THE ROLE OF ICT AND AI IN THE TRANSFORMATION OF BUSINESS MODELS SACHDEV RAMAKRISHNA

THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 389 THE ROLE OF ICT AND AI IN THE TRANSFORMATION OF BUSINESS MODELS SACHDEV RAMAKRISHNA Sachdev Ramakrishna PhD researcher of the Center for Research of Problems in Privatization and State Assets Management, Tashkent, Uzbekistan https://doi.org/10.5281/zenodo.17768497 Abstract. In a business world that is increasingly digital, the role of information and communication technologies (ICT) and artificial intelligence (AI) is central to business model transformation. While ICT provides the foundational infrastructure for connectivity, data exchange, and digital collaboration, AI value adds through the power of automation, advanced analytics and informed decision-making. Brought together, they help reshape value creation across industries. This paper aims to explore how ICT and AI combine synergistically to transform business models. It also showcases opportunities, challenges, and implications for organizations that are focused on reshaping their business models for the modern economy. Keywords: ICT, AI, Business Model Transformation, Digital Transformation, Cloud Infrastructure, Personalization, Algorithms, Servitization, Datafication, Platformization Introduction Business models define the many approaches and options for organizations to create, deliver, and capture value. They help design the way innovations deliver value to customers, meeting their met and unmet needs in unique and unprecedented ways. Traditionally, these models were shaped by assets which were principally physical such as plant and machinery, human resources ,materials, energy sources and linear supply chains. However, the rise of ICT and AI has rewired the supply chains, making them global, networked, real-time and just-in-time, fueling greater consumer demand in an upward virtuous consumption chain. For the past several decades, ICT made steady progress, automating parts of a company’s internal and external environment through global connectivity and digital infrastructures. In the recent past, AI has amplified its power, bringing adaptive, predictive, and autonomous capabilities to the enterprise. The coming together of these technologies has more than an incremental impact. It represents a whole new manifestation of business logic, organizational design, and competitive dynamics (Bharadwaj et al., 2013; Marston et al., 2011). The transformation owing to ICT and AI is omnipresent, across a variety of industrial sectors—manufacturing, healthcare, finance, retail—, however with mixed success. Often, firms undertake digital projects to enable parts of their business, but do not realize the disruptive value of transformation through digital business models powered by AI. This paper aims to address some of these issues these gaps by offering a view of embedding AI’s role in business model innovation within ICT’s infrastructural logic (Iansiti & Lakhani, 2020; Parker et al., 2016). Conceptual foundations: Business models, ICT, and AI Innovation in business models can be engineered through new value propositions for enhanced value creation (resources, processes, partnerships), newer ways of value delivery (channels, customer engagement), and re-imagined mechanisms for value capture (pricing, revenue, cost structure) (Osterwalder & Pigneur, 2010; Teece, 2010). ICT influences each of these THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 390 aspects through a systems approach, enabling modularity in digitalization of each component of the enterprise, and then integrating information flows through interoperability and connected data flows. On top of this, AI algorithms constantly modify and improve decision rules within these components. Digital transformation then becomes less about pure technology deployment and more about rearranging and recombining components to realize new value logic, often in platformmediated ecosystems (Bharadwaj et al., 2013; Parker et al., 2016). AI’s role typically enriches business decision making through all the three aspects of supervised, unsupervised and reinforcement learning. Supervised learning helps to reduce uncertainty (better forecasts); reinforcement learning helps in decision optimization, and generative models help in creating targeted content and code. The real impact is AI’s ability to reduce uncertainty (better prediction and forecasts), rearrange labor (automation and augmentation), and design mass customization or personalization at scale (micro-segmentation, real-time experimentation), each with distinct implications for revenue and cost structures (Davenport & Harris, 2007; Brynjolfsson & McAfee, 2014). ICT provides infrastructural agility ICT’s primary contribution is its ability to help organizations with capabilities to sense, process, and act across distributed digital assets at scale. The following are ICT’s main contributions: 1. Connectivity and modularity: ICT’s ability to help data and information flows across applications through standardized interfaces and APIs help build networked firms, connecting suppliers, manufacturers and buyers through an integrated information chain. (Bharadwaj et al., 2013; Parker et al., 2016). 2. Cloud computing and elasticity: Moving data from on-premise storage to cloud platforms helps move fixed IT costs into pay-as-you-use variable costs. Cloud infrastructure creates elasticity in data storage, helping companies scale demand up or down based on business conditions. Cloud infrastructure helps distribute work around the world to enable global service delivery (Marston et al., 2011). 