Agentic artificial intelligence (Agentic AI) assumes a differentiated role in customer support service delivery, whereby it executes automated customer journeys for organizations. Information technology (IT) service provision vendors and large customer service centers are categorized. In contrast, Singaporean small and medium-sized enterprises (SMEs) rely on external platforms and business partners to meet market demands. While Agentic AI constitutes a cost-effective solution for automating specific customer activities, these front-end services still require oversight from human business advisers. A customer’s journey with an SME depends on specific sequences of synchronous and asynchronous service interactions involving both the customer and the SME, which may include informational prompts or events that trigger an Agentic AI-supported inquiry or response.
Diversification and specialization of customer journeys occur naturally due to the prevalent reliance on business partners, external service providers, and customer preferences and initiatives. Customer journeys supported by Singaporean SMEs typically consist of multiple touchpoints, including those that provide inbound and outbound communication functions, enable hands-on-assisted experiences, and feature instructional presentations. Four architectural paradigms are established to support Customer Journey as an Eco-System. Novelties emerge in the areas of data integration and interoperability; simultaneous delivery of personalized, context-specific, at-scale journeys; efficient orchestration of channels and touchpoints; and decisions made on behalf of the SME with differing levels of autonomy.
Theoretical Foundation of Agentic AI
Agentic AI describes systems and applications which operate with levels of semi-autonomy and augmented decision-making capabilities in order to impact a real-world outcome. The definition as currently proposed is composed of four elements: agency, impact, autonomy, and augmentation. Agency connects to the ability to perceive one’s environment, make decisions, and perform actions while remaining conscious of one’s existence in the world. Impact addresses the greater or lesser significance of the effects of actions within the context in which agency is exercised. Autonomy refers to the amount of detail in decision making that is not prescribed by another party, while augmentation indicates the extent to which AI enhances decision-making capability.
Such systems fulfil specific thresholds for decision-making and action without human oversight, or at least with actions receiving a low degree of scrutiny. The applicable model of autonomy encompasses machine learning to function as nodes within a larger agentic construct while lacking the previously discussed attributes of agency and impact. Agentic AI can operate at any level of the Automation Continuum, provided that the decisions and action taken impact the outcome of a customer journey. The definition remains sufficiently broad to capture the absence of a definitive model or example. These elements function as a profile of participating components within complete systems rather than a rigid specification of architectural interrelations.
Outlook of Singapore SMEs and Customer Journeys
Singapore’s vibrant economy is characterized by a global outlook and a deep-seated tradition of entrepreneurship. Small and medium-sized enterprises (SMEs) comprising about 99% of the economy form the backbone of Singapore’s economy in total value-added and total employment. Across all economic sectors, SMEs contribute 50% or more to value-add. Looking beyond Singapore, SMEs account for over 90% of the business ecosystem in most economies, representing about 50% of global value-add and employment. Their sheer numbers mean that any disruption will have an influence on societies and economies.
Despite this resilience, the COVID-19 pandemic has caused many SMEs to rethink their business model as a result of the level of disruption experienced during this period. The increase in customer uncertainty, changes in customer buying behaviour and expectations, the pressure of supply chain issues, and the geopolitical environment all require companies to rethink the management and orchestration of every customer journey associated with the business. A customer journey describes the interaction from the moment a customer becomes aware of a product or service through to post-sale service and support. Every journey is a complex orchestration of different channels and touchpoints operated independently by either humans or machines within the company and is often in the control of different business units. Automating this orchestration at the level of the customer journey presents a major opportunity for SMEs using Agentic AI.
Governance, Ethics, and Data Privacy in Singapore
Diversifying the customer base for growth while maintaining those customers are key SME requirements that can lead to significant operations problems and cost blowouts. These problems can then result in layoffs or even bankruptcy. In particular, supporting customer journeys with the best service at lowest cost is paramount through such an SME growth phase. To make this happen much better and cheaper, new technologies like cloud software-as-a-service (SaaS) products and computing capacity, particularly Agentic AI (AAI) assistance-based services enabling much wider usage by SME staff, can help: AAI tools simultaneously delivering best customer journeys at lowest cost throughout each journey are emerging. With this in mind, it is not surprising that major AAI-enabled products will support a single vendor succeeding in holding onto its AAI-empowered end-users throughout a customer journey.
