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Role of AI in Product Management Automation and Effectiveness

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Hari Gonaygunta at University of the Cumberlands
Hari Gonaygunta
Pawankumar Sharma at University of the Cumberlands
Pawankumar Sharma
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Abstract

Technological advancement has revolutionized the onset of artificial intelligence (AI) in product management across various organizations. This research paper explores the impact of artificial intelligence on the automation of routine product management tasks within organizations, hence the efficiency improvement. The study further incorporated leveraging the theoretical framework of the Resource-Based Theory (RBT) and the incorporation of the various AI capabilities in identifying the critical AI-specific resources essential for developing robust AI capabilities in product management. The research methodology incorporated included the development of the AI essential for measuring the AI capabilities and the interrelation with organizational creativity, hence enhancing performance. The findings drawn from this research provided compelling evidence of AI's capability to enhance organizational creativity and performance.
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Role of AI in Product Management Automation and Effectiveness
Hari Gonaygunta1, Pawankumar Sharma2
1,2School of Computer and Information Sciences, University of the Cumberlands, KY
1hgonaygunta3255@ucumberlands.edu, 2psharma8877@ucumberlands.edu
Submitted: December 2021
Abstract: Technological advancement has revolutionized the onset of artificial intelligence
(AI) in product management across various organizations. This research paper explores the
impact of artificial intelligence on the automation of routine product management tasks within
organizations, hence the efficiency improvement. The study further incorporated leveraging
the theoretical framework of the Resource-Based Theory (RBT) and the incorporation of the
various AI capabilities in identifying the critical AI-specific resources essential for developing
robust AI capabilities in product management. The research methodology incorporated
included the development of the AI essential for measuring the AI capabilities and the
interrelation with organizational creativity, hence enhancing performance. The findings drawn
from this research provided compelling evidence of AI's capability to enhance organizational
creativity and performance.
Keywords: AI, Product Management, Resource-Based Theory, Organizational Creativity,
Performance
Introduction
Artificial Intelligence (AI) forms the major transformative change witnessed in the current and
contemporary business landscape. The technology, as influenced by the burgeoning data
reservoirs and cutting-edge computational technologies, has confounded the origin of AI in
various technological innovations and inventions (Sharma & Dash, 2020). The various
organizations have consequently transformed into the technological paradigm, hence the
adoption of AI to solve the various complex contexts in organizational operations (Pan &
Zhang, 2021). However, even upon adopting the technologies, the organization still grapples
with the challenges of realizing the full AI potential capabilities and effectiveness.
This research explored the intricacies of AI capabilities, especially regarding the resources
requisite for establishing a robust AI framework. The research study centered on automating
repetitive product management tasks alongside the spheres through which AI accords profound
promise (Li et al., 2019). Further, the research elucidates that AI deployment is significant in
the augmentation of organizational efficiency and performance, hence the competitive aspect
within the dynamic business context.
AI forms the emphasis aspect within the organization's technological, strategic adoption. The
adoption follows the increasing benefits of AI adoption, hence the vast incorporation. The
increased AI adoption centers on the aspiration to improve the business output and consequent
growth. Some organizations have encountered different challenges in adopting the technology,
as some reports recognized minimal to almost negligible business impact as accrued from the
AI ventures (Dash et al., 2018). Hence, these companies witnessing almost negligible benefits
grapple with the high costs of implementing AI technology in their operations against the likely
benefits realized. Hence, these aspects sparked curiosity in investigating the benefits of
incorporating AI in automating repetitive product management tasks and improving efficiency.
Figure 1. AI model for use in business
Research Questions
• What are foundational resources essential for developing an AI capability to automate
product management tasks?
• What are the benefits of establishing AI capability in organizational creativity and
performance within product management?
• To what extent does the organizational maturity in AI adoption influence AI-driven
effectiveness in product management automation?
