Abstract

The integration of artificial intelligence (AI) and technology is a growing trend that will define the remainder of the 21st century. Notably, every era goes through a revolution of industry — human history and society have endlessly been shaped by a steady drumbeat of technological advancement. Hundreds of thousands of years in the past, mankind was reliant on tools made of bone, wood, and stone. Sometime after, controlled-fire was discovered — propelling the world forward even more. This spark would eventually lead to several revolutions of technology that were shaped by steam, mass production/manufacturing, and a digital age (Schwab, 2021). But beyond the first, second, and third industrial revolutions, a fourth revolution is rapidly rising: AI. As a computer science division focused on the creation of a system that performs “human-like tasks, such as speech and text recognition, content learning, and problem-solving”, AI enables technology to both analyze and recognize “huge amounts of data” and “recurrent patterns” (Softengi, 2022). While digitization becomes the norm, and while process complexity and the acceleration of innovations expand (Softengi, 2022), companies such as those involved in information technology (IT) can only compete by relying on more intelligent processes. As such, the growing adoption of AI in various sectors is quickly altering how value is created, exchanged, and distributed (Schwab, 2021) — profoundly changing lives, business performance, market competition, and consumer relationships with products. This holds true for the technology industry, and there is a considerable degree of excitement — but also apprehension — over where the future of AI is headed in that regard.

At a glance, artificially intelligent technology has provided revolutionary advantages such as improved processing speeds and decision-making, increased productivity, and significant reductions in human error (Anderson & Rainie, 2018). Nonetheless, despite the societal benefits of AI, this mega-trend is disadvantaged by a multitude of side-effects such as higher costs for hardware and operations — as well as the risk of AI-inspired automation replacing human labor; the risk of AI increasing unemployment as people are replaced by advanced, higher-performing technologies (Anderson & Rainie, 2018). These are but a few of the opportunities and challenges posed by the increased adoption of AI. As such, there is a need for government intervention to better regulate AI — no different than other technological areas. Therein, it is necessary to address the tensions that exist in the tech industry over the growing incorporation of artificial intelligence (AI) in daily operations and products — the opportunities and challenges that have emerged — so that measures can be taken to ensure the future of AI is defined by stability and value for humankind. It is also worth recognizing the value AI adds to strategic business execution, as well as portfolio and risk management — the competitive advantage to be derived in these areas with the rise of AI.

AI’s impact on the technology industry should not be understated. Tech Mahindra’s chief executive officer (CEO) — leading a company that provides IT services and networking solutions — stated that AI is revolutionary because it will bridge the divide between the limits of human capacity and “what is actually possible” (Gurnani, 2019). Moreover, AI’s growing prevalence in the tech industry will result in the increased robotization, mechanization, and automation of basic tasks once performed by humans — with a AI becoming integral to daily life (Gurnani, 2019). While it is true that most pronouncements of AI-assisted automation of tasks remain fanciful — as there are “far more instances of augmentation of human work by smart machines than of full automation” (Davenport & Miller, 2022) — the limitations of AI in “real-world work settings” (Davenport & Miller, 2022) will one day give way to the unlimited productivity of AI-supported, automated labor. Relatedly, motor vehicles have progressed in safety, speed, fuel efficiency, and general reliability when compared to a hundred years ago — and some cars are now driverless with the integration of AI. Likewise, AI is amplifying human productivity and efficiency; matching or exceeding “human intelligence and capabilities” (Anderson & Rainie, 2018). From this comes a belief that AI can solve a multitude of issues in the IT space that are beyond humans’ current capacity to address — and recent evidence suggests the increased adoption of AI technologies has optimized solutions to these challenges (MyComputerCareer, 2022). Integrating AI with technology has generally improved efficiency, enhanced productivity, and further assured quality — thereby reducing “the burden on developers” (MyComputerCareer, 2022). No doubt, AI’s opportunities are endless.

AI is developing “advanced algorithmic functions” that will bolster the “development and deployment of IT systems at large scale” — a feat once considered nearly impossible (MyComputerCareer, 2022).

