As a CEO of the software industry, I am excited about the power and potential of AI. Yet AI could evolve from a rising tide that lifts all boats to a perfect storm of unmanageable risk unless it is given effective guardrails and governance.
With the increase in generative AI applications this year, executives in every boardroom are asking the critical question as they plan for 2024: “What does AI mean for my business?”
The promise of profound new forms of productivity and unimaginable intelligence is significant, but with that potential also comes unprecedented fear.
Leaders face uncertainty about how to adapt to technology, optimize it, and ultimately harness its power. I’m a big believer in unleashing AI-powered innovation and putting it in the hands of as many people as possible to unlock possibilities we’ve never seen or imagined before. But just as quickly, companies need guidance on how to experiment, test and adopt AI in an ethical and safe way.
Here’s an AI governance roadmap to help leaders guide their efforts in 2024 and beyond:
1. Reliable data: In my world, so-called unstructured data requires refinement, cleaning, and sorting to capture bits of business intelligence. But AI is only as good as the data that drives it, so the first step to strong AI governance is to ensure you can trust the data itself.
According to a recent survey conducted by the Eckerson Group, 46% of data leaders say their organization has insufficient controls around data quality and data management. Even then, most business leaders don’t strive for enough – they strive for excellence.
What does that look like? High-quality data is accurate, complete, consistent, current, valid and unique. In healthcare, for example, it is essential to have complete, correct and unique patient records, without duplication, to ensure proper treatment, monitoring and billing. Just one mistake in managing that data can have costly – even devastating – consequences, and that’s before AI or large language models (LLMs) for generative AI are built on top of it.
The challenge, of course, is that most organizations still have highly fragmented data and no comprehensive governance framework. The natural starting point is to assess the current state of your data – what you have, where it is located, how it moves and how it is protected to diagnose any quality issues. Then apply rules to manage and monitor it to ensure quality is maintained over time.
2. Modern Governance: Data governance is not a new concept, but it is becoming increasingly important amid the proliferation of AI and generative AI applications, stakeholder and shareholder demand, and global data regulations.
Many leaders have now outlined data governance practices that cover the people, processes and technologies in their organizations to create common rules and guidelines for collecting, storing and using data. Most of these frameworks are based on risk and compliance – and for good reason. Ethically securing, protecting and using data is an essential part of any successful data strategy to align with current and future data and AI regulations. For example, US President Joe Biden’s recent executive order on AI will increase corporate reporting standards and government oversight of AI, while the EU is poised to announce AI regulations and enforcement practices in the near future.
Yet traditional approaches to corporate governance will fall short when it comes to ensuring secure and effective data management for AI and generative AI. Why? For the most part, they were not designed for the scale and democratization of data use that companies now need. The sheer volume, variety and velocity of data is too much for human management alone.
Modern data management builds on the key features of security and compliance and integrates automation, adaptability and flexibility. It considers and automates data integration, cataloging, management and observability to increase productivity and results – essentially becoming an exercise in competitive advantage, not just compliance.
3. Getting Started: AI will be a top priority as leaders finalize their organizations’ 2024 strategies. This is partly driven by democratization and growing access to generative AI. In fact, McKinsey reports that 40% of organizations will invest more in AI overall due to advances in generative AI. But the smartest companies will also make AI governance a priority to maximize value and minimize risk.
To start, assess the current quality of your data and any underlying issues or gaps in your data management processes. Form a team that works together to develop and oversee your governance practices, to ensure continued accuracy and reliability of your data and AI-powered insights.
“Put the technology in the hands of as many people as possible and work together to secure our future with it. ”
Finally, keep repeating. Just as feedback is essential for product development and improvement, it is equally important for the performance of AI models. Your approach must be flexible and take into account new innovations and ways to use the technology and, more importantly, new security threats that emerge every day.
While we’ve never seen anything like AI, history is filled with countless innovations and opportunities that created fear and required guardrails, from the gold rush and transportation boom of the mid-19th century to telecom and pharmaceutical developments in the twentieth century .e century.
History has shown time and time again that we can and will rise to the challenge through the public and private sectors. AI will be the same if we put the technology in the hands of as many people as possible and work together to secure our future with it.
Amit Walia is the CEO of Informatica, an enterprise cloud data management platform.
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