Background

Malaysia's communications and multimedia (C&M) industry has long been recognised as a hub of technological innovation, spearheading the adoption of cutting-edge technologies to drive industry growth and transformation. As the global landscape continues to evolve, the industry is increasingly embracing the transformative power of artificial intelligence (AI) to enhance its operations, improve service delivery, and drive innovation (Noranee & Bin Othman, 2023; Tiwari et al., 2021).

The Malaysian government has proactively undertaken initiatives to support the integration of AI within the C&M industry, recognising its potential to contribute to the country's digital economy age (Ariffin et al., 2023).

However, the implementation of AI within the Malaysian C&M industry has been observed to be relatively low, with various challenges and barriers hindering its widespread adoption. These challenges include insufficient investment in AI infrastructure and data management, a lack of skilled talent in AI-related fields, concerns over data privacy and ethical implications, as well as uncertainty surrounding regulatory frameworks and governance structures to guide the responsible deployment of AI technologies (Amato et al., 2019).

Problem Statement

AI has progressed far beyond mere theoretical concepts and experimental research, now demonstrating a wide range of practical, real-world solutions through the deployment of AI-powered technologies (Ashfaq et al., 2023). The rapid development and proliferation of diverse AI-based applications have emerged as highly valuable and transformative tools, revolutionising how businesses and organisations across various sectors operate and deliver services (Osama et al., 2023). This technological revolution has ushered in new levels of efficiency, productivity, and innovation, empowering companies to optimise their processes, enhance customer experiences, and drive strategic decision-making in unprecedented ways.

While there has been significant research on the impact of AI in industries such as hospitality and construction, there remains a gap in understanding the specific implications of AI adoption within the C&M industry (Chan-Olmsted, 2019; Chen et al., 2022). The C&M industry is one such industry that has experienced a significant impact from the adoption of AI. For instance, AI-powered content creation tools have enabled the industry to generate more personalised and engaging content for consumers, allowing for more effective targeting and increased customer satisfaction. Similarly, AI-driven personalisation algorithms have optimised customer experiences, leading to increased user engagement and loyalty. Additionally, the industry has utilised AI to automate various operational processes, such as media planning, content distribution, and performance analytics, improving efficiency and productivity (Noranee & Bin Othman, 2023; Zaman, 2022).

The implementation of AI C&M is sufficient C&M industry has the potential to offer a wide range of benefits. AI-powered content creation tools can generate highly customised and engaging content, catering to individual consumers' unique preferences and interests. This personalised approach can lead to improved targeting, increased customer satisfaction, and enhanced brand loyalty (Ljepava, 2022). Furthermore, AI-driven personalisation algorithms can optimise customer experiences by providing tailored recommendations, streamlining service delivery, and anticipating user needs. This can result in higher user engagement, loyalty, and overall satisfaction.

Additionally, integrating AI into operational processes within the industry can drive significant improvements in efficiency and productivity. AI-enabled automation of tasks such as content distribution and performance analytics can reduce manual effort, minimise errors, and accelerate decision-making. This can lead to cost savings, enhanced resource utilisation, and more informed strategic decisions (Chowdhury et al., 2023; Tiwari et al., 2021).

While it is evident that AI has significantly influenced the C&M industry in Malaysia, the extent to which different areas of this industry have benefited from AI adoption, as well as the specific mechanisms through which AI has impacted performance and efficiency, remain unclear. Further research is needed to fully understand the transformative potential of AI in this dynamic industry and to identify best practices for its effective implementation (Votto et al., 2021; Yang, 2022)

This research explores AI's specific use cases and applications within this industry, focusing on its potential to enhance AI governance, develop robust AI frameworks, and optimise operations, as discussed in works by Bélanger et al. (2013), Belk et al. (2023) and Holmström (2022). By shedding light on the multifaceted implications of AI integration, this study contributes to a deeper understanding of AI's transformative role in shaping Malaysia's contemporary communications and multimedia landscape. Navigating these challenges effectively will be essential for harnessing the full potential of AI and positioning the industry for a future-ready digital landscape.