Literature Review
AI in Product Management Automation
AI technology in modern organizational development, rooted in data availability and
sophisticated techniques, forms the foundation for the automation witnessed in various
organization operations. AI can potentially revolutionize product management operations,
especially in the various activities surrounding product development within a particular
product. Modern organizational development, especially in inventory management, demand
forecasting, and customer segmentation, has revolutionized with technological advancement.
The optimization of these operations through the implementation of AI-driven perspectives has
revolutionized business operations, especially with the demonstration of the various likely
benefits (Paul et al., 2021). AI-driven approaches such as machine learning algorithms form a
foundation for backing the AI system in analysing vast amounts and facilitating accurate
predictions and recommendations, which reduces the augmentation of manual strategies in
daily operations.
Efficiency Gains Through AI Adoption
The vast technological integration, such as AI in product management tasks, has proved the
various likely efficiency gains realization. For instance, the automation of repetitive tasks has
culminated in improved time management when designing a particular product alongside
reduced human errors, improving product development efficiency. The precision machine
learning technology, as incorporated through AI, enhances the accuracy in product
development alongside precision and augmented decision-making. For instance, the AI-
powered demand and future market trends forecast have influenced the effective adjustment in
the inventory levels alongside the assurance that the products remain readily available to the
esteemed company consumers (Kokina & Davenport, 2017). This aspect has improved
customer confidence, especially in product availability, hence the improvement in customer
loyalty. Besides, the organizations that have embraced this technology have reported the
adjustments in the restocking plans essential in avoiding overstocking alongside the
improvement in the company’s continuous supply and revenue generation, hence the
sustainability (Verma et al., 2021). In addition, the current dynamic nature of business
operations requires modernized technological responsiveness to ensure continuous production
and sustainability.
Resource-Based Theory (RBT) and AI Capability
The extensive and robust RBT is essential for the analysis and comprehensive understanding
of the development of AI capabilities within various organizations. For instance, according to
the RBT, aspire for a competitive advantage in the scramble for product development using the
leveraging of the diverse and valuable resources that have previously culminated the obstacles
in the development of substitutes. The context of the AU has influenced organizations in
identifying and cultivating various specific resources, as exemplified by the data, technology
infrastructure, and human skills alongside the establishment of extensive AI capabilities
(Makridakis, 2017). The capability has, therefore, influenced the organization in the extensive
deployment of various AI-driven technologies in the automation of the product management
task, hence improving operational efficiency.
AI Capability and Organizational Creativity
Establishing an efficient AI provides prospects of a profound impact on organizational
creativity, influencing the company's efficiency in the various activities surrounding product
development. The automation of routine tasks has influenced the liberation of employees from
mundane responsibilities, influencing strategic and innovation endeavors (Huang & Rust,
2021). The increasing shift has led to the onset of an innovative and creative culture alongside
the idea generation within the organization. This approach accrues from the employees’
empowerment in exploring the various ideologies and approaches in the company operations.
AI accords the valuable insights and recommendations essential for inspiring novel product
development initiatives within the business organization.
AI Capability and Organizational Performance
Integrating AI capabilities within product management is essential in enhancing organizational
performance. Various empirical studies have depicted organizations successfully developing
and implementing robust AI capabilities with enhanced key performance indicators (KPIs).
Revenue generation has significantly increased as AI-driven technologies facilitated the
identification of new production streams, enhancing revenue generation (Mikalef & Gupta,
2021). Besides, organizations have taped the pricing strategies optimization and enhanced
cross-selling and up-selling opportunities.
AI technologies have efficiently improved the cost-optimization crucial in enhancing
organizational efficiency. The automation of the various operations, especially in the product,
facilitates data-driven decision-making as AI streamlines the organization's operations
alongside the reduction of manual interventions and resource allocations (Javaid et al., 2021).
This approach has reduced operation costs, improving profits and resource utilization.
Streamlined operations reduce product deficiency alongside continuous production, improving
customer satisfaction. Leveraging AI-driven insights gives organizations an extensive
understanding of customer preferences and behaviors, hence easily adopting new production
models to improve customer satisfaction (Zhang et al., 2021). Besides, the approach enhances
personalized and tailored experiences, improving revenue generation through increased
customer loyalty.
