Suits the C-Suite

22 July 2024

Marnelli Eileen J. Fullon

GenAI: The gamechanger in creating lasting customer relationships

Generative AI (GenAI) is more than a mere buzzword or a fad; it’s an exciting new avenue that businesses are exploring to help reinvent customer experience (CX). In the push towards a more digitalized society in the 2010s and 2020s, customers have not only become more well-informed, but now also expect their experiences and interactions to be more personalized, efficient, and engaging, regardless of the channel they are using. This has prompted organizations to rethink their CX strategies and explore how new technologies can transform CX.GenAI early adopters have already leveraged the technology’s transformative power, avoiding the pitfalls of usual tech trends or fads by focusing their deployment strategies to create real, tangible business outcomes. According to the 2023 EY Innovation Realized Survey, nearly half of the C-Suite respondents cited sales and marketing as the function with the highest priority for deploying GenAI. However, there has also been a movement pushing for GenAI to play a pivotal role in the future of CX interactions, with three significant areas for transformation:Personalization at scale, where we can leverage AI to design hyper-personalized experiences for our customers;Automation and efficiency, where we leverage on AI’s ability to manage routine and repetitive tasks; and,Innovation and engagement, where AI can analyze behavior to predict and anticipate the needs of tomorrow and even leverage customer insights to create new ideas for products and services. It must be noted that the intent is not to replace human resources with AI, but instead create a collaborative environment where AI turns humans into superhumans. With AI’s help, companies will be able to redefine existing roles and lay the groundwork for a working model of the future where human ingenuity and creativity is at the forefront of the work we do in the field of CX transformation.GenAI's role in today’s business landscapeGenAI is now at the center of CX’s future, enticing businesses with new possibilities and opportunities to answer the complex challenges of today’s world. GenAI technology is already reshaping the business landscape by enabling companies to analyze and synthesize large datasets without the need for the heavy workloads that characterized yesterday’s analytics. This also extends to AI being able to integrate consumer preferences such as purchase history and individual interests into strategic considerations. By leveraging AI-driven analytics suites, businesses would no longer be required to spend considerable effort and manhours on data collection and analysis.Businesses can then focus their efforts on the next step of CX: personalization. People can focus their efforts on the actual design and implementation of personalized and targeted marketing and product or service suggestions and work towards driving real engagement with their target consumer bases. AI’s ability to not only analyze data, but make recommendations based on that analysis, helps businesses continuously adapt their approaches and strategies to the changing needs of their customers, ensuring that every interaction and touchpoint feels uniquely tailored and special.GenAI also offers considerable benefits in terms of operational efficiency and decision-making. By automating routine, time-consuming tasks such as customer data analysis and insights generation, business leaders can allocate their human resources to more strategic, creative, and customer-facing roles. This reorientation of people will lead to a more concerted effort to providing customers with the care and attention they deserve and create a more responsive and customer-centric approach. As organizations continue to integrate GenAI into their workflows, they will be able to build upon these examples and further innovate new ways to maximize both their people and GenAI, enabling themselves to deliver more value to their customers.As an example, a sports company aiming to revolutionize its marketplace and deepen consumer connections faced challenges in realizing its vision. Partnering with EY professionals for enterprise data and AI solutions, they were able to work together to address a range of CX transformation objectives, such as customer segmentation, churn prevention, and market strategy refinement. Utilizing AI-powered tools, they focused on a particular opportunity to optimize product substitutes. This strategic focus on personalization not only tripled their e-commerce sales but also captured market demand exceeding US$1.5 billion, enabled by an innovative app feature.Providing support for the new age of customer touchpointGenAI can also be used to redefine specific touchpoints across the customer journey, primarily through its ability to automate certain actions and interactions. AI chatbots are already being trained to engage in meaningful, context-rich dialogues with customers. Where we would once have needed customers to queue up to speak to agents, AI bots would be able to meet customers, analyze customer asks and sentiments to determine the assistance needed, and, if doable, assist the customer in utilizing self-service to solve simpler problems.The benefits are numerous, both for the customer and for the agent who is at the frontline of customer interactions:The AI chatbot initiating contact and analyzing the customer’s situation already cuts down significantly on the response time as encoding of the problem is already automated.AI analysis of the customer situation lets it identify who the call should be routed to and prevent customers from being passed around between lines. AI’s effect on follow-up or repeat calls are tangible as AI will be able to detect unresolved issues or reasons for calling. All these will have the knock-on effect of reducing stress and pressure on agents, meaning they will be happier, more productive, and more responsive to the needs of the customer, which in turn means happier customer relationships.Strategies and challengesIt should be noted that the implementation of GenAI, while promising in its potential, is not without its challenges. Ensuring data privacy, developing the requisite skill sets, and seamlessly integrating AI into existing systems are just some of the challenges businesses must overcome. It must be stressed that upholding transparency with customers on the use of AI in our business is a key element to ensuring that the implementation does not fall through. It is also essential to avoid common pitfalls, such as overlooking the customer experience or underestimating the importance of human oversight in AI-driven processes.Furthermore, as businesses embark on their GenAI journey, they must navigate the complexities of aligning AI initiatives with the broader organization. This does not just extend to operational, strategic, or technical integration, but also cultural adaptation. GenAI is a gamechanger, as much so for the employees of the business as for their customers because it impacts the way that work is done. Managing the change and the resistance to it will be a fundamental challenge to its implementation, which is why it is important for the business to establish clear communication channels and training programs right from the top. Only through effectively navigating these implementation challenges can businesses unlock the full potential of GenAI and transform it into a core driver of customer satisfaction and business success.The CX revolutionGenAI is set to be the newest driver of the digital and technological CX revolution. When done right, GenAI can enable businesses to deliver a modern and innovative customer experience that is personalized and uniquely emblematic of their company’s brand.However, while GenAI is a technology filled with untapped potential, it is still not without its risks. It remains imperative that businesses implement the technology properly, correctly, and responsibly. Misuse of GenAI can lead to detriments just as large and impactful as the potential gains.With that in mind, the question we should ask ourselves at this crossroads is: Are we content to let potential remain potential? Or are we ready to take steps into a great unknown in pursuit of something greater than what we have today? Marnelli Eileen J. Fullon is a Business Consulting Partner 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.