Research Objectives (RO) & Research Questions (RQ)

AI Governance: A Five-Subsystem Approach

Effective AI governance ensures responsible development, deployment, and use of AI systems, mitigating potential risks while maximising stakeholder benefits. Bélanger et al. (2013) the framework posits that successful governance hinges on the interplay of five interconnected subsystems: personal, organisational structure, technical, environmental factors, and joint optimisation.

The personal subsystem emphasises the human element within the organisational system. A skilled and ethically aware workforce is crucial for responsible technology adoption. This includes ensuring employees at all levels fully understand the concepts, capabilities, and limitations to engage with new systems effectively (Guzman & Lewis, 2020).

The organisational structure subsystem examines how organisational structures and processes facilitate effective governance (Guzman & Lewis, 2020). Defining clear lines of accountability for AI development, deployment, and oversight is crucial to ensure transparency and mitigate potential risks. Robust data management practices are essential to ensure data quality, privacy, and security, forming the foundation for responsible AI utilisation.

The technical subsystem focuses on the technological infrastructure and tools for responsible organisation. This includes utilising explainable AI systems that provide transparent and interpretable outputs, fostering stakeholder trust and accountability. Implementing robust cybersecurity measures and privacy-enhancing technologies is paramount to protecting sensitive data used in AI systems. Furthermore, employing techniques to identify and mitigate biases in AI algorithms is crucial to promoting fairness, equity, and non-discrimination in AI-driven outcomes (Hicham et al., 2023).

The environmental factors subsystem acknowledges the influence of external factors on governance. This includes a thorough analysis of existing and emerging regulations related to AI, data protection, and consumer rights to ensure compliance and ethical alignment. Critically aligning AI development and deployment with broader societal values and ethical principles is crucial to fostering public trust and ensuring the responsible use of AI (Belk et al., 2023).

Finally, joint optimisation emphasises the importance of a coordinated and collaborative approach to AI governance. This fosters dialogue and collaboration among industry players, policymakers, researchers, and civil society to ensure a multi-stakeholder perspective is integrated into AI governance mechanisms. To assess their impact and adjust governance mechanisms, continuous monitoring and evaluation of AI systems are necessary, ensuring ongoing alignment with ethical considerations and societal values.

In conclusion, Bélanger et al. (2013)'s five-subsystem approach provides a comprehensive roadmap for achieving AI governance while emphasising the crucial interplay of personal responsibility, robust organisational structures, cutting-edge technical safeguards and a commitment to collaborative optimisation.

Digital Transformation in The Malaysian Communications and Multimedia Industry: A Landscape of Innovation and Challenges

The Malaysian government, recognising the transformative potential of digital technologies, has implemented policies and initiatives like the Multimedia Super Corridor and the Digital Economy Blueprint to foster a vibrant digital economy (Artificial Intelligence Roadmap 2021 - 2025, 2021). These initiatives have attracted foreign investment, fostered the growth of ICT infrastructure, and accelerated digital adoption across sectors, including C&M. Simultaneously, Malaysian consumers, particularly the digitally savvy demand for seamless, personalised, and on-demand access to information and entertainment. This shift in consumer behaviour compels the C&M industry to adapt, embrace digital platforms, and deliver innovative products and services to remain competitive.

Further accelerating this transformation is the rapid pace of technological advancements in areas like mobile broadband, cloud computing, and big data analytics. These technologies have unlocked unprecedented opportunities for AI integration, enabling the development of new content formats, delivery platforms, and business models, transforming how content is created, distributed, and consumed. The digital wave presents many opportunities for the Malaysian C&M industry (Guzman & Lewis, 2020).

Notwithstanding that, the rise of over-the-top platforms, streaming services, and online gaming has created a burgeoning market for digital content, allowing the C&M industry to leverage creative talent and cultural insights to develop compelling content for local and global audiences. Digital technologies empower C&M companies to enhance customer experiences by personalising content recommendations, offering interactive experiences, and providing seamless customer support through multiple channels (Hicham et al., 2023).

In conclusion, the Malaysian Communications and Multimedia industry is undergoing a remarkable transformation driven by government initiatives, shifting consumer preferences, and rapid technological advancements. This digital wave presents many opportunities for Malaysian C&M companies to innovate, expand their reach, and captivate domestic and global audiences. While challenges like bridging the digital divide and navigating regulatory complexities remain, the industry's resilience and adaptability position it for continued growth and success in the digital era.