Agentic AI products are a specialized type of Automation technology. Existing IT resourcing and operating costs also need to be streamlined. AAI products will greatly reduce the need to hire additional sales and support personnel, but the positive impacts will not be without their own problems, especially for Singapore SMEs targeting Asia and Singapore companies supporting such end-users. AAI products work by processing customer supportive information obtained from a set of customer interactions before assigning them back into their respective customer journey points along an integrated data format. Building such integrated internal and external data environments for AAI products to access in order to better coordinate with other applications and processes come with significant globally recognized ethical challenges.
Architectural Paradigms for Agentic AI in Customer Journeys
The architectural paradigms considered in the Agentic AI context highlight distinctive agentic properties for the internal architecture as well as the customer journey automation application domain. These domains include data integration and interoperability across underlying IT and operating/collaboration platform silos, contextual and personalized experience at scale, orchestration of channels and touchpoints across organizations and travel stages, and decision making with appropriate levels of autonomy and control.
Data Integration and Interoperability Data are foundational blocks for AI systems and decision making at all levels. An agentic AI system integrates internal/external sources of structured and unstructured data across the various systems of systems underpinning the organization and its customer journeys. It orchestrates these data sources via a structured multidimensional architectural framework that emphasizes semantic interoperability of underlying data exposed by operation and collaboration platforms. Such a framework lays the groundwork for demanding yet crucial customer privacy and data consent requirements while enabling personalization at scale both across journeys and across different customers, roles, and profile segments.
Personalization at Scale Delivering contextualized and personalized experiences at scale requires the ability to infer the relevant profiles, intents, needs, and interests across customers and all touchpoints along and around the journeys. Agentic AI uses past data, such as previous interactions, journeys, and website visits, as well as future data, for example, using marketing campaigns, to identify those variables to be inferred via deep learning before feedforward loopbacking the results into unique layers of the networks thus enabling personalized recommendations of the next action for everyone on the continuum.
Data Integration and Interoperability
To enable a seamless customer experience, data from multiple sources must be integrated to enable contextualized and relevant customer interactions. The interaction patterns of customers change naturally over time, and so do their preferences. Such changes must be captured and reflected in relevant customer communications at scale to allow for proactive engagement and appropriate nudges. This requires orchestrating all channels and touchpoints along each customer’s journey. The content should resonate with customer needs and the stage of the customer journey, such as awareness, engagement, consideration, purchase, or post-purchase.
An essential requirement for such personalized experiences is data integration and interoperability across customers’ lifecycle data, marketing applications, and manages services. Dedicated data integration frameworks have been developed Externally Supported Data Integration (ESDI) and Manually Supported Data Integration (MSDI). ESDI requires the sharing of customers’ lifecycle data and marketing application services among the service providers of these marketing application services, to prepare the marketing application services for personalized engagement with customers. In situations where such data sharing is not possible, customers’ lifecycle data must still be periodically replicated by the marketing service provider into its internal application. However, the marketing service provider must remain responsible for the integrated marketing messages to its customers. A hybrid variant of these two extreme options is also possible.
Personalization at Scale
Automating customer journeys powered by agentic AI at scale entails delivering personalized experiences in product, service, and scenario choices based on valid and inferred customer data. With the current generative tools, SMEs can deploy hyper-personalized marketing at scale at low cost. Actively encouraging customer participation in the marketing creation process promotes positive and continuous company-customer engagement. Over a long-time horizon, customer incentives and support play determinants in guiding a customer behaviour trajectory that meets company goals. Nonetheless, customers may have only short-term incentives to participate in the behaviour being promoted. Decision path experiences further take the combined context and data to construct custom marketing and product elements before letting the assistant distribute them across selected media.
Many chatbots today can be viewed as agentic AI consulting on product and service choice within a specific context and choice scenario. Actor-based personas represent realistic embodiments of company values. Through combined context and data such implementations can guide customer interaction towards enterprises that offer relevant solutions. At higher levels of autonomy, the AI agent can identify, plan, and coordinate choices to achieve trajectories leading to the optimum decision path for the customer persona.
Orchestration of Channels and Touchpoints
Effective customer engagement requires synchronised interactions across multiple channels and touchpoints. Presenting a coherent story and a consistent brand experience becomes even more critical for SMEs when they aim for global expansion.