15 July 2024

Rossana A. Fajardo

How AI is fundamentally changing recruitment

The recruitment process has always been a critical aspect of organizational success, serving as the means to acquiring the talent that drives innovation and growth. With the advent of artificial intelligence (AI), the talent acquisition landscape is undergoing a fundamental transformation. This article explores how AI is revolutionizing recruitment, the benefits and challenges it presents, as well as the strategies for striking the right balance between technology and human interaction.Recalibrating the hiring processThe pandemic has led to more companies using advanced HR technology to find and keep valuable employees. Because hiring the right talent is crucial for the success of any organization, this has increased the use of automated hiring tools to make better hiring decisions. These tools use AI to look at candidates more deeply than just their resumes, considering their potential for innovation and other important qualities, which helps to avoid bias and makes hiring more efficient.AI in hiring doesn't just make things more efficient; it also changes how companies plan for their future workforce. With AI, companies can predict what skills they will need and address any shortages by hiring or training people in advance. AI can look at job candidates from all over the world, helping companies find a diverse range of employees and create teams that are more innovative and successful in a global business setting. AI also helps make hiring fairer by reducing bias, leading to a more diverse and inclusive workplace.Digital hiring solutions could revolutionize the way organizations identify, assess, and recommend candidates. By analyzing talent data, AI can build predictive profiles that align with the company’s cultural context and can look at data to predict which candidates will fit the company's culture well, which could result in longer tenures and better performance. Leveraging AI for long-term successAI-powered digital accelerators are dynamic tools that continuously improve themselves by updating and adjusting their algorithms. This ensures they stay effective over time. These technologies can be smoothly incorporated into a company's overall management system, which helps the company look at hiring as part of the big picture. They make finding candidates faster and help companies make smarter hiring choices.AI in talent acquisitionAI is changing how companies hire people, but it's not perfect. Relying too much on AI might mean missing good candidates if the AI doesn't understand all the details in resumes. People also worry about losing the personal side of hiring, which is about knowing people and how they work together. To fix these issues, companies are using both AI and human judgment together. Examples are as follows:Resume Screening with Human Review: AI software initially screens resumes to filter out candidates based on specific criteria such as skills, experience, and education. Recruiters then manually review the shortlisted candidates to consider additional factors that AI might overlook, such as unique experiences or potential for growth.AI-driven Assessments with Human Interviews: Candidates might be asked to complete online assessments powered by AI, which evaluate their skills, personality, and cognitive abilities. The results are then reviewed by hiring managers who conduct personal interviews to get a better sense of the candidate's soft skills and cultural fit.Chatbots with Recruiter Follow-ups: AI chatbots can engage with candidates for initial data collection and answering FAQs. Recruiters can then follow up with candidates who pass this initial screening for more in-depth conversations and relationship building.Predictive Analytics with Human Decision-making: AI can analyze large datasets to predict which candidates are most likely to succeed in a role. Hiring managers use these insights to inform their decisions but also rely on their professional judgment and experience when making the final call.Automated Sourcing with Personalized Outreach: AI tools can identify potential candidates from various sources such as job boards, social media, and professional networks. Recruiters then personally reach out to these candidates to ensure a more human touch in the recruitment process.Video Interviews with AI Analysis and Human Review: Candidates might be asked to record video interviews that AI software analyzes for speech patterns, facial expressions, and body language. Recruiters review these analyses alongside the actual video to make more informed decisions about the candidates. By integrating AI tools with human expertise, companies can benefit from the efficiency and data-driven insights of AI while still maintaining the critical human elements of intuition, empathy, and complex decision-making that are essential for successful talent acquisition.Additionally, not all companies can afford to use AI for hiring, especially if they don't hire often or the jobs are very specialized. To help with this, there are new AI services that let companies pay only when they use them, making AI available to more businesses.AI’s impact on Filipino RecruitersAccording to the 2024 Work Trend Index from Microsoft Corp. and LinkedIn, 89% of Filipino leaders think their organization must leverage AI to stay competitive in the global market. The Philippines, like many other countries, has been adopting AI in various aspects of recruitment including 1) Automated Resume Screening, 2) Use of Chatbots for Candidate Engagement, and 3) AI for Candidate Sourcing. The adoption of AI in recruitment and retention in the Philippines reflects a broader global trend towards digital transformation in HR. However, the extent of AI adoption can vary widely among organizations, depending on their size, industry, and resources. It's also important to note that while AI can significantly enhance HR functions, it is typically used in conjunction with human expertise to ensure that the recruitment and retention processes remain balanced and fair.Striking the right balanceAI is fundamentally changing recruitment by providing innovative solutions that enhance efficiency, reduce bias, and improve the overall candidate experience. However, the successful integration of AI in recruitment requires a holistic approach that maintains the human element, ensuring that the technology serves to complement rather than replace the talent that is vital to the acquisition process. Understanding how AI is reshaping the hiring landscape is crucial for recruiters and chief human resources officers (CHROs) to meet evolving business and employee expectations. As AI and the HR function continue to evolve, organizations must balance traditional competencies like risk and compliance while paving the way for future innovations. Rossana A. Fajardo is the Consulting 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.

