OpenAI has brought about incredible awareness of what large language models (LLMs) and AI, in general, are capable of. Some organisations then trained LLMs in the hope of observing impressive ChatGPT-like capabilities on their own organisational data.
However, such training and serving a massive LLM requires a large investment. Specialisation of massive LLMs incurs higher economic and environmental costs as it utilises more powerful, larger and costlier models than necessary for simpler business use cases.
In addition, this approach often lacks specific business requirements. Even though the resulting AI tool can still be useful as a side tool to aid with some business processes, it cannot specifically target and automate tedious parts of a business process.
When keeping the organisational data internal is not a concern, some organisations utilise APIs from external sources such as OpenAI. However, using models from external providers (with their own response regulation mechanisms in place) can obstruct exceptional knowledge and desired responses. See our earlier blog articles:
Regulating the Generative AI Systems Obscures Exceptions to the Knowledge
Long-term Generative AI Adoption Strategy: Human Empowerment not Replacement
Some organisations may not see a good return on their investment and decide to stop the AI adoption altogether. Others, on the other hand, persevere and turn to specialise these massive LLMs to different business process needs.
However, specialisation of massive LLMs is not required for many business processes. In the early phases of AI adoption, many use cases do not require massive LLMs to be solved; they can be more effectively approached through problem decomposition and specialised AI models. The end users drive the specialisation of AI models and LLMs. The need-driven pace makes the adoption easier. The organisations gain full control of both the customisation and regulation mechanisms, and this leads to fewer trials.
Another advantage of using specialised AI models is that task-specific evaluation metrics (including business utility functions) can be applied at each phase of the business process. Any performance drift can be detected, and if required, additional helper models added. In this way, the user gets a more eagle-eye view of functioning and performance as opposed to more general evaluations that are used when massive general-purpose LLMs are used. The efforts can be focused on improving specific components without experiencing trade-off on performance for other tasks.
In addition to lower economic and environmental costs, specialised AI models enable users to monitor and control individual components and improve business performance at large. Hence, they offer a more sensible choice than fine-tuning larger LLMs to improve on one of the business tasks.
September AI Labs Approach
At September AI Labs, we begin by providing core functionalities as easy-to-use, fast, not resource-intensive tools with dynamic user interfaces. These tools empower individuals in many tedious tasks (e.g., document management) and continue to evolve to tackle more complex business problems through specialised AI models or larger/fine-tuned LLMs, in line with specific business use cases needs. Most importantly, organisations own their tools and keep all their data internal.
Our AI adoption approach utilises Generative AI, LLMs and traditional machine learning techniques to target three broad categories: (I) Automation, (II) Optimisation and Recommendation, and (III) Prediction. It can span across all business functionalities and combine all available data (e.g., structured/database, unstructured text, media) to automate, predict or optimise to the highest extent possible in a business processes-aware manner.
Feel free to contact us for a discussion on how this AI adoption strategy can benefit your business. We can also demonstrate the type of tools you could own, and evolve from while keeping all your organisational data internal.