Research Design

This research employed a mixed methods approach to address its objectives, utilising primary and secondary data collected in two distinct stages, as illustrated in Table 1.

Table 1: Research Approach.

The conceptualisation of the themes is presented in Tables 2 and 3.

Table 2: Conceptualisation of the Theme for In-Depth Interview.

Table 3: Conceptualisation of the Theme for Benchmarking Exercise.

Findings and Analysis

Several sub-themes were derived from the five main themes, as explained in Table 4.

No.	Theme	Description	1	2	3	4	5
1.	Budget and governance	Optimised Processes: AI is used to enhance efficiency through process optimisation, situational cost analysis, and support for decision-making.
		Predictive and Personalised Sales: Leverage AI to forecast sales trends and tailor offerings to customer preferences.
		Strategic Budget Allocation: The budget is allocated on a case-to-case basis.
		Innovative Organisational Models: The organisation is working towards new operational models utilising neural networks, blackboard systems, algorithms and establishing an AI Committee.
		AI Collaboration and Partnerships: Foster partnerships and collaborative efforts for effective AI development and deployment.
		Cost Reduction: AI is used for cost savings by maximising output with the optimal use of human resources.
2.	Infrastructure and data (R&D)	Operational Efficiency: AI is used to enhance operational efficiency through process optimisation and situational cost analysis, such as using drones for telco tower repairs.
		In-House AI Approach: Leverage internal resources, cloud computing, and chatbots for AI implementation.
		AI-Driven Data Collection: Utilise AI applications to streamline and enhance data collection processes for operation efficiency.
		Business Operation Optimisation: Apply AI to improve business operations and optimise workflows.
		AI-Enhanced Work Efficiency: Increase work efficiency and productivity through AI-grounded strategies and tools.
3.	Talent and innovation	Training and Upskilling: Provide training and upskilling opportunities in AI-related to cybersecurity, financial management, HR management, and change management.
		AI-Supported Business Innovation: Foster business innovation through AI applications and technologies.
		Skill-Based Recruitment: Conduct targeted recruitment efforts to headhunt AI experts with specialised skills.
		Support for AI-Driven Innovation: Encourage and support innovation initiatives that leverage AI.
		Sustaining Industrial Existence: Utilise AI-based innovations to ensure the ongoing relevance and sustainability of the organisation.
4.	Regulatory and ethical considerations	Fair Business Strategy: Implement fair AI strategies that avoid biases in decision-making.
		Equality towards Customers: Ensure AI (especially chatbot) treats all customers equally and inclusively.
		AI Committee for Ethical Guidelines: Establish a dedicated committee to set and enforce ethical guidelines for AI use.
		PDPA Compliance: Ensure AI operations comply with the Personal Data Protection Act to protect individuals' privacy and data rights.
5.	Stakeholder involvement	Customer Experience: Focus on enhancing customer satisfaction through AI-driven service delivery and interaction improvements.
		Service Personalisation: Implement AI solutions that tailor services to meet individual customer needs and preferences.
		AI Solutions Provider: Engage with AI providers to develop and integrate solutions that enhance stakeholder engagement and operational efficiency.
		Input on Customer Usage Patterns: Gather and utilise feedback on customer usage patterns to refine AI strategies and improve service delivery.

Table 4: Theme and Subthemes of Use Cases and Applications of AI Deployment.

Table 5: Current State of AI Adoption Readiness of C&M Industry in Malaysia.

CHALLENGES

Table 6 explains the challenges faced when adopting AI in the C&M industry, according to the findings of the interviews conducted with this study's participants.

Table 6: Challenges in AI Adoption.

OPPORTUNITIES

The opportunities for adopting AI in the C&M industry are explained in Table 7.

Table 7: Opportunities in AI Adoption.

