June 2024

SGV thought leadership on pressing issues faced by chief executives in today’s economic landscape. Articles are published every Monday in the Economy section of the BusinessWorld newspaper.
24 June 2024 Roderick M. Vega

Instilling integrity into the corporate DNA

The current climate of persistent macroeconomic, geopolitical, and market volatility, coupled with stringent regulatory scrutiny, continue to put the moral compass of organizations to the test. These global conditions underscore the critical importance of the values businesses uphold, particularly trust and integrity. Trust serves as a significant competitive advantage, particularly when market unpredictability challenges business resilience. Without trust from employees, customers, suppliers, and investors, an organization’s future viability is jeopardized.  At the same time, companies rooted in integrity ensure long-term sustainability by adhering to ethical practices, which reinforce their brand and operational stability. A lack of integrity erodes trust, leading to significant operational and strategic challenges. The interplay between these two raises a critical question: "How can trust endure without integrity?" This query forms the crux of the EY Global Integrity Report 2024, which surveyed over 5,400 respondents across 53 countries and territories. On the upside, 49% of global respondents believe that compliance with their organization's standards of integrity has improved over the past two years, marking a seven percent increase from the EY Global Integrity Report 2022.  However, 38% of global respondents acknowledge a willingness to engage in unethical behavior to advance their career or remuneration. This pervasive mindset creates substantial risks that could lead to various adverse impacts within an organization. The cost of low corporate integrity is high. Specifically, corporate violations in the United States and the United Kingdom incurred penalties totaling US$1 trillion, as a result of half a million infractions between 2010 and 2023.  This article explores actionable insights from the EY Global Integrity Report 2024 and identifies human-centered approaches that leaders can use to build an integrity-first culture within their organizations. The current state of integrity Despite improved perceptions of organizational standards of integrity, companies continue to grapple with significant incidents and violations. The EY report highlights that 20% of companies acknowledge experiencing major integrity breaches, such as fraud, data privacy or security incidents, or regulatory compliance violations, within the past two years. Notably, among those reporting significant integrity incidents, over two-thirds indicate the involvement of third parties. An analysis of over 500,000 corporate violations from 2010 to 2023 reveals that certain financial and employment violations, including accounting deficiencies, AML deficiencies, tax violations, labor standards, workplace safety, and consumer privacy issues, have become 2 to 10 times more frequent since 2010. Conversely, there has been a notable decline in violations related to employee compensation, public safety, banking, and environmental issues. However, progress remains limited in addressing anti-competitive behavior, discrimination, and whistleblower retaliation. Employees’ approach toward integrity Although a majority of employees (58%) take a principled approach to integrity, there remains a substantial proportion (42%) who may compromise these standards under certain conditions.  In this dichotomy, the report shows that potentially compromised employees have a more negative view of their organization’s compliance environment. They are nearly three times more likely to say that unethical conduct is ignored within their teams, and more than five times more likely to say that unethical conduct is ignored within their organization’s supply or distribution chain. Leaders' integrity dilemma An unethical mindset towards career or pay is predominant in the upper echelons of organizations, with 67% of board members admitting they would consider unethical actions for their own benefit compared to only 25% of employees. Moreover, 47% of board members and 40% of senior management have observed actions within the past two years that could damage their organization’s reputation if made public, yet no internal response was taken. This lack of action highlights a critical gap in ethical oversight and accountability. What breeds misconduct The survey identifies several root causes of integrity incidents globally, including failure of financial processes and controls (27%), lack of internal resources to manage compliance and integrity activities (27%), employees not understanding policy and requirements (26%), and lack of appropriate tone from senior leadership (25%). Equally significant, 45% of global respondents who reported integrity incidents attribute them to poor leadership tone or management pressure. This issue is compounded by the apparent reluctance among leaders to address misconduct.  Such factors contribute to an environment conducive to misconduct, emphasizing the need for robust controls, resources, and leadership commitment to foster a culture of integrity. High cost of low integrity Misconduct is an unpleasant reality, surfacing even within the most ethical organizations. Corporate infractions come at a high cost—not just in resources spent on internal investigations and remediation but also in fines and penalties paid to government regulators. For instance, recent research indicates that corporate fraud shaves approximately 1.6% off a company’s equity value each year. In monetary terms, that equates to US$830 billion in 2021 alone.  But the costs extend beyond the financial. A top-down, all talk, no walk mentality erodes trust both within the organization and in the public eye, placing the company's reputation and financial health in jeopardy. Building an integrity-first culture Embracing the following integrity-first approaches — which put the right programs in place to drive behavior to create a strong culture and a strong belief in their commitment to integrity — can help organizations keep pace with evolving regulations and increasing societal expectations: Lead from the top. Integrity can’t be built or sustained with all talk and no action. Organizations need to focus on preventing and addressing misconduct by starting from the top. Moreover, leaders need to listen and practice what they preach to instill integrity further down the line. Words alone won’t inspire integrity; it demands actionable leadership. Design and implement a structure to execute strategy. To prevent unethical actions from the top down, organizations must implement robust governance structures within their integrity programs and strategies. Breaking down silos is also crucial to encourage a 'speak-up' culture against any misconduct. Strengthen a culture of integrity across the organization. Organizations must recognize that integrity is a collaborative endeavor, not merely a stand-alone function. Embedding compliance directly into operations—from new business development to vendor payments—transforms corporate policies into actionable workflows.  Boost awareness, training and communication. The report indicates that fewer than 47 percent of management teams frequently communicate to their employees the importance of behaving with integrity. Making the rationale behind policies crystal clear fosters a resilient organization capable of thriving in both good and bad times. Create a virtuous circle of integrity. In times of rapid change and difficult market conditions, maintaining, let alone enhancing, corporate integrity can seem daunting. But it is precisely in these challenging times that integrity must not only be preserved but also prioritized. Roderick M. Vega is the Forensic and Integrity Services Leader of SGV & Co.This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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18 June 2024 Christiane Joymiel C. Say-Mendoza and Joseph Ian M. Canlas