3. Data infrastructure: AI cannot work on raw and unstructured data, Data needs to be sourced, captured, organized, stored, duplicated and archived to create value. These are done through data architectures and their governance, ensuring that data is available in a customized and usable manner for business users, and are often the single source of truth across the organization. Big data is the raw material for AI engines to infer, learn from and improve their predictions and outputs. (Chen et al., 2014; Wamba et al., 2015). In itself, ICT does not transform business models. It however builds the foundations and connections to enable easy connections with suppliers and customers and across the organization to enable transparent, rapid and sound decision-making. (Marston et al., 2011; Parker et al., 2016) AI adds algorithmic power AI amplifies the value created by ICT infrastructure with its ability to improve managerial decisions, automate operational workflows, and generate outputs at near-zero marginal cost. Here are ways in which AI improves business models 1. Automation and augmentation: Predictable, repeatable processes are best handled through Robotic process automation. Automating these rules-based tasks based on machine learning frees humans from routine tasks. For complex judgements, decision-support THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 391 systems can augment human thinking by helping simulate scenarios and outcomes. (Brynjolfsson & McAfee, 2014). 2. Prediction and personalization: AI helps improve demand forecasting, reducing waste in supply chains. Dynamic pricing helps to attract and retain customers. Recommendation systems showcase products to customers segmented by their demographic, psychographic, geographic or behavioural segmentation. These actions translate into higher conversion, reduced churn, and optimized inventory (Davenport & Harris, 2007; Iansiti & Lakhani, 2020). 3. New value logic: There is more” intelligence” in the demand chain and the customer journey. Servitization makes products become “smart services” (predictive maintenance, usage-based billing). Platforms help monetize data/algorithms (model-as-a-service, API marketplaces) (Lee et al., 2015; Kagermann et al., 2013). All the above help reconfigure margins and growth trajectories for companies (Iansiti & Lakhani, 2020). Modes of transformation There are five distinct ways how ICT and AI reshape business models: • Digitization: This aspect focuses on converting paper and data stored in analog processes and products into digital artifacts. The improvement in searchability, reproducibility, and distribution at low cost transforms business efficiencies (Bharadwaj et al., 2013). • Datafication: This aspect is about capturing previously uncaptured data e.g. from a customer’s daily activities to create richer behavioral, operational, and contextual signals of buyer behaviour.. The new data coming from sensors and mobile phones helps in curating data to feed learning systems; creating secondary data markets (Wamba et al., 2015; Chen et al., 2014). • Automation: This aspect is about process optimization, simplifying work processes and replicating basic human intelligence in machines to conduct tasks that are rules-based, repeatable and predictable. (Brynjolfsson & McAfee, 2014). • Platformization: These business models are about creating two-sided platforms e.g. Uber, Booking.Com, Airbnb to make customer interactions simpler, harness network effects, and monetize via access, transactions, or analytics (Parker et al., 2016). • Servitization: This involves shifting from a product sales focus to looking at prodcuts doing jobs in a customer’s life. The outcomes-as-a-service model with subscription, usagebased, or performance-based pricing creates a whole different value propositon; (Lee et al., 2015; Kagermann et al., 2013). All of these aspects are modular but interact to create immense enterprise value. Datafication enables personalization. Automation improves productivity, reducing time and costto-serve. Platformization helps expands reach and assists a company acquire customers rapidly. Servitization creates greater value for customers and helps in stabilizing revenue. Firms that connect these modular blocks tend to outpace peers in growth and resilience (Iansiti & Lakhani, 2020). THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 392 There is evidence from industrial applications. In E-commerce, ICT helps Platform-based retailers manage global logistics, payments, and marketplaces while AI helps with ranking, personalized recommendations, and fraud detection. In advertising and marketing, A/B testing at scale (Parker et al., 2016; Davenport & Harris, 2007) makes efficient spend of marketing dollars. In Manufacturing (Industry 4.0), with the leverage of IoT sensors and digital twins, asset maintenance improves significantly. AI enables predictive maintenance and quality control. Servitization creates value, offering uptime guarantees and outcome-based contracts. The factory becomes a cyber-physical system; the firm becomes a steward of lifecycle outcomes rather than just a producer (Lee et al., 2015; Kagermann et al., 2013). In Healthcare, ICT leverages telemedicine and interoperable records. It forms the foundation for AI to support diagnostics, triage, and treatment planning. Healthcare reorients to be more personalized and patient centric. (Topol, 2019). Synergies Between ICT and AI The integration of ICT and AI creates synergistic effects: • Digital Ecosystems: ICT provides connectivity, while AI optimizes interactions within ecosystems, such as smart cities or Industry 4.0 networks. • Platform Intelligence: ICT-based platforms (e.g., Amazon, Alibaba) leverage AI for recommendation systems, fraud detection, and logistics optimization. • Data-Driven Innovation: ICT enables data collection, while AI transforms data into actionable insights, driving innovation across industries. This synergy results in adaptive, scalable, and resilient business models. Challenges and Risks The new paradigm of a digitally rich business brings its inherent risks and downsides. The opportunities of ICT and AI adoption brings concomitant challenges: • Cybersecurity: The more personal data exists in digital form, the higher is the vulnerability of ICT infrastructures to