Figure 2. AI and Strategic Marketing Decisions
Theoretical Framework
Resource-Based Theory (RBT) forms the guiding principle for this research study. According
to the RBT proposition, organizations compete against each other based on the resources
owned by the organization, upon which the rare and difficult imitating products lead to
increased performance gains. In the context of AI, this approach emphasizes the need to
identify and develop the specific resources essential for formulating robust AI capability
(Raisch & Krakowski, 2021). The approach posits the tangible resources, such as data and
technology, while the intangible resources, such as inter-departmental coordination and
organization change capacity, are the cofounding technologies essential for driving the
competitive advantage possessed by the various organizations.
Methodology
Data Collection: This research study incorporated the multifaceted data collection approach.
For instance, the data included a review of the 50 existing literature materials on AI,
organizational capabilities, and product management. The approach provided a comprehensive
overview of the essential concepts and identification of the key resources essential for building
the AI capability. Surveys conducted on approximately 150 study participants provided
insights into the benefits and roles of AI in improving repetitive product management tasks and
efficiency.
Theoretical Framework: This research’s theoretical approach centers on Resource Theory
(RBT), paramount in strategic management. The organizations gain a competitive advantage
by harnessing and leveraging unique, valuable, and non-substitutable products. This research
study contained the various automation of the product management tasks assessment, including
the tangible data repositories and the advanced infrastructure alongside the intangible aspect,
as exemplified by cross-functional coordination and adaptability to organizational change.
The operationalizing of the RBT framework comprised a comprehensive literature review
emphasizing identifying the critical resources fundamental for AI formulation. The review
comprised academic papers, industry reports, and case studies, hence the nuanced
comprehension of the pivotal resources in AI-driven product management.
Data Processing and Analysis: Thematic data analysis application in this research study
helped identify the recurring patterns and the key themes of AI capabilities and organizational
resources. This qualitative data analysis only dealt with the survey data. Statistical techniques
like regression and correlation analysis aim to quantify the interrelation between AI
capabilities, identified resources, and organizational performance metrics. The mixed data
analysis approach included the research questions and a comprehensive basis for formulating
the conclusions.
Results and Discussions
For scientific observation, the statistical power of results is crucial. The research findings
indicated a positive correlation between the development of AI capabilities and enhanced
organizational performance within product management (Dash & Ali, 2019). The correlation
underscores the leveraging of RBT in enhancing repetitive product management and improving
efficiency (Shneiderman, 2020). The organizations that have effectively harnessed AI
technologies have managed to augment their product management capabilities, hence the
transformative enhancement of the key performance indicators (Zhang et al., 2021).
This research study has also depicted the increased revenue generation within the organizations
that have implemented AI technologies, especially pricing strategies. The well-established AI
capabilities are fundamental in enhancing the substantial growth revenue streams with the
surge attributed to the AI-driven technologies driving the identification and exploitation of the
various new revenue generation streams, especially in product development (Abioye et al.,
2021). The harnessed data-driven insights have led to optimizing the pricing strategies and
identifying the ultimate technologies for tangible bottom boost.
The integration of AI has culminated in the improvement in cost optimization. Automating the
production processes has streamlined the processing and enhanced data-driven decision-
making and resource allocation. The reduction in manual interventions and improved resource
utilization enhance cost savings. The inefficiencies have increased profitability alongside the
organizational positioning in resource allocation to innovation and growth initiatives.
The AI technologies integration in product development has increased customer satisfaction.
The AI-driven insights provide a profound analysis of customer preferences and behaviors. The
technology empowers organizations to adapt and respond to the swift changes in the dynamic
market dynamics (Wamba-Taguimdje et al., 2020). The assurance of product availability and
customer experience personalization has enhanced customer loyalty satisfaction, increasing
customer retention and revenue growth.
