Today, businesses across sectors are in a race to embed AI effectively in their products and services. What kicked off this race is undoubtedly the massive popularity gained by ChatGPT when it emerged a few months ago. Or more specifically, we should say the underlying AI tech that made ChatGPT a reality – Generative AI. It is estimated that the worldwide enterprise spending on Generative AI will exceed USD 143 billion by 2027. From customer service to healthcare diagnostics, new use cases for Generative AI are continuously being developed and upgraded by enterprises.
However, adopting GenAI at scale is not without its fair share of challenges. Most businesses react to the hype and hasten their attempts to incorporate GenAI capabilities, often without giving the move detailed thought. Due to the relatively new domain, decision-makers can make mistakes in the selection of the technology as well as other critical components needed to make GenAI capabilities more sustainable in their business.
Let’s try to change that.
Here’s a look at a usable framework to meaningfully adopt GenAI.
The major hurdles in GenAI adoption
Organizations often fail to pick a large-language model (LLM) that can handle unique communications and conversations that are specific to their business. For example, a healthcare business would find limited use in leveraging an LLM model designed for retail customer experience.
Developing prompting techniques to acquire insights from their GenAI solution is another major challenge. If your AI model is unable to understand deep and complex relevancy in conversations, then the results generated would not be accurate and serve the intended purpose.
Another big hurdle that enterprises face is the inability to fine-tune their model parameters to continuously improve the model’s ability to generate accurate responses. Feedback integration is a critical component of any AI model development exercise. It is a supplementary initiative that adds more relevance from real-life scenarios to training data.
The ultimate solution – a useable framework that enables GenAI adoption
If we observe the major hurdles above, the core issue that plagues enterprises with their GenAI choices is the lack of a sturdy framework that aids in GenAI capability development. There needs to be a structured framework with governance and standards in place that helps enterprises easily adopt GenAI in their business and evolve it over time.
This is where our Digital Workers as a Service (DWaaS) platform could offer huge relief for enterprises. DWaaS offers a GenAI adoption framework that businesses can leverage to build highly sustainable GenAI capabilities in their AI innovations. The core offering revolves around a set of “digital workers” available for ready use in the platform. Businesses can leverage these DWaaS and customize them according to their needs. These digital workers abide by a framework that allows a high degree of flexible business customization, evolution, and scale for GenAI capabilities.
Let us explore the top benefits that a GenAI adoption framework like DWaaS can provide businesses when they decide to adopt GenAI meaningfully. Here’s why it’s a great idea to test-drive Recode’s DWaaS:
Flexibility to select LLM
Businesses can first focus on identifying the most accurate requirements in their business for which generative AI initiatives will be needed. Once it is done, the DWaaS framework offers them a diverse range of LLM choices they can pick from to suit their unique needs and preferences. They can explore from among several listed LLMs and select one. They can pick an LLM that has the characteristics needed to deliver conversational experiences in their unique business domain. Even better, the framework allows businesses to compare ratings for accuracy, efficiency, and other key metrics for different LLMs. This helps decision-makers make the LLM choice most likely to help them accomplish the key requirements identified initially.
Easy prompt engineering
With the DWaaS framework, there is flexibility to implement dynamic inputs across different LLMs simultaneously. This allows enterprises to see how different LLMs are responding to queries. This is a great way to fine-tune prompt engineering for the best LLM that delivers the most accurate responses. This will help in eventually enabling extended interactions and more relevant conversations could be established for prompts without worrying about lost context. The framework operates by leveraging data in the form of vectors. This allows for faster retrieval and easier computation for responses.
Seamless feedback integration
One of the biggest advantages that the uber-useable framework of DWaaS offers for enterprises is the ability to integrate feedback into LLM through observation-driven optimization. It allows the live observation of different LLM responses, and measurement of their performance, effectiveness, and quality. Decision makers can provide ratings as feedback to each LLM from the diverse range of LLMs available. This feedback score will be used to continuously optimize the LLM stack with more refined training data. It will help in propagating more relevancy in outputs and will ultimately improve conversational performance.
Get real results faster
For enterprises, gaining an edge in AI capabilities, especially generative AI can help in building competitive advantage. A useable ready-to-deploy framework-based solution will accelerate the time to market for such AI innovations. With the added benefits of trusted results, continuous improvement, optimization, and easy scalability, the useable framework will significantly help to advance GenAI penetration across business services.
Get in touch with us to explore in detail how DWaaS can be the game-changing GenAI sandbox and toolkit for your business.
