By: Margarita Garcia - Managing Director, Naoitech
The current landscape of Gen AI business implementation and development relies heavily on the use of Generic LLMs as a basis for their projects. The problem with this approach is that it starts with a fallacy: All the data that Generic LLMs use is accurate, fair, and usable. However, generic LLMs have been trained on massive amounts of unqualified data, which makes them unreliable to perform important and relevant tasks without the assistance of a Subject Matter Expert who can validate the accuracy and relevance of their responses.
LLMs are great tools if they are used in the correct context and under the appropriate implementation strategy; however, companies are developing and deploying projects assuming that Gen AI will self-correct and circumvent the planning and evaluations required. In many cases companies are relying completely on the results of the work performed by Gen AI Tools. A recent example involved a large consulting firm that was tasked with creating a report for the Australian Government and was caught using Gen AI. The issue was uncovered by one of the Government experts who found several areas that had been fabricated by the LLM judges with incorrect last names, who were attributed books that do not exist. The consulting firm had failed to perform its quality assurance and source validation, which led the Australian Government to request a portion of the payment for this service to be returned.
The issue of unqualified data on generic LLMs gets magnified significantly in the business world because organizations don’t have a single source of truth, ubiquitous, available data or well-defined interdepartmental guidelines and governance. This is evident when implementing projects; generally, stakeholders of different departments within the same organization have contradictory information, expectations, goals and objectives.
The successful deployment of AI and Gen AI is a strategic mandate. By focusing on a structured roadmap that prioritizes business value, data quality, ethical governance, and strategic partnerships, At Naoitech, we have developed a comprehensive implementation roadmap designed to navigate the complexities of AI deployment, ensuring every project directly addresses a real business issue and delivers measurable results, such as revenue growth, process efficiency, and accelerated product delivery.