Challenges of implementing artificial intelligence for smart and sustainable industry: Technological, economic, and regulatory barriers

Smart and sustainable is the way forward when it comes to industries, and, although artificial intelligence (AI) is the pathway to the transformation, it has its own set of challenges for massive incorporation. First, it costs a lot to build an AI infrastructure, investment would be hard for a lot of organizations, for instance, small and medium-sized enterprises (SMEs), to made coveted in the market. The field of AI is multi-layered, demanding technically sound workforce specializing in data science and machine learning, facilities a scarce resource at the global level. Also, in any industry with sensitive data there are big issues that arise alongside the adoption of AI systems, specifically related to data privacy and security. Ethics is another important issue and without careful handling tradition biased humans through AI can lead to a turbocharged outcome. In addition, operational issues are rampant; integrating AI into legacy systems and operations can be complex and time-consuming. This is not scalable at run-time due to the dynamic nature of AI technologies and results in incrementing operational burden of continuous updates/maintenance. Even so, legal issues accompany AI as it continues to grow in popularity, as the rules that apply to AI are forming and have significant differences depending on the region. Addressing such challenges will demand an integrated system approach, encompassing government ordinance, academic learning and industry exposure to enact a conducive policy environment, educate and train manpower properly and encourage innovation in creating efficient and sustainable AI-based solutions.

Keywords: Challenges, Artificial intelligence, Data quality, Data privacy, Cost, Data security, Ethics.

Citation: Rane, N. L., Desai, P., & Choudhary, S. (2024). Challenges of implementing artificial intelligence for smart and sustainable industry: Technological, economic, and regulatory barriers. In Artificial Intelligence and Industry in Society 5.0 (pp. 82-94). Deep Science Publishing. https://doi.org/10.70593/978-81-981271-1-2_5

5.1 Introduction

The technology is all set to take several industries by a storm as it has the potential to provide them with streamlined projects and an innovative and sustainable approach towards their business thereby driving efficiency on how things are done. Automation, data-driven decision-making, and optimized processes are the hallmarks of the new smart industries powered by artificial intelligence (AI) technologies deployed in industrial operations (Sun, & Medaglia, 2019; Peres et al., 2020; Rane et al., 2024a; Rane et al., 2024b). But deploying AI for industry is a complex task, which is fraught with difficulties that can limit its opportunity for promoting more sustainable businesses (Kaplan, & Haenlein, 2020; Celik et al., 2022; Peres et al., 2020; Paramesha et al., 2024a; Rane et al., 2024c). A major obstacle arises from the technical complexity of AI systems (Bhima, et al., 2023). Both AI solutions need to be steeped in machine learning, data science, and engineering expertise. Many industries find it hard to gather the talent pool and resources to build strong AI models. In addition, deploying AI on current industrial systems is a challenging enterprise because of interoperability issues, the inability to scale AI applications, and the quality of data (Aldoseri et al., 2023; Pansara, 2023). AI works from the organization end as well. Resistance to change, poor strategic vision and a lack of leadership to understand and support AI are a key impediment in the adoption of AI technologies. For instance, a lot of businesses may be challenged with integrating their AI strategy towards a bigger business goal, and the same is true for sustainability goals. This misalignment leads to suboptimal outcomes and wastes resources and undermines the potential benefits of AI. A second great challenge is, of course, posed by ethical considerations (Du, & Xie, 2021; Slimi, & Carballido, 2023; Paramesha et al., 2024b). AI in industries triggers worries about job loss, privacy, and prejudice. It is crucial to get public trust and acceptance that AI systems are transparent, fair, and accountable (Arrieta et al., 2020; Longo et al., 2020; Das, & Rad, 2020). Navigating the ethical landscape is important so that industries are able to avoid adverse societal impacts and create more responsible AI. This research attempt to put as much light into the challenges faced by the industry towards implementation of AI for smart and sustainable industries.

Contributions of the research:

• The current research aims at systematically reviewing the literature associated to the application of AI in industries and to establish a sense of the key areas covered, and also identifying themes and gaps of AI-based implementation in industries.
• Using the challenges keywords that can be extracted from each publication, the pipelines are able to determine the main themes that cover most of the publications and provide the landscape of the current research field.

