How to Navigate the Risks and Rewards of Generative AI

As the debate about generative AI continues, the technology’s potential to revolutionize business operations remains a focus of organizations across industries. In fact, the buzz around Chat GPT has motivated 45% of executives to increase investment in artificial intelligence (AI).

Governance and a risk management strategy are key to making the most out of AI, and when you combine its unique capabilities with the power of intelligent automation, the benefits of digitalization are impactful. Intelligent automation combines technologies, like generative AI, robotic process automation (RPA), business process management, and other complementary technologies, to reengineer processes and drive business outcomes.

This article focuses on the best practices when it comes to using generative AI.

Accelerating Digital Transformation with Generative Artificial Intelligence (GAI)

The range of capabilities and accessibility of AI systems is unprecedented, marking an exciting time for the automation space and any sector standing to benefit from advancements in natural language processing, including healthcare, finance, and customer service. However, generative AI is still limited, in a sense, to its own domain knowledge. 

Intelligent automation enhances the practical application of generative AI, using it to automate tasks that were previously only possible for humans to perform, such as generating new marketing copy, designing new product prototypes, or creating personalized content for each customer. Generative AI can make suggestions on what to automate and enable a greater cross-section of workers to initiate the development of automation thanks to its ease of use. Automation can then be designed within designated governance parameters and best practices. 

Workers Benefit From AI-Supported Data Collection

When it comes to creative work, humans add color and empathy, which technology can only try to mimic. Generative AI can offer a starting point and help with idea generation. Employees provide their uniquely human abilities to read between the lines and their emotional empathy for which AI is no substitute.

According to our research, most workers believe that AI’s impact will improve their role by enabling them to complete tasks faster, save time, and drive efficiencies. 

One of AI’s most important application areas is data, with 88% of workers reporting they want data collection automated and presented with analytics and proposed actions.

AI systems help with day-to-day operations. For example, automated emails can exhibit a greater degree of personalization and improve resolution times. For more complex or high-level emails, generative AI can be used to draft an adequate, personalized email, with all needed information, and a human can then review and tweak if needed. 

Bots utilizing generative AI have been invaluable to contact center processes, enhancing customer communications significantly before needing to loop in a human worker. This ensures employees’ time is used effectively and that as many customers as possible are being serviced. Bots that harness generative AI, after all, can work around the clock. Error handling is improved with error messages providing context that enables immediate resolution.

Intelligent document processing (IDP) solutions, which use a combination of optical character recognition and AI to extract information that is locked away in documents, are enhanced by generative AI capabilities. This is especially important for financial and healthcare services. Generative AI’s understanding and learning features better equip it to contend with unstructured data, an area that has been a weak point for IDP solutions, which have been confined to structured and semi-structured documents at best. 

Generative AI can also help improve the overall performance of intelligent automation systems by allowing them to adapt and learn over time by analyzing the results of previous tasks and using that data to generate new content or output. 

Top AI ML News: Fujitsu Launches Technology to Automatically Generate New AI Solutions Specific to Customers’ Business Needs

6 Steps to an Effective Generative AI Strategy

It is essential that the use of generative AI aligns with your organization’s ethical principles and that the right information is used for the right purposes to protect sensitive information and privacy. Proper governance and risk management also help to ensure that AI-generated content does not violate intellectual property, privacy, or other laws. 

When done right, generative AI can support an automation strategy that is even more innovative and productive than anything we have seen before.

A clearly defined corporate governance risk management strategy and set of operating principles around this need to be developed. This is key to maintaining your organization’s quality standards and confirming that outputs are consistent with expectations. 

What’s Required to Exploit Generative AI Responsibly?

Developing a strategy involves several steps:

1. 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.
2. 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.
3. 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.
4. Develop a risk management plan: This may include risk assessments, monitoring, and regular reviews of governance practices, as well as processes for identifying and addressing any issues that arise.
5. Train employees: It’s important to train employees on governance and risk management practices. Make sure all employees who will be working with generative AI understand the risks and their responsibilities for mitigating those risks. 
6. 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.