Responsible AI: Transforming risk management in the Philippines

As the digital age continues to evolve, artificial intelligence (AI) is rapidly becoming a cornerstone of innovation and efficiency. In 2021, the Philippines launched the National Artificial Intelligence Roadmap, which prioritizes inclusive, resilient, and sustainable development. Furthermore, the country’s President believes that AI can uplift the lives of the nation’s citizens, drive enterprise productivity, and increase the Philippine economy’s competitiveness. According to a recent study from IBM’s Institute for Business Value, three out of four CEOs think that organizations with the most advanced generative AI (GenAI) are at an advantage, with nearly half already utilizing GenAI to guide their strategic decisions. As organizations expand their AI adoption, it is imperative that they adhere to Responsible AI practices, which promote the ethical, transparent, and beneficial use of the technology. AI adoption in the Philippines The country’s AI adoption is evident across multiple sectors, each harnessing its capabilities to enhance operations and manage risks. Financial institutions. Some local universal banks are leveraging on AI for risk assessment, fraud detection, and customer service, utilizing solutions provided by tech giants such as Microsoft. Healthcare. Some healthcare platforms are leveraging AI for medical data analysis, improving patient care, and expanding telehealth services. Telecommunications. Local telecom companies employ AI for network optimization, customer service enhancement, and predictive maintenance. E-commerce/Retail. Online marketplaces and retailers utilize AI-driven recommendations and predictive analytics to refine customer experiences and operational efficiency. AI's impact on risk management AI is revolutionizing risk management by offering enhanced data analysis, predictive capabilities, real-time risk assessments, and advanced cybersecurity measures. These technologies enable businesses to identify and respond to risks with unprecedented speed and accuracy. However, the integration of AI into risk management is not without its challenges. Concerns around data privacy, algorithmic bias and fairness, transparency, and regulatory compliance must be addressed to ensure the responsible use of AI. Data privacy and security. AI systems rely on data. There's a risk that sensitive customer or business information could be exposed, particularly if appropriate cybersecurity measures are not in place. Algorithmic bias and fairness. AI systems are only as good as the data they're trained on. If the data is inaccurate, incomplete, or biased, it can lead to unreliable or discriminatory decisions. Lack of transparency. Complex AI models may lack transparency, making it challenging for stakeholders to understand how decisions are made. If the reason behind a decision by AI can't be explained, it can lead to legal and ethical implications. Regulatory compliance. The legal environment for AI is complex, fluid, and still developing. Companies can face risks relating to non-compliance with data protection regulations and other industry-specific laws. Navigating AI risks with responsible practices Responsible AI covers transparency, fairness, accountability, ethical use, privacy protection, reliability, safety, sustainability, inclusivity, and governance. To integrate Responsible AI into risk management, companies can adopt the following best practices: Ethical framework development. Create a comprehensive ethical framework that aligns with regulatory standards and industry-specific best practices. Data governance and privacy protection. Implement data governance practices to ensure data privacy and transparency in AI models. Transparency and explainability. Make AI outputs understandable and provide justifications for AI-generated decisions. Bias detection and mitigation. Conduct thorough bias assessments to identify and mitigate biases in AI models. Human-AI collaboration. Augment human expertise with AI, promoting collaboration through accessible interfaces like visualizations and interactive dashboards. Examples of Responsible AI in action Banks. Major local banks are incorporating AI in risk management, with a focus on fraud detection. Responsible AI usage involves stringent data protections and privacy measures. Telecommunications. Local providers use AI to manage infrastructure risks and predict outages. Ensuring responsible AI usage means preventing wrongful service denials. E-commerce. Some platforms employ AI for product recommendations, with a responsibility to avoid discriminatory biases. Health Tech. Certain local companies use AI for disease diagnosis, requiring the protection of sensitive health information. The trajectory of Responsible AI in the Philippines The future of Responsible AI in the Philippines includes broader AI adoption across sectors, enhanced regulations, and workforce upskilling, among others. With the Philippines set to propose the creation of a Southeast Asian AI regulatory framework to the ASEAN in 2026, Responsible AI could become a standard in business operations. As AI becomes more pervasive in the country’s business landscape, its impact on society will be profound, shaping the future of work, influencing broader socio-economic development, and driving positive change. It is therefore imperative for organizations to embrace Responsible AI principles in risk management and collaborate with stakeholders to navigate the opportunities and challenges presented by local AI-driven innovations.  Christiane Joymiel C. Say-Mendoza and Joseph Ian M. Canlas are Business Consulting Partners of SGV & Co. This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the authors and do not necessarily represent the views of SGV & Co. 