Some other opportunities include increased system security, improved coding productivity and automation, enhanced quality assurance, “better application deployment during software development, and better server optimization” (MyComputerCareer, 2022). Data security is vital in the technology industry for “securing personal, financial, and confidential data” — and AI relies on advanced algorithms and Machine Learning (ML) to fortify protections for this data (MyComputerCareer, 2022). Furthermore, coding is essential to technological development and AI enhances “coding productivity” via a similar reliance on advanced algorithms (MyComputerCareer, 2022). AI contributes to efficient and productive coding by noticing patterns and providing suggestions — which not only saves time but also keeps the code “clean and bug-free” (MyComputerCareer, 2022). Therein, human error is automatically corrected by AI. Additionally, AI’s automation lends itself to a reduction in manual labor — a reduction supported by “deep learning applications” and automated “backend processes” that cut into work hours and reign in costs for IT departments (MyComputerCareer, 2022).

Nevertheless, while AI is a path for optimizing technologies and seamlessly integrating “business and technological functions” (MyComputerCareer, 2022), the aforementioned opportunities of AI are balanced by the undeniable threats that come with it. Accompanying AI’s benefits are challenges such as the high costs of adopting AI technologies — costs that counteract the reduced business costs AI can lead to (Gurnani, 2019). AI technologies are not cheap — and so, not currently cost-effective for every business. There is also the issue of data and algorithms that “reinforce gender, racial or ideological biases” (AI for Good, 2018). AI is not yet fully immune to the human error of its creator — and so, if the input of the dataset by human operatives is faulty, AI can reach the wrong conclusion and incorrect output. As a further matter, AI utilizes deep-learning algorithms/technologies that are “opaque” in their decisions — this can spell trouble when seeking to understand why AI has either failed in its assignment or made certain recommendations (AI for Good, 2018). To add to this, the vague workings of deep-learning algorithms/technologies can also impede efforts to assess “when and how AI may be reproducing bias” (AI for Good, 2018). Additionally, as celebrated as the intelligence and predictive potential of AI might be, AI is not immune from security risks — the software that is found in electrical grids, as well as cellular and camera technology, is not invulnerable to attack simply because it is supported by AI (AI for Good, 2018). Although current AI is a growing answer to such threats, security risks continue to necessitate some form of human oversight.

Furthermore, there are economic and national security threats that characterize AI’s growing adoption in the technological sector. Due to globalization and the internet, the peace and stability of the international world have never been more interconnected at any point in time. As a result, the abuse of AI technologies and machine learning to generate ‘deep-fakes” or fake videos and audio of an individual — to influence perceptions — is a growing problem (AI for Good, 2018). Political, social, and economic outcomes — matters of peace and war — can be tilted one way or another in the absence of sufficient organizational and policy-oriented governance. AI has also led to increased augmentation and automatization which can deepen economic insecurity and societal inequalities by substituting humans with machines for routine tasks (AI for Good, 2018). As jobs are displaced and unemployment climbs, the AI revolution is at risk of succumbing to the same societal ills that plagued industrial revolutions of the past. Some economists have lectured as to how the AI revolution might “yield greater inequality, particularly in its potential to disrupt labor markets” (Schwab, 2021). The mechanization of cheap labor will devalue or outright eliminate some occupations — forcing a collision between the public and private sectors as citizens and their government debate profits over people and other moral questions. This holds true for AI’s automation of jobs across the tech industry — for example, tech is full of analytical positions that are prone to disruption by AI (Markolf et al., 2021). Likewise, AI’s advanced capabilities — when coupled with increasing data availability and decreasing computing costs — threaten to upend other positions in the tech job market (Markolf et al., 2021).

Granted these threats and opportunities, it is worth considering the progress that has been made — what the tech industry has done to date. Numerous organizations have already implemented AI into their daily operations and products. The integration of AI into operations has optimized the processes of several tech companies (MyComputerCareer, 2022).

