Today, almost all enterprises in most domains want to leverage the power of generative AI. However, how do they leverage these Large Language Models (LLMs) remains a challenge. Vishesh Tewari. partner at Deloitte India, during the ongoing Cypher 2023, India’s biggest AI conference delved into how enterprises can find true value from generative AI.
He says enterprises are venturing into the realm of generative AI to unlock its potential, seeking opportunities for innovative services and business models. In his talk titled ‘ Driving enterprise value from generative AI, he delves into ways organisation’s can harness Generative AI to drive revenue, strategically select and prioritise use cases, and systematically explore its applications.
How can enterprises leverage generative AI?
Tewari says enterprises can leverage generative AI in multiple ways from back-office functions to front-office functions. Areas such as HR, finance, and tax, which are not customer-facing are ripe for experimentation with generative AI. It promises efficiency gains, reduced turnaround times, and cost reduction.
“That is the sweet spot for starting with generative AI for a lot of enterprises. The next bucket is your front office, which is customer-facing,” he said.
In fact, some organisations have already started experimenting with customer-facing functions like customer and sales teams. These are often early adopters, customising models to better serve their clientele. Moreover, generative AI is even influencing software engineering, with models capable of generating code. This helps improve the efficiency of software development.
A generative AI strategy
However, it’s important for enterprises to have a generative AI strategy which aligns closely with the overall enterprise strategy, ensuring that the technology supports broader organisational goals. He says Ensuring their generative AI strategy is aligned with enterprise strategy is critical.
“A lot of conflict in an organisation happens when it comes to trying new technologies due to a lack of this.” Also, it’s equally critical for enterprises to determine why they want to leverage generative AI. Is it for cost reduction or revenue growth, for example?
Tewari also notes that multidisciplinary teams are essential for a successful generative AI strategy. Representation from various departments, including legal and compliance, is crucial.
Ensure the collection and curation of proprietary data
Another important factor Tewari notes are that enterprises should focus on collecting, organising, and structuring their data to create customised Gen AI models. “Today, most of the models out there are trained on public data, but the real power of generative AI will come once enterprises start harnessing their own data. But that comes with a lot of challenges.”
Initially, it’s essential to pinpoint the location of your data. While many organisations have structured data stored in data warehouses, some are transitioning towards data lakes or data meshes. This shift might introduce unstructured data into the mix. However, a significant portion of data remains scattered throughout the organisation, lacking a centralised repository.
Assess against GenAI ethical principles
Tewari also states that it is important for enterprises to develop a robust framework for the ethical use of generative AI, emphasising privacy, fairness, and accountability. Compliance with emerging regulations is essential.
In conclusion, generative AI holds immense potential to reshape how enterprises function, streamline processes, and enhance customer experiences. As enterprises continue to explore its capabilities, the future of generative AI in the business world looks promising, according to Tewari, provided they align their strategies, address ethical concerns, and leverage the technology wisely.