08 July 2024

Rajiv Kakar

Key GenAI cybersecurity challenges and risk mitigation strategies

Generative artificial intelligence (GenAI) has the capacity to understand, learn, adapt, and implement knowledge across a broad range of tasks at a level equal to or beyond human capability. Unlike Narrow AI, which is designed to perform a specific task such as voice recognition or recommendation algorithms, GenAI can apply intelligence to any problem, and be able to perform any intellectual task that a human being can do.While it holds extraordinary promise for the future, GenAI comes shrouded in various concerns, extending from ethical dilemmas to security susceptibilities. This article will explore some of the key challenges of GenAI and risk mitigation strategies from a cybersecurity perspective.Key challenges of GenAI A persistent issue of AI is the lack of transparency, frequently referred to as the black box problem. It’s difficult to understand how complex AI models make decisions, and this can create a security risk by allowing biased or malicious behavior to go unchecked.Businesses are rapidly exploring GenAI solutions with little forethought on the security implications on the rest of the IT estate. There is currently no limit for the complexity of attack surfaces of AI systems or other security abuses enabled by AI systems. In addition, AI models heavily rely on third-party technologies, where the large language models (LLMs) like ChatGPT are outside the control of an enterprise. Consequently, the learning parameters where AI systems may be trained for decision-making outside an organization’s security controls or trained in one domain and then “fine-tuned” for another raises concerns about intended and actual usage.Datasets used to train AI systems may become detached from their original and intended context, or may become stale or outdated relative to deployment. This introduces the problem of decisions made on incorrect data. Moreover, changes during training of models may fundamentally alter AI system performance and outcomes.LLMs typically capture more information than they process, and considering the privacy policy of ChatGPT, the platform may regularly collect user data such as IP address, browser info and browsing activity. These may be shared with third parties, competitors, and regulators. The use of pre-trained models that can advance research and improve performance can also increase levels of statistical uncertainty and cause issues with bias management, scientific validity, and reproducibility.On top of the computational costs for developing AI systems and their impact on the environment and planet, it is very difficult to predict failure modes for the emergent properties of large-scale pre-trained models. AI systems may require more frequent maintenance and triggers for conducting corrective maintenance. Additionally, it is challenging to perform regular AI-based software testing, or determine what to test, since AI systems are not subject to the same controls as traditional code development.“Artificial stupidity,” the term used to describe situations where AI takes decisions that may seem illogical to humans due to its inadequate understanding of the real-world context, presents another challenge. Talks of AI singularity, a hypothetical scenario where AI outstrips human intelligence, have also started to gather momentum. Critics argue that a super-intelligent AI poses a real existential risk, as it might spin out of human control. The dehumanizing effects of GenAI are another cause for concern. Over-reliance on AI risks devaluing human skills and minimizing human interactions. Moreover, the widespread application of GenAI may give rise to economic disparity, as the benefits of AI may not distribute evenly across society. Finally, the misuse of GenAI, particularly for harmful purposes like illegal surveillance, spreading propaganda, or weaponization, cannot be overstated.The already dense and complex AI landscape is further complicated by substantial hype and a multitude of diverse solutions. The resulting application environment is scattered with multiple third-party technology solution components which require thorough vetting in enterprise contexts. Types of GenAI attacksThere are various types of GenAI attacks manifesting across enterprises. Adversarial attacks involve manipulating an AI model's input data to make the model behave in a way that the attacker desires, without triggering an alarm. For example, an attacker could manipulate a facial recognition system to misidentify an individual, allowing unauthorized access. A data poisoning attack involves maliciously manipulating the data used to train AI