Generative AI in the Enterprise

Lumenia Guest Post from Vignesh Subramanian, senior director, product management, Infor.

AI has certainly been hitting the headlines. Generative AI (GenAI) models, such as ChatGPT, seem to be some of the most discussed but how can businesses harness the potential power of this truly transformative technology and to what end?

Although the field of GenAI is still pretty nascent, we are definitely at an inflection point. Most of the large language models making a splash in the generative AI space are good at Natural Language Processing (NLP). Across a multitude of industries, these GenAI models can help with NLP based applications, such as providing interactive help.

Another benefit is to provide an NLP-based enterprise-wide search capability on business data. This is an ever-evolving space, with enterprise software businesses already hard at work investigating how GenAI models can complement existing NLP solutions and AI offerings. This could be by enhancing contextual experiences, integrating voice chat capabilities with digital assistants or machine learning (ML) models through AI platforms, and extending enterprise search into image recognition capabilities.

And, because GenAI models enable users to tap into a variety of data sources, they can be used for a variety of enterprise use cases. These include writing e-mails, reports and web content; creating job descriptions; performing product comparisons, and assembling photos and music tracks for marketing campaigns.

GenAI in action

So, what does this look like in practice? Companies with IT and software engineering departments, for example, can initiate a healthy practice of leveraging tools such as Microsoft’s Copilot or 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.

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Challenges ahead

Ideally, businesses should be embracing this powerful technology but there are certainly a number of challenges to address before GenAI models can gain widespread adoption in enterprise environments.

  1. Reliability. While the generated content from a large language model looks original, it is actually mimicking a pattern based on a similar training data set. Many times, the generated information is known to be false and the same question can generate different answers.
  1. Privacy issues. The data and the input conditions that the users share are used to train the larger model. Valuable trade secrets or PII data can be shared, inadvertently leading to compliance violations. Also, the generation and exchange of business-specific content must adhere to strict legal and data privacy requirements.
  1. Bias. Content generated by AI is tailor-made based on the input prompt. You can also train the model using favourable data points only without exposing it to the full picture, meaning you can mould the output the way you want.

Moderation filters

One way to combat these threats is to apply the proper moderation filters on the end user interface. And, for business use, enterprises must follow a ‘human in the middle’ approach. i.e., all generated content moderated by a real person. This 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. And, enterprises need to develop a point of view of how GenAI applies to them, following the best practices from GenAI vendors.

Rapid evolution

Over the next five-to-ten years, investments in Generative AI technology will increase tremendously – both in terms of generating better models as well as in the hardware space. All media content we consume in the coming years will be influenced by GenAI; the internet search will move towards a tailored, conversational experience; tools that detect content generated by AI will get smarter, and regulatory and compliance will get ever-tighter.

While we expect enterprises to adopt this powerful technology, we hope they are aware of the potential risks, inaccuracy and privacy concerns involved. It’s only a matter of time before the GenAI space matures and addresses such concerns, but, in the meantime, with human control and moderation, GenAI models have the potential to revolutionise enterprise environments.

Infor is a global leader in business cloud software products for companies in industry specific markets. Infor builds complete industry suites in the cloud and efficiently deploys technology that puts the user experience first, leverages data science, and integrates easily into existing systems.

Over 65,000 organisations worldwide rely on Infor to help overcome market disruptions and achieve business-wide digital transformation.

We look forward to meeting with you at the Lumenia ERP HEADtoHEAD event, please do stop by our booth to discuss anything of interest from this article or your specific ERP needs. Find our more here on Infor CloudSuite at the ERP HEADtoHEAD event. 

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