cyberattacks, requiring robust security frameworks. The battle is a continuous and ongoing one, between the hackers and defenders of enterprise infrastructure and data. • Ethical Concerns: The foundational language models could bias AI algorithms towards unfair recommendations, raising issues of bias, transparency, and accountability. • Organizational Resistance: Newer business models requires massive shifts in mindsets and organization cultures, often facing cultural and structural resistance. • Regulatory Uncertainty: Often, these technologies innovate at speeds ahead of the ability of regulators to control and regulate AI applications.. Future Directions The future of business models will be shaped by: 1. Human-AI Collaboration: Humans and AI systems work together to co-create value. Tactical actions based on pattern recognition are best done by machines, whilst logical reasoning is best done by humans. 2. AI-Driven Ecosystems: AI will help supply chains to be more sensitized to demand, avoiding overproduction and helping products in the right quantities to market demands. Customer centricity will drive corporate actions across industries. THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 393 3. ICT and AI will build increased personalization, based on rich customer analytics, driving economic efficiencies in enterprises. Cloud-based infrastructure will continue to power an always-on digital commerce business. 4. Artificial Generative Intelligence will transform content creation for marketers, enabling them to produce content precisely targeted to customer segments, accelerating purchase conversions for brands. Strategic implications for incumbents and digital natives There are different implications of these impactful new technologies on established and emergent companies. These are discussed below. • Incumbents: These companies will need to embrace digital and AI technologies on a priority basis. Often, they risk creating a separate division to focus on the applications of these technologies, and blindsided with the fact that the traditional and new models serve the same customers. Therefore, there needs to be an omni-channel view of serving the customer; building sound data structures to drive analytics from and integrating products and services through servitization. On the human resources side, it will call for a change in company cultures, reskilling and governance that balances innovation with risk (Teece, 2010; Marston et al., 2011). • Digital natives: These companies have it better, being used to technologies from the very beginning. Their main focus should be building multi-sided platforms and in acquiring buyers and suppliers to participate through the platform. These companies tend to be data rich and are positioned to leverage technologies such as analytics and AI better. Their challenge is scaling responsibly—managing to build online trust and being compliant. , markets (Parker et al., 2016; Floridi & Cowls, 2019). • All firms: Firms realize that competitive advantages in a digital economy move away from static products or prices to rapid experimentation leading to product development and dynamic pricing, and leveraging data for improved decisions and offerings.While ICT enables seamless dataflows, AI enables the learning to shape new business model designs for improved monetization(Davenport & Harris, 2007; Iansiti & Lakhani, 2020). Conclusion The contributions of ICT and AI are significant, having helped create trillion-dollar companies. While ICT laid the foundation for a global economy through connectivity and networks over the past few decades, AI has amplified the transformative power of technology in reshaping business models through augmenting human intelligence for improved and agile decision-making. Together, these technologies are creating data-driven organizations for the future. It calls for a balance to harness the immense transformative power for human good and to build and sustain smart, ethical, responsible and innovative organizations of the future (Bharadwaj et al., 2013; Teece, 2010; Iansiti & Lakhani, 2020). REFERENCES 1. Jobstreibizer, J., Beliaeva, T., Ferasso, M., Kraus, S., & Kallmuenzer, A. (2025). The impact of artificial intelligence on business models: A bibliometric-systematic literature review. Management Decision, 63(13), 372–396. 2. Peniaz, L. (2024). AI-driven transformation of business models: New opportunities for startups in the global marketplace. Scientific Research Journal, 12(8), 990–1005. THE VI INTERNATIONAL SCIENTIFIC CONFERENCE “SCIENTIFIC FOUNDATIONS FOR THE USE OF INFORMATION TECHNOLOGIES OF A NEW LEVEL AND MODERN PROBLEMS OF AUTOMATION”, NOVEMBER 20, 2025 394 3. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482. 4. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing— The business perspective. Decision Support Systems, 51(1), 176–189. 5. Parker, G., Van Alstyne, M., & Choudary, S. (2016). Platform revolution: How networked markets are transforming the economy. W. W. Norton & Company. 6. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company. 7. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. 8. Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Review Press. 9. Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. German National Academy of Science and Engineering. 10. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. 11. Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. 12. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). 13. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. 14. McKinsey Global Institute. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey & Company. 15. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. 16. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press. 17. Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Review Press. 18. Osterwalder, A., & Pigneur, Y. (2010). Business model generation. John Wiley & Sons. 19. Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194.