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10 June 2024 Maria Kathrina S. Macaisa-Peña

Leveraging GenAI to transform the finance function

The evolution of artificial intelligence (AI) has been remarkable, beginning with the conceptualization of neural networks in 1943 and progressing to the birth of machine learning (ML) in 1959 and the advent of deep learning in 2006. This trajectory led to the era of Generative AI (GenAI), which emerged around 2017. GenAI refers to the subset of AI that focuses on creating new content, from text to images, by learning from vast datasets. This leap forward enables machines to not only interpret data, but also to generate original outputs that can mimic human creativity and reasoning. As the technology becomes more sophisticated, consumers are increasingly integrating large language models (LLMs), an application of GenAI, into their daily lives. From asking virtual assistants for weather updates to receiving personalized recommendations, the comfort and confidence in using such technology are on the rise. This adoption signifies a shift in the public's perception of AI, viewing it as a reliable and integral part of modern living. Part of this shift can be seen in how GenAI is revolutionizing strategic business thinking, enabling businesses to unlock new revenue sources, achieve productivity gains, and innovate existing business models, ultimately leading to value creation. In particular, firms and departments dealing with finance and accounting can leverage GenAI to enhance data entry and reconciliation, enrich forecasting and analysis, and fortify risk management. GenAI applications in the finance function The finance function is pivotal in supporting optimized enterprise decisioning – and rethinking enterprise structures through the lens of GenAI is key to unlocking a spectrum of new possibilities for value creation. Knowledge management and decision support in particular are among the most potent use cases for scaled AI capabilities.  GenAI can enhance an organization's data value by asking better questions, optimizing multi-variable choices, and enabling actions at scale. In addition, GenAI can write code on demand to extract information from data sources, create reports with appropriate data visualization, and provide persona-based analysis. It also enables conversations with virtual agents for a deeper understanding of results.  In monthly financial reporting cycles, analysts would traditionally spend hours writing code to extract data from various sources, compiling it into spreadsheets, and then painstakingly creating visualizations. GenAI allows them to input their requirements, after which the AI writes the necessary code on demand, pulling information from databases, cloud storage, and even real-time market feeds. The data is not just tabulated – it's transformed into compelling visual reports that highlight key financial metrics and trends. Moreover, GenAI can provide persona-based analysis, tailoring insights to the specific needs of each stakeholder. The CFO receives a high-level overview emphasizing strategic implications, while line managers get detailed breakdowns relevant to their departments. Content creation, a repetitive and complex task, has also been redefined by GenAI. Finance teams can leverage GenAI to assist in generating various analytical documents. Variance reports, budgets, and forecasts are produced with a level of detail and accuracy that was previously unattainable. GenAI sifts through historical data, identifies anomalies, and presents findings in a clear, concise manner. Moreover, GenAI can extend its capabilities to responding to common queries from colleagues or clients. Instead of drafting individual responses, finance professionals can rely on GenAI to provide accurate and contextually relevant answers, freeing up their time for more strategic tasks. GenAI has become an essential collaborator in meetings and project planning as well. It helps document discussions, distilling them into actionable items and comprehensive plans.  Last but not the least, perhaps the most transformative application of GenAI within the finance function is in forecasting. A GenAI model can take in vast amounts of historical financial data and current market trends to predict future performance with remarkable accuracy. It identifies patterns that might elude even the most experienced analysts and uses natural language processing to incorporate insights from news articles and external data sources. This ability allows organizations to anticipate market movements and adjust their strategies proactively. Whether in terms of revenue, expenses, profit, or cash flow, forecasts can provide more than just numbers — they can become strategic tools that inform decision-making at the highest levels. Realizing GenAI advantages  To fully realize the advantages of GenAI in finance and accounting, companies need to enhance their finance and accounting functions with innovation intelligence, invest in infrastructure and develop talent in AI while putting proper governance and controls in place. Amidst the possibilities and efficiencies that AI can create for the finance function, blind optimism and hype around this disruptive technology can have a counterproductive impact on a business that is unaware of its risks. To avoid this, companies can take the “innovation intelligence” approach through implementing planning, education and an agile test and learn strategy. Another critical determinant of an organization’s success will be how they enhance their comprehension of and refine their data infrastructure. Companies should have a tech stack with a solid foundation and support from experts to ensure their legacy data and technologies are unimpeachable before adding any GenAI applications on top of existing systems. Based on the EY 2023 Financial Services GenAI Survey, 44% of leaders identify access to skilled resources as a barrier to GenAI implementation. Part of the solution is to deploy upskilling programs that can equip the current workforce with the necessary skills in an increasingly AI-centric world. The human role of AI implementation is just as important as technology infrastructure. The GenAI imperative in the finance function Incorporating GenAI into finance is not just an option – it has become an imperative for long-term value creation. However, while it brings significant gains, it is also crucial to be mindful of potential risks. While aligning GenAI across the organization will be essential to unlock greater value, organizations must consider how GenAI can be used not only to transform the finance function, but also to redefine the future of business decision-making. Maria Kathrina S. Macaisa-Peña is a Business Consulting Partner and the PH Finance Fields of Play Leader of SGV & Co. This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co. 