5.2 Methodology

This research consists of a detailed literature from databases like IEEE Xplore, Scopus, Google Scholar, etc of academic articles, conference papers and relevant publications. The search keywords were "artificial intelligence," "smart industry," "sustainable industry," "challenges," "AI challenges" and "industry 4.0." Looking at the keywords from the collected literature, analysis is further used to discover key themes and trends. That process has included the extraction and examination of percentiles with respect to keywords in the abstracts and titles of these selected articles to gauge the frequency and the relationship among them. The results from these analyses served to offer a structured understanding on the current state of research and emphasized areas and gaps that must be addressed at different fronts on AI integration into industrial environments.

5.3 Results and discussions

Challenges of implementing AI for smart and sustainable industry

AI has the potential to transform industry, connected with efficiency increases, waste reductions, and more sustainable business practices (Sun, & Medaglia, 2019; Peres et al., 2020). The achievement of those benefits is not free from critical challenges. AI in creating intelligent and green industries suggests a complex interplay among technological, economic, social, and regulatory dimensions. One of the main challenges is that AI needs to be fitted into legacy industrial infrastructure. Most of the industries work with old systems, none of which were designed for any kind of AI technology. Making the systems AI-compatible means an enormous investment in finance and time. Much of it implies upgrading the hardware by installing sensors, all the way to finally ensuring that the data makes some sense for AI algorithms. Moreover, heterogeneity in industrial environments implies that no single solution is suitable for all, which requires customized solutions and can be pretty expensive and complex. Table 5.1 shows the summary of the challenges of implementing AI for smart and sustainable industry.

Data is life for AI, but more than anywhere else, the problems in this regard have been singled out in the industrial sector. Although industries generate enormous volumes of data, it is unstructured mainly, heterogeneous, and spread across several departments. For AI implantation to be effective, clean, and quality data is needed, entailing massive efforts in cleaning, normalization, and integration (Lotfian et al., 2021; Aldoseri et al., 2023). In addition, the privacy and security of data are paramount, especially where sensitive industrial information is handled. This adds to the challenge because of the risks of cyberattacks and data breaches in today's world, coupled with the requirement that appropriate cybersecurity measures be taken for the integrity of the data (Bécue et al., 2021; Sontan, & Samuel, 2024). Another major challenge is the shortage of skilled talent (Mukherjee, 2022; Vishwakarma, & Singh, 2023). To implement AI, industries need multidisciplinary teams, including experts in data science and AI, and domain experts who understand the particular context within which an industry operates. Currently, there is a global shortage of such skilled professionals, so it can be difficult for industries to find the right kind of talent that will either lead or support AI initiatives. This also requires the retraining of existing staff to work with AI technologies, which is a resource-intensive task itself.

The costs of implementing AI solutions may also be prohibitive (Mun et al., 2020; Rane, 2023). For instance, the development and deployment of any AI system require substantial upfront investments in technologies, infrastructures, and highly skilled personnel. This could be a cost that not all industries, especially small and medium enterprises (SMEs), can comfortably meet. Further, the return on investment (ROI) from the varied AI projects is mostly indefinite since benefits take time to realize and these are majorly intangible. However, it cannot disregard the ethical and social considerations regarding AI deployment in industries. Finally, an AI system can replace human workers, leading to massive unemployment and social disturbances. It therefore becomes very critical for the sectors to strike a balance between automating and employing people with no adverse effect on the workforce. That would mean reskilling and upskilling at work, an organizational culture of continuous learning, and the identification of strategies for man-machine collaboration. Finally, how AI will be used ethically to make decisions affecting workers and the environment should be taken into cognizance, ensuring transparency and fairness of the AI-driven processes.

Table 5.1 Summary of the challenges of implementing AI for smart and sustainable industry

References

Challenge

Description

Potential Solutions

Lotfian et al., (2021); Aldoseri et al., (2023); Pansara, (2023)

Data Quality

Ensuring high-quality, diverse data for AI models.

Implement data governance, use cleaning tools, collect diverse data.

Irani et al., (2023); Misra et al., (2023)

Legacy Systems Integration

Integrating AI with existing systems.

Use middleware/APIs, phased implementation, modernization.

Shaw et al., (2019); Rajendran, (2021); Dhar Dwivedi et al., (2024)

Scalability

Scaling AI solutions across operations/sites.

Design scalable architectures, use cloud/edge computing.

Mun et al., (2020); Rane, (2023)

Cost

High initial investment for AI technology and personnel.

Cost-benefit analysis, seek grants/subsidies, public-private partnerships.

