Over the past year, the buzz around generative AI (genAI) has reached a fever pitch. Today’s gen AI technologies can generate content at speed and with unprecedented ease.
With global data creation projected to grow to more than 180 zettabytes by 2025, there is enormous business value potential for GenAI to unlock data insights and become a data-driven business. GenAI heralds a transformative era for industries. This innovation offers the perfect capability for decision-makers with no data science skills to deliver insights via a natural language prompt, effectively democratising AI for those without deep expertise in AI and data science.
However, the rapid pace of technological progress presents a dual-edged sword. While it unlocks possibilities, it also widens the digital divide and forces boardrooms to play catch-up with emerging technologies. With access to more data than ever before, businesses leveraging genAI can uncover new patterns and insights in their data to make faster, better decisions and drive business value. But recognising that you can't simply go from zero to genAI success overnight is essential. It requires incremental steps to assess and improve data analytics maturity, organisational readiness, and buy-in to deliver trusted insights.
Data literacy and command of data analytics are some of the most important skills for unlocking the full potential of intelligent systems and AI. Equally, companies looking to take advantage of genAI must have the capability to accelerate the data journey to ensure the business can thrive in this ‘era of intelligence’.
To do so requires an AI-ready data stack so the business can drive strategic innovation or create competitive advantages from AI. Alteryx research indicates that only 21% of companies surveyed in Asia Pacific feel their current data stack is well-equipped to handle gen AI tools effectively, while 37% desire more effective utilisation of this technology.
Balancing innovation with responsibility
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As more organisations look to flourish with genAI capabilities, the true success differentiator lies in establishing a clear organisational structure and defined ownership of technological processes. Business leaders must not only bridge the knowledge gap but cultivate leadership qualities that prioritise adaptability, strategic foresight, and ethical responsibility in the deployment of gen AI technologies.
With nearly 70% of organisations in Asia Pacific now investing in and utilising genAI technologies, there's a growing need for business leaders to assess whether they are equipped to create value from genAI while managing its risks. Their understanding of genAI directly influences their ability to make informed decisions about investments, risk management, talent acquisition, and technology strategies that will affect the organisation and its stakeholders. This expertise extends beyond mere familiarity with the technology—it requires a deep understanding of its underlying algorithms, data requirements, and potential applications across various business functions.
To address the complexities of genAI, decision-makers need to adopt a multi-faceted leadership approach. This involves designating a senior executive to champion genAI initiatives while assembling a cross-functional team with a wide range of expertise. This team could include domain experts, data scientists, legal specialists, cybersecurity professionals, and others—all working together to develop a comprehensive strategy that prioritises innovation and responsible risk management.
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As genAI transforms industries, it becomes critical that companies' leadership structures evolve accordingly. By fostering a continuous learning and collaboration culture, organisations can empower their leaders to make informed decisions about genAI that align with the company's values and long-term goals.
Navigating the evolving technology landscape
Whether you are just starting your AI journey or upgrading your toolkit, leaders across the board, regardless of company size, grapple with a common challenge: How to integrate new tech into operations with minimal disruption?
The first step on this journey is to drive upskilling and learning about the power of data. The second is implementing effective guard rails that include practical checks on data quality, privacy, and governance. The third is comprehensively assessing your organization's needs and goals to identify potential blockers and craft a detailed architecture plan for integrating the new technology with legacy systems.
Companies serious about adopting genAI across their entire organisation must ensure they have the expertise and mechanisms to manage risk and adopt the technology responsibly. This approach means that the leadership landscape is also undergoing a significant transformation. Alteryx’s Enterprise of the Future report found that 58% of business leaders across Asia Pacific anticipate the emergence of the Chief AI Officer role will be critical to a more holistic approach to AI strategy that facilitates collaboration between business units – from IT and compliance to Legal, HR and People teams. For context, just over 400 globally have CAIO in their LinkedIn job titles.
While the CAIO's far-reaching role and scope of work are tailored to each business's unique demands and strategic objectives, they sit at the heart of ensuring the responsible use of generative AI to ensure initiatives are regulatory compliant and ethical. AI processes vast amounts of data, which makes ethical considerations and data privacy even more critical. Emerging roles like the CAIO and Data Steward also profoundly impact the cultivation of a data-driven culture across the business, all key to ensuring effortless collaboration across technical and non-technical domains.
The business imperative of Generative AI
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McKinsey estimates that gen AI adoption could unlock a staggering US$7.9 trillion ($10.8 trillion) across industries – by comparison, Singapore’s entire GDP in 2023 was $673.3 billion. In the global race to gain and maintain a competitive edge, businesses are intensifying investments in AI and data analytics. However, this rapid integration has also introduced significant challenges. Ethical, compliance and legal limitations must not be overlooked when deploying AI.
Singapore, a leading AI powerhouse, exemplifies this duality. Government initiatives like the AI Trailblazers programme have fostered collaboration between businesses and government, leading to over 100 gen AI solutions for real-world problems.
However, Singapore remains acutely aware of the potential pitfalls. To counteract these risks, the government introduced a proposed framework that tackles the primary concerns of gen AI: hallucinations, accelerated disinformation, copyright challenges, embedded biases, impersonation reputation attacks, and malicious code generation. Singapore’s policy principles underscore a critical point for all organisations—effective oversight is essential for designing and using gen AI.
Ultimately, success in leveraging gen AI hinges on the ability to strike the right balance between innovation and responsibility. With a risk mitigation and compliance approach to data democratisation and data culture, an AI-enabled workforce will more likely ask the right questions when building a LLM or other generative AI application. That’s why self-service platforms that empower non-technical users to access and analyse data while harnessing AI in a secure and trusted environment are ideal for fostering trustworthy genAI.
Philip Madgwick is the regional vice president for Asia at Alteryx