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03 June 2024 Jan Ray G. Manlapaz and Mary Andrea T. Bacani

Building efficient and resilient supply chains with GenAI

In the wake of the global pandemic, businesses remained focused on advancing their artificial intelligence (AI) supply chain pilot projects into fully functioning applications. Companies are turning more to AI for demand planning and procurement within their supply chains, and are also investigating its potential for streamlining processes and enhancing efficiency in final-stage delivery. However, the rapid emergence of Generative AI (GenAI), brought to prominence by ChatGPT, has dramatically shifted perceptions about the capabilities of AI. GenAI is adept at producing new content that includes images, text, audio, or video, drawing from its training data. This technology isn’t new, but recent developments have streamlined its use and enhanced its practical value. As funding flows into this technology, leaders are swiftly assessing how it affects their operations and business structures, aiming to capitalize on its benefits. For those who diligently and strategically engage with innovation while maintaining an awareness of its limits — rather than impulsively chasing trends — GenAI can serve as a dynamic collaborative partner and a force multiplier in fortifying supply chains.What might have once been considered fictional is now part of serious conversations. AI applications are already being put into practice in real-world scenarios throughout the entire supply chain. These are made possible by GenAI's capabilities to organize and sort information based on visual or textual inputs, rapidly assess and adjust strategies, plans, and the distribution of resources in response to live data, produce various types of content on-demand, leading to quicker reaction times, summarize vast amounts of data while highlighting essential insights and patterns, and quickly help retrieve relevant information and deliver immediate responses, whether through voice or text.While it does have its limitations, GenAI provides a multiplier in what technology and humans can achieve together in building efficient and resilient supply chains, whether in planning, sourcing, making or moving. PlanningGenAI streamlines engagement across technology-driven planning activities. Modern GenAI applications are also capable of proposing multiple strategies in case of unforeseen complications. The area of risk management stands out as particularly promising, especially in anticipating risks that supply chain planners might not have previously contemplated. Numerous organizations are leveraging AI to sift through extensive historical sales data, market movements, and other factors to construct real-time models of demand. In addition, GenAI enables the formulation of ideal inventory quantities, manufacturing timetables, and distribution strategies to efficiently satisfy consumer needs.AI can assist in orchestrating production and timetabling by taking into account elements such as changes in customer orders, production capacity, resource availability, and the priority of orders. Similar to its capabilities in forecasting demand, GenAI can devise production plans, scheduling sequences, and efficiently allocate resources to reduce bottlenecks and optimize production efficiency. Currently, AI can be utilized to scrutinize historical data, market dynamics, climatic trends, and geopolitical occurrences, among other information sources, to pinpoint potential risks within the supply chain. Rather than relying on preset dashboards, for instance, GenAI can be prompted to generate on-the-spot risk evaluations, simulate various scenarios, and craft strategies for risk mitigation to aid planners in proactively overseeing and lessening risks.SourcingBeyond negotiating, GenAI offers a chance to enhance supplier engagement and oversight, providing guidance on subsequent actions. These useful tools can quickly pull information from extensive contracts, potentially helping prepare for discussions about contract renewals. In managing suppliers, companies can utilize natural language processing to derive insights from supplier communications and various data points. It can support the supervision and analysis of supplier interactions, pinpoint potential problems, and foster stronger supplier partnerships.Moreover, GenAI can assist in the process of choosing suppliers by evaluating a broad spectrum of supplier data and producing