Google Cloud recently held a conference in Las Vegas, where they focused heavily on generative AI. While Google is primarily a cloud infrastructure and platform vendor, they dedicated a significant amount of time to AI-related announcements and demonstrations. However, some of these demos seemed simplistic and relied heavily on examples within the Google ecosystem, rather than considering the data repositories that companies typically have outside of Google.
Despite this, generative AI does have powerful use cases, such as creating code, analyzing content, and understanding log data. Google also introduced task and role-based agents to help developers and other professionals leverage generative AI in practical ways.
However, implementing generative AI within large organizations can be challenging. Technological advancements like AI often come with promises of potential gains, but they also introduce complexity. Large companies tend to move cautiously when adopting new technologies, resulting in slower adoption rates than expected. Furthermore, various factors like organizational inertia, a complex technology stack, and resistance from different corporate groups can impede the adoption of new technologies.
According to Vineet Jain, CEO at Egnyte, companies that have already made the shift to the cloud will find it easier to adopt generative AI compared to those that have been slow to adopt cloud technologies. Late cloud adopters will need to address their existing issues, such as data security and governance, before fully embracing AI.
One crucial aspect of implementing generative AI is the quality of data used for training models. If a company’s data is not clean or properly organized, it will be difficult to train models effectively. Therefore, companies must prioritize data management before reaping the benefits of generative AI.
Google has developed tools to help data engineers connect and clean data from various sources, both inside and outside the Google ecosystem. These tools aim to speed up the data engineering process for companies further along their digital transformation journey. However, companies that are still in the early stages of digital transformation may face additional challenges even with these tools.
Apart from implementation challenges, companies must also consider governance, liability, security, privacy, and ethical implications when deploying AI solutions. These factors are not trivial and require careful consideration.
Overall, Google’s focus on AI at the conference may have left some attendees feeling overwhelmed or unprepared if their organization is not ready to fully embrace AI. It will likely take time for less digitally mature companies to take advantage of these technologies beyond the packaged solutions provided by Google and other vendors.