models. By introducing false or misleading data into the training dataset, attackers can compromise the accuracy and reliability of AI systems. This can lead to biased predictions or compromised decision-making. On the other hand, a model theft or model inversion attack may attempt to steal and/or reverse-engineer AI models to obtain sensitive information. In a transfer learning attack, an attacker manipulates an AI model by transferring knowledge gained from one domain to another, resulting in the AI system producing incorrect or harmful outcomes when applied to new tasks. In input manipulation, interacting with a chatbot or an AI-driven system can lead to incorrect or harmful responses simply by changing words or asking tricky questions. For instance, a medical chatbot might misinterpret a health query, potentially providing inaccurate medical advice.AI can also be used by malicious actors to automate and enhance their cyberattacks. This includes using AI to perform more sophisticated phishing attacks, automate the discovery of vulnerabilities, or conduct faster, more effective brute-force attacks.GenAI security risk managementTo mitigate attack vectors, organizations must establish comprehensive regulations and standards that can guide the responsible use and development of GenAI. A GenAI Risk and Control framework can be very helpful in highlighting areas of vulnerability and risk mitigation in some of the following areas: Threat recognition. Identify possible threats GenAI might enable, such as autopilot system hacking, data privacy threats, decision-making distortion, or manipulation.Vulnerability Assessment. Evaluate weak spots in the system that might be exploited due to GenAI characteristics.Risk Impact Analysis. Look into potential implications if any threats were actualized (financial implications, impact on company reputation, etc.)Mitigation Strategy Development. Develop strategies to mitigate these risks, whether that means strengthening your network security system, creating backup systems, securing data privacy with improved encryption, or continuously auditing & updating the AI’s programming against potential manipulation.Contingency Planning. Develop a plan for responding to any breaches or issues that occur, despite mitigation efforts. Include steps to fix the issue, mitigate the damage, and prevent future occurrences.Constant Monitoring & Updating. GenAI systems should be regularly monitored and updated to patch vulnerabilities and keep up with the evolving threat landscape.Training & Awareness. Ensure that all users of GenAI systems are properly trained on security best practices and are aware of the potential threats.External Cooperation. Cooperate with other firms and institutions to share threat intelligence and promote a collective defense strategy.Regulation Compliance. Ensure compliance with all applicable laws and regulations surrounding data security and AI, such as general data protection regulation (GDPR).Incident Response Plan. Prepare a clear and concise plan to follow when a breach occurs, which includes reporting breaches, managing and controlling the situation.Organizations must consider upgrading cloud security and moving towards zero trust principles, whereby every access request is authenticated, authorized and validated every time. Antivirus systems should be upgraded from the current norm of using a pre-programmed list of known attack vectors (signature based) to systems that can observe unusual patterns and alert on deviations (anomaly based). Embracing GenAI monitoring by introducing the appropriate tools allows organizations to monitor AI prompts and see that they do not deviate from original scenarios.Review and strengthen security around a GenAI application stack emphasizing on integration points between systems (API’s) and identify AI systems and assets by drawing up a plan of usage. Organizations can assign a dedicated team to test AI models at base and application level, as well as introduce moderation and control on user developed applications, tools and products. Any experimental or uncontrolled work on GenAI within the enterprise must be monitored.Applying these strategies can minimize the risks associated with GenAI and help efficiently manage cybersecurity.Navigating AI pitfalls by mitigating risksWhile the potential of GenAI is undeniable, a cautious, forward-thinking approach is crucial to navigating its potential pitfalls. It is imperative to establish comprehensive risk mitigation, standards, and frameworks that can guide the responsible use and development of GenAI. Rajiv Kakar is a Technology Consulting Principal 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.