The organizations that have embraced AI-driven technology have recorded profound AI
capabilities. For instance, the employees have indicated reduced mundane and repetitive tasks,
hence facilitating the redirection of the focus toward extensive strategic and innovative
endeavors (Benbya et al., 2020). Therefore, the shifting work dynamics have nurtured
creativity in the organization and innovation within the production, improving product
development and efficiency gains.
Figure 3. AI Capability and categorization of resources
This research study's findings further underscore the transformative potential of AI in product
management. The integration of the AI alongside the strategic critical resources. Integrating AI
capabilities alongside critical resource allocation has improved tangible and substantial
revenue generation enhancements, cost optimization, and customer satisfaction (Benbya et al.,
2020). The findings validate the RBT theoretical framework in providing actionable insight to
organizations seeking to harness the AI tools' powers and competitive advantage.
Conclusion
This research study has substantiated the essential roles of AI capabilities in automating
repetitive product management tasks and enhancing organizational efficiency. The study has
also leveraged the RBT theoretical framework in identifying and examining the fundamental
resources for developing a robust AI capability. According to the empirical evidence, the
tangible benefits of AI integration in product management have ultimately enhanced
organizational success.
The research study has encountered some limitations in its formulation. For instance, the
research findings continue to become obsolete and invaluable due to technological
advancement and the formulation of sophisticated technologies. The study has also focused on
the larger organizational perspective rather than blending both small and large organizations to
enhance the relevance and extensive nature of the study. Future research can delve into the
applicability of AI capabilities within smaller enterprises and likely compare the imperative
findings. Besides, a longitudinal study is likely to provide a more valuable framework for
organizations seeking to harness the power of AI and enhance the efficiency of the
organizations alongside the competitive advantage in the dynamic and evolving business
landscape.
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... smart supply chains, connected health, and smart cities. Agricultural, linked car, and industrial internet of things (IoT) projects have seen a 25% decline in completion rates in the US and EU (Forbes, 2018). With an estimated 11,099,999 people expected to call densely populated urban areas home by the end of the century (Pewresearch, 2019), (Hari Gonaygunta, et. al.,(2021)) the concept of a "smart city" may play an increasingly important role in easing the burden on already-stretched urban infrastructure. ( Amin Gharehbaghi. et. al., 2023) ...
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Explainable AI (XAI) has emerged as a crucial field of research in machine learning, aiming to address the black-box nature of complex models and provide human-interpretable explanations for their decisions. As machine learning models become increasingly sophisticated and deployed in critical domains, such as healthcare, finance, and autonomous systems, there is a growing demand for transparency and accountability in their decision-making processes. This abstract provides an overview of the concept of explainable AI, highlighting its significance, key challenges, and various techniques used to interpret and understand machine learning models. The abstract begins by emphasizing the importance of interpretability in machine learning models. While highly accurate models like deep neural networks have achieved remarkable performance across numerous domains, their decision-making processes often lack transparency, hindering their adoption in real-world applications. Explainability is crucial to building trust, ensuring fairness, and avoiding potential biases and discrimination in automated decision systems. Next, the abstract discusses the challenges associated with building interpretable models. It explores the trade-off between model complexity and interpretability and the inherent tension between accuracy and explainability. Furthermore, it highlights the need to strike a balance between model transparency and preserving privacy and proprietary information. The abstract then provides an overview of various techniques and approaches employed in XAI. It discusses rule-based methods, which generate decision rules to explain model decisions in a human-readable format. It also explores feature importance techniques, such as permutation importance and SHAP values, which assign relevance scores to input features. Additionally, it covers model-agnostic approaches, such as LIME and SHAP, which provide post-hoc explanations by approximating the model's decision boundary locally. The abstract also mentions the use of visualizations and interactive tools to aid in understanding complex models. Lastly, the abstract highlights the importance of evaluating and validating the explanations generated by XAI methods. It discusses the need for metrics and standards to assess the quality and reliability of explanations and highlights ongoing research in this area. Introduction:
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Importance of Hypothesis Testing, Type I, and Type II Errors -A Study of Statistical Power
Conference Paper
Full-text available
  • Dec 2019
The statistical interpretation of data is crucial to research techniques and scientific studies. To infer something statistically, one must estimate and test a hypothesis. This is known as statistical inference and the 'Testing of Hypothesis' discussion is crucial when doing a statistical observation. The null and alternative hypotheses used in hypothesis testing result in Type I and Type II errors depending on whether they are accepted or rejected. We focus more on Type II mistakes when discussing statistical power since they are less likely to occur as statistical power increases. This study offers fresh perspectives and a thorough examination of each of these statistical variables, as well as how statistical power affects daily statistical inference and decision-making.
