What the Cloud Era Teaches Us About GenAI Adoption
What the Cloud Era Teaches Us About GenAI Adoption: Ensuring the secure utilization of data-driven solutions poses significant challenges in today's landscape. Data has gained a new dimension as consumers become increasingly aware of how companies handle and use their data.
According to a 2022 study by Statista, 70% of European consumers fear that companies may use their personal data for purposes other than intended. This statistic underscores that data security is an absolute priority for consumers in the EMEA region when deciding to engage with a company.
Faced with the complexities of adopting generative AI, we must remember the lessons learned from the cloud era and prioritize data management and security. While recognizing the immense opportunities at hand, it is crucial to remain fully aware of the potentially heavier penalties for companies that make mistakes.
No Universal Generative AI Strategy Exists All types of businesses can benefit from AI, provided they make the necessary investments. To become a trailblazer in generative AI, investing in recruiting professionals who master the technology—data scientists, data analysts, or data engineers—is essential. According to a study by the University of Oxford, since 2015, the demand for AI-related skills has multiplied by five globally. The clear conclusion for businesses is simple: if you don't understand the lifecycle of your data or the regulations related to AI, you need to hire specialists.
Businesses hesitant to immediately adopt generative AI are wise to wait and learn from those who take the plunge. Once these early adopters have made the necessary investments, we will witness a more widely accepted way of managing data in large language models.
Generative AI Should Be Considered Long-Term Value Any technology leading to transformation, be it the cloud, generative AI, or otherwise, follows the same pattern. The classic media hype cycle begins with a period of anticipation, followed by rapid adoption, an intermediate stage characterized by caution, and ultimately, laggards who remain slightly behind. Generative AI is still in the very early stages of this cycle. A Dell study reveals that only 44% of companies are currently in the rapid or intermediate adoption phases of generative AI, meaning that most have not made significant progress.
Moreover, misinformation abounds on the subject. We can draw another parallel here: when the cloud first emerged, leaders thought it would be less cost-effective, less secure, and less reliable than traditional IT infrastructure.
The only true way to address this issue and navigate the generative AI cycle is to let things unfold naturally. We are beginning to see what generative AI looks like for businesses in practice. Examples of new use cases include:
Idea Generation: Generative AI to address the "blank page" problem and contribute to brainstorming and idea generation within the company.
Evaluation, Ranking, and Recommendation Capability: Generative AI to summarize large amounts of data or long reports/logs, classify information, and provide recommendations and reasoning based on that data.
Content Generation: Generative AI to suggest emails, social media posts, weekly summaries, or responses to IT ticket management service requests.
Generative AI adoption is a journey. Companies must go at their own pace and start by streamlining a foundational layer of AI or automation.
Mature capabilities of generative AI, such as predictive intelligence, statistical analysis, natural language understanding, to name a few, truly change the game. It is crucial to determine precisely how the technology can foster a use case in your business and build from there.