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What the Cloud Era Teaches Us About GenAI Adoption

What the Cloud Era Teaches Us About GenAI Adoption: Generative AI (GenAI) has experienced rapid growth over the past two years, presenting business leaders with a major technological disruption. We all want to understand how changes like these will impact us and what the next steps will be. Fortunately, we've been through this before.

When cloud computing emerged in the mid-2000s, it faced skepticism and concerns. Today, it's rare to find a leader who doesn't rely on it. Drawing parallels between the adoption of cloud computing and generative AI provides essential insights as we move towards widespread GenAI adoption.

1. Generative AI is an Operational Investment Similar to the cloud, Generative AI should be relevant to all types of businesses. The technology can be adopted by everyone, from the world's largest corporations to small independent businesses. McKinsey estimates that Generative AI is poised to increase the impact of overall AI by 15 to 40%, representing an annual amount of $4.4 trillion.

The cloud era allowed businesses to shift from capital-intensive projects to more sustainable operational investments. Generative AI is set to replicate this trend, involving a similar shift from CapEx to OpEx that suits all types of businesses.

It is possible to leverage Generative AI for business, regardless of its structure. Those who embrace this accessible approach to technology should experience increased productivity, efficient work processes, and reduced operating costs.

2. Generative AI Requires Security and Data Management Generative AI requires a massive amount of data to function effectively. Businesses are still in the early stages of considering the security implications of feeding AI solutions with their data.

In the early days of cloud adoption, businesses faced similar concerns. Industry leaders had to demonstrate that the cloud was reliable enough to encourage companies to place large amounts of data on it. They also had to determine which partners to work with to ensure security. There was a sense of uncertainty or ambiguity, which many leaders feel today regarding Generative AI.

Safely harnessing data-driven solutions involves significant challenges. Data has taken on a new dimension as consumers are increasingly aware of how companies manage 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 figure highlights that data security is a top priority for consumers in the EMEA region when choosing to buy from a company.

Given the complexity of Generative AI adoption, we must remember the lessons from the cloud era and prioritize data management and security. Recognize the vast opportunities but remain fully aware of the potentially heavier consequences for companies that make mistakes.

3. There is No Universal Generative AI Strategy All types of businesses can benefit from AI, provided they make appropriate investments. To become a pioneer in Generative AI, investing in recruiting employees who master the technology, such as data scientists, data analysts, or data engineers, is essential.

According to a study from the University of Oxford, since 2015, the demand for AI-related skills has multiplied by five globally. The 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 adopt Generative AI outright are better off waiting and learning from those who dive in. Once these early adopters make the necessary investments, we will see a more widely accepted way of managing data in large language models.

4. Generative AI Should be Considered a Long-term Value Addition Any technology leading to transformation, whether it's the cloud, Generative AI, or something else, 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 finally, laggards who slightly trail 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 yet.

Additionally, misinformation is circulating on the subject. We can establish another parallel: 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 solving the "blank page" problem and contributing to brainstorming and idea generation within the company.
• Evaluation, Ranking, and Recommendation Capability: Generative AI summarizing large amounts of data or long reports/journals, categorizing information, and providing recommendations and reasoning from that data.
• Content Generation: Generative AI proposing emails, social media posts, weekly summaries, or responses to IT ticket management service requests.

Generative AI adoption is a journey. Companies need to 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's crucial to determine exactly how the technology can foster a use case in your business and build from there.