5 Key Factors To Adopt Generative AI 

With many companies lacking the resources or expertise to adopt generative AI, the complexity and cost behind this technology seems like something to fear. 

What business cases are you solving? What will it cost to produce and utilise these AI models? How does one even get started?

But, if you’re a business looking to get into the generative AI game – it’s actually quite simple.

As a business that’s looking to the potential success that will come from generative AI, there are a few requirements to have a few things in place.

  • A strong AI infrastructure, 
  • Access to multi-model foundational models
  • AI-powered collaboration tools 
  • A broad ecosystem of partners

Companies that are interested in adopting generative AI should start by assessing their needs and resources. And, once they have a clear understanding of their needs, they can start to develop a plan for adoption.

World-class AI infrastructure

To unleash the full potential of generative AI, your biggest requirement is having a robust AI infrastructure. 

One that can handle substantial computing power and data storage for those powerful AI models. Oh, and a scalable data storage solution to ensure that you have the capacity to handle the vast amounts of data required for both training and generating them.

According to Gartner, by 2024, 40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020. This indicates a growing need for world-class AI infrastructure to support the deployment and operation of generative AI systems.

Multi-model foundational models 

Training generative AI models is no walk in the park. 

It requires access to massive datasets of not only text and images, but also code. Luckily, multi-model foundational models come to the rescue. These models act as a solid foundation, enabling you to bootstrap your own generative AI models.

By leveraging these pre-trained models, you can kickstart the training process and enhance the accuracy of your models right from the start.

According to McKinsey, the value chain of generative AI systems includes foundational models which are critical for the development and deployment of these systems. The complexity of generative AI systems makes them more challenging to deliver, and hence, companies need multi-model foundational models to navigate these complexities.

AI-powered collaboration

Collaboration is the fuel that ignites innovation. 

Generative AI projects involve a lot of complexity, often requiring cross-functional teamwork. That’s where AI-powered collaboration tools enter the scene. These tools facilitate seamless data sharing, model collaboration, and knowledge exchange among your team members.

 With efficient collaboration, you can boost productivity, unleash creativity, and streamline the development process of your generative AI projects.

According to Gartner, Generative AI is set to make a significant impact on collaboration within enterprises. By 2026, Gartner predicts that over 100 million humans will engage with AI-driven “robocolleagues” to contribute to their work. This signifies the importance of AI-powered collaboration in the workplace.

Broad ecosystem of partners

To thrive in the rapidly evolving field of generative AI, it’s crucial to build a diverse ecosystem of partners.

These partners keep you in the loop on the latest trends and technologies. 

By establishing connections with thought leaders and experts, you gain invaluable insights and stay ahead of the game. A broad ecosystem of partners also enables knowledge sharing and best practice exchange, fostering a collaborative environment for continuous growth and improvement.

To effectively implement and utilise generative AI, companies will need to build an effective ecosystem of partners, communities, and platforms. This will help them navigate the business opportunities and technical complexities associated with generative AI, reveals McKinsey.

Deep integration with Hyperscaler Platforms 

When it comes to unleashing the true potential of generative AI, Cloud and hyperscaler platforms (like GCP or AWS) are your ultimate allies. 

With their extensive range of tools and services, you gain a powerful arsenal for building and deploying generative AI models. The deep integration with these platforms simplifies the development and deployment process, saving you time and effort. 

Whether it’s leveraging Google’s advanced AI capabilities or tapping into their scalable infrastructure, the integration empowers you to take your generative AI projects to new heights.

According to McKinsey, cloud platforms provide the necessary infrastructure and resources for the development and deployment of AI systems. Therefore, deep integration with a robust cloud platform like Google Cloud Platform could be beneficial for companies wanting to adopt generative AI.

Get Started With Generative AI Today!

With so many businesses around the world tapping into the power of AI, there’s no denying the impact and reach of this unprecedented technology.

By breaking down the complexities behind the barriers to Generative AI adoption, it’s easier to understand how to approach implementation. Taking a careful look at cloud technologies, collaborative AI toolsets and your infrastructure as a whole, you’re able to make wiser, data-derived decisions in your approach.

If it’s AI that you want: We can help you.

Our specialist teams of data engineers, machine learning experts and AI torchbearers are ready and waiting to take your business to an AI-driven world.

Contact us today and begin your Generative journey with us.

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