Mukherjee, (2022); Vishwakarma, & Singh, (2023)

Talent Shortage

Lack of skilled AI professionals.

Invest in training, collaborate with academia, offer competitive salaries.

Chaudhary et al., (2020); Bécue et al., (2021); Sontan, & Samuel, (2024)

Cybersecurity Risks

Increased vulnerability to cyber-attacks.

Implement robust cybersecurity, conduct regular audits, use AI for threat detection.

Cath, (2018); Igbinenikaro, & Adewusi, (2024)

Regulatory Compliance

Navigating complex regulations.

Stay updated on regulations, use compliance systems, engage with regulatory bodies.

Du, & Xie, (2021; Slimi, & Carballido, (2023

Ethical Concerns

Addressing bias, job displacement, transparency.

Develop ethical guidelines, ensure transparency, detect/mitigate bias.

Chui et al., (2018); Nishant et al., (2020); Ahmad et al., (2021)

Energy Consumption

Managing AI's increased energy demands.

Optimize algorithms, use energy-efficient hardware, invest in renewable energy.

Valtiner, & Reidl, (2021); Lemos et al., (2022); Smith et al., (2022)

Change Management

Resistance to AI adoption among employees/stakeholders.

Conduct change programs, provide training, involve stakeholders.

Zeid et al., (2019); Macharia et al., (2023)

Interoperability

Ensuring AI works with various hardware/software.

Adopt open standards, use interoperable platforms, collaborate with consortia.

Feng et al., (2022); Steidl et al., (2023); Aldoseri et al., (2023)

Continuous Improvement

Updating AI models to adapt and improve.

Implement continuous learning, monitor performance, incorporate feedback.

Nishant et al., (2020); Zhao & Gómez Fariñas (2023)

Environmental Impact

Balancing AI's footprint with sustainability goals.

Conduct impact assessments, optimize resource use, invest in green tech.

Wang et al., (2015);

Lin, & Zhao, (2020)

Resource Allocation

Allocating sufficient resources for AI projects.

Prioritize projects, secure executive support, manage resources efficiently.

Arrieta et al., (2020);

Longo et al., (2020);

Das, & Rad, (2020)

Transparency and Explainability

Making AI decision processes understandable.

Use explainable AI techniques, provide clear documentation, transparent communication.

ÓhÉigeartaigh et al., (2020); Budhwar et al.,

(2022)

Cultural Barriers

Fostering a culture of innovation and sustainability.

Promote innovation culture, provide cultural training, highlight success stories.

Lins et al., (2021); Kokkonen et al., (2022)

Vendor Lock-in

Avoiding dependence on specific AI vendors.

Choose open-source solutions, diversify vendors, negotiate flexible contracts.

Reyna et al., (2022); Kulkov et al., (2023); Walk et al., 2023); Fan et al., (2023)

Performance Metrics

Establishing metrics to evaluate AI's impact on sustainability.

Define clear KPIs, use monitoring tools, regularly review/adjust metrics.

 

There are also challenges associated with regulatory and compliance issues (Cath, 2018; Igbinenikaro, & Adewusi, 2024). Although the regulatory environment for AI has not yet been settled, different regions and countries are reaching out to their frameworks and standards. Stakeholders in industries will have to be ahead of such a complex and sometimes fragmented regulatory environment, ensuring that AI implementations align with applicable laws and regulations. This includes adherence to copyright and data protection regulations, standard safety, and environmental guidelines. Staying on top of regulatory change and fulfilling compliance requirements needs dedicated resources specialized in this expertise. Sustainability is an essential objective for innovative industries and has its challenges. Whereas AI can fundamentally work to enhance sustainability through the optimization of resource utilization and waste reduction, the sheer short-term impact of AI technologies on the environment cannot be overlooked. Thereby, AI systems, especially those that cover machine and deep learning, use energy intensively, increasing carbon footprints. The challenge will be one of balancing AI-driven efficiency against environmental costs from the technology (Nishant et al., 2020; Zhao & Gómez Fariñas 2023). It becomes very imperative that industries seek out sustainable AI solutions, such as energy-efficient algorithms and the powering of AI systems through renewable sources of energy.

