Overcoming the Challenges of Generative AI
With the growing interest in generative AI (genAI), many organizations are facing significant challenges in bringing their AI programs to life. Despite the excitement and investment in AI, few businesses have successfully moved genAI models into production. In this article, we’ll explore the challenges limiting AI impact and provide solutions for overcoming them.
Challenges Moving Generative AI into Production
The challenges limiting AI impact can be broadly broken down into four categories: technical skills, culture, confidence, and infrastructure.
Technical skills: Organizations lack the tactical execution skills and knowledge to bring Gen AI applications to production, including the skills needed to build the data infrastructure to feed models, the IT skills to efficiently deploy models, and the skills needed to monitor models over time.
Culture: Organizations have failed to adopt the mindset, processes, and tools necessary to align stakeholders and deliver real-world value, often resulting in a lack of definitive use cases or unclear goals.
Confidence: Organizations need a way to safely build, operate, and govern their AI solutions, and have confidence in the results. Otherwise, they risk deploying high-risk models to production, or never escaping the proof-of-concept phase of maturity.
Infrastructure: Organizations need a way to smooth the process of standing up their AI stack from procurement to production without creating disjointed and inefficient workflows, taking on too much technical debt, or overspending.
Building GenAI Infrastructure with an Enterprise AI Platform
Successfully delivering generative AI models demands infrastructure with the critical capabilities needed to manage the entire AI lifecycle.
Build
An enterprise AI platform should allow teams to create AI-ready datasets, augment as necessary, and uncover meaningful insights so models are high-performing.
Operate
The best enterprise AI platforms allow teams to put models into production, integrate AI use cases into business processes, and gather results.
Govern
An enterprise AI platform should provide the observability and governance to ensure the entire organization has trust in their AI process and in model results.
Harnessing Google Cloud and the DataRobot AI Platform for GenAI Success
Google Cloud provides a powerful foundation for AI with their cloud infrastructure, data processing tools, and industry-specific models.
DataRobot supercharges this foundation by giving teams an end-to-end enterprise AI platform that unifies all data sources and all business apps, while also providing the essential capabilities needed to build, operate, and govern the entire AI landscape.
Frequently Asked Questions
Q1: What is Generative AI?
Generative AI (genAI) refers to the use of artificial intelligence and machine learning to generate new, original content such as images, videos, music, and text. genAI has the potential to revolutionize various industries such as healthcare, finance, and marketing.
Q2: What are the challenges of implementing Generative AI?
The challenges of implementing genAI include technical skills, culture, confidence, and infrastructure. Organizations need to overcome these hurdles to successfully bring their AI programs to life.
Q3: How can organizations overcome these challenges?
Organizations can overcome these challenges by building infrastructure with the critical capabilities needed to manage the entire AI lifecycle. They can also leverage platforms such as Google Cloud and DataRobot AI Platform to streamline their AI development process.
Q4: What is the DataRobot AI Platform?
The DataRobot AI Platform is an end-to-end enterprise AI platform that unifies all data sources and all business apps, while also providing the essential capabilities needed to build, operate, and govern the entire AI landscape.
Q5: What industries are using Generative AI?
GenAI is being used across various industries including banking, healthcare, retail, insurance, and manufacturing. Elite AI teams are already seeing results from these powerful capabilities.
Conclusion
In conclusion, overcoming the challenges of generative AI requires a flexible infrastructure that can handle the demands of the entire AI lifecycle. With the right genAI stack and enterprise AI platform, companies can confidently build, operate, and govern generative AI models. By leveraging platforms such as Google Cloud and DataRobot AI Platform, organizations can overcome common genAI challenges and deliver actual AI solutions to their customers. Whether starting from scratch or an AI accelerator, the 13% of organizations already seeing value from genAI are proof that the right enterprise AI platform can make a significant impact on the business.