Integrating all touchpoints into a single view of the customer journey enables contextualised interactions. Whenever AI-generated content or recommendations are shared via a channel, the information is also made available to all downstream touchpoints in the journey. Automated processing thereby becomes much easier. All channels can act upon the same information, thereby reducing expensive rework and inconsistency. Channels can also make adjustments based on what they already know about the customer, achieving lower-cost interactions that still feel personal.
Automation can even be applied to real-time channel-switching during an engagement. Adding a human touch can be activated automatically when customers show irritation or boredom. On the other hand, if a visitor’s questions are exceedingly simple and can be easily answered by a bot, escalation to a bot remains an option. A human takes over the conversation only when the visitor exhibits even greater interest and complexity. Otherwise, the bot will continue serving the customer to closure, complete with all contextual knowledge flowing from past touchpoints auditory, visual and affective.
Decision Making and Autonomy Levels
Continuing with the overall theme of Customer Journey Automation through Agentic AI, an important aspect to explore concerns the levels of decision-making and autonomy that SMEs can implement for their Customer Journeys through Agentic AI. SMEs generally have limited resources at hand and the level of automation in operation of Customer Journeys needs to be aligned with the available capabilities and the ratio between potential opportunity and associated risk.
In general, Customer Journeys with Agentic AI should do as much as possible through automation for convenience and efficiency. However, the risk of failure or errors during the execution of Customer Journey, especially in unusual situations, needs to be contained under the acceptable level. Failures during the execution of Customer Journey can hurt customer perception about the speed of response and quality of service and can damage longer-term business relationships. Therefore, for automation of Customer Journey with Agentic AI, an escalation process supervised by Human agent should be ensured. Automatic execution of identified steps of Customer Journey can cater speed and cost advantage while Escalation Process can mitigate the risk of damaging business relationships.
Impacts on Operations, Efficiency, and Customer Experience
For companies operating online, the COVID-19 pandemic caused a surge in demand that distorted traditional trade-offs between revenue and costs. Rather than generating a profit maximization problem with straightforward demand elasticity, override requests from senior management created a new kind of optimization. Travel demand for low-cost flights and hotel holiday packages soared, such that SMF Group set up a holiday camp business in Singapore. Razer and Shopee sold food and sex toys during lockdowns. Grab even provided tent rentals. New ventures and tweaking the product inventory to chase fresh demand benefitted from superior revenue per employee. The efficiency element in the operation equation became revenue generation for high street retailers unlucky enough to be declared essential by the government.
The need to shorten customer journeys and to bring touchpoints together became paramount in enabling customers to pursue purchasing journeys that matched their shifting behaviour. Agency AI was therefore deployed at the extreme to create a virtual operating environment that would automatically cater for the customer as their behaviour dictated. Agents from multiple domains automatically integrated technology, moved between domains as customers migrated, captured demand in a single transaction to ease payment, and minimised effort by keying data only once. In the process, all agents removed awkward and unsightly crime scene tape, and even created new income streams. Singapore’s respecting of international personal data privacy laws had simplified governance data flows and regulatory approval, but posed challenges on scale; approaching a million transactions risked becoming a proportionate burden.
Case Studies from Singaporean SMEs
Fewer than half of the SMEs in Singapore undertook digital transformation initiatives during the pandemic, and many digital platforms launched in a rush were deployed without foresight. A visible minority regarded the turmoil as a useful opportunity to redesign, rebuild, or reinvent totally. A few embraced AI at a different level creating a group of “early adopters,” responding to the disruption with new initiatives directed towards either their customers or supply chains. Two such companies now provide advertisements and services or instruments for those who either browse for travelling ideas or look for opportunities to travel. An AI Chatbot determines when and to whom an advertisement should be directed or how proper service or instrument might be offered if they were to look for help.
Retailers currently provide their customers with the ability to inquire about a product in an online store, receive product recommendations, or provide fast answers to frequently asked questions. These are typically simple Chatbots. However, an increased version could chat about various topics, acting as a travel friend, providing insights on a specific location and advising the chances of rainfall or required clothing according to the temperature. Furthermore, it could suggest other destinations the user might like to explore. An agentic AI Chatbot developed for a travel-related site adds personalisation at scale and integrates a much wider selection of sources for a product recommendation. It even offers a complete travel package instead of just addressing a single product before interaction. A fantastic answer service acting as a conversation friend has been implemented. When a question is outside the provided answer bank, it can search the Internet and other APIs to return background information to the user.
