It is no exaggeration that almost every company is researching generative artificial intelligence. 90% of organizations say they are starting their genAI journey, meaning they are prioritizing AI programs, scoping and/or experimenting with their first models. Despite this enthusiasm and investment, however, few businesses have anything to show for their AI efforts, with only 13% reporting that they have successfully moved genAI models into production.
This inertia rightly causes many organizations to question their approach, especially when budgets are tight. Overcoming these genAI challenges in an efficient, results-oriented manner requires a flexible infrastructure that can handle the demands of the entire AI lifecycle.
Challenges Moving generative artificial intelligence into manufacturing
The challenges of limiting the impact of AI are varied, but can be divided into four categories:
- 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 power the models, the IT skills to effectively deploy the models, and the skills needed to monitor the models over time.
- Culture: Organizations have failed to adopt the mindset, processes and tools necessary to connect stakeholders and deliver real value, often resulting in a lack of definitive use cases or unclear goals.
- Confidence: Organizations need a way to securely build, operate and manage their AI solutions and have confidence in the results. Otherwise, they risk deploying high-risk models to production or never getting past the proof-of-concept maturity stage.
- Infrastructure: Organizations need a way to streamline the process of building their AI from procurement to production without creating disjointed and inefficient workflows, too much technical debt or excessive spending.
Each of these issues can hold back AI projects and waste valuable resources. But with the right genAI stack and enterprise AI platform, companies can confidently build, run and manage generative AI models.
Building a GenAI infrastructure with the Enterprise AI Platform
Successfully meeting the infrastructure requirements of generative AI models with the critical capabilities needed to manage the entire AI lifecycle.
- Create: Building models is all about data; aggregate, transform and analyze. An enterprise AI platform should enable teams to create AI-ready datasets (ideally from dirty data for true simplicity), augment them as needed, and uncover meaningful insights to make the models high-performing.
- Operate: Operational models mean putting models into production, integrating AI use cases into business processes, and collecting results. The best enterprise AI platforms enable
- Drive:
An enterprise AI platform solves a number of inefficient workflows and costs by unifying these capabilities into a single solution. Teams have fewer tools to learn, fewer security issues, and easier cost management.
Leveraging 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:
- Google Cloud it provides the simplicity, scalability and intelligence that helps companies build the foundation for their AI stack.
- BigQuery helps organizations easily leverage their existing data and discover new insights.
- Data Fusionand Pub/Sub enabling teams to easily migrate their data and prepare it for AI, maximizing the value of their data.
- Vertex AI provides a basic framework for building models and Google Model Garden provides over 150 models for any industry use case.
These tools are a valuable starting point for building and scaling an AI program that delivers real results. DataRobot supercharges this foundation by providing teams with an end-to-end enterprise AI platform that unifies all data sources and all business applications while providing the core functionality needed to build, operate and manage an entire AI environment.
- Create: BigQuery data – and data from other sources – can be brought into DataRobot and used to create RAG workflows that, combined with models from the Google Model Garden, can create complete genAI blueprints for any use case. These can be staged in the DataRobot LLM Playground and tested against each other, ensuring teams launch the most powerful AI solutions possible. DataRobot also provides templates and AI accelerators to help companies connect to any data source and fast-track their AI initiatives,
- Operate: DataRobot Console can be used to monitor any AI application, whether it’s an AI-powered application within Looker, Appsheet, or a completely custom application. Teams can centralize and monitor critical KPIs for each of their predictive and generative models in production, making it easy to ensure each deployment performs as intended and remains accurate over time.
- Drive: DataRobot provides observability and governance to ensure the entire organization trusts their AI process and model results. Teams can create robust compliance documentation, manage user permissions and project sharing, and ensure their models are fully tested and wrapped in robust risk mitigation tools before deployment. The result is complete control of each model, even as regulations change.
With more than a decade of enterprise AI experience, DataRobot is the orchestration layer that transforms the foundation laid by Google Cloud into a complete AI pipeline. Teams can accelerate deployment of AI apps to Looker, Data Studio, and AppSheet, or empower teams to confidently build customized genAI apps.
Common GenAI use cases across industries
DataRobot also enables companies to combine generative AI with predictive AI for truly customized AI applications. For example, a team could create a dashboard using predAI and then summarize those results using genAI for simplified reporting. Elite AI teams are already seeing the results of these powerful features across industries.
Chart showing examples of real-world applications of genAI for banking, healthcare, retail, insurance and manufacturing.
Google provides businesses with the building blocks to leverage the data they already have, and DataRobot gives teams the tools to overcome common genAI challenges to deliver true AI solutions to their customers. Whether you’re starting from scratch or using an AI accelerator, the 13% of organizations that already see the value of genAI is proof that the right enterprise AI platform can have a significant business impact.
Launching the GenAI Journey
90% of companies are on the genAI journey, and no matter where they may be in the process of realizing the value of AI, they all face similar obstacles. When an organization struggles with skills gaps, a lack of clear goals and processes, low confidence in its genAI models, or costly, sprawling infrastructure, Google Cloud and DataRobot give companies a clear path to predictive and generative AI success.
If your company is already a Google Cloud customer, you can start using DataRobot through the Google Cloud Marketplace. Schedule your own demo to see how quickly you can start building successful genAI applications.