It’s no exaggeration to say that nearly every company is exploring generative AI. Ninety percent of organizations report that they have begun their genAI journey, meaning they are prioritizing AI programs, scoping use cases, and experimenting with their first models. But despite this excitement and investment, few companies are seeing results from their AI efforts, with only 13 percent reporting that they have successfully moved their genAI models into production.
This inertia has led many organizations to question their approach, especially as budgets become tight. Overcoming these genAI challenges in an efficient and results-focused manner requires a flexible infrastructure that can handle the demands of the entire AI lifecycle.
Challenges of moving generative AI to production
The challenges limiting the impact of AI are varied, but they can broadly be divided into four categories:
- Technical ability: Organizations lack the tactical execution skills and knowledge required to deploy Gen AI applications into production. This includes the skills required to build the data infrastructure that feeds data to models, the IT skills required to deploy models efficiently, and the skills required to monitor models over time.
- culture: Organizations have failed to adopt the mindset, processes, and tools needed to align stakeholders and deliver real value, often resulting in a lack of clear use cases or unclear goals.
- faith: Organizations need a way to securely build, operate, and manage AI solutions and have confidence in the results. Otherwise, they risk deploying high-risk models into production or never getting beyond the proof-of-concept stage of maturity.
- infrastructure: Organizations need a way to seamlessly move their AI stack from procurement to production without creating inefficient and disjointed workflows, accruing too much technical debt, or overspending.
Each of these challenges can hinder AI projects and waste valuable resources. However, with the right genAI stack and enterprise AI platform, businesses can confidently build, operate, and manage generative AI models.
Building GenAI Infrastructure through Enterprise AI Platform
Successfully delivering generative AI models requires infrastructure with critical capabilities needed to manage the entire AI lifecycle.
- compose: Building models is all about data. It’s about aggregating, transforming, and analyzing data. An enterprise AI platform should enable teams to create AI-ready data sets (ideally from dirty data for true simplicity), augment them as needed, and uncover meaningful insights to make models performant.
- Work: The operational model means putting models into production, integrating AI use cases into business processes, and capturing the results. The best enterprise AI platforms allow for:
- Rule over:
Enterprise AI platforms integrate these capabilities into a single solution that addresses multiple workflow and cost inefficiencies. Teams have fewer tools to learn, fewer security concerns, and easier cost management.
Leveraging Google Cloud and DataRobot AI Platforms for GenAI Success
Google Cloud provides a strong foundation for AI with cloud infrastructure, data processing tools, and industry-specific models.
- Google Cloud We deliver simplicity, scalability, and intelligence to help enterprises build the foundation of their AI stack.
- BigQuery We help organizations easily leverage existing data and discover new insights.
- Data fusionand Pub/Sub Maximize the value of your data by making it easy for your team to get it and prepare it for AI.
- Vertex AI Google Model Garden provides a core framework for building models, with over 150 models for all industry use cases.
These tools are a valuable starting point for building and scaling AI programs that produce real results. DataRobot strengthens this foundation by providing teams with an end-to-end enterprise AI platform that integrates all data sources and all business apps, while providing the essential capabilities needed to build, operate, and manage the entire AI environment.
- compose: You can bring BigQuery data and data from other sources into DataRobot to create RAG workflows, which can then be combined with models from Google Model Garden to create complete genAI blueprints for any use case. These can be staged in DataRobot LLM Playground and tested on different combinations to ensure your team can launch the most performant AI solution possible. DataRobot also offers templates and AI accelerators to help companies connect to all their data sources and accelerate their AI initiatives.
- Work: DataRobot Console can be used to monitor all your AI apps, whether they are AI-powered apps within Looker, Appsheet, or completely custom apps. Your team can centralize and monitor critical KPIs for each predictive and generative model in production, making it easy to ensure that all deployments are performing as intended and maintaining accuracy over time.
- Rule over: DataRobot provides observability and governance that enables the entire organization to have confidence in AI processes and model results. Teams can create robust compliance documentation, control user permissions and project sharing, and ensure models are fully tested and wrapped in robust risk mitigation tools before deployment. The result is complete governance for all models, even as regulations change.
With over 10 years of enterprise AI experience, DataRobot is the orchestration layer that transforms the foundation laid by Google Cloud into a complete AI pipeline. It can accelerate the deployment of AI apps to Looker, Data Studio, and AppSheet, or empower teams to confidently build custom genAI applications.
Common GenAI use cases across industries
DataRobot also enables enterprises to combine generative AI with predictive AI to create truly personalized AI applications. For example, a team can build a dashboard using predAI and then summarize the results with genAI for streamlined reporting. The Elite AI team is already seeing the results of these powerful capabilities across industries.
Chart showing real-world examples of genAI applications for banking, healthcare, retail, insurance, and manufacturing.
Google provides the building blocks for enterprises to leverage the data they already have, while DataRobot provides teams with the tools to overcome common genAI challenges and deliver real-world AI solutions to their customers. Whether starting from scratch or with an AI accelerator, the 13% of organizations already seeing value in genAI is proof that the right enterprise AI platform can have a significant impact on their business.
Start your GenAI journey
90% of enterprises have started their genAI journey, and no matter where they are in their journey to realizing value from AI, they all face similar obstacles. Whether organizations struggle with skills gaps, lack of clear goals and processes, low confidence in genAI models, or costly and massively scaled infrastructure, Google Cloud and DataRobot provide enterprises with a clear path to predictive and generative AI success.
If your company is already a Google Cloud customer, you can get started with DataRobot through the Google Cloud Marketplace. Schedule a custom demo to see how quickly you can build a successful genAI application.