For example, AI has improved communication — enabling the automatic transmission of “reminders to departments, team members, and customers” (MyComputerCareer, 2022); AI has proven beneficial for monitoring network traffic; AI has also helped reduce the number of repetitive tasks so that employee attention can be focused toward the more “critical aspects of the business” (MyComputerCareer, 2022). Additionally, AI has given technology a more “personalized customer experience” as customer service — ranging from answering questions and providing recommendations, to uncovering hard-to-find products — has become more intelligent and automated in its analysis of in-store and customer data (MyComputerCareer, 2022). Similarly, AI’s ability to sift through large quantities of data has given companies a competitive advantage by improving “strategic insights and business intelligence” (MyComputerCareer, 2022). Various surveys indicate a minimum of 75% of companies and tech leaders believe AI technology will make their business more competitive, support new business ventures, boost efficiency and productivity, and create new jobs (MyComputerCareer, 2022).

 

Still, some companies find the task of implementing AI daunting — the biggest obstacle for “roughly 37% of executives” being the alignment of the corporate vision and strategy with the possibilities of AI (MyComputerCareer, 2022). It is important to have senior leadership and management not only on board but in the know about how new technologies, like AI, function — but evidence suggests the pairing of AI and IT will make the integration of AI much easier for these companies (MyComputerCareer, 2022). So far, the integration of AI has shown itself to be successful in service management, IT operations, business process automation, and fraud detection (MyComputerCareer, 2022). AI has integrated with service management for more efficient resource management that supports cheaper and faster deliveries; AI for IT Operations (AIOps) relies on AI for the continuous, automated management of IT on multiple platforms and the management of increasingly complex information sources, data collection, and controlled systems; deep learning technologies and business process automation decrease the reliance of IT departments on “direct human intervention”; while AI has been abused to commit fraud, AI/ML has made fraud detection easier and faster by sifting through data and identifying “patterns of fraudulent behavior” (MyComputerCareer, 2022).

Although many opportunities and their progress have been noted, the threats endure — however, field and policy experts have presented some solutions. For one, it is imperative that “relevant international standards” are developed and adopted (AI for Good, 2018). The recognition of international standards for AI, alongside the use of open-source software, is suggested to be one way to achieve a “common language” by which diverse stakeholders can contribute to AI’s development — thereby rooting out the threat of bias with “datasets that are accurate and representative of all” (AI for Good, 2018). Field and policy experts have made additional recommendations for safeguards that support the transparent, “legal, ethical, private and secure use of AI” (AI for Good, 2018). Transparency of AI’s design will address the challenge of assessing AI’s vague determinations — human operators will be able to understand and agree or disagree with the reasoning behind some of AI’s conclusions (AI for Good, 2018). Overall, the pursuit of AI and machine learning that is “ethical, predictable, reliable and efficient” (AI for Good, 2018) has led to the proposal of regulations and the intervention of domestic and international government agencies.

The CEO of SpaceX and Tesla, Elon Musk, once iterated a need for humanity to be careful with AI — that AI was “potentially more dangerous than nukes” (Etzioni, 2017).

Musk also shared a need for regulatory oversight of AI “at the national and international level” (Etzioni, 2017). Objectively, global governance of AI is worthwhile — no different than the laws that check nuclear proliferation or the international cooperation that supports the internet. The International Telecommunication Union (ITU), the United Nations’ (UN) “specialized agency for information and communication technologies”, has been working with various stakeholders — from “governments, industries, academic institutions, and civil society groups” — to address ways in which AI can be both used for good and regulated for the public interest (AI for Good, 2018). As such, one of the concerns raised by ITU is over the threat AI poses to employment. While AI affords the benefit of new employment opportunities, this is jeopardized by the mass augmentation and automation of repetitive tasks found in lower-level positions. High unemployment has the potential to destabilize communities — if not entire countries/regions. To address the negative employment consequences of AI — along with the other threats and challenges that are posed technologically — regulations that support safety, data security, digital literacy, and accountability are encouraged (AI for Good, 2018).

