AI is the New Panacea with the Old Challenges

AI is everywhere these days - it's the hot topic at most conferences and seems impossible to avoid. While it's seen as transformational, there are significant concerns about potential harm at societal, organisational, and personal levels. Despite these concerns, AI adoption continues to grow in organisations, with many comparing its impact to that of the Industrial Revolutions. The Life Sciences industry is a perfect example, where AI is already demonstrating real value. 

But here's the interesting part - despite all this progress, data remains the primary barrier to entry for AI initiatives. So, what's the solution? Many suggest tying Data Governance and AI Governance more closely together. While this might seem obvious, the real challenge isn't in identifying what to do, but in figuring out how to do it effectively. 

Let's take a step back and consider what needs to be in place before we can even think about governing AI. Quality data is absolutely crucial - it's the lifeblood of AI. Yet it's fascinating how often this gets reduced to a throwaway line like "of course, you need to make sure that you have good quality data." It's so obvious that it often goes unnoticed.

But here's where it gets more complex - AI is broader than just data. We need to consider multiple factors, including Ethics, Transparency, Explainability, and Responsibility. Data is just one piece of this larger puzzle. This raises an important question: how can we implement governance for such a rapidly evolving technology? 

Unified Governance Framework

This is where the Unified Governance Framework by amino comes into play. It's an innovative approach that takes existing data governance as the foundation of AI Governance while recognizing that AI Governance extends beyond just data. The framework builds upon existing structures to cover both Data AND AI, creating a unified governance mechanism. It provides processes, templates, and accelerators that organisations can tailor to their needs, taking into account regulations, geographies, model governance, and expected outcomes - all aligned with business strategy. 

What makes this framework particularly valuable is its flexibility. Whether an organization already has robust Data Governance in place or is starting from scratch, the framework can adapt. For those with existing Data Governance, it offers a baseline process to identify what evolution is needed for AI Governance. For organisations with minimal formal data governance, it enables specific governance initiatives with AI as the end goal. The framework can even be retrofitted onto existing AI initiatives. 

One crucial point to understand is that data governance is a component of AI Governance, not the other way around. 

Principles

The framework is built on four key principles: 

Data – without quality data, there simply is no AI. This makes data governance essential for ensuring data quality meets requirements. 

Leverage investments and evolve – this means not just evolving data governance but also other governance mechanisms, like risk management frameworks. It's about examining existing processes and identifying necessary changes for AI implementation. 

Innovation – since AI is inherently innovative, governance must drive innovation rather than impede it. While this can be challenging, taking time at the start to consider requirements enables a "fail fast but safely" approach. 

AI without IA is futile – technical and business aspects must be integrated, with Information Architecture providing the foundation for AI. 

Evolution

When organisations already have data governance in place, they've developed specific structures, processes, and clearly defined roles and responsibilities. Instead of dismantling this existing structure, these are used as the baseline and then expanded to accommodate the new requirements that AI brings to the table. 

This evolution involves bringing more people into the conversation than might have been originally considered. With AI governance, we need to expand our circle of stakeholders and be very explicit about who's responsible for what decisions and why they need to be included in the process. 

One of the most crucial aspects of this evolution is ensuring that business teams and data science teams work hand in hand. These groups sometimes speak different languages, but the framework helps bridge this gap by creating structured ways for them to collaborate effectively. 

But here's where it gets really interesting - this evolution isn't limited to just data governance. Any governance process that touches AI can and should evolve. Take risk management, GDPR compliance, or privacy governance, for example. These all need to work together in harmony within the unified governance structure, like different systems in a smart home that need to communicate with each other. 

However - and this is important - not everything needs to come under one umbrella. Some processes might work better remaining separate but connected. The key is understanding how these different elements link together and influence each other. 

Approaches

When it comes to implementation, there's often tension between governance and innovation speed. Some argue there isn't time to implement Enterprise AI Governance first, fearing loss of competitive advantage. However, rushing into initiatives without considering business strategy alignment can lead to pilots never scaling to production. 

The solution? A hybrid approach using "Just in Time Governance." This ensures enough overarching principles and guidance are in place before considering the specific governance needs of each AI use case. It's about asking the right questions: for example, who's developing these use cases? Who's assessing risk? Who needs to approve pilot entry and exit?  Answering these questions will inform who needs to be part of the governance structure. 

Conclusion

When implementing AI into an organisation, consider the following areas:

• Review existing governance structures – don’t start from scratch and look how to leverage existing investments to meet AI requirements.
• Data Governance is not AI Governance – but it does form the basis for AI Governance. There are other areas that need to be considered as part of AI Governance, including whether this should be deployed.
• 360-degree Risk Review – AI governance will only work if all the relevant groups are brought together. This includes both technical resources, business resources and areas such as legal and commercial to understand the risks associated with each initiative.
• Use ‘JIT’ Governance – establish the overarching principles and processes, just enough to get going. As part of each initiative prioritisation, review the stakeholders needed through the development and deployment process, and bring these in at the appropriate time.

For further information, please contact  Amino Data to discuss your AI and Data Governance requirements. Download our Unified Governance brochure  here