insights. By considering aspects such as supplier performance, capabilities, pricing, and risk assessments, GenAI algorithms can offer suggestions or rankings to support well-informed decision-making. MakingGenAI is revolutionizing the supply chain by significantly accelerating the journey from concept to commercialization, even when it involves new materials. Organizations are educating algorithms on their proprietary data and then employing AI to uncover methods to enhance productivity and efficiency. Predictive maintenance is yet another area where GenAI can pinpoint which machinery or production lines are at risk of malfunctioning and when, thereby enhancing overall equipment effectiveness (OEE) — a critical metric in manufacturing.In product design, GenAI can rapidly generate and assess numerous design alternatives based on set criteria, drastically accelerating the innovation cycle. This approach can be applied to a wide range of design challenges, from engineering new components for industrial machinery to creating consumer goods that are more efficient, robust, or visually attractive. Informed by data from factory machinery, GenAI models can also devise new maintenance strategies that align with predicted failure times of equipment. This enables manufacturers to fine-tune their maintenance timetables to intervene only when necessary, minimizing operational interruptions and expenses while also prolonging machinery lifespans.In addition, GenAI can be used to unearth new materials and refine existing ones. By analyzing extensive data on material characteristics and experimenting with various combinations, it can recommend new materials with specific desired traits or enhance the properties of current materials. This innovation could lead to the development of materials that are more efficient, sustainable, or durable for manufacturing purposes.MovingAlthough GenAI application in the field of logistics isn't new, the generative aspect introduces new levels of adaptability. For example, it can be used for route optimization for reduced fuel usage, the prioritization of specific shipments, or integration of various factors into an accessible platform. GenAI can optimize global trade by assessing a wide range of factors, such as tariffs, customs rules, trade agreements, and shipping expenses, to propose the most effective and economical routes and strategies. This helps businesses to maneuver through intricate global trade networks, ensuring compliance while cutting costs. Additionally, GenAI can improve the design of logistics networks by considering elements such as warehouse locations, transportation links, and demand patterns to generate efficient configurations. This results in shorter delivery times, decreased expenses, and heightened service quality.One of the significant challenges in logistics is real-time routing, which GenAI can address by constantly refining and enhancing delivery or collection routes in response to evolving conditions such as traffic, weather, and delivery priorities. This leads to heightened efficiency, lower fuel usage, and greater customer satisfaction.Realizing value with GenAIGenAI is a potent instrument with its own set of constraints, but it should not be mistaken for a strategy in itself. Organizations must focus on the business benefits and establish a roadmap, guided by the following steps:Focus on domain-wide transformation. Identify use cases with significant potential, aiming to create an integrated ecosystem that complements traditional business practices and unlocks new opportunities.Coordinate and collaborate. Discuss the broader implications of using GenAI and pinpoint the competencies needed across various departments, extending beyond just the technical roles.Maintain an open mindset while being mindful of risks. Launch exploratory pilot projects to gain insights, secure early successes, and work towards a model that can be expanded and adopted on a larger scale.Utilizing AI in supply chain management can help organizations become more resilient and sustainable while transforming cost structures. With recent developments that make AI easier to use and more effective in realizing value, organizations must evaluate how its advances can impact their sectors. Jan Ray G. Manlapaz is a Consulting Partner and Mary Andrea T. Bacani is a Supply Chain and Operations (SCO) Senior Manager of SGV & Co. This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.

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