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A Data-Driven AI Framework to Improve Urban Mobility and Traffic Congestion in Smart Cities
Article
Full-text available
  • Nov 2018
One of the most talked-about problems at the start of the twenty-first century is effective transportation, which is one of the numerous challenges the globe is experiencing. Technology is playing a critical role in helping to tackle the current transportation problems as smart cities evolve. Smart cities feature the modernized form of civilization worldwide as they leverage increasing technological advancement, including Artificial Intelligence, in running city initiatives alongside addressing urban challenges. Traffic forms a major challenge in urban development due to various factors, such as poor planning. The innovative approaches, exemplified by integrating shop and delivery options in smart cities, will alleviate traffic congestion challenges. This research study pinpoints the Actor-Network Theory (ANT) principles and pragmatism as the guiding approaches in the research and enhancing the integration. The data collection methods included in the research included interviews, reports, and media content essential for understanding the complex interrelations of smart city initiatives. As depicted in the data analysis, the ANT and pragmatic framework form the foundation of shaping the future urban landscapes.
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Big Data-IoE Relationships and the Future of Smart Cities
Article
Full-text available
  • Jun 2020
Urbanization has become a serious problem across most countries. Many developing countries like China and India are identified as suffering from the rise of urbanization over the past few years. The development of smart cities is an effective solution to major problems within most cities today. Internet of Everything (IoE) is identified as a critical solution needed in developing smart cities of the future. The Internet of Everything is identified as the networked connection of data, things, processes, and people. This means that IoT devices are a crucial element of the IoE but big data plays a key role in storing and processing those information for insight extraction. The study adopts a systematic review methodology. The selected articles are reviewed and analyzed to provide answers to the objectives selected. Each of these concepts is guided by the multidisciplinary theoretical framework. The Framework identifies that the components within a smart city should be interconnected, as identified in the pillars of the IoE.
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Blockchain Adoption in Enterprises: Opportunities and Challenges
Article
Full-text available
  • Feb 2020
Blockchain is one of the greatest inventions of the 21 st century. Since its inception in 2009 to till date, its applicability has increased manyfold in all business operations. Initially, blockchain technology appeared to be too risky for corporate use since it featured a decentralized, peer-to-peer architecture that allowed anyone to join. That began to alter in 2016 with the development of full enterprise platforms by a rising open-source community, including the Ethereum programmable blockchain and Hyperledger, a framework and set of tools for creating industry-grade blockchains. Blockchain is being used in many businesses not just as a cryptocurrency but also as a tool to protect data, privacy, and authentication methods. Because it increases the level of confidence in technical use, it becomes easier to transact with unidentified parties, thus extending markets and driving up demand for goods and services, which can increase profitability. It causes a lot of hurdles for simple and adaptable adoption in modern organizations, as well as many opportunities to preserve and develop opportunities. In this post, the specifics are laid down while taking into account both aspects of blockchain usage.