The following case study discusses how an agentic AI Chatbot was developed from a traditional one in order to exploit its capabilities fully. The goal of having this type of Chatbot is twofold: to engage customers and capture data on their intentions; and, whenever the platform identifies a possible match between customer queries with submitted offers, to propose it proactively.
Metrics and Evaluation Frameworks
Like all complex business systems in the digital economy, automated customer journeys must deliver results. Revenues must meet expectations, not just to satisfy investors but also to fund future operations, development, and growth. The company must generate sufficient profit to provide a reasonable return on capital invested and to fund a reasonable level of taxation. Efficiency also matters, in any business system, the costs incurred cannot exceed the revenues generated by its operation, adjusted for an appropriate level of risk. In the context of businesses in the digital economy whose continuing success is predicated on customer experience, performance must be measured not only in terms of efficiency and profitability but also, and arguably more important in the longer term, in terms of customer satisfaction and the overall customer experience. Evaluation frameworks are needed to capture each of these performance dimensions and to identify any gaps in satisfactory performance in the context of operating objectives. Importantly, they must align with the goals of Singapore’s Digital Economy Strategy and the broader national interest.
A framework for evaluating automated customer journeys based on these goals is presented in Wong (2022) and Wong and Sharma (2020). The operation of each customer journey is evaluated in terms of the following general dimensions: four areas of performance (business outcomes, operational efficiency, customer satisfaction, and overall experience), relative performance (actual performance relative to expectations), and sufficiency (risk-adjusted performance above an acceptable level). The evaluation framework, together with industry case studies, can serve as a guide for performance assessment and diagnosis of automated customer journeys in Singapore SMEs.
Implementation Pathways and Change Management
Potential pathways to enabling Agentic AI to automate customer journeys for Singaporean SMEs are presented, along with their governing principles. Data governance and compliance, talent and capability development, and vendor selection and procurement represent key change management areas. Singaporean SMEs can then harness Agentic AI automation for customer journeys, lift performance, and create a resilient foundation for future business transformation.
Data Governance and Compliance
Implementing Agentic AI to automate customer journeys and operations requires sensitive personal and transactional data. This data must come from customers in a manner aligned with local data governance and privacy regulations. Future-proof frameworks integrating technical, operational, and training processes are crucial to prevent detrimental effects on compliance and risk management.
Deliberate planning can aid compliance during data-sharing processes between Singapore SMEs and technology vendors. Clear communication regarding data usage encourages customers and potential users to share valuable information that enables richer, more accurate customer profiles. Deepening customer understanding through this data improves the customer experience by allowing for a more tailored journey.
Talent and Capability Development
Success in enabling Agentic AI to automate customer journeys hinges on strong internal talent and capability development. Singapore SMEs should foster technical skills in machine learning, natural language processing, and AI. Skills in integration of backend systems and third-party offerings, use and configuration of any no-code/low-code frameworks, and understanding of customer experience are likewise critical. Furthermore, building awareness of AI pitfalls helps avoid over-hype and resulting disappointment.
Vendor Selection and Procurement
Successful adoption of Agentic AI by Singapore SMEs requires careful vendor selection. Partners must support a clear roadmap that aligns with technology adoption or refresh cycles. Enabling Agentic AI capabilities through outsourcing reduces risk and expedites delivery. The success of Agentic AI largely relies on training data, making evaluation of historical data quality essential. Selection of a partner with domain knowledge raises the likelihood of quality training data and reducing inherent biases.
The selection process should also consider business vision, product roadmap, data quality, and breadth of industry knowledge. A comprehensive approach determines if they can effectively automate a customer journey. Criteria should be easily tracked and weighted to support justification of the chosen partner.
Data Governance and Compliance
The design and deployment of Agentic AI systems underpinning automated customer journeys mandate the strictest adherence and compliance to data governance and privacy requirements of the state and industry. Hence, data governance frameworks are generally defined at a country and industry level, detailing the key policies and principles to which enterprises must adhere in their collection, storage, and usage of customer, partner, and employee data. Grounded on key instruments such as the Personal Data Protection Act and the model AI governance framework, Singaporean enterprises may thus leverage the assistance provided by the Personal Data Protection Commission and the Infocomm Media Development Authority in the establishment of appropriate policies. These policies may be further elaborated by industry-specific bodies such as the Monetary Authority of Singapore with the guidance of the Association of Banks in Singapore or the Monetary Authority of Singapore in relation to the financial services industry so as to meet enterprise, industry, and customer needs.
