In just 2 years, generative AI has catapulted into the consciousness of CXOs. It’s in the consideration set, maybe even in the upper echelons of the priorities in the enterprise technology stack. And it’s not without reason too. Users, uses, and usage continues to surge. For instance, every week, over 100 million people use ChatGPT globally for various needs.
Enterprise leaders from startups to Fortune 500 companies now recognize the role generative AI can play in transforming several customer-facing and operational roles in their business. However, that recognition comes with a statutory warning because not everything is as easy as it seems!
For businesses, generative AI is not just about using ChatGPT to find answers to queries. They must find ways to adapt the core capabilities of the tech into their mainstream workflows. As they consider fresh applications, they must be able to craft, validate, launch, and scale those applications beyond superficial pilot projects or trivial use cases. And this is where many enterprise GenAI strategies seem to be running aground. The chatrooms and user groups are littered with stories of enterprises that have piloted use cases in different business departments but have stalled thereafter. Clearly, achieving transformation on a real-world scale with generative AI can be a challenge.
The challenges in driving GenAI initiatives to real real-world scale
Acquiring datasets
For GenAI systems to work at their full potential, there is a need for rapid acquisition and processing of datasets. For large organizations working on known problems or in areas that have been operationalized for long, this may not be a huge challenge as they would have enough data inventory to help with the supply. However, for small and mid-range organizations, or where innovative new approaches or methods are on the agenda data acquisition will be a major obstacle. The solution will need access to contextual data that represents operational paradigms in the business domain or unique business workflows that will be transformed later.
Specialized data is often hard to acquire and even when it is somehow available, there is a further need to apply classification, categorization, and quality checks to make it usable. Businesses need extensive data quality measures to ensure that generative AI models are offered access to data of the highest quality and business relevance. The accuracy of data will directly influence the interpretation characteristics of the system.
Selecting the right LLM
Large Language Models (LLMs) are among the foundational attributes of generative AI systems. Their relevance and quality play a crucial role in determining how successful the outcomes are. They are responsible for enabling GenAI systems to have contextual sense and memory about situations that will eventually result in more accurate interpretations and predictions.
However, when enterprises target GenAI deployment at a real-world scale, they face the glaring challenge of picking the right LLM. A deep dive into the market unveils a diverse range of LLMs available from different players. Decision-makers do not often have the tools or experience to evaluate the accuracy, efficiency, and other critical parameters of an LLM in light of their specific needs. This harms their efforts to finalize the right LLM option for their business needs. This difficulty inevitably prevents the scaling of generative AI initiatives.
Difficulties with prompt engineering
Achieving the desired level of operational reliability for generative AI systems is directly influenced by the simplicity of prompt engineering. If users can trigger the most accurate responses from LLMs with minimally complex prompts, it is an indication of success. However, when enterprises scale up their generative AI scope, the efforts involved in enabling prompt engineering also increase.
Adding to the problem of LLMs above, prompt engineering becomes challenging as enterprises need to achieve a system where continuous dynamic prompting is possible across different LLMs to help drive the right LLM for their business needs. They need to be able to figure out how to enable extended interactions as well as to support complex conversations. GenAI systems need to retain context when intense or involved prompting scenarios come about, and the relevance of the results matters a lot in deciding the growth trajectory for the model.
Continuous training and optimization
Once an LLM has been finalized by an enterprise for its generative AI initiatives, it must be possible to constantly update and optimize it. This will help the GenAI business capabilities evolve as the market demands shift and morph. That’s the only way to ensure that the GenAI-led solution approach can accommodate new scenarios, context, and interpretations only if there is an ongoing training activity available for the model.
Such training activities can be challenging for enterprises as they look to scale up GenAI services. It is important to fine-tune model parameters periodically or else they will generate irrelevant results eventually. The requirements for training data too can be a challenge.
The way forward for GenAI scaling
Articulating these challenges may create the feeling that GenAI suffers from some technology deficiencies that inherently limit it from being practically scalable in real-world scenarios. However, this is not true.
The key factor behind all these challenges is not technical but strategic. Most businesses approaching GenAI do so without a framework to build and deploy their own GenAI solution. Such a framework would have the essential foundational building blocks that make it easier for businesses to pick the right LLMs, ensure continuous improvement through training, facilitate better data acquisition and processing, and enable better relevance for results continuously.
In other words, enterprises need access to GenAI capabilities in a ready-to-deploy environment where all underlying technical complexities, architecture, and continuous learning foundations are well established. This is where platforms like TestDrive become a major asset. Get in touch with us to know more.
