By Naveen Krishnamoorthy
We are in the second year of GenAI revolution, but the hype cycle for such future technology continues with several proprietary and open-source LLMs getting launched with multi-model capabilities. Even though LLMs dispose bias and hallucination, the adoption of GenAI and implementation of real-world use cases are skyrocketing.
Extending on the prominence of such future technology, we see more and more software products and tools that we use daily get infused with GenAI features. GenAI adoption for personal productivity using ChatGPT kind of assistants are surging day by day. Beyond personal use, Gen AI’s influence can also be seen in enterprises, where their focus on generating vast amounts of data from their inter-connected operations helps streamline production processes and design complex systems.
According to the Gartner 2023 Hype Cycle for Emerging Technologies, the pace of generative AI adoption is slow (atleast in the near term) despite multiple experimentation, pilot implementations and planned investments. BCG captures that alongside the Generative AI hype more than 50% of executives are deeply worried about incorporating it into their operations, leading to discouraging its adoption. Major questions that arise in this regard are: how disruptive will GenAI be to our business? How can we capture “quick wins”? How can we rapidly and effectively build GenAI capabilities?
At any Enterprise, GenAI enablement and solution offerings impact internal (Engineering) and external (Business) use cases.
- Engineering – Enables engineering teams across software development life cycle by improving code development, testing process, knowledge management, and accelerating research efforts etc
- Business – Enhance customer service through conversational AI, streamline content creation processes, and deliver personalized user experiences through advanced language understanding and recommendation capabilities.
Currently, departments in the enterprise are exploring GenAI use cases and trying to implement standalone solutions. However, this approach takes time, requires more resources, and may not move forward beyond the proof-of-concept stage. Enterprise leaders need to adopt the platform strategy for effective and rapid implementation of GenAI and realize the benefits. To realize the benefits of GenAI effectively and rapidly, enterprise leaders need to adopt a platform strategy and partner with the right GenAI-based digital engineering vendors.
Imagine a world where implementing cutting-edge GenAI solutions is not just feasible, but effortless. This is what Gen AI platforms bring to enterprises. With such solutions, organizations can easily implement custom, private GenAI models, without needing technical expertise or hiring expensive AI talent.
There are several vantages of platform based GenAI implementation. In just a few clicks, you can set up your own secure GenAI models, control usage, and build unlimited use cases – all while ensuring the safe and responsible deployment of this transformative technology. Your content and data remain completely private and protected within your enterprise. Such development platforms empower quick deployment of chat agents in mere minutes, unlocking new levels of efficiency and innovation, without compromising on ethics or security.
Through Gen AI platforms that offer capabilities to build custom GenAI solutions for engineering and business use cases, focusing on enhancing the current team’s skillset with improved methods becomes key to accelerating Gen AI adoption. These platforms, which are designed with advanced RAG quantization techniques, helps optimize data vectorization and faster search, enabling the creation of engineering and business use cases through pre-defined prompts which in turn creates governance over usage, and accurate outcomes on usage.
A robust Gen AI platform provides options to connect to various fine-tune LLMs such as OpenAI ChatGPT, Antrop Claude etc., allowing enterprises to experiment use cases against desired models effectively and effortlessly.
Such platforms also become beneficial in providing a single pane of glass view to usage across organizations and use cases to understand the token usage and cost of usage.
Along with the innovations posed by such platforms, it also becomes important to pay attention to developing and deploying GenAI technologies in a safe, fair, and trustworthy manner, prioritizing ethical considerations and mitigating potential risks. This not only protects enterprises against the risk of maintaining subpar operational efficiency and providing low-quality services but also fosters public trust and facilitates the sustainable and equitable advancement of AI technologies, which in turn becomes revolutionary in infusing Gen AI technology across industries and enterprises.
As Generative AI continues to evolve, it becomes important to acknowledge its integration into engineering services, setting new standards in the field and enhancing new possibilities for innovation and growth.
(The author is Naveen Krishnamoorthy, Director, Engineering Management, Ascendion, and the views expressed in this article are his own)