Another critical challenge is interoperability and standardization (Macharia et al., 2023; Paramesha et al., 2024c; Paramesha et al., 2024d). Industrial environments are typically characterized by several systems and equipment purchased from different vendors based on protocols and standards. Since AI systems must be integrated and communicate well with diversified components within a highly industrialized environment, coupling would become a central aspect of deploying them effectively. Deficient protocols and interfaces can lead to incompatibility problems and thus hinder smooth deployment. To overcome these, developing industry-wide standards and collaboration among vendors, as well as all stakeholders, becomes crucial. The rate at which technological change is occurring is forcing another challenge parameter to implementation. AI technologies themselves are changing very fast as new algorithms, tools, and techniques are in continuous development. This will need to be kept up with by industries to remain competitive, yet it will have heavy resource constraints coupled with a constant learning curve. A strategic approach and investment will be needed to remain current with new developments in AI and to ensure that solutions implemented are not replaced by innovations that make them obsolete. There are cultural and organizational barriers to the adoption of AI within industries (Budhwar et al., 2022; Paramesha et al., 2024e). Few quickly embrace change, and hence, employees and management resist new technologies. This could be because of a lack of knowledge about AI, fear of loss of jobs, or skepticism concerning the returns from the outputs that could come from the use of AI. This will require strong leadership, apparent communication of the value and impact of AI, and a collaborative approach where all stakeholders involved in the adoption process for AI are partakers. Openness to innovation and change has to be created for industries to implement AI.

Fig. 5.1 summarizes in detail the problems in ML support implementation in manufacturing. Overcoming these challenges require robust data handling practices, advanced algorithm development, and practical application strategies. Handling these obstacles is essential if all the potential of ML for enhancing efficiency, cutting costs, and stimulating innovation in manufacturing is to be realized. It identifies three thematic areas where challenges are encountered: Data Acquisition and Processing, Algorithmic Model, and Application & Development.

Data acquisition and processing

Missing or corrupted data sets: Incomplete or damaged data can significantly impede the training and effectiveness of ML models.

Unlabeled datasets: Lack of labeled data makes it hard to train any supervised learning model; extra effort is required in the data labeling process.

Fig. 5.1 Challenges in Implementing Machine Learning (ML) in Manufacturing

Data sharing: Sharing data across different systems or organizations is not without claiming challenges in privacy, security, and interoperability.

Algorithmic Model:

Speed and accuracy: Fast and accurate models are some of the most challenging tasks that real-time apps related to manufacturing require in their development phase.

Specialized applications: Specific algorithms might be called for in every manufacturing process, making the development of adaptable and general models challenging.

Multi-source data blending-Something as complex as integrating multi-source data is indispensable for deep analysis and decision-making.

Applications and Development:

Real-world application: Translation of ML models from theory into practice means that they must be translated subject to real-world constraints and operational integration.

Standardization and normalization: the same data standards and normalization have to be followed similarly across different datasets and systems readings that help in effectual ML deployment.

5.4 Conclusions

There are various hurdles to overcome when it comes to implementing AI as the creator of smart and sustainable industries, these challenges are both technical and non-technical. While AI has the power to completely shift how businesses operate by increasing efficiencies, cutting wastes and promoting environmental sustainability, the path to adopting largely remains difficult. This is a difficult task mainly due to the cost and complexity it affords to develop and keep up with any AI system. Businesses, particularly small and medium-sized enterprises (SMEs) that need to invest to purchase, maintain, and build knowledge of complex machinery. There are also substantial challenges related to data. However, AI ultimately hinges upon its data sets. Quality data is becoming increasingly hard to find. But a lot of industries struggle with amassing and processing data, mostly because of the level of regulation. In addition to addressing the bias in these systems, overcoming biases in AI algorithms and protecting for data integrity are essential to avoid creating flawed decision-making processes that could hinder sustainability initiatives. Apart from this integrating AI in the existing industrial process is what is seems a challenging part of same. Most of the industries are running on legacy systems which are hard to integrate with modern AI. It would require more effort and it can potentially mess up with the current run of things. This also has to do with a large pool of workers not possessing the necessary skills to implement AI; expertise is needed not only in AI technologies but also in specific industries. The closing of this gap demands extensive training programs and a change in the corporate culture. Of course, there are significant ethical and regulatory challenges. The fast development of AI has overwhelmed the established regulatory structures and generated uncertainties and potential obstacle-risks. It is vital that AI applications should comply with ethical standards and contribute to sustainability goals. AI is poised to transform smart and sustainable industries but they must first overcome these wicked problems for successful adoption.

 

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