Abstract
This report is the strategic analysis of Tesla, which is an electric car company and a sustainable energy solution company worldwide. It starts with an explanation of the business model, operating in a market and the competitive environment in Tesla. Next, the report will cover the environmental analysis, in which the most significant technological and social changes are cited. The implications of these changes have been considered in the background resource management, supply chain management, and strategic decision making and reveal their impacts on the efficiencies, innovativeness, and operational agility. Opportunities and challenges that these changes bring are also found, such as workforce development, sustainability efforts, and better performance measurements. The report ends with a summary of organisational culture and the existing practice that assesses Tesla’s ability to capitalise on strategic opportunities in a changing technological environment.
Individual Report
Background Information
Tesla Inc. was founded under the guidance of the strong vision and charisma of Martin Eberhard and Marc Tarpenning in 2003. It is a world-leading multinational company that focuses on electric cars and renewable energy technology, as well as energy storage (Masoud et al., 2023). Tesla is based in Austin, Texas, and it has redefined the automotive and energy sectors by encouraging sustainable transportation and the global shift to clean energy (Chen, 2024). The company conducts its activities under a number of divisions such as automotive manufacturing, energy generation and storage, digital technologies, autonomous driving and artificial intelligence systems.
The current performance of Tesla in the market is healthy, as it has been among the most valuable car manufacturers in the world based on the market capitalisation. Irrespective of the growing threats posed by existing competitive automakers in the market and newcomers in the electric vehicle segment, Tesla maintains a dominant market presence in electric mobility as a result of its brand image, technological development capacity, and extensive networks of supercharger stations (Maradin, Malnar, and Kastelan, 2022). Nevertheless, the success of Tesla is placed in a fast dynamic competitive landscape that is characterised by technological shocks, changing social priorities and world sustainability needs.
Environmental Analysis
Tesla’s external environment has a complex interaction between the economic, environmental, social, and technological factors. With the PESTEL framework, it is possible to identify several key dimensions. Politically, various governments around the world have been promoting decarbonisation policies, providing tax incentives to purchase electric vehicles, and establishing emission reduction goals (Pan and Shittu, 2025). These laws provide new opportunities to Tesla but cause compliance and localisation issues. Economically, the changing prices of raw materials, especially lithium and nickel, that are incorporated in the manufacturing of batteries, influence the cost structure and profitability of Tesla (Le and Ho, 2021). Moreover, macroeconomic risk has put a strain on efficiency in production.
Technological advancements represent the most defining external factor in Tesla’s environment. The intensive development of battery and artificial intelligence technologies and autonomous driving systems has changed the competitive landscape of the car industry (Abro et al., 2023). The Tesla design of the Dojo supercomputer, the machine learning used in self-driving cars, illustrates how the firm utilises technology in its strategic plan (Zhang, 2024). Simultaneously, the rise in the spread of digital connectivity, data analytics and smart infrastructure emphasises the move towards the intelligent mobility ecosystem.
At the social level, consumers get more eco-aware and seek sustainable products more often. This shift has also enhanced the adoption of electric cars and renewable energy systems (Mishra and Shukla, 2024). The society has also heightened its demands on ethical supply chains, equitable practices of labour and data security. Tesla has been questioned in aspects such as the ethos of the working environment, factory activities, and data processing, which have contributed to the determination of its social legality (Jacobsen, 2025). Tesla is also affected by the strategic environment. The issue of climate change, resource insufficiency, and environmental deterioration compels businesses to introduce the principles of the circular economy and reduce their carbon footprint (Yang et al., 2023). These requirements are seen in the Tesla mission to accelerate the adoption of sustainable energy.
Implications of Technological Change for Strategic Management
The Tesla strategic management has been greatly changed through the fast technology of artificial intelligence, automation and digitalisation. As one of the pioneers in electric cars and clean energy, Tesla employs technological innovation as one of the selling points of the product, not to mention the building blocks of the competitive strategy (Lang, Reber, and Aldori, 2021). The adoption of resource allocation, supply chain management, and strategic choice in Tesla is influenced by the progressive advancement of AI, data analytics, and robotics (Islam Khan, Barua, and Das, 2025). These technologies may assist the company to be flexible and conform to the changes of the dynamic market environment, to improve its performance in the business sphere, and retain its position as an innovative technological enterprise.
