Navigating the Gen AI Frontier With Strong Data Governance
5 min readIn an era where artificial intelligence (AI), and Generative AI (Gen AI) in particular, dominates the headlines and boardroom discussions, the path to successful adoption remains elusive for many. In fact, only 22% are seeing real value from their Gen AI deployments. Why? At a recent gathering hosted by SoftServe and AWS at London's House of Lords, IT leaders from diverse industries converged to tackle this challenge. The consensus was clear; strong use cases, robust data governance, and collaboration are essential for the Gen AI journey.
Read on to learn how data governance paves the way for success in Gen AI projects.
Setting the bar — high hopes for AI projects
When it comes to AI initiatives, success isn't guaranteed — projects must meet stringent criteria to deliver real value. Creating value with Gen AI doesn’t require industry-changing deployments. Rather, it is a matter of strategically identifying use cases.
As our experts confirmed with practical examples from their own companies, Gen AI has already added tangible value. It can cut costs by efficiently summarising survey data and translating materials or supplementing traditional photography by creating marketing images.
These examples underscore the potential of AI, but they also highlight the critical need for setting realistic expectations as a condition of success.
Data governance: the key to successful Gen AI deployments
Targeting the right use cases goes hand in hand with a business-value framework or AI operations (AIOps) methodology. Both initiatives rely on a sober understanding of the governance, risk, and compliance (GRC) aspects of Gen AI projects.
Effective governance ensures that AI projects are grounded in reliable data and aligned with business objectives. It acts as a safeguard against potential pitfalls, such as overhyped expectations or ill-defined use cases. Implementing a robust governance framework is crucial in steering Gen AI projects towards success:
Privacy and poisoned models
Proactive data governance measures, such as regular audits and adherence to best practices, are vital to maintaining trust. Additionally, safeguarding against poisoned models — where malicious data skews Gen AI outputs — is essential.
Mitigating accuracy risks
Companies need a structured approach to managing data, ensuring that it is accurate, accessible, and secure. By correctly implementing data management protocols, you can prevent inaccuracies that could compromise Gen AI outcomes.
Reliable data provenance
It's essential to track the origins and transformations of data used in Gen AI models. This transparency not only enhances trust but also aids in pinpointing issues when problems arise.
Regulatory security
Proactive governance enables organisations to navigate regulatory landscapes swiftly and confidently. It ensures compliance with current and future regulations, reducing the risk of legal repercussions.
Data governance for future-proofed Gen AI applications
How can companies begin implementing the data governance that will set them up for short- and long-term success with Gen AI? Our panel had three key recommendations:
Private models for governed data
While public models offer speed and lower price tags, they pose privacy challenges. In contrast, well-governed private models provide enhanced security, protecting sensitive data from unauthorised access.
Reliability with reinforcement learning
Reinforcement learning, with human-in-the-loop oversight, enables continuous improvement of algorithms. It refines them based on expert human feedback — preventing potential brand damage and enhancing reliability while improving upon their algorithms.
AIOps and strategic roadmaps
It is critical to establish an AIOps methodology and build a roadmap for a comprehensive, integrated, and intelligent data ecosystem. That includes essential technology components but also developing talent and knowledge through collaborations with partners and ecosystems.
Confident Gen AI deployment with strong data governance
The ability to realise Gen AI's potential is based on data quality and governance. That requires collaboration across all stakeholders, fostering a culture where governance is ingrained; all stakeholders are accountable for compliance, and good governance needs to be baked into the culture.
Our panel of experts was unanimous: embarking on an AI journey requires more than technological prowess — it demands strong data governance based on collaboration and oversight. By prioritising data governance, organisations can harness Gen AI's transformative potential while safeguarding against risks.
How far along are you in tackling the data obstacles to successfully deploying Gen AI in your company? Compare your progress to the answers of 750 decision-makers across industries in a 2024 commissioned global study conducted by Forrester Consulting on behalf of SoftServe. They might just offer insights into which next steps are right for your goals.
If you’re looking to explore further, SoftServe offers a wealth of resources and expertise to guide organisations in navigating the Gen AI frontier with confidence.