01 July 2024

Ryan Gilbert K. Chua

Realizing potential with GenAI

The significance of managing generative artificial intelligence (GenAI) initiatives is underscored by a white paper from Pactera Technologies, a leading global technology company, which indicated that a substantial 85% of these projects fall short. Forbes corroborates that the majority of GenAI projects do not meet expectations, underscoring a problematic trend in the field. GenAI projects possess characteristics that differentiate them from standard software development undertakings. Consequently, the strategies employed in overseeing and realizing the potential of GenAI projects demand a tailored approach distinct from conventional software project management. To address this issue and enhance AI project management methodologies, this article will discuss the following fundamental principles designed to refine the management of GenAI-related projects.Establishing clear business objectives and the importance of planningTo fully harness the potential of GenAI, it's essential to establish specific, measurable, achievable, relevant, and time-bound (SMART) goals for the GenAI solution to achieve. This crucial step involves a deep understanding of the underlying business problem or challenge that the GenAI solution intends to address. It is also vital to consider whether GenAI is the most suitable solution, ensuring that the technology is not simply being used for its own sake.Identify and rationalize potential use cases for GenAI that are in sync with core business objectives. This involves a process of prioritization - pinpointing which GenAI applications can deliver the highest value in alignment with the strategic direction of the organization. By focusing on areas where GenAI can make a significant impact, businesses can channel their resources more efficiently and create a tailored approach that maximizes its benefits. For instance, a common application of GenAI is in knowledge management, which could provide value across the enterprise.Understanding the project life cycle is another fundamental aspect of managing and executing a GenAI project successfully. Establish the stages the project will go through, including a comprehensive methodology that covers various phases such as planning, developing, testing, deploying, and monitoring the GenAI solution. While each stage of the project is important, an emphasis on key differences in developing and monitoring traditional and GenAI solutions is important. For example, in developing GenAI solutions, model “training” directly impacts the performance of the solution in production. Likewise, monitoring performance for its accuracy and precision would be continuous throughout the use of the solution.Selecting the appropriate tools and methodology is equally critical. Whether in terms of data processing software, programming languages, and platforms for deployment, these must be chosen with the aim of enhancing the productivity and effectiveness of the GenAI solution.Understanding dependencies and prerequisites GenAI solutions depend on a process often referred to as "learning," which involves feeding them a substantial volume of historical business data. This data acts as the foundation upon which the GenAI model is adjusted and refined, making it crucial that this information is of high quality. The principle of "garbage in, garbage out" is applicable here, as any shortcomings in the data can lead to flawed results. As the system continues to process new data, its effectiveness is influenced by the accuracy, completeness, and overall integrity of the information it receives.Another key aspect is the existing technological infrastructure and the broader system of the company. The current architecture and its capacities must be evaluated to determine how they might integrate with or support the effective deployment of the GenAI system. This includes considering the capability of current systems to communicate with the GenAI solutions and manage the additional workload. Scalability also cannot be overlooked. While GenAI can be a powerful business enabler, it requires the proper infrastructure to unlock its full potential. For example, GenAI solutions require significant computational power to function properly, thus, a powerful hardware component will accelerate “learning” of complex algorithms. The implications of GenAI on existing business processes are profound. The adoption of GenAI systems can lead to a complete overhaul of current processes, possibly making some obsolete. This makes it essential to perform a meticulous gap analysis to understand the differences between current state and future state