Businesses today are deeply influenced by Generative AI (GenAI), a cutting-edge technology that has transformed the landscape. While organisations grasp its potential, many are actively strategising on how to optimise GenAI usage, all the while considering its ethical implications and the necessity of developing appropriate models. During a recent roundtable hosted by Brio Technologies in association with YourStory in Bengaluru, industry leaders discussed their progress in their GenAI journey, while also addressing critical roadblocks that require resolutions and exploring use cases to facilitate widespread adoption.
The panel comprised of Kumar Shailove, Vice President of Engineering, Hiver; Jatin Garg, Co-founder and Chief Technology Officer, GoCodeo AI Technologies Private Limited; Biru Kumar, Vice President of Engineering, Betterhalf.ai; Sancheeta Kaushal, Head of Cloud, Bolt. Earth; Umar Shah, Co-founder and Chief Technology Officer, Simplyphi Software Solutions; Milind Naik, Head of Technology, DOQFY Internet Private Limited; Nikhil Vaidya, Chief Technology Officer, Beep Innovations and Praveen Castelino, Chief Technology Officer, CodeCraft Technologies.
The potential of GenAI
The discussion started with participants sharing several use cases to highlight the potential of GenAI that has helped to drive productivity and efficiency in their respective businesses.
One of the examples was its efficacy in the customer support industry, where GenAI has played an important role towards enhancing customer experiences by ensuring efficiency towards customer responses, powered by multiple data points. It has also enabled support executives to deliver data-driven actionable insights on areas of improvement.
However, experts also noted that while GenAI ensures faster turnarounds and heightened efficiency, there is often a disparity in mindset regarding its implementation within organisations, including the level of employee receptivity to the technology.
Tackling challenges
During the round table, technology leaders also spoke about several challenges for adoption of GenAI. While some mentioned the lack of access to the graphic processing unit (GPU) as a roadblock, for others it was about lack of association with developer groups around the world. Many participants also highlighted the limitations of being able to use data effectively, to scale or build innovative solutions.
Cost optimisation was another important factor highlighted during the discussion, where participants spoke about the availability of pre-trained models, which do not offer the same outcome for specific business cases. This requires them to develop data sets, which in turn becomes an added expense.
Apart from technical concerns, certain leaders also brought up organisational challenges. In the race to cater to the current GenAI trends or uses, most businesses get caught up in building quick solutions, which then becomes talent and expense heavy for organisations.
Best practices to align data strategy with business outcome
Experts in the discussion also highlighted some outcomes that could help businesses take on the adoption of GenAI. Some of the suggestions included:
Adopt a consumer mindset: Start by embracing GenAI as a consumer to grasp the value of data. This shift often requires a cultural change within businesses to maximise returns on investments.
Building the right skills: Businesses need to focus on building the right skill sets for using AI responsibly and being an enabler for their larger goals.
Consider the broader perspective: Regardless of whether one is a startup, an enterprise, or a cloud service provider, it’s crucial to start with the desired business outcome and then determine how various components can contribute to achieving it. Without this approach, organisations may struggle to reach the ultimate goal they aim for with AI implementation.
In conclusion, the roundtable highlighted the immense potential of GenAI in boosting productivity. It also discussed challenges like technical limitations and organisational barriers. Key takeaways included adopting a consumer mindset, building relevant skills, and aligning data strategy with business outcomes for successful GenAI implementation, for businesses navigating AI adoption in the evolving technological landscape.