In addition, enterprises may opt to apply for assurance frameworks in order to assess the robustness of their data governance practices. Data governance and privacy assessments are hence critical as they help to identify data management shortcomings or non-compliance that could raise consumer trust issues. Furthermore, the threat of steep financial penalties and reputational damage for breaches motivate enterprises towards conforming with data governance principles. However, many roadblocks remain. Many enterprises lack the personnel with the training and experience to develop comprehensive data governance policies, and even those that do seek the assistance of external experts. In addition, procurement often focuses narrowly on cost considerations and can overlook the trustworthiness of a supplier’s data governance practices.
Talent and Capability Development
Investments in agentic AI introduce new methods and competencies, accelerate technology adoption, and require change management across ICT as well as business functions. Although customer journeys are often managed by marketing teams, other functions must help define journey steps, sequences, and business rules governing decision making for each journey. Designing and managing data integration and data-sharing facilities uses ICT expertise, while ensuring data privacy and security requires legal, governance, and compliance skills. Role-based authorizations must be defined for accessing integrated customer datasets and customer data from external sources; personal data must be anonymized before use in AI-training and analytics exercises. Procurement activity uses enterprise and supplier management capabilities to select SMA partners, ERP suppliers with robust channel connectivity, and support for information hybridization and respect. Finally, the implementation path is a multi-year activity that combines customer journey enlightenment with governance, risk, and compliance functions and the role of external vendors.
Adopting agentic AI involves acquisition of business expertise, analytical skills, and knowledge of CSML. Universities and private universities train graduates in general AI and configures CSML. CSML use requires a new combination of industry experience, ICT domain knowledge, and familiarity with business decision making. Skillsets are often applied in analytics functions for a short duration before moving to business functions. Market-positioned curriculum offerings raised agentic AI awareness. Short courses increased broad awareness of the possibilities of agentic AI technology and how it can enable customer journey automation.
Vendor Selection and Procurement
Vendor selection and procurement processes can be time consuming, especially when multiple technologies need to be considered and integrated. Agency frameworks should include rapid decision-making procedures for selecting vendors that provide the best overall value, balancing up-front costs against ongoing maintenance costs and implementation time, as well as ease of integration with existing technologies. Investments in open-source software can accelerate progress if the software meets functional needs or if internal capability allows for forked modifications.
Contractual agreements can be streamlined and simplified if funds are readily available. Special care should be taken in engaging vendors capable of delivering products for the distinct characteristics of the Singapore context: multi-ethnic languages and dialects, cultural preferences, strong tendencies towards Singaporean-made products and services, an aptitude for regional tourism, and holiday seasons that revolve around Chinese New Year. Funding may be available from IMM and enterprise test-beds to speed deployment. SMEs that join grants like the Digital Resilience Initiative can pool resources to quicken the setup of cross-channel service offerings and share maintenance budgets.
Risks, Mitigations, and Resilience
Risk management is the process of mitigating, managing, and monitoring risk. Risk definitions vary depending on the field of science and/or practice. In enterprise risk management (Perry & Towers, 2013), for instance, it is defined as “the risk of not achieving objectives or realizing opportunities.” This implies that risk can be considered both a downside, something to be avoided, and an upside, something to be pursued. The IT industry has seen significant investments in risk management and a mature body of literature; Sutherland (2014) describes how broadly defined risk is as well as recent developments in the specific domain of IT security, and a survey by Beranek (2007) analyzes IT security risk management in the context of enterprise risk management.
A useful definition for understanding how risk management intersects with change management is that of Kreitner and Kinicki (2008, p. 324): “a stage of the management process in which managers set a direction, determine how to allocate resources, nurture relationships, and build a network of supporters to create a future that would not happen by itself.” Kreitner and Kinicki link this stage of the management process to four transitions from one state to another: (1) from one form of stability to another; (2) from one form of growth to another; (3) from one form of decline to another; or (4) a combination of the first three transitions.