business processes. This helps businesses ensure they can capitalize on the advantages GenAI offers while mitigating any operational disruptions.Cross-functional collaborationGenAI initiatives will require cross-functional collaboration. A diverse team composition is necessary due to GenAI projects intersecting multiple domains, requiring a holistic understanding of each area to create solutions that are not only technically advanced but also practical and relevant to the business. For example, a GenAI solution includes business process, application, infrastructure, and data components. To be able to design the solution, it will require the business unit to define the business problem, legal unit to provide compliance requirements, IT unit to provide data, infrastructure and other system requirements, HR unit to manage change, and senior leaders to drive its adoption. Adequate training will be crucial in ensuring that each team member can contribute effectively and understand the complexities of the tasks at hand. A data scientist, for example, must understand not just the intricacies of algorithms and model-building but also the business problems the technology is meant to solve. It is also imperative to involve cross-functional teams from the earliest stages. Collaboration should be established from the beginning, mixing technical expertise with business insights and ethical considerations. This allows every aspect of the project to be scrutinized from multiple perspectives, fostering an environment where technical feasibility, business viability, and ethical implications are all weighed and balanced. This blended approach ensures that the solutions developed are realistic, beneficial for the business, and designed with a consideration of their impact on stakeholders and society at large.Change managementOne common issue in implementing a GenAI solution is resistance. While people may be hesitant to adopt new technologies in favor of established routines, it's essential for companies to anticipate this resistance and prepare with strategies to address concerns and ease the transition for everyone involved.To facilitate adaptation, the company should provide substantial training and dedicated support. Instructional programs designed to enhance understanding of the new GenAI system can empower employees. Additionally, a hypercare support system, which offers intensive post-implementation assistance, ensures that immediate help is available for any issues or questions that may arise during the initial stages of using the new technology.Stakeholder management is also a critical component in ensuring a smooth transition. Clear and transparent communication regarding sunk costs associated with GenAI systems is necessary, as well as assurances that the investments are calibrated for long-term benefits. Stakeholders must also understand the timeframes involved, from the initial implementation phase to when positive returns can be expected. By managing expectations with clarity, the company can secure sustained commitment and support for GenAI initiatives.Performance monitoring and optimizationDetermine baseline metrics that act as a standard against which the added value of the GenAI system can be measured. Once the system is operational, the company must assess its performance, leveraging both qualitative and quantitative methods in its evaluation while utilizing appropriate metrics and benchmarks. For instance, the company might compare the output generated by the GenAI system against previously established baseline metrics, such as output produced by humans prior to when the GenAI system was implemented.In addition to monitoring technical GenAI metrics such as accuracy and precision, the company must measure the impact of the system through a business-focused lens. This means putting a spotlight on how the system influences business metrics, outcomes, and the overall impact on company operations. Realizing the potential of GenAI The potential of GenAI transcends simple enhancements in organizational efficiency. Its profound ability to generate, model, and interpret intricate data place it at the forefront of driving business innovation. GenAI empowers corporate leaders to envision a new horizon for their organizations, leveraging this rapidly advancing technology well past the bounds of simple gains in productivity. Through GenAI, businesses are not just improving processes, but revolutionizing their approach to problem-solving and strategic planning, planting the seeds for long-term value. Ryan Gilbert K. Chua is the Business Consulting Leader and Technology Assurance 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.