In terms of resource management, this means that the growing extent of AI-based systems integration in Tesla demands a change of its human and technological resources. It also requires the company to spend on data scientists, software engineers, and machine learning experts to ensure the company remains ahead. This transformation necessitates strategic realignment of the human capital management as it entails recruitment, development, and retention of digital talent. Additionally, the organisational resources of Tesla are getting more knowledge-intensive, and intellectual property, data and algorithms are some of the key strategic assets (Gitelman, Hebert, and Romero, 2022). Changing a traditional manufacturing-oriented model of resources towards a technology one will not only require new skills, but also a culture of constant learning and innovation.
Regarding the supply chain management, AI and automation are changing the way Tesla models, procures and distributes its products. The vertically integrated model of Tesla, including battery manufacturing as Gigafactories and direct distribution channels, can be controlled and managed more effectively (Naor, Coman, and Wiznizer, 2021). Nevertheless, there are also cybersecurity, data management, and reliability vulnerabilities introduced through the adoption of AI technologies. The future of AI-based supply chains is getting more complex, and thus, the logistics is becoming more dependent on complex analytics and prediction models to remain on top of such disruptions (Grover, 2025). For Tesla, the ability to implement AI in managing its worldwide network of suppliers is key to maintaining a stable market with regard to production after the uncertainty in the market of raw materials and geopolitics. In addition, ethics-related and sustainability-oriented objectives should be incorporated within a supply chain plan at Tesla that will have to be fulfilled via ethical sourcing of particular resources, such as cobalt and lithium.
The next influential effect of AI-driven technological advances in Tesla is the strategic decision-making process. Data-driven and risk-tolerant decision-making processes have long been incorporated in the leadership of the company, spearheaded by Elon Musk. AI increases the level of strategic intelligence and contributes to better decision-making and responsiveness by generating real-time data insights, predictive analytics, and simulating scenarios (Anastasios and Maria, 2024). The use of data analytics by Tesla to inform the policies of product development, customer experience, and energy management is an example of the transition to algorithmic governance in strategic management. Nevertheless, the adoption of AI also raises concerns of transparency, accountability, and moral decision-making (Akinrinola et al., 2024). The strategic dilemma is to use technological efficiency in order to maintain control over and to make sure that strategic decisions do not conflict with corporate values and the interests of stakeholders.
Opportunities and Challenges
The application of artificial intelligence and automation to Tesla’s strategic management structure opens a number of business development opportunities. AI allows improving the efficiency of organisations through optimising production, cutting operational costs, and increasing the quality of products (Qudus, 2025). Predictive analytics and machine learning will enable Tesla to predict component failures, streamline the use of energy and improve the precision of manufacturing. Such technological potential enhances the operational agility of Tesla and allows for constant innovation. Moreover, AI can allow Tesla to customise customer experiences using data-driven insights, build new value propositions and promote brand loyalty.
The other opportunity is employee empowerment and engagement created by making them participate in decision-making. With an increasing number of operational processes delegated to AI systems, the functions of humans in Tesla have to change to strategic, creative, and problem-solving ones. Cross-functional teamwork and decentralised decision-making will help increase the levels of employee commitment and innovation (Ahmad, Boit, and Aakula, 2023). With this kind of culture instilled by the leadership of Tesla, a culture of participation by employees in technological adaptation can be developed, and a shared vision towards sustainable innovation can be achieved.
Following a sustainable strategic management strategy is another opportunity that Tesla can consider. A combination of AI, renewable energy, and sustainability can provide a one-of-a-kind platform on which the company can push its environmental stewardship mission. It will be possible to optimise the renewable power production and consumption by embedding AI into energy management systems, which will in turn increase the efficiency and scalability of the energy products (Ukoba et al., 2024). Furthermore, the social legitimacy of Tesla may be reinforced by incorporating sustainability into its corporate strategy by evaluating lifecycle emissions.
There are numerous challenges associated with these opportunities. One of the issues is how to control the ethical and governance implications of AI implementation. Data privacy, decision accountability, and algorithm bias are rooted in issues that might violate the trust of the stakeholders unless managed in a transparent manner (Farinu, 2025). Tesla should hence come up with moral AI systems and regulatory systems in order to achieve responsible innovation. The other issue is the organisational resistance towards change. The process of introducing new modern technologies tends to alter the traditional work patterns and values, which may lead to some nervousness or resistance in employees (Bhattacharyya, 2024). This resistance needs to be overcome by estimable change management plans, appropriate communication and dedication of leadership to developing a learning-based culture.
