Imagine a world where machines not only perform mundane tasks but create and innovate. This world is no longer a distant future and is within our reach today thanks to Intelligent Automation, the powerful synergy of Robotic Process Automation (RPA) and Generative Artificial Intelligence (Gen AI). It’s an exciting frontier in digital transformation, one that calls for an equally transformative approach to its governance and risk management.

The symphony of RPA and Gen AI

RPA automates mundane tasks – streamlining operations and ensuring efficiency. On the other hand, Gen AI takes creativity to the next level, enabling systems to generate new content, ideas, or solutions. When these two come together, businesses can achieve outcomes that have not yet been realised.

Think about the potential. Routine processes are automated, ensuring that resources are optimised. Meanwhile, Gen AI infuses creativity into the framework, allowing for innovation even in the most standardised operations. This duality brings a new kind of dynamic to the digital realm.

Recent SS&C Blue Prism research reveals that 35 per cent of organisations in Singapore are turning to intelligent automation to adapt to the challenging economic outlook. Intelligent automation combines technologies like generative AI, robotic process automation (RPA), business process management, etc., to reengineer processes and drive business outcomes.

Generative AI can be used to automate tasks that were previously only possible for humans to perform, from assisting with customer service to processing large volumes of data from documents to supporting a wide variety of everyday office tasks, such as writing copy or creating personalised content for each customer. It can even suggest new processes which could be automated and enable a greater cross-section of workers to initiate the development of automation due to its usability.

Also Read: Generative AI: Unprecedented adoption rates in 2024

While historically, AI projects have been long, expensive, and complex, generative AI has the potential to reduce time to value for digital transformation initiatives and make advanced technologies more accessible to a greater cross-section of people thanks to its ease of use and learning capabilities.

Governance: The key to getting Gen AI right

Governance in the context of Gen AI means setting clear boundaries and guidelines on how this technology is used. It’s about understanding its capabilities and limitations. Most importantly, it’s about ensuring that as we leverage Gen AI, we do so ethically, responsibly, and with a clear purpose in mind.

Businesses need to consider certain factors before they explore adding generative AI to their toolkit to accelerate digital transformation since its outputs can have a significant impact on a company’s reputation, revenue, and legal liabilities.

A clearly defined corporate governance risk management strategy and set of operating principles around this need to be developed. Done right, generative AI can support an automation strategy that is even more innovative, cost-effective, and productive than anything we have seen before.

Reasons why governance and risk management considerations are important when using generative AI:

• Help ensure AI-generated content does not violate intellectual property, privacy, or other laws
• Make sure the use of generative AI aligns with your organisation’s ethical principles
• Maintain your organisation’s quality standards and confirm outputs are consistent with expectations
• Ensure the right information is used for the right purposes to protect sensitive information and privacy.

How to develop a governance and risk management strategy

A clearly defined strategy and concomitant operating principles maximise the benefits of generative AI while mitigating any outfall. Developing a strategy involves several key steps:

• Define the scope: This includes the types of content you will be generating, the data you will be using, and the intended use cases for the content. This helps with identifying the specific risks and governance requirements that apply to your initiatives.
• Identify risks: These may include legal risks, such as infringing on intellectual property; ethical risks, such as bias in generated content, and security risks, such as the potential for data breaches. You may need to engage with legal and compliance experts to identify all potential risks.

Also Read: Riding the affluence surge: How Generative AI can power growth in financial advisory

• Establish governance requirements: Based on the risks you’ve identified, establish governance requirements that will mitigate those risks. These may include policies and procedures for data handling, content review, and compliance with regulations.
• Develop a risk management plan: Outline how your organisation will mitigate and manage risks. This may include risk assessments, monitoring, and regular reviews of governance practices, as well as processes for identifying and addressing any issues that arise.
• Train employees: It’s important to train employees on governance and risk management practices. This may include training on data handling, content review, and compliance with regulations. Make sure all employees who will be working with generative AI understand the risks and their responsibilities for mitigating those risks.
• Monitor and review: Monitor and review your governance and risk management practices on an ongoing basis. This will help you identify any gaps or issues that need to be addressed and ensure that your practices remain effective over time.

Final thoughts

Like all advanced technologies, generative AI’s impact is positive – so long as you take the steps necessary to ensure you’re using it the right way. There’s no turning back the train. Generative AI is here to stay – full steam ahead.

The best approach is going to be to embrace it with care and work with providers when it comes to decision-making around implementation. The possibilities behind generative AI are exciting – so let’s work to get it right and make it a force for good.

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This article was first published on April 10, 2024

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