Terry Smagh, Senior Vice President and General Manager, Asia Pacific and Japan, Infor
Terry Smagh, Senior Vice President and General Manager, Asia Pacific and Japan, Infor
Artificial intelligence (AI) has emerged as a transformative technology, revolutionising enterprises by empowering them to streamline and optimise their operations. Specifically, generative AI (GenAI) has showcased remarkable potential for exploration and groundbreaking innovation. GenAI, including models like ChatGPT, is at the forefront of these discussions, drawing attention to its capabilities and implications across various sectors.
Although GenAI is at a nascent stage, it marks an inflection point in AI and computing. Many large language models in the GenAI domain demonstrate proficiency in natural language processing (NLP). Enterprises aim to integrate GenAI with existing NLP solutions for enhanced contextual experiences and innovative applications, spanning from content creation to strategic functions. GenAI’s versatility enables a wide array of enterprise uses, from content generation to summarising books and reviews, significantly streamlining operations, and improving content quality.
GenAI in action
So, what does this look like in practice? Well, for example, companies with IT and software engineering departments can establish healthy practices by leveraging tools such as Microsoft’s Copilot or Amazon’s CodeWhisperer for code generation. Businesses that need to build their own industry-specific language models, verify general information, get reviews and recommendations from the web, or integrate private enterprise data and enhance it with information from the public domain, can integrate GenAI tools and platforms such as OpenAI’s ChatGPT or Amazon’s Bedrock.
Challenges ahead
The rapid evolution of GenAI necessitates that enterprises adapt promptly or risk falling behind. Embracing this powerful technology is ideal, but it is crucial to recognise that a one-size-fits-all approach does not apply to GenAI models. Various challenges need to be addressed before its widespread adoption in enterprise environments. Firstly, there is the concern of reliability, as the generated content, while appearing original, often mimics patterns from training data, potentially spreading misinformation. Secondly, privacy issues arise as user data and input conditions contribute to training the models, risking inadvertent exposure of trade secrets or personally identifiable information data, and thus demanding compliance with stringent legal and data privacy requirements such as the General Data Protection Regulation. Lastly, there is the challenge of bias, as AI-generated content can be selectively trained to suit perspectives, raising the risk of manipulating user opinions and amplifying fake news and media creation.
Moderation filters
Nevertheless, these challenges are not insurmountable. One way to combat these threats is to apply proper moderation filters on the end user interface through which GenAI tools can be used by “normal” users. For business use, enterprises must follow a “human in the middle” approach – that is, all generated content must be moderated by a real person before being rolled out for regular consumption. For the foreseeable future, human control and moderation will be required to boost the accuracy and consistency of generated content, reduce socio-political bias and ensure that a company’s competitive edge is not compromised.
Taking these considerations into account, enterprises need to develop a point of view on how GenAI applies to them. Additionally, it will be vital to follow the best practices from GenAI vendors – for example, the use of moderation filters from OpenAI. We are also seeing countries scrambling to come up with their own AI policies, another aspect that businesses must take into account, making sure the local AI policy is adhered to, following proper protocols as outlined by the respective governments.
Rapid evolution
In the next five to ten years, investments in GenAI will increase tremendously – both to generate better models as well as in the hardware space – driven by faster, more powerful chips and the need for more network bandwidth. Its impact should not be underestimated. In the coming years, all media content we consume will be influenced by GenAI; the internet search as we know it will move more towards a tailored, conversational experience; tools that detect AI-generated content will become smarter; and regulatory compliance will get increasingly stringent.
ChatGPT and other GenAI models represent disruptive solutions that are already helping consumers refine the search process, automate content creation and boost individual productivity. While we expect enterprises to adopt this powerful technology rapidly, we also hope they are aware of the potential risks, inaccuracies and privacy concerns associated with it. Naturally, it is only a matter of time before the GenAI space matures and addresses such concerns. In the meantime, with human intervention and moderation, GenAI models have the potential to revolutionise enterprise environments.
