30,000 individuals gathered in Las Vegas this week to receive the most up-to-date and innovative information from Google Cloud. The only thing they heard was continuous generative AI. Google Cloud primarily operates as a vendor that provides cloud infrastructure and platform services. Without prior knowledge, it is possible that you overlooked this information amidst the abundance of AI news.

While acknowledging the impressive showcase by Google, it is worth noting that, similar to Salesforce’s event in New York City last year, the company did not focus much on its primary operations, save when discussing generative AI.

Google unveiled a range of artificial intelligence (AI) improvements aimed at assisting users in leveraging the Gemini large language model (LLM) and enhancing productivity throughout the platform. Undoubtedly, it is a commendable objective. During the primary keynote on Day 1 and the subsequent Developer Keynote, Google incorporated a substantial number of demonstrations to exemplify the capabilities of these solutions.

However, several of them were overly basic, especially considering the constraint of being condensed into a keynote presentation with a restricted time frame. Their reliance was primarily on cases within the Google ecosystem, despite the fact that most companies store a significant portion of their data in repositories that are not affiliated with Google.

Several of the examples seemed feasible without the use of artificial intelligence. During an e-commerce demonstration, the presenter contacted the vendor to finalize an online transaction. The purpose of its design was to demonstrate the communication capabilities of a sales bot. However, in reality, the customer could have completed the task on the website with ease.

Generative AI possesses powerful applications, such as code generation, content analysis with query capabilities, and log data analysis to determine the cause of a website outage. In addition, the company has produced task- and role-based agents that may effectively utilize generative AI to assist individual developers, creative individuals, employees, and others.

However, when it comes to developing AI tools using Google’s models, rather than using the ones created by Google and other vendors for their customers, I couldn’t help but notice that they were downplaying many of the challenges that could hinder the successful implementation of generative AI. Despite attempts to downplay the difficulty, the truth is that integrating advanced technology into large businesses poses a significant obstacle.

Significant transformation is not simple
Similar to earlier technological advancements over the previous 15 years, such as mobile, cloud, containerization, and marketing automation, there have been numerous claims of potential benefits. However, each of these developments brings up its own degree of intricacy, and prominent corporations proceed with more prudence than we could envision. Artificial intelligence (AI) appears to require significantly more effort and resources than what Google or other major vendors are openly acknowledging.

Our experience with past technological revolutions has shown that they often generate excessive excitement and ultimately result in widespread disappointment. After several years, we continue to observe prominent corporations that, while having the opportunity, are only experimenting with or completely abstaining from utilizing these sophisticated technologies, even long after their introduction.

Companies may fail to take advantage of technological innovation due to various factors. These include organizational inertia, a rigid technology infrastructure that hinders the adoption of newer solutions, and a group of individuals within the company who oppose any well-intentioned initiatives. These individuals may belong to legal, HR, IT, or other departments and may reject substantive change for reasons such as internal politics.

Vineet Jain, the Chief Executive Officer (CEO) of Egnyte, a company specializing in storage, governance, and security, identifies two categories of companies: those that have already made a substantial transition to the cloud and will find it relatively easier to adopt generative AI, and those that have been slow in embracing new technologies and are likely to face challenges in adopting generative AI.

He engages in conversations with numerous firms that predominantly rely on on-premises technology and have a significant amount of progress to make before considering the potential benefits of AI. “We engage with numerous ‘late’ cloud adopters who have either not initiated or are in the initial stages of their pursuit of digital transformation,” Jain informed.

The introduction of AI may compel these organizations to carefully consider pursuing digital transformation, although they may encounter difficulties due to their significant lag in progress, according to his statement. “Before incorporating AI, these companies must first address and resolve the existing issues and establish a robust data security and governance framework,” he stated.

The data was consistently the main factor
Major industry players such as Google present the implementation of these solutions as straightforward, but the apparent simplicity on the surface does not guarantee that it is not complex behind the scenes. Throughout this week, I frequently encountered the notion that the quality of the data used to train Gemini and other extensive language models is crucial. It is evident that if the input data is of poor quality, the output generated by generative AI will also be of poor quality.

The process begins with the collection and analysis of data. If your data is not organized, it will be challenging to prepare it for training the LLMs for your specific use case. Kashif Rahamatullah, a principal at Deloitte responsible for overseeing the Google Cloud practice, expressed his admiration for Google’s recent developments. However, he also highlighted that certain firms without organized data may encounter difficulties when using generative AI solutions. “The initial AI conversation often transitions into a focus on data cleaning and consolidation, as this is crucial for maximizing the benefits of generative AI,” Rahamatullah explained.

Google has developed generative AI tools to facilitate data engineers in constructing data pipelines that connect to both internal and external data sources within the Google ecosystem. “The purpose is to enhance the efficiency of data engineering teams by automating the labor-intensive tasks associated with data movement and preparation for these models,” explained Gerrit Kazmaier, Google’s Vice President and General Manager for Database, Data Analytics, and Looker, in an interview with.

This will be beneficial for data integration and data cleansing, particularly in firms that have made significant progress in their digital transformation. However, for firms like the ones mentioned by Jain, who have not made significant progress in terms of digital transformation, these tools developed by Google could pose further challenges.

Furthermore, it is important to note that AI presents additional hurdles beyond mere implementation. According to Andy Thurai, an analyst at Constellation Research, this is true whether one is developing an application based on an existing model or attempting to create a customized model. “During the implementation of either solution, companies must consider governance, liability, security, privacy, ethical and responsible use, and compliance with these implementations,” stated Thurai. And all of that is significant.

Executives, IT professionals, developers, and other attendees of GCN this week may have sought insights into Google Cloud’s future offerings. However, if they were not actively seeking AI or if they were not adequately prepared as an organization, they may have left Sin City feeling overwhelmed by Google’s intense focus on AI. Organizations that lack digital expertise may need a significant amount of time before they can fully utilize these technologies, especially those that are not as comprehensive as the solutions provided by Google and other suppliers.