Lastly, when extracting value from AI, there are specific business processes/disciplines to consider. Whether natural language processing or deep learning, studies show the implementation of AI has failed to significantly benefit “40% of organizations” that make major investments — regardless, there is sustained interest with “91.5% of firms reporting ongoing investment in AI” technologies and solutions (Davenport, 2020). From the perspective of a business professional, AI has tremendous value despite ongoing difficulties in extracting this value in all industries. Specifically, AI supports and provides technological solutions to the following: portfolio and risk management, as well as strategic business execution. In the world of business, risks abound — there are countless opportunities to take advantage of and threats to address. Effective risk management seeks to prevent as many risks as possible from becoming issues, while at the same time embracing risks with positive potentiality. Risks refer to uncertainties — unknown conditions or futures whose occurrence might result in a positive or negative effect on business objectives (Canvas, 2021). Risk can also be understood as the likelihood of failure to meet some objectives (Canvas, 2021). When managing risks, an assessment is conducted to determine the risk’s probability and potential impact. Thereafter, several risk response strategies and contingency measures are formulated to effectively address the risk.

With respect to strategic business execution, it refers to efforts to achieve business objectives by planning and implementing various strategies (Canvas, 2022).

When it comes to the strategies that shape the planning, operations, and changes an organization undergoes, the capability of an organization to achieve such is critical — as is the ability of the organization to effectively align its strategies with the execution of any initiative (Canvas, 2022). Strategic capabilities relate to an organization’s “strategy and vision” and can be thought of as the resources required to transform ideas into successful realities (Boatman, 2022). All organizations should build up their strategic capabilities because this will help organizations achieve greater success with their projects. Strategic capabilities optimize business performance “through a disciplined approach of identifying, prioritizing and approving” project, program, and portfolio elements (Canvas, 2022). Moreover, strategic capabilities address the various strengths of the company that can contribute to a competitive advantage — “people, resources, skills and capacities” (Hartman, 2019). Organizations rely on strategic capabilities to remain competitive in their respective industries/business environments. In total, improved strategic capabilities provide a competitive advantage, greater adaptability/flexibility to respond to change, and drive business performance upwards via stability (Boatman, 2022). Therein, the ability of an organization to compete is directly tied to the successful implementation of its strategies — this necessitates investment in those areas that advance strategic capabilities and strategic business execution.

As for portfolio management, it is considered a “bridge” between strategy and execution because it sits between each measure as a determinative element (Canvas, 2022).

Portfolio management resolves which projects business strategy should be focused on and, thereafter, which projects to execute — and so, portfolio management supports organizations in making “key investment decisions” (Canvas, 2022). Thus, the main focus of portfolio management is determining “the right projects” for the organization to invest in (Canvas, 2022). An unworthy investment should not be executed — and so, portfolio management is a metaphorical bridge that is crossed to reach the side of project execution. Portfolio managers ascertain if a business initiative is worth the time, money, and other resources of an organization — whether the bridge is worth crossing; whether the initiative is safe or too risky to execute. As an intermediary bridge, portfolio management enables successful strategic business execution because it aligns the execution of business with the organization’s strategy, guides decisions pertaining to project investments and resource allocation, strengthens organizational legitimacy, focuses on portfolio performance, and informs risk management (Canvas, 2022). From this, a connection is drawn between risk management, portfolio management, and successful strategic business execution — and AI can be beneficial in each of these areas.

The integration of AI with risk management has born positive results in the form of “risk intelligence” (OnSolve, 2022). Granted AI excels as a predictive asset — “detecting and anticipating problems” that might arise during different stages of technological development (MyComputerCareer, 2022) — risk intelligence is a beneficiary of the combination of AI with risk management. Together, AI and risk management elevate an organization’s ability to assess and respond to risks by proactively and continuously monitoring data sources (OnSolve, 2022). Because AI enables technology to quickly analyze and recognize “huge amounts of data” and “recurrent patterns” (Softengi, 2022), AI bolsters risk management by improving the speed at which risks are identified and remedied. Moreover, AI is benefited by automation such that a human operative may not be required to analyze a risk’s probability of occurrence or severity of impact — a simple assessment of risk histories, other data sources, and pattern recognition may prove more than enough to protect an organization from threats while also capitalizing on opportunities that a human operative might overlook. Also, the increased system security of AI’s advanced algorithmic processes supports the management of risk even further (MyComputerCareer, 2022). Not only will AI better filter project vulnerabilities, but it will also suggest — given the same artificially-intelligent predictive model — the optimal actions to address risk in orders of likely severity.