Policy Context and Ecosystem Support in Singapore
Singapore is well positioned for the successful adoption of agentic AI in automating customer journeys. Its safe and trusted business environment balances privacy with data economy. Data are the new oil of the digital economy. Used in safe and trusted ways, data can enrich lives, create value for businesses, and underpin Singapore’s growth as a smart nation, Connecting lives, Business and Government through data innovation anderson set in their Data strategy. Its Data under its Data strategy. Its Data governance and use Policy alliance forms the basis beyond the integrity of data in Singapore society and economy. to allow Businesses and governments to harness and share data securely and Proprietorialy, while Economizing Privacy. Its confirms that Companies should get the consent of customers before collecting their personal data and Use the data only for the purpose that customers have agreed to. Relevant data protection and personal privacy laws are Strictly utilized.
Ecosystem support for agentic AI-enabled automated customer journey technologies is also evident Singapore offers a vibrant tech ecosystem, with a Ready Market for AI and high Potential for AI sustainment through a growing base of Enterprise A. Becaused in us FleetApt ranked. In the Start-Up Rail Smt. Also aims to make Singapore a regional hub for niche that AI class for a. The technology has been identified. By the Infocomm Media Development Authority (IMDA) is a Licensed Segment for the country, with strong Growth prospects for Cyber Solution Structure, capable Service, Datal Lake and Logineny Vector Offerings, Cyber System, Risk Informatics, Market For Big Data Analytics and Data Security within the national budget. 12-Stom and also livens. A rapid Deployment of data speeds Data Resident Generaction Target, spearheaded by AI,36 and Enabling Capabilities to use Public Data set AI Taxes. The Office of the Power generation. The Office of the Power generation. The Central Virgin Islands and the Infocomm Media Authority are key players the Sustainable Roadmap and Trace Roadmap for the Future Heat. Singapore’s local delivery system is of high priority to reform.
Future Scenarios and Strategic Considerations
Looking further into the future, the scenario of devices and agents automatically adapting the environment for users able to move into it becomes increasingly tangible. Agents will continue to interrogate data on known and predictable user behaviour patterns, monitoring recent anomalies, and adjusting delivery of the most effective presets supporting agent attunement, experience alignment, and engagement fulfilment through priority selection of channel, medium, type, and timing adapted to the considered delivery context and individual user history.
Customer journeys could increasingly transmute into user journeys, and the focus widen to include also suppliers, such as food vendors, that guarantee sufficient range and standard of delivery. Here, agents already monitor channel status, weather and event calendars, predict talk time concentration and consequent uptimes, determine best early and late hour planning for delivery, modularise routing across channels to manage robots shift patterns more energy-efficient in planning, delivery, or recharging, and check ad-hoc deliveries synchronizing supply across customers. User-agent disintermediation risk could hence also arise in demand prediction through collaborative long-tail fulfilment.
At such stage, customer experience focus might evolve back towards brands, that end up being the only connecting element in journeys across many suppliers, primarily focusing on ensuring quality reception within guarantees. Consideration of digitally-automated customer journeys might then re-enter as a tool to further improve efficiency and enable reliable brand association. The final dimension, however, cannot be eliminated and remains that things are required to be kept on delivery track of customers, end customers, suppliers, vendors, and employees at all levels.
The analysis and supporting case studies demonstrate how Agentic AI allows for automation of aspects of customer journeys that previously depended on human agency and effort. Furthermore the research considers how Agentic AI can address distinct operational challenges in two Singaporean SMEs while improving customer experience. These contributions substantiate the potential of Agentic AI for small and medium-sized enterprises in Singapore: in facilitating continued service and support even at lower levels of operational staffing, and in promoting increased sales revenue from the customer relationship management domain. Consequently, the research outlines a structured approach for deploying Agentic AI to enhance SME digital capabilities and customer experience.
Change is typically hard, and change in small businesses often more so. Motivation to change, however, frequently arises precisely when change seems most difficult. During the COVID-19 pandemic, wide-ranging operational disruption and the greater role of technology acted as a catalyst for many small and medium-sized enterprises in Singapore to accelerate their digital adoption and investment journeys. Embedding Agentic AI into customer journeys can deliver a major shift in operation, redistribute resource demands, and disclose vulnerability in non-digital aspects of customer interactions. Such embedding not only constitutes meaningful change but also delivers very positive outcomes.
Thilina is a multi-skilled digital marketing professional and technical specialist at Sotavento Medios. He manages essential technical SEO audits, search engine indexing, and automated workflows. With experience spanning website management, Google Ads, and campaign execution, he ensures digital assets remain optimized for generative search engines.
