, the deployment of AI in the business world could be riddled with responsibility gaps whereby the involved parties abdicate their obligation to make ethical decisions when using machines to accomplish duties hitherto done by humans. Business ethics should inform technology ethics and not the other way round (Martin, 2019) as the underlying principle of the ethical use of AI.

The Future of Evie.ai

Evie.ai is expected to grow and establish a formidable presence in the international market by carving its niche to offer value to its clients. Hiring the right staff will be required to fill the current deficit and build a competent workforce with the commensurate skillsets for Evie to address all arising issues in the virtual PA space. The software’s architecture will also continue to evolve and according to Joseph et al. (2018), plans are underway to expand Evie’s functions to include human resources management and finance. The software is also likely to have messaging options to accommodate common platforms in the space, such as WhatsApp and Facebook Messenger.

References

Ekwoaba, J. O., Ikeije, U. U., & Ufoma, N. (2015). The impact of recruitment and selection criteria on organizational performance. Global Journal of Human Resource Management, 3(2), 22-33.

Hernández-Orallo, J., Martínez-Plumed, F., Schmid, U., Siebers, M., & Dowec, D. (2017). Computer models solving intelligence test problems: Progress and implications. Artificial Intelligence, 230, 74-107. Web.

IBM. (2019). AI Hardware Center. Web.

Johnson, D. G. (2015). Technology with no human responsibility? Journal of Business Ethics, 127(4), 707-715. Web.

Joseph, D., Lim, W. K., & Chun, C. T. (2018). Evie.ai: The rise of artificial intelligence, and the future of work. Harvard Business Review. Web.

Li, H., Yu, L., & He, W. (2019). The impact of GDPR on global technology development. Journal of Global Information Technology Management, 22(1), 1-6. Web.

MarketWatch. (2019). Artificial intelligence market 2019 share, trends, segmentation and forecast to 2025 | CAGR of 36.2%. Web.

Martin, K. (2019). Designing ethical logarithms. MIS Quarterly Executive, 18(2), 129-142. Web.

Rajan, K., & Saffiotti, A. (2017). Towards a science of integrated AI and Robotics. Artificial Intelligence, 247, 1-9. Web.

Reisinger, D. (2019). Why Tesla quietly acquired DeepScale, a machine learning startup that’s ‘squeezing’ A.I. Web.

Spacey, J. (2018). 4 examples of problem analysis. Web.

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