GUEST OPINION: The adoption of AI technologies by several industries has gathered momentum since the commercial launch of Generative AI technology in the last couple of years. Spending on generative AI is only going to increase worldwide over in the coming months. According to Infosys’ recent AI Business Value Radar report, APAC companies plan to increase spending by more than 140% (from $1.4 billion to $3.4 billion) over the next 12 months. As AI adoption increases across various sectors, organisations must actively begin embedding and adopting AI into their core business strategies to remain competitive.
Setting the stage for AI adoption to enhance business benefits
Based on our survey, 50% of AI initiatives already deliver value, and enterprise-level adoption of AI is fundamentally shifting in how businesses operate, compete, and deliver value to customers. There is a broad spectrum of AI applications and businesses that must take a well-rounded approach to AI strategy – one that considers innovation, security, and ethical implications, equally.
The business benefits of AI are promising, including productivity enhancement, personalised customer experience, cost reduction, improved quality, and better decision-making. While these benefits drive enthusiasm for AI adoption, organisations must also understand the risks and challenges associated with rapid and widespread implementation.
The risks of unchecked AI
In the rapidly evolving realm of AI, the promise of significant benefits comes with increased risk. The adoption of AI has technical, legal, and ethical imperatives.
- The World Economic Forum’s annual global risk report identified Misinformation, disinformation, and the adverse outcomes of AI as key technological challenges over the next 10 years. This highlights the urgent need for responsible AI frameworks that prioritise transparency and accountability.
- Unregulated AI may lead to discriminatory outcomes, exposing companies to regulatory scrutiny and reputational damage. As Dr. Muneera Bano, a principal Scientist at CSIRO, recently suggested, carefully chosen models are essential for fairness and accuracy. Ensuring fairness in AI models is not just a compliance requirement-it is a foundational pillar of building trust with customers and stakeholders.
- Poor security measures in AI can increase cyber threats, making governance a key factor in risk management. A quick internal network audit conducted by an entertainment company’s CIO discovered that 20% of their workforce uses 80+ different AI tools, which amounted to a data transfer of 10 GB over a few months. This reinforces the need for strict AI monitoring and governance policies in order to prevent data leakage and unauthorised use.
- The lack of a well-managed AI use case lifecycle process will impact managing the model training, containing the inference costs, and achieving the intended benefits.
Strengthening Risk and Governance Insights
Organisations with clear governance frameworks experience better efficiency, decision-making, and risk mitigation. By embedding governance into their AI strategy, businesses can mitigate risks while harnessing AI’s full potential for operational efficiency and innovation.
The board and management must work collectively to ensure successful AI adoption within their organisation and fully reap the benefits.
- Strategic context – The leadership board understands the emerging trends impacting their business and defines strategic objectives. This could mean altering strategic objectives if the current trend of an AI-enabled workforce and agentic AI may affect their future workforce planning and skill sets. The board must also be able to provide strategic oversight to ensure the organisation is poised to benefit from AI Adoption. An AI-informed board is better equipped to guide long-term investments and navigate industry disruptions.
- Regulatory context – AI regulatory frameworks worldwide have evolved over the last few years and are yet to mature. The Australian Government, through the National AI Centre, has been actively formulating regional regulations. Organisations need to constantly examine the regulatory environment and tune their current AI practices within their organisations by formulating technical and legal guardrails to ensure safety, transparency, fairness, privacy and IP protection.
- Operational context – The challenge for business and its leaders is to look beyond the maze of products in the current market and develop AI-enabled capabilities aligned with long-term business strategies. Defining and adhering to a comprehensive AI lifecycle process will benefit the organisation in realising the value of investments ethically and responsibly. A structured AI lifecycle process ensures that AI implementations remain scalable, cost-effective, and aligned with business objectives.
Businesses that weave governance into their AI strategy enhance operational resilience and maintain stakeholder trust. The key components of such a foundation include:
- AI strategy statement – the organisation’s strategic vision for implementing AI initiatives that align with the business objectives over the short and long term. This statement should define a set of measurable goals for strategic oversight.
- Risk appetite statement – the board should include AI, along with other, in their risk appetite statement and define the various tolerance levels that tie the AI risks to its strategic intent and risk appetite.
- AI use case lifecycle – defines the various phases of the AI use case lifecyle, along with the involvement of relevant teams. The phases can range from ideation, training to deployment. A disciplined approach to managing AI use cases helps optimise costs, minimise risks, and maximise AI-driven value creation.
- A cross-functional team – unlike IT governance, AI involves various skills ranging from human resources, data privacy, intellectual property, legal and technical. Cross-functional AI governance fosters collaboration between technical and non-technical stakeholders, ensuring well-rounded decision-making.
- Effective risk management – the Three Lines Model, from the Institute of Internal Auditors, is essential for managing the risks associated with AI adoption with clear responsibilities. The governing body provides oversight. First-line roles are aligned with product delivery, and second-line roles assist with managing risk. The third-line roles provide the internal audit function. The division of roles and responsibilities ensures accountability across the organisation.
- Regular reporting – Continuous reporting mechanisms help identify emerging AI risks and opportunities before they become critical business concerns.
A strong AI foundation will promote AI practices critical for long-term business success. Risk management practices through the AI lifecycle will provide transparent AI decision-making that fosters trust among customers, employees, and regulators.
Australian businesses must adopt a well-rounded AI game plan to future-proof their AI initiatives and stay ahead. By taking a proactive stance on AI governance today, organisations can drive innovation while ensuring they meet the evolving expectations of regulators, customers, and society.