AI GOVERNANCE: BENCHMARK STRATEGY

The European Union (EU), the U.S., China, and Singapore are considered to be important in benchmarking AI Governance. In the U.S., AI is regulated and driven by government programmes and partnerships between the public and private sectors. The National Institute of Standards and Technology (NIST) and the National Science Foundation (NSF) are government agencies that help run AI. As part of its goals, the NIST conducts basic research to make AI technologies more reliable; to use AI research and innovation across all its laboratory programmes; to set standards, data, and metrics for assessing AI technologies; to lead and take part in the development of technical AI standards; and to offer technical input to discussions and the formation of AI policies. The NSF spends USD 700 million a year to encourage the development of new AI methods and the use of AI techniques and tools, making AI research resources more accessible to everyone, developing trustworthy and ethical AI, and improving education and human resources. On the legislative side, the White House recently officially required federal agencies that use AI to adopt 'concrete safeguards' to protect Americans' rights and to ensure safety as the government expands AI use in a wide range of applications. The US Office of Management and Budget issued a directive to the federal agencies to monitor, assess, and test the impact of AI on the public, to mitigate the risks of algorithmic discrimination, and to provide the public with transparency regarding how the government uses AI.

Meanwhile, the EU was the first to regulate AI by introducing the European Artificial Intelligence Act (EU AI Act)2024, which sought to ensure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory, and environmentally friendly. This act was proposed by the European Commission in April 2021 and passed in March 2024, and has come into force as of 1 August 2024. The Act categorises the AI-related risks, and the new rules establish obligations for providers and users depending on the level of risk of AI; the different risk levels will mean either more or less regulation. Under the Act, cognitive behavioural manipulation of people or specific vulnerable groups, social scoring, 'real-time' remote biometric identification (RBI) in publicly accessible spaces, and the compilation of facial recognition databases via the untargeted scraping of facial images from the internet or CCTV footage are prohibited.

In 2017, China announced an ambitious programme for the domestic development of AI technology to become the world's major AI innovation centre by 2030. By this deadline, China intends to expand AI in various production, governance, and defence sectors. The next-generation AI development plan issued by the state council aims to achieve three strategic goals. The first goal is that by 2020, China's AI technology and applications will have advanced to a global level, the AI industry will have become a new economic growth driver for the country, and AI technological application will have become a new approach to improving people's livelihoods, in support of the goal of becoming an innovation-driven country and establishing a moderately prosperous society in all aspects. The second goal is that by 2025, the country will have made a breakthrough in AI basic theory, which means that AI will become a major driving force for industrial upgrading and economic restructuring, as well as assisting in the development of an intelligent society that is able to progress. The final goal is that by 2030, AI theory, technology, and applications will have reached a globally advanced level, and China will become a global AI innovation hub. Moreover, the intelligence economy and society will have advanced significantly, laying the groundwork for an innovative and economically powerful country.

Singapore is currently leading the Association of Southeast Asian Nations (ASEAN) region's efforts to regulate AI and use it for public good. In 2019, Singapore released its first National AI Strategy that outlined plans to expand the use of AI for economic transformation and growth. Under this plan, using AI engendered the formation of approximately 150 R&D teams and 900 startups exploring new AI ideas. In 2023, the Singapore National AI Strategy 2.0 (NAIS 2.0) was introduced. Seeking to promote Singapore's advance as an AI leader, three key changes were implemented from the initial National AI Strategy to strengthen NAIS 2.0. These ranged from opportunity to necessity, local to global, and projects to systems. As a result, NAIS 2.0 aims to achieve excellence and empowerment in the three fields of activity drivers: people and communities, infrastructure, and the environment.

Malaysia can benchmark these regulations to national initiatives by leveraging existing frameworks such as the National AI Roadmap and National AI Governance and Ethics, which the Ministry of Science, Technology, and Innovation (MOSTI) spearheads. These initiatives aim to foster the deployment of trustworthy and ethically sound AI across various sectors. By integrating regulatory measures with technological advancements, Malaysia can effectively mitigate the risks of algorithmic discrimination. Legislative mandates could categorise different AI applications based on risk levels, thereby enhancing measures to safeguard public security. Furthermore, initiatives like 'AI Untuk Rakyat' under MyDigital highlight Malaysia's commitment to augmenting AI infrastructure and prioritising human capital development. Through robust R&D efforts and educational programmes, Malaysia seeks to position itself as a regional leader in secure AI adoption and innovation. By aligning regulatory frameworks with strategic investments in AI and human resources, Malaysia aims to meet international standards and aspire to global leadership in AI.

To comprehensively assess global regulatory approaches to AI governance and promotion and to enable the integration of these strategies into the C&M industry in Malaysia, the benchmarking findings are presented in Table 8.