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.

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.

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.

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.

27 May 2024

Randall C. Antonio

How GenAI can accelerate business transformations

Given the fast-paced nature of digital evolution, businesses are increasingly turning to innovative technologies to stay ahead of the curve. Generative Artificial Intelligence (GenAI), which refers to AI algorithms that generate outputs based on existing data, has emerged as a transformative force that can revolutionize operations like product development, customer engagement, and software programming. However, integrating this technology into business processes requires strategic planning and careful execution. The EY 2023 Work Reimagined Survey shows that 84% of employers are expecting to implement GenAI within a year. Additionally, 33% of employees and employers see potential benefits for productivity and new ways of working. GenAI’s transformative capabilities are expected to augment human work and increase efficiency, which will have long-term effects on the global business landscape.The technology can transform business processes and unlock new levels of creativity and efficiency. To help ensure the success of GenAI implementation, this article will share five key strategies to effectively harness the power of GenAI in business transformation.Anchor everything to the enterprise strategyBegin by clearly defining business objectives and assessing how GenAI fits into the organization’s broader strategy. Identify specific areas where implementing an AI solution can drive value, such as streamlining operations, enhancing creativity, or personalizing customer experiences. By aligning AI initiatives with strategic goals, businesses can ensure that resources are allocated efficiently and that AI investments deliver measurable, tangible returns. Prepare quality data The success of GenAI will depend on the quality and diversity of data used to train models. It encompasses algorithms that leverage upon neural networks to generate new data that resembles the patterns found in the inputs it has been trained on. Access to relevant and high-quality data is therefore crucial for training and validation. Organizations must invest in data collection, cleansing, and augmentation processes to ensure that AI systems are trained with accurate and representative datasets. Additionally, diverse training data will be imperative to capture a wide range of scenarios and edge cases. This can improve the robustness of AI models, help mitigate biases, and ensure fair outcomes. Collaborate with the right teams The effective implementation of GenAI requires collaboration among multidisciplinary teams, highlighting the need for partnerships between AI specialists, data scientists, domain experts, and business stakeholders. Involve a cross-functional team in the decision-making process, blending technical expertise with business acumen and ethical considerations, to create a balanced and forward-thinking AI strategy.By fostering a collaborative ecosystem, organizations can leverage diverse perspectives and domain knowledge to develop AI solutions that address real-world challenges. Cross-functional teams should work together iteratively, from ideation to deployment, to ensure that AI solutions are aligned with business needs and user requirements. Apply responsible AI Ethical and responsible AI practices are paramount in today's data-driven world. Prioritize transparency, fairness, and accountability throughout the AI lifecycle. Implement measures to mitigate biases, ensure data privacy, and establish mechanisms for explaining AI-generated outputs. Bias in particular often manifests in ways that harm certain parts of the population. When the data that is used to train a model does not accurately reflect the group it is intended to serve, it can create imbalances in the model's outcomes. For example, imbalances could stem from a lack of diversity in the types of data collected. However, there are other types of imbalances that may compromise the precision of the GenAI model without negatively affecting a particular group. Although preventing such imbalances entirely is challenging, the development team must investigate potential sources of imbalance and seek ways to reduce it. By embedding ethical considerations into AI development processes, businesses can build trust with stakeholders and mitigate potential AI-deployment risks. Learn, adapt, and improve continuously AI implementation is a journey, not a destination. Embrace a culture of continuous learning and adaptation, where feedback loops drive incremental improvements. Monitor the performance of AI systems in real-world environments and gather insights from user interactions. Furthermore, use this feedback to refine AI models, optimize algorithms, and adapt strategies according to evolving business dynamics. Learning and growing from the project should be treated as an essential component of an AI endeavor, instead of a last-minute consideration. Foster an environment of ongoing education, prompting those involved to thoughtfully evaluate all triumphs and challenges. By staying agile and responsive, organizations can harness the full potential of GenAI to drive innovation and secure a competitive advantage. Transforming in the long-termWhile previous technological advancements mostly focused on automation, GenAI can also assist with complex cognitive functions like predictive analytics, machine learning, and natural language processing. Also, its use-cases encompass a diverse range of industries, occupations, and tasks. For example, the case study Generative AI at Work showed that customer service agents could resolve 13.8% more customer inquiries per hour with the help of GenAI tools.The successful implementation of GenAI requires a holistic approach that encompasses strategic alignment, data excellence, collaborative engagement, ethical considerations, and continuous learning. By adopting these key strategies, businesses can unlock new opportunities, drive operational efficiencies, and stay ahead in today's digital economy. Through concrete, actionable steps, GenAI can boost efficiency and innovation, reshaping today’s ways of working. Randall C. Antonio is an AI Technology Consulting Principal 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.

