Generative AI has sparked a renewed interest in artificial intelligence (AI). Seeing its transformative potential, some companies are already looking for ways to adopt generative AI functions into workflows to realize a competitive advantage. Many businesses see generative AI as an opportunity to bring AI adoption into the mainstream and to finally realize the business impact that AI has promised for so long.
While individuals often use experimentation and generative AI to increase productivity, using generative AI for enterprise transformation is still not very common. Surprisingly, less than 30% of organizations report using generative AI in more than one function. This share has remained essentially unchanged since 2021.1
The Data Dilemma: A Reality Check for Companies
According to a recent survey conducted by the Eckerson Group, only a few companies can manage the business risks associated with using generative AI in enterprises.2 The survey also revealed that 46% of data leaders feel that their organization lacks adequate data quality and data governance controls to support the application of generative AI.3
As organizations progress through different stages of AI adoption — first as a standalone tool, then as a function within the tool and finally as embedded workflows — they will likely face familiar challenges arising from unresolved data management issues.
Accelerate your AI journey: For a deeper dive to explore the adoption stages of generative AI and learn more about the associated challenges and how to overcome them, download the white paper Governed Data Management for Generative AI.
Trusted Data Drives AI Adoption
To fuel AI applications with precise and reliable data, managing large volumes of data efficiently is crucial. However, this can be daunting due to concerns about data privacy, security and regulatory compliance. To address these challenges, it is necessary to eliminate any inconsistencies in data and ensure its quality.
Data management for AI can provide a significant advantage in adopting AI. Not only does the availability of trusted data combat data challenges at scale and speed that deliver competitive advantage, but it also produces far more accurate results by helping to prevent data drifts and unethical use of AI. Such data management boosts users' confidence in AI-generated results and accelerates their adoption journey by providing more reliable outcomes in a shorter time with less effort. Put another way, reliable data management is crucial to the successful adoption of AI.
Governed Data Management: A Generative AI Imperative
Managing large amounts of reliable data is critical for training the large language models (LLMs) used for generative AI. However, traditional data governance approaches are not enough to ensure safe and effective data management. Organizations need modern data governance solutions that utilize AI technology to address this. These solutions should support risk and compliance and enable data democratization while maintaining data quality and observability across data pipelines.
Having clean, accurate and context-rich data is crucial to make the most of AI-enabled workflows and systems. One way to achieve this is by automating critical processes for governed data management. This includes data integration, data cataloging, master data management, data observability and data governance. By doing so, enterprises can increase productivity and transform their operations with AI.
Join us for an informative webinar on improving data management processes to speed up generative AI adoption and outcomes. We'll explain how trusted data can help you get better AI results. Register now for Drive Better AI with Trusted Data to learn more.
AI Needs AI to Thrive
Successfully adopting generative AI depends on three critical factors:
- Enable seamless data integration for data from various sources with efficient data management.
- Enable data accuracy and reliability with early identification and continuous remediation of data issues.
- Improve the performance of the AI model with a robust feedback mechanism that’s based on the data it processes.
Companies need more than manual data management methods to meet the data requirements for generative AI. Advanced AI-powered data management capabilities are required to automate data classifications, apply intelligent data quality rules, match data, manage master data and more. The Informatica Intelligent Data Management Cloud™ (IDMC) provides an all-in-one AI-powered solution that delivers trusted data for smart decision-making. Informatica's AI engine, CLAIRE, can help automate data management across different environments, such as multi-cloud, on-premises and hybrid. This can transform a company's data foundation to provide the necessary support for AI.
Getting Started with Data Governance for AI
It's no secret that generative AI has enormous potential to transform businesses. However, to fully reap the benefits, companies must first address the underlying issues with data management. This means that they need to strengthen their processes to ensure that the outputs generated by AI are trustworthy and can drive intelligent action. According to the Eckerson Group, this can be achieved by breaking down the task into three simple steps:
- First, define the use case.
- Second, assemble the team.
- Third, iterate to improve.
Advancements in AI technology have created a lot of excitement. With the right tools, strategies and a unified and composable data management platform, you can accelerate your journey to successful AI adoption.
Discover how trusted, high-quality data can help you create success with generative AI in this Eckerson Group white paper, Governed Data Management for Generative AI.
Register now for our webinar, Drive Better AI with Trusted Data.
Read the solution brief, How CLAIRE AI Engine Can Help You Automate Data Governance
1QuantumBlack AI by McKinsey, The state of AI in 2023: Generative AI’s breakout year
2Eckerson Group White Paper, Governed Data Management for Generative AI