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 for various sectors.
Although GenAI is at a nascent stage, it marks an inflection point in AI and computing. Most of the notable 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 information technology (IT) and software engineering departments can initiate a healthy practice of leveraging tools such as Microsoft’s Copilot or Amazon Web Services (AWS) CodeWhisperer for code generation. For businesses that need to build their own industry specific language models, simply verify general information, get reviews and recommendations by sourcing the web, or have a need to combine their private enterprise data and enrich this with information in the public domain, they can integrate with GenAI tools and platforms such as Open AI’s ChatGPT or AWS Bedrock.
The rapid evolution of GenAI necessitates enterprises to 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 addressing before widespread adoption in enterprise environments. Firstly, there is the reliability concern, where 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 model, risking inadvertent exposure of trade secrets or personally identifiable information (PII) data, demanding compliance with stringent legal and data privacy requirements like general data protection regulation (GDPR). Lastly, the challenge of bias emerges, 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.
That’s not to say that these challenges are insurmountable. One way to combat these threats is to apply the proper moderation filters on the end user interface through which GenAI tools can be used by normal users. And, without a doubt, for business use, enterprises must follow a human in the middle approach. i.e., all generated content must be moderated by a real person before being rolled out for regular consumption. Human control and moderation will be required for some time to boost the accuracy and consistency of the generated content, help reduce socio-political biases and ensure that a company’s competitive edge is not compromised. Considering all of the above, enterprises need to develop a point of view of 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 Open AI. What we are also seeing is individual countries scrambling to come up with their own AI policies, something else that businesses will need to take into account, making sure the local AI policy is adhered to, following the proper protocols as outlined by respective governments.
In terms of how GenAI will evolve over the next five-to-ten years, investments in the technology will increase tremendously, both in terms of generating better models as well as in the hardware space, with faster more powerful chips and the need for more network bandwidths. Its impact should definitely not be underestimated. All media content we will consume in the coming years will be influenced by GenAI; the internet search as we know it will move more towards a tailored, conversational experience; tools that detect content generated by AI will get more smarter, and regulatory and compliance will get ever-tighter.
ChatGPT and other GenAI models represent disruptive solutions that already are helping consumers refine the search process, automate the creation of content and boost individual productivity. While we expect enterprises to adopt this powerful technology rapidly, we also hope they are aware of the potential risks, inaccuracy and privacy concerns involved too. Naturally, it is only a matter of time before the GenAI space matures and addresses such concerns. In the meantime, with human control and moderation, GenAI models have the potential to revolutionise enterprise environments.