It is no exaggeration that almost all companies are exploring Generative AI. 90% of organizations report that they have begun their genAI journey, meaning they are prioritizing AI programs, analyzing use cases, and/or experimenting with their first models. However, despite this enthusiasm and investment, few companies have anything to show for their AI efforts: only 13% report successfully bringing genAI models to production.
This inertia is rightly causing many organizations to question their approach, especially when budgets are limited. Overcoming these genAI challenges efficiently, results driven This way requires a flexible infrastructure that can handle the demands of the entire AI lifecycle.
Challenges in bringing generative AI to production
The challenges limiting the impact of AI are diverse, 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 feed the models, the IT skills to efficiently deploy models, and the skills necessary to monitor the 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 insights. use cases either unclear objectives.
- Trust: Organizations need a way to securely 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 maturity phase.
- Infrastructure: Organizations need a way to ease the process of modernizing their AI stack from procurement to production without creating disjointed and inefficient workflows, taking on too much technical debt, or spending too much.
Each of these issues can hinder AI projects and waste valuable resources. But with the right genAI stack and enterprise AI platform, companies can confidently build, operate, and govern generative AI models.
Building GenAI infrastructure with an enterprise AI platform
Successfully meeting the demands of generative AI models infrastructure with the critical capabilities necessary to manage the entire AI lifecycle.
- Build: Building models is about data; add it, transform it and analyze it. An enterprise AI platform should allow teams to create AI-ready data sets (ideally from dirty data for true simplicity), scale up as needed and uncover meaningful insights to make models perform highly.
- Work: Operational models mean putting models into production, integrating AI use cases into business processes, and collecting results. The best enterprise AI platforms enable
- Govern:
An enterprise AI platform solves a number of cost and workflow inefficiencies by unifying these capabilities into a single solution. Teams have fewer tools to learn, there are fewer security concerns, and it’s easier to manage costs.
Leveraging Google Cloud and the DataRobot AI Platform for GenAI Success
Google Cloud provides a powerful foundation for AI with its cloud infrastructure, data processing tools, and industry-specific models:
- Google cloud provides simplicity, scale and intelligence to help companies build the foundation of their AI stack.
- Great consultation helps organizations easily leverage their existing data and discover new insights.
- Data fusionand Pub/Sub Enable teams to easily ingest their data and prepare it for AI, maximizing the value of their data.
- Vertex AI provides the core framework for creating models and Google Model Garden provides 150+ models for any industry-specific use case.
These tools are a valuable starting point for building and scaling an AI program that produces real results. DataRobot powers 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 essential capabilities needed to build, operate and govern the entire AI landscape. .
- Build: BigQuery data (and data from other sources) can be brought into DataRobot and used to create RAG Workflows which, when combined with models from Google Model Garden, can create complete genAI blueprints for any use case. These can be staged in the DataRobot LLM Playground and different combinations can be tested against each other, ensuring that teams release the highest-performing AI solutions possible. DataRobot also provides templates and AI accelerators that help businesses connect to any data source and accelerate their AI initiatives,
- Work: DataRobot Console can be used to monitor any AI application, whether it is an AI-powered application within Looker, Appsheet or in 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 works as intended and remains accurate over time.
- Govern: DataRobot provides the observability and governance to ensure the entire organization trusts its AI process and model results. Teams can create robust compliance documentation, control user permissions and project sharing, and ensure their models are fully tested and included in robust risk mitigation tools before deploying. The result is complete governance of each model, even when regulations change.
With over a decade of experience in enterprise AI, DataRobot is the orchestration layer that transforms the foundation laid by Google Cloud into a complete AI pipeline. Teams can accelerate the deployment of AI applications in Looker, Data Studio, and AppSheet, or enable teams to confidently build custom genAI applications.
Common GenAI Use Cases Across Industries
DataRobot also enables companies to combine generative AI with predictive AI for truly personalized AI applications. For example, a team could create a dashboard using predAI and then summarize those results with genAI to streamline reporting. Elite AI teams are already seeing results from these powerful capabilities across industries.
A graphic showing real-world examples of genAI applications for banking, healthcare, retail, insurance, and manufacturing..
Google gives businesses the building blocks to leverage the data they already have, then DataRobot gives teams the tools to overcome common genAI challenges to deliver real AI solutions to their customers. Whether starting from scratch or with an AI accelerator, the 13% of organizations The value already being seen in genAI is proof that the right enterprise AI platform can have a significant impact on business.
Starting the GenAI journey
90% of companies are on their AI journey, and regardless of where they are in the AI ​​value journey, they are all experiencing similar obstacles. When an organization struggles with a lack of skills, a lack of clear goals and processes, little confidence in its genetic AI models, or a sprawling and expensive infrastructure, Google Cloud and DataRobot provide companies with a clear path to AI success. predictive and generative AI.
If your company is already a Google Cloud customer, you can start using DataRobot through the Google Cloud Marketplace. Schedule a custom demo to see how quickly you can start building successful genAI applications.
