Understanding Generative AI: Benefits, Risks and Key Applications
By Rayan Potter | 16 January, 2024 in Generative AI | 3 mins read
Generative AI gleans from given artefacts to generate new, realistic artefacts that reveal the attributes of the training data without repeating it. A number of model content can be produced which include images, video, music, speech, text, software code, along with product designs.
It utilizes numerous techniques which are continuously evolving. At the forefront are AI foundation models that can be trained on a wide range of unlabeled data which can be utilized for various tasks with extra fine-tuning. Complicated math and immense computer power are needed for creating these trained models which are essentially prediction algorithms.
Generative AI: Key Benefits
Foundation models, a part of key AI architecture innovations, are used for automating, enhancing the performance of humans or machines, and independently executing businesses and IT processes. The key benefits of Generative AI are fast product development, superior customer experience, and increased employee productivity. However, the specifics rely on use case.
The target users must have realistic expectations with regard to the value they are hoping to achieve especially when utilizing a service with major roadblocks. Generative AI generates artefacts which might be inaccurate or biased necessitating validation by humans along with setting a limit on the worker’s time.
Gartner has recommended that use cases be connected to KPIs with the aim of ensuring that a project may enhance operational efficiency or create new revenue streams or better experiences.
According to a recent webinar poll by Gartner of over 2500 executives; the main objective of their generative AI investments:
38% indicate customer experience and retention
26% indicate revenue growth
17% indicate cost optimization
7% indicate business continuity
Market Impact of Generative AI on Enterprises in the Next Five Years
According to Gartner,
• By 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020.
• By 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.
• By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps.
• By 2026, over 100 million humans will engage rob colleagues to contribute to their work.
• By 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today.
Generative AI: Risks
Generative AI comes with major risks that are evolving very quickly. The technology has been utilized by a broad range of threat actors for creating “deep fakes” or copies of products, and for generating artefacts for supporting scams that are very complicated.
Foundation models like ChatGPT acquire training on vast quantities of data that’s publicly available. It’s not meant to be compliant with General Data Protection Regulation (GDPR) and other copyright laws, hence, it’s essential that one pays close attention to their enterprise’s platform usage.
The risks are outlined below:-
- No transparency: The unpredictability of Generative AI and ChatGPT models make it very tough for even the companies behind them to acquire detailed knowledge regarding their working.
- Accuracy: At times, Generative AI systems generate answers that are incorrect or made up. All outputs need to be assessed for accuracy, appropriateness and usefulness before they become reliant or information is distributed publicly.
- Bias: Policies or controls need to be in place for detecting biased outputs in order to deal with them in a way that’s consistent with company policy and any major legal requirements.
- Intellectual Property and Copyright: At the moment, there are no verified data governance and protection assurance with regard to confidential enterprise information. Users must take into account that any given data or query entered into ChatGPT and other similar models can become public information, hence, it is advised that enterprises must instil controls to inadvertently avoid exposing the IP.
- Cybersecurity and fraud: Enterprises should be ready for malicious actors’ to utilize generative AI systems for cyber and fraud attacks. These include deep fakes for social engineering of personnel for ensuring mitigating controls are placed.
- Sustainability: Large quantities of electricity is utilized by Generative AI. Vendors must be selected to limit power usage and utilize high-quality renewable energy to mitigate the impact on sustainability goals.
In the near future Generative AI models will go beyond responding to natural language queries and begin making suggestions that have not been asked for. This will lead to increased worker productivity however, it also poses a challenge to conventional thinking regarding humans playing a leading role in developing strategy. There will be a dramatic change in the workforce like industry, location, size, and offerings.
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