There are also issues relating to sustainability. Even though Tesla is marketed as environmentally sustainable transportation, its manufacturing operations are energy-intensive, and supply chains rely on the exhaustion of raw materials. Circularity based on battery recycling, substitution of materials, and design-efficient materials will prove crucial to handling these problems. Moreover, the international race in the field of AI and electric vehicles is increasing, and old and new companies are creating similar technologies. The problem faced by Tesla is how it can maintain its innovative advantage and balance regulatory policies and economic cycles across the world. Lastly, new performance measurement systems must be developed to ensure that AI-related results and sustainability goals are tracked successfully.
Critical Analysis of Current Practices and Organisational Culture
The organisational culture and management practice of Tesla comprises a distinctive mix of innovation-based ambition, entrepreneurial liveliness, and operation-intensive. Its culture, which has been characterised as a mission-oriented culture and a performance-based culture, has been a decisive factor in the success of the company. Employees hold a common mission to move faster to the sustainable energy source, which brings a coherent sense of belonging (Siddiqui, 2025). Nevertheless, the culture is also characterised by high pressure, demanding work settings, and poor tolerance of failure. This environment encourages a high rate of innovation and the speed of implementation, but on the other hand, it limits psychological safety and employee retention in the long run.
Tesla has strengths and weaknesses regarding the culture of technological change and AI integration. The level of agility in embracing new technologies is facilitated by the flat structure of the company, the lack of time-consuming decisions, and the focus on experimentation. Management at Tesla promotes radical thinking and allows taking calculated risks, and this practice allows the company to become a first mover in such domains as battery development and self-driving (Peng, 2024). Conversely, burnout reports, minimal transparency in intra-organisational communication, and a centralised control of leadership indicate the possibility of impediments to long-term adaptability. The predominance of Elon Musk’s leadership style, at times, impedes innovation via collaborative decision-making and bottom-up innovation.
Tesla will need to develop a more engaging and inclusive organisational culture to effectively apply the identified opportunities. Shared governance systems, systematic feedback, and perpetual learning programs would empower employees to be prepared to integrate technology (Nuraini, 2024). Moreover, the integration of sustainability principles into the organisational routine and performance analysis may enhance the consistency between the strategic mission and organisational behaviour (Hristov et al., 2022). Although Tesla has a very good culture of innovation, a more equalised culture focusing on ethical responsibility, employee welfare, and relations with stakeholders will be required.
Summary
Tesla Inc. is at the centre of global technological change in the transition to sustainable and intelligent mobility. Innovation aptitudes and solid performance in the market have made the firm a leader in electric cars and renewable energy. Nonetheless, it is being redefined by the external environment that is marked by technological development, demands of society to be sustainable, and regulatory authorities. The development of artificial intelligence and autonomous driving technologies has transformative implications for the management of resources, operations, and the strategic decision-making process of Tesla. The advantages of changes are the possibility to become more efficient, empower people and make sustainability a Corporate Strategy, yet it presents ethical, organisational, and environmental concerns.
The opportunity to overcome the challenges of managing these implications will be available to Tesla based on its capacity to modify its organisational culture and managerial practices in line with the requirements of the technological and societal change. The establishment of a culture of participation, collaboration, and learning will be the most significant indicator of responsible AI usage and the possible maintenance of the competitive advantage in the long run. The Tesla strategic management of its current endeavour of going beyond the definitions of innovation ought to continue being guided by the merit of sustainability, inclusivity, and responsible leadership. Tesla, therefore, will act as a prototype on how the world of business can be sustainable by implementing changes.
References
Abro, G.E.M., Zulkifli, S.A.B., Kumar, K., El Ouanjli, N., Asirvadam, V.S. and Mossa, M.A., 2023. Comprehensive review of recent advancements in battery technology, propulsion, power interfaces, and vehicle network systems for intelligent autonomous and connected electric vehicles. Energies, 16(6), p.2925.
Ahmad, T., Boit, J. and Aakula, A., 2023. The role of cross-functional collaboration in digital transformation. Journal of Computational Intelligence and Robotics, 3(1), pp.205-42.
Akinrinola, O., Okoye, C.C., Ofodile, O.C. and Ugochukwu, C.E., 2024. Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability. GSC Advanced Research and Reviews, 18(3), pp.050-058.
Anastasios, P. and Maria, G., 2024. Predictive AI in Business Intelligence Enhancing Market Insights and Strategic Decision-Making. American Journal of Technology Advancement, 1(8), pp.72-90.
Bhattacharyya, S.S., 2024. Co-working with robotic and automation technologies: technology anxiety of frontline workers in organisations. Journal of Science and Technology Policy Management, 15(5), pp.926-947.