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Substantial Capabilities of Robotics in Enhancing Industry 4.0 implementation
Article
Full-text available
  • Jun 2021
There is the increased application of new technologies in manufacturing, service, and communications. Industry 4.0 is the new fourth industrial revolution, which supports organisational efficiency. Robotics is an important technology of Industry 4.0, which provides extensive capabilities in the field of manufacturing. This technology has enhanced automation systems and does repetitive jobs precisely and at a lower cost. Robotics is progressively leading to the manufacturing of quality products while maintaining the value of existing collaborators schemes. The primary outcome of Industry 4.0 is intelligent factories developed with the aid of advanced robotics, massive data, cloud computing, solid safety, intelligent sensors, the Internet of things, and other advanced technological developments to be highly powerful, safe, and cost-effective. Thus, businesses will refine their manufacturing for mass adaptation by improving the workplace's safety and reliability on actual work and saving costs. This paper discusses the significant potential of Robotics in the field of manufacturing and allied areas. The paper discusses eighteen major applications of Robotics for Industry 4.0. Robots are ideal for collecting mysterious manufacturing data as they operate closer to the component than most other factory machines. This technology is helpful to perform a complex hazardous job, automation, sustain high temperature, working entire time and for a long duration in assembly lines. Many robots operating in intelligent factories use artificial intelligence to perform high-level tasks. Now they can also decide and learn from experience in various ongoing situations.
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Artificial intelligence in marketing: Systematic review and future research direction
Article
Full-text available
  • Jan 2021
Disruptive technologies such as the internet of things, big data analytics, blockchain, and artificial intelligence have changed the ways businesses operate. Of all the disruptive technologies, artificial intelligence (AI) is the latest technological disruptor and holds immense marketing transformation potential. Practitioners worldwide are trying to figure out the best fit AI solutions for their marketing functions. However, a systematic literature review can highlight the importance of artificial intelligence (AI) in marketing and chart future research directions. The present study aims to offer a comprehensive review of AI in marketing using bibliometric, conceptual and intellectual network analysis of extant literature published between 1982 and 2020. A comprehensive review of one thousand five hundred and eighty papers helped to identify the scientific actors' performance like most relevant authors and most relevant sources. Furthermore, co-citation and co-occurrence analysis offered the conceptual and intellectual network. Data clustering using the Louvain algorithm helped identify research sub-themes and future research directions to expand AI in marketing.
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Artificial Intelligence Capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance
Article
Full-text available
  • Jan 2021
  • INFORM MANAGE-AMSTER
Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.
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Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse
Article
Full-text available
  • Apr 2021
  • INT J INFORM MANAGE
Despite heightened interest, integrating artificial intelligence (AI) into businesses remains challenging. Recent surveys show that up to 85 % of AI initiatives ultimately fail to deliver on their promises. Studies on successful AI applications that could provide invaluable lessons for organizations embarking on their AI journey are still lacking. Therefore, this study aims to understand how AI technology, people, and processes should be managed to successfully create value. Building on the resource orchestration perspective, this study analyzes the successful applications of AI at Alibaba's e-commerce fulfillment center. The findings indicate that the key AI resources include data, AI algorithms, and robots. These resources must be orchestrated (e.g., coordinated, leveraged, deployed) to work with other related resources, such as warehouse facilities and existing information systems, to generate strong AI capabilities. The key AI capabilities generated include forecasting, planning, and learning. More importantly, AI capabilities are not independent – they interact and coevolve with human capabilities to create business value in terms of efficiency (e.g., space optimization, labor productivity) and effectiveness (e.g., error reduction). The implications of understanding these social informatics of AI for research and practice are discussed.
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Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges
Article
  • Oct 2021
The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An advanced digital technology, Artificial Intelligence (AI), is currently revolutionising industries such as manufacturing, retail, and telecommunications. The subfields of AI such as machine learning, knowledge-based systems, computer vision, robotics and optimisation have successfully been applied in other industries to achieve increased profitability, efficiency, safety and security. While acknowledging the benefits of AI applications, numerous challenges which are relevant to AI still exist in the construction industry. This study aims to unravel AI applications, examine AI techniques being used and identify opportunites and challenges for AI applications in the construction industry. A critical review of available literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimisation was conducted. Furthermore, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study provides insights into key AI applications as it applies to construction-specific challenges, as well as the pathway to realise the acrueable benefits of AI in the construction industry.
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