Question

(IT for Managers course) MBA level

GENERAL INSTRUCTIONS
The theme of this research paper is Artificial Intelligence (AI) and Business. For this assignment, you must think about the future of business, considering current trends toward automation of business processes. You must research current trends and summarize your research as a report. The general structure of the report is as follows:
1. An introductory section about current technological trends providing context for your report;

2. A description of select applications of AI to business (minimum 3). Have particularly business areas (of your interest) in mind (e.g., HR, Marketing and Sales, Finance, Operations). You must also describe and analyze these technologies based on their potential to be game changers;

3. The challenges for such applications to become widespread and beneficial to companies; 4. A conclusion section (with your takeaway from the discussion).

SPECIFIC REQUIREMENTS
To write the report you will need to cover at least the following:
1. Your report must adhere to APA formatting;

2. You must research at least 5 recent (i.e., less than 10 years) peer-reviewed articles. Exceptionally, highly regarded industry sources (e.g., IBM, Oracle, SAP, McKinsey, Accenture, Deloitte, Capgemini) will be accepted.

Answer 1

As technology is rapidly increasing the rate of transition, companies need to take advantage of any chance to maximize performance, cut costs and enhance profitability so that they can raise the bottom line and remain ahead of the curve. Automation techniques are becoming extremely relevant with the digitization of business operations to help businesses maximize capital, minimize labour costs , streamline procedures, promote teamwork and increase revenues. In fact, 61 percent of survey respondents believed that workflow automation helps improve customer experience, 59 percent said that automation leads to higher productivity, and more than 50 percent used automation to increase knowledge sharing within the survey

Many automation devices are accessible on the market. Understanding the main trends in business process automation (BPA) is essential, so you can select the right technology for optimizing your ROI. The best thing is that they are very available to all of those technologies. They can be introduced efficiently and affordably, providing scalable approaches that respond to the increasingly changing operating climate for organisations of all sizes. BPA is the secret to ensuring that knowledge is exchanged throughout the enterprise in a timely fashion, whereas regular processes involving consent and permission are directed to the right individuals for optimum organizational productivity at the right moment.

In fact, when integrated with existing BPA applications, small automation can improve the productivity of individual processes by 80-100 percent and overall functions by more than 20 per cent. These technologies are often easily configurable and have the ability to dynamically "learn," so they can be deployed in highly variable input and output environments, such as customer service centres. Small automation technology 's inherent flexibility allows for fast implementation, allowing individual departments to respond quickly to changes in the business environment. Many of these methods meanwhile require department-wide collaboration to help prevent a silo approach.

Because small automation focuses on individual processes or tasks, its success is based on a solid data base and standardized processes established by a broader IT initiative. After all, without a well-defined structure and an appropriate plan for automation, you won't be able to coordinate your activities and further maximize performance simply by adding more innovations into the mix. As such, you need to find the BPA process management system that is based on your company objectives. Remember also the gradual benefits rendered possible by integrating emerging innovations into an existing system to maximize the automation technology investments.

Trends:

Big Data Optimization – It is expected that the amount of data generated each year will continue to increase over time, posing significant challenges for data scientists who manage and analyze large data workloads. In the future, improvements in AI will see more of those tasks giving way to automation. Gartner hopes that by 2020, AI can perform more than 40 per cent of data scientist activities. These "augmented analytics" will enable businesses to use data sets more efficiently and cheaply, enabling them to react faster to changing market circumstances, and deliver products and services that are more closely aligned with consumer needs.

Automated business-process decision taking — Companies are constantly inundated by user queries and data with the proliferation of digital infrastructure and the Internet of Things ( IoT) tools. According to Forrester Research, between 60 to 73 percent of the data collected is never even used somewhere, which means that decision-makers are often forced to make difficult business strategy choices without a complete picture of what is happening on the ground. Machine learning and artificial intelligence-driven automated systems may help close the distance by addressing repetitive facets of customer care and data processing. Decisions driven by data such as lending approvals etc.

Machine Learning and Data Center Infrastructure — Data centers continue to play an important role in the IT strategy, whether in the form of private facilities, colocation data centers, or cloud computing providers' massive hyperscale operations. Managing these facilities' power demands is a major challenge , especially considering that they consume about three per cent of the world 's electricity available. Automation programs powered by machine learning have already been instrumental in driving better energy efficiency practices in the data center. Google, which handed control of the cooling systems in many of its data centers, is the most notable example of this.

Systematic Automation — To date, many Technology and business process automation developments have proved to be somewhat ad hoc and intermittent, introduced in a few test cases, but rarely carried out through an enterprise in a holistic way that is fully exploiting the power of automation. Despite possible cost reductions and efficiency benefits, these innovations are applied on a scale by just 39 per cent of businesses, and only then in only a few instances in limited use. That is expected to evolve in the next few years as businesses merge big data 's value with digital consumer services and process management technologies that optimize employee productivity.

As IT automation is a bigger part of change management, the way businesses implement machine learning software and artificial intelligence to their technology and services should get more innovative. By responding to the current developments in the development of market processes and recognizing where technology would support The operations, they will put themselves on a road to become more efficient in the future by increasing the importance of their established capital.

References:

Hofmann, E., Brunner, J. H., & Holschbach, E. (2019). Research in business service purchasing: current status and directions for the future. Management Review Quarterly, 1-40.

Niederman, F., Kaarst-Brown, M., Quesenberry, J., & Weitzel, T. (2019, June). The Future of IT Work. In Proceedings of the 2019 on Computers and People Research Conference (pp. 28-34).

Heilig, L., Schwarze, S., & Voß, S. (2017). An analysis of digital transformation in the history and future of modern ports.

Janiesch, C., Koschmider, A., Mecella, M., Weber, B., Burattin, A., Di Ciccio, C., ... & Oberweis, A. (2017). The internet-of-things meets business process management: mutual benefits and challenges. arXiv preprint arXiv:1709.03628.

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.

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