Chen, B., 2024. Study on sustainable development of electricity resources in the United States. In SHS Web of Conferences (Vol. 181, p. 04005). EDP Sciences.
Farinu, U., 2025. Fairness, Accountability, and Transparency in AI: Ethical Challenges in Data-Driven Decision-Making. Available at SSRN 5128174.
Gitelman, A., Hebert, D. and Romero, R., 2022. Can Businesses Succeed with Open Intellectual Property? The Case of Tesla, Inc. The Case of Tesla, Inc, pp.93-100.
Grover, N., 2025. AI-Enabled Supply Chain Optimisation. International Journal of Advanced Research in Science, Communication and Technology, pp.28-44.
Hristov, I., Appolloni, A., Cheng, W. and Huisingh, D., 2022. Aligning corporate social responsibility practices with the environmental performance management systems: A critical review of the relevant literature. Total Quality Management & Business Excellence, pp.1-25.
Islam Khan, M.R., Barua, A. and Das, N., 2025. Artificial Intelligence and Business Analytics: Driving Efficiency in Digital Supply Chain Management. International Journal of Innovative Science and Research Technology, 10(6), pp.1501-1510.
Jacobsen, S.D., 2025. Dan O’Dowd on Tesla’s Toxic Culture, Failing Hype, and the Rise of BYD. International Policy Digest.
Lang, J.W., Reber, B. and Aldori, H., 2021. How Tesla created advantages in the ev automotive paradigm, through an integrated business model of value capture and value creation. Business & Management Studies: An International Journal, 9(1), pp.385-404.
Le, L. and Ho, Q., 2021. Factors affecting the valuation of electric vehicle company in 2020: case Tesla Inc.
Maradin, D., Malnar, A. and Kaštelan, A., 2022. Sustainable and clean energy: the case of tesla company. Journal of economics, finance and management studies, 5(12), pp.3531-3542.
Masoud, R.M., Mostafa, R.M., Khattab, A.A., Ahmed, A.M.H.A. and Wahib, R., 2023. Assessing the transformative impact of Tesla’s strategic change interventions and technology implementation on the electric vehicle and clean energy industries. 10.13140/RG.2.2.14984.39684.
Mishra, M.S. and Shukla, M.T.T., 2024. Going Green on Wheels: Unearthing the Journey of Consumer Shifts to Electric Vehicles and Sustainable Mobility Choices. The Global Green Economy Leading to Sustainability: A Multidisciplinary Approach: EDITED BOOK, 114.
Naor, M., Coman, A. and Wiznizer, A., 2021. Vertically integrated supply chain of batteries, electric vehicles, and charging infrastructure: A review of three milestone projects from theory of constraints perspective. Sustainability, 13(7), p.3632.
Nuraini, N., 2024. Creating Environments for Continuous Employee Development and Learning. Economics and Digital Business Review, 5(1), pp.769-789.
Pan, W. and Shittu, E., 2025. Assessment of mobility decarbonisation with carbon tax policies and electric vehicle incentives in the US. Applied Energy, 379, p.124838.
Peng, L., 2024. Incremental Innovation: Range Development and Innovation in Tesla’s New Energy Batteries. Industrial Engineering and Innovation Management, 7(1), pp.61-68.
Qudus, L., 2025. Leveraging Artificial Intelligence to Enhance Process Control and Improve Efficiency in Manufacturing Industries. International Journal of Computer Applications Technology and Research, 14(02), pp.18-38.
Siddiqui, N.N., 2025. Effective Leadership Strategies for Enhancing Employee Motivation and Reducing Turnover in Distributed Energy Conversion Companies. In Hybrid Electric Vehicles and Distributed Renewable Energy Conversion: Control and Vibration Analysis (pp. 337-364). IGI Global Scientific Publishing.
Ukoba, K., Olatunji, K.O., Adeoye, E., Jen, T.C. and Madyira, D.M., 2024. Optimising renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7), pp.3833-3879.
Yang, M., Chen, L., Wang, J., Msigwa, G., Osman, A.I., Fawzy, S., Rooney, D.W. and Yap, P.S., 2023. Circular economy strategies for combating climate change and other environmental issues. Environmental chemistry letters, 21(1), pp.55-80.
Zhang, K.Z., 2024, November. Applications and prospects of ai in autonomous cars-take tesla as an example. In 2nd International Conference on Mechatronic Automation and Electrical Engineering (ICMAEE 2024) (Vol. 2024, pp. 355-360). IET.
Comments