With the advent of a highly predictive AI, the notion that risk planning for large and complex projects is difficult due to the challenge of predicting risks beyond the project team’s foresight (Wu, 2020, p. 187) becomes increasingly obsolete. As an example, AI has already been incorporated into banking risk management. Some of the AI-affiliated technologies relied on by banks for risk management include Machine Learning, Deep Learning, Natural Language Processing, Analytics, and Big Data (Intel, 2022). The tech industry supplies banks with these AI risk management tools “to mitigate losses, spot market opportunities, and improve their bottom line” (Intel, 2022). By accessing “a vast number of data points…to spot patterns and predict outcomes”, AI technologies allow banks to better understand risks and more effectively address them (Intel, 2022). Overall, by informing business-critical decisions with “actionable intelligence” — and expediting “critical event response and risk mitigation” for enhanced harm reduction — AI greatly contributes to the upward propulsion of business value (OnSolve, 2022). These benefits are found throughout numerous industries, not just the financial — revealing the extent to which AI has made every industry more dependent than ever before on the tech industry for a competitive edge.

As for AI’s role in portfolio management and optimizing strategic business execution, benefits can similarly be extracted. As “a bridge” between strategy and execution (Canvas, 2022), portfolio management can benefit from AI when determining which projects should be the focus of business strategy and execution by automatically making “key investment decisions” (Canvas, 2022) based on past failures and successes. AI can analyze data to recognize previous failures and successes, and patterns of alignment between the organizational strategy and the execution of portfolio components — patterns that lend themselves to determinations for where to invest resources without waste. The “advanced algorithmic functions” of AI that support quality assurance and “increased automation” (MyComputerCareer, 2022) will lead to the automated selection of worthy projects for investment. AI, in conjunction with effective portfolio management, will better guide key investment decisions, improve resource allocation, and fortify risk management and protections against evolving threats (Canvas, 2022). As a consequence, AI adds tremendous value to organizations that rely on portfolio management as this combination enhances overall business performance (The Standard for Portfolio, 2017, p. 5).

Finally, a major rule of business execution conveys the importance of establishing good metrics and reward systems. There is a saying that you can only achieve what you measure — this applies to both “business and people” (Canvas, 2022).

However, it is not always easy to determine the correct “metrics” (Canvas, 2022). Therein, to optimize success, organizations should consider if operations occur under a “consistent set of metrics” or “Key Performance Indicators (KPIs)” — metrics that support and bolster predictions and analysis (Canvas, 2022). Current metrics should be critically challenged and checked to ensure “the desired outcome” is being measured (Canvas, 2022). For all of this, AI will be able to utilize machine learning and data analysis to automatically identify the best metrics to optimize predictions, performance, and business outcomes. AI can rely on past performance measures and other data sets to strengthen the linkage between the organization’s corporate vision/strategies and business initiatives — with research already showcasing the clear impact the quality of this link has on the success of strategic business execution (Canvas, 2022). Moreover, when it comes to allocating the right resources to effectively execute business strategies and the “optimization of internal business operations”, AI is coupled with an empowering “surge in data generation and computing power” to achieve this (Duhaime et al., 2021, p. 625). When it comes to the impact of internal and external organizational environments, the advanced algorithms of AI will expand “the scale, scope, and speed of analysis” of the business climate and market (Duhaime et al., 2021, p. 628) to assess what initiatives are worth pursuing. As such, AI truly has the potential to “transform organizations” (Duhaime et al., 2021, p. 625) — and the rapid rise of AI will only further reward the objectives of businesses in the future.

About the Author:

John Izuchukwu is a graduate student at the Feliciano School of Business. He previously earned a bachelor’s in International Justice from Montclair State University. He has an avid interest in the technological and international business sectors. See the author’s LinkedIn profile here: https://www.linkedin.com/in/johniz-izuchukwu2010/

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