20 May 2024

Aaron C. Escartin

The evolving role of financial controllers

The financial controller role has transformed dramatically, with emerging technologies and advanced data analytics, along with the growing importance of environmental, social, and governance (ESG) considerations, introducing a fresh perspective to company planning. The traditional duties of financial controllers, which used to focus on historical financial reporting and regulatory compliance, now demand a broader range of skills and responsibilities.Their responsibilities have broadened beyond basic bookkeeping – they are now expected to adopt a proactive and visionary mindset, taking on the role of strategic business overseers. Modern controllers must be well-versed in a variety of competencies; they must excel in accounting and be capable of managing data, participating in strategic corporate conversations, and acting as reliable counselors. Their role has progressed into one that focuses on directing and ensuring the achievement of value, positioning them at the core of financial strategy.The increasing need for real-time and predictive financial reporting has been a major catalyst for this shift, with the role now including elements of financial planning and analysis (FP&A). Though controllers used to focus on internal transactional duties, technological advancements and evolving business expectations are pushing the role to become more extroverted.Incorporating ESG factors into fiscal planningAs ESG factors gain prominence in corporate planning and risk evaluation, it is essential for controllers to weave them into the fabric of financial forecasting and disclosure practices. This integration should be in harmony with the company's sustainability objectives and effectively communicated to all stakeholders.Some organizations are now appointing ESG-specific controllers, positioning the controllership role at the vanguard of this pivotal strategic initiative. With the growing need for verified ESG reporting, controllers are well-placed to spearhead this domain within their companies. This marks a considerable shift from previous times when compliance with statutory or similar regulatory reporting might not have been at the forefront of many corporate controller agendas.Familiarity with non-financial reporting standards, such as those set by the Global Reporting Initiative (GRI) and the Sustainability Accounting Standards Board (SASB), is becoming indispensable. These standards provide a blueprint for evaluating and disclosing sustainability efforts, tasks that now fall under the purview of financial controllers.In a globally intertwined business environment, the challenge lies in ensuring adherence to a variety of regulations across different markets while keeping financial management practices consistent with both international benchmarks and local mandates. Controllers are expected to perform thorough due diligence and maintain a comprehensive international outlook to protect the company's cross-border activities.The controllership should embrace a "glocal" (globally local) operational framework, capitalizing on centralization to enhance value while also ensuring that compliance, resilience, and risk management are supported at the local level. This new model for controllers aims to strike a balance between shared services and business partnership roles, with compliance functions remaining centralized. To adapt effectively, controllers must integrate strategies that utilize technology and data to streamline and standardize processes, all while upholding a local presence that supports compliance and risk oversight.In the era of digital finance, the sheer amount and velocity of financial data add unprecedented complexity to the task of ensuring precision in financial reporting. Controllers have the critical responsibility of guaranteeing that financial statements are free of material misstatements and reflect a true and just representation of the company's financial status. The rapid evolution of technology and regulatory frameworks demand financial controllers to dedicate themselves to continuous learning, enabling them to anticipate trends and challenges by adapting their knowledge and practices to stay relevant and efficient.Expanding the financial controller roleThe expanding role of the financial controller now encompasses a more prominent role in strategic decision-making processes, including steering investment approaches, navigating risks, and pinpointing growth opportunities. They are emerging as pivotal figures in formulating business strategies, charged with navigating their companies through market volatility with a decisive grip on fiscal instruments.Moreover, they must master sophisticated financial software platforms that not only simplify financial processes but also unlock the potential for detailed data analysis. Controllers must become fluent in the language of technology, providing a nuanced perspective on the financial well-being of the company, and facilitating predictive insights. They should approach their role with an open mind and curiosity, ready to embrace new tools, functionalities, and technologies. At the same time, they must exercise discernment to choose technologies that are appropriate for their organization and specific circumstances.Controllers must cultivate a dual expertise: a deep grasp of financial principles coupled with skills in data analytics. With these capabilities, they can translate intricate data into clear insights, formulate corporate strategies, spur innovation, and promote ethical leadership. By nurturing sustainable business operations and maintaining the integrity of financial disclosures, controllers establish themselves as vital consultants within their organizations, equipped to manage the intricacies of today's business landscape.From traditional bookkeepers, financial controllers can become "value articulators" – guardians of value delivery who evaluate the financial outcomes of investments. Today's controllers transcend transactional duties, embracing data and technology with a forward-looking mindset crucial for steering sound decisions, ensuring regulatory adherence, and propelling the organization towards resilience and expansion. Preparing for the future of controllershipTo navigate the evolving landscape of controllership and prepare for its future, financial controllers must proactively refine their expertise and adapt to new challenges. A commitment to continuous professional development is essential, with a focus on acquiring knowledge in data analytics and mastering advanced financial software platforms. Controllers should immerse themselves in the latest fintech innovations, selecting tools that align with their company's specific needs. This discernment will ensure they remain competitive, leveraging automation and predictive analytics to drive business success.Additionally, understanding and integrating ESG principles into financial strategies is becoming increasingly important. Controllers should become well-versed in non-financial reporting frameworks, enabling them to align financial strategies with sustainability goals and communicate these efforts effectively to stakeholders.In our interconnected global economy, maintaining awareness of international regulations is paramount as well. Controllers must develop strategies that ensure compliance across various markets while harmonizing financial management practices, safeguarding company operations across borders. Cybersecurity vigilance is another critical area. Financial controllers must prioritize financial data security, implementing robust data governance measures and staying informed about the latest cybersecurity best practices to protect the company's financial information and reputation.Finally, controllers should actively engage in strategic business discussions and investment decisions. By doing so, they position themselves as chief value officers and vital business partners, contributing significantly to the company's strategic direction and value creation. This strategic business involvement ensures that controllers are not just number crunchers – but key players in shaping the future of their organizations. Aaron C. Escartin is a Global Compliance and Reporting (GCR) Tax Partner 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.