As we move deeper into 2023, AI breakthroughs are dominating the global business landscape. The capabilities of generative systems and large language models have gripped the public’s attention, with companies across various sectors investing heavily in these AI-centric technologies. However, the success of these investments heavily relies on the quality of data used to train these systems and the in-house expertise of the organisations implementing them.
Vall Herard, the CEO and co-founder of compliance solutions developer Saifr®, recently explored how to leverage data to make the most of AI.
Many enterprises are witnessing the exciting potential of AI and are keen to harness this technology. To do so effectively, one essential strategy could be to apply AI to trusted in-house data, as opposed to data procured from the public internet, Herard said. AI can leverage this trusted data to generate valuable insights from the history, mission, and unique skills within your enterprise.
Despite the promising capabilities of AI, leaders should exercise caution when using generative AIs trained on data scraped from the internet before 2021. The reliability of such data sources can be uncertain, with possible issues surrounding the ethical use of proprietary data. Institutions including the U.S. government, the European Union, the U.K.’s Competition and Markets Authority (CMA), and various academics and tech entrepreneurs, have all called for a cautious approach towards this potential risk.
As a cautionary measure, providers and business leaders have been urged to ensure that their AI implementations are “ethical, trustworthy, responsible, and serve the public good”. This sentiment was echoed by the White House, encouraging adherence to the advisory AI Bill of Rights, a suggested code of conduct for AI usage.
While AI technology can be thrilling and user-friendly, it’s important to approach it with practical sense and professional skepticism. The attractive ease of use and rich outputs can potentially lead users into overreliance on AI for business insights, skilled outputs, and thought leadership, potentially neglecting their in-house skills and expertise.
One key concern with cloud-based AI tools is the quality and ethics of their training data. There is a risk that these tools have been trained on web data that could be inaccurate, biased, misleading, or false. By uncritically accepting AI outputs, businesses could inadvertently automate societal biases and flawed human behaviours from previous decades.
The most promising AIs are those trained on trusted, industry-specific data sets, designed for use in specific contexts. When applied to focused use cases, they can generate valuable content in niche areas, especially in heavily regulated industries such as healthcare and financial services.
In conclusion, a critical aspect of successful AI adoption in 2023 will be investment in quality data and in-house expertise. The value you extract from your AI system directly corresponds to the quality of data you feed into it.
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