Table 8: Benchmarking Findings.

THE IMPACT OF THE US-CHINA TECH WAR ON MALAYSIA

The tech war between China and the US is only just getting started. The recent series of bilateral meetings between the countries within a week marked a notable departure from the norm of diplomatic standoff that has characterised their relationship in recent years. The first meeting, held in Washington on 9 May 2024, centred on climate issues. Subsequently, the delegations from both countries convened in Switzerland to initiate discussions concerning AI, signalling a tentative step towards cooperation in a more nascent and complex arena. These discussions may be particularly sensitive given the technological rivalry between the two nations. The Biden administration emphasised the need for communication regarding AI-related risks in the absence of technical cooperation. Both countries compete to dominate future technologies, including AI, while seeking to shape global norms and regulations. Despite the nascent nature of the AI dialogue and the absence of concrete plans for future meetings, the ultimate measure of success lies in advancing strategic interests and innovation in AI.

The increasing tensions between the US and China in the field of AI are likely to have serious consequences for Malaysia. The growing competition may pressurise Malaysia to affiliate with one of these two AI giants, potentially affecting its foreign policy and economic ties. Furthermore, the US-China AI competition may impact Malaysia's AI development policies and collaborations as the country strives to negotiate the geopolitical landscape and capitalise on growing AI innovation and adoption prospects. Moreover, Malaysia may face hurdles related to technology transfer, intellectual property rights, and cybersecurity as it strives to use AI for economic growth and societal betterment in the midst of the US-China AI conflict.

Recommendations

Informed by the findings of this research regarding AI adoption by the C&M industry, the recommendations, as presented in Table 9, are proposed based on the three sectors studied.

Table 9: Sector-Based Recommendations.

To address the need for a structured approach to AI initiatives in Malaysia, it is essential that an ecosystem model is proposed to the relevant authority. This model should consider which ministry, or agency should oversee AI initiatives and whether the ecosystem should be centralised or decentralised, as presented in Table 10.

Table 10: Proposed AI Ecosystem Model.

CONCLUSION

This study provided valuable insights into the multifaceted landscape of AI adoption within the C&M industry via a comprehensive examination of the four research objectives. By synthesising the findings of previous empirical research and benchmarking exercises against key regulatory documents and national AI strategies from around the globe, this study contributed to a deeper understanding of the challenges, opportunities, and regulatory considerations shaping the future of AI in this industry.

The findings related to the four (4) research objectives are summarised below.

First, identifying AI use cases and applications deployed or planned within the C&M industry revealed the presence of a diverse array of applications spanning operational optimisation, customer experience enhancement, and strategic innovation. From predictive analytics to personalised services, AI technologies are reshaping traditional business models and opening new avenues for growth and differentiation within the industry

Second, examining the challenges, opportunities, and potential impacts of AI adoption in the C&M industry highlighted the transformative potential of AI technologies alongside the inherent risks and challenges. While AI promises to drive operational efficiencies and strategic innovation, data privacy, bias mitigation, and regulatory compliance concerns underscore the need for robust governance frameworks and ethical guidelines.

Third, the benchmarking initiatives in other countries provided valuable insights into global best practices and regulatory approaches to AI governance. Documents such as the AI Act of the European Parliament and the national AI strategies of Malaysia, Singapore, China, and the U.S. offered valuable frameworks and strategic roadmaps for fostering AI innovation whilst addressing regulatory and ethical concerns.

Finally, the recommendations regarding the regulatory and ethical considerations of AI usage in the C&M industry emphasised the importance of a holistic approach to AI governance. Regulatory frameworks must balance fostering innovation and protecting consumer rights, while ethical guidelines should prioritise fairness, transparency, and accountability in AI adoption and usage.

In conclusion, while this qualitative small-scale study's findings may not fully represent the entire C&M sector, they provide valuable insights into the current adoption of AI based on identified respondents. As the C&M industry continues to embrace AI technologies to drive growth and innovation, it is imperative to prioritise ethical considerations and regulatory compliance. By adopting a proactive approach to AI governance and leveraging insights from global benchmarks, the industry can navigate the complexities of AI adoption while fostering trust, transparency, and responsible innovation for all stakeholders involved.

References