July 2024 Articles - Suits the C-Suite | SGV & Co. | EY

29 July 2024

Vivian C. Ruiz

How AI is transforming the accounting and auditing professions

Artificial intelligence (AI) can transform the business landscape for accountants and auditors. Given the fast-paced technological advancements in data mining; machine learning, which in turn fuels generative AI (GenAI); and quantum computing, which can speed up and enhance machine learning, the potential to transform the accounting and audit professions is immense.As technology continues to evolve, so do accounting and audit professionals as they gain access to huge amounts of data and leverage AI to streamline workflows. Besides data analysis, AI can also be used to improve various accounting and audit processes to save time, reduce human error, and increase efficiency. Despite AI's potential, fears persist that it could replace humans by performing tasks faster and more accurately. However, experts from the World Economic Forum (WEF) predict that automation will result in an increase of 58 million jobs, with two-thirds of which being highly skilled. While AI has the power to revolutionize accounting, its real strength lies in supporting the work of highly skilled professionals.Technological advancements and applicationsFollowing a technological lull, the past years saw an uptick of AI applications across various industries. This nascent period saw AI becoming more adept at handling, organizing, and analyzing large sets of both structured/quantitative and unstructured/qualitative data. The digital revolution, which is data-rich, has also sparked interesting AI use-cases in different fields. For example, lease accounting analysis is usually performed by humans; although, some pilot programs show that AI tools could execute the same task more quickly. That is, AI can possibly review up to 80% of the contents of simple lease arrangements, thus allowing humans to focus on more challenging tasks or, in this case, more complex leases.Nevertheless, AI cannot replace the judgment, experience, and creativity that humans bring to their work. Making value judgments and weighing opportunity costs are still out of the scope of AI.Predictive capabilities Overall, one of AI’s main strengths lies in its predictive capability. AI could help audit teams reasonably predict future risks and recalibrate their approaches. Additionally, AI presents interesting opportunities for accounting areas like fraud detection.Another lucrative area for AI is anomaly detection, the predictive value of which underscores AI’s evolution and allows auditors to work more efficiently. Consequently, organizations must ensure that their AI algorithms are compatible with their current infrastructures and workflows, which requires a balance in planning, training, and monitoring.Real-world interactions and implicationsAI’s second developmental phase, shaped by its interactions with the world, can be seen in voice recognition and similar tools. Other technologies like the Internet of Things (IoT), the network of physical devices and objects connected to the Internet that collect and share data, could enable AI to synergize with the material world. This shift is often called the Fourth Industrial Revolution.AI will impact not only audit work but also talent recruitment since it will demand new, diverse profiles, rather than replace existing talent. As such, the industry will need skilled individuals across a wide range of disciplines; moreover, they must understand accounting, its industry, and emerging technologies such as AI, blockchain, and machine learning. By keeping pace with technological advancements, organizations can continue to deliver high-value, high-quality audits. Building confidence in AI adoptionGoldman Sachs forecasted that global AI investment could reach 200 billion USD by 2025. However, survey data from the International Data Corporation (IDC) — a global market intelligence firm — showed that only 22% of organizations are planning to adopt AI tools, with 52% citing a lack of specialized talent as the top blocker. Moreover, an EY survey showed that 65% of CEOs believe that more work is needed to address various AI-related risks like data privacy, misinformation, and intellectual property.Building stakeholder trust takes time, and it requires a balanced approach that encourages innovation while minimizing risks. As such, EY has started shaping responsible AI guidelines and frameworks through the EY.ai Confidence Index, a tool that integrates ethical, societal, and public policy considerations. Driving sustainable growth through AIAI will reshape the global economy, which will come with new risks and opportunities. Organizations must identify opportunities, leverage AI, and create long-term value to gain a competitive advantage over their peers.Leaders face the critical task of navigating change management, clarifying AI-related misconceptions, and establishing AI governance. With responsible and people-centered approaches to AI, organizations can drive sustainable growth, empower talent, and transform the accounting and audit professions. Vivian C. Ruiz is the Vice Chair and Deputy Managing 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.

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.

July 2024

How AI is transforming the accounting and auditing professions

GenAI: The gamechanger in creating lasting customer relationships

How AI is fundamentally changing recruitment

Key GenAI cybersecurity challenges and risk mitigation strategies

Realizing potential with GenAI

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