Is Your Association Ready for AI?

As more and more organizations consider integrating AI into their operations, the question on everyone's mind is: How do we even begin? Perhaps the first step is to envision the potential benefits that this transformative technology can offer your association. Once this is realized, it's time to identify the best ways to put AI to work by adopting a strategy. In this post, we’ve gathered useful insights from a recent InfoTech report to provide expert guidance on how to create a solid AI use case that's tailored to your association's needs. Our hope is to inspire you to craft a solid AI strategy that's designed for success. 

It’s All About the Data 

When it comes to harnessing the power of artificial intelligence (AI), it's essential to provide a vast amount of varied and high-quality data. While incorporating a greater volume of data can make up for a dip in data quality in some AI applications, it's still critical to remember that the results are only as good as the data you provide. The importance of data quality cannot be overstated, particularly in the context of AI, as it can have a significant impact on human health, freedom, and life. Additionally, poor data quality can expose your business to risks, litigation, and regulatory penalties. Given that AI is increasingly being used to enhance decision-making and even automate it altogether, we must ensure that the quality of the data used to make these decisions is top-notch since the quality of the decisions, their results, and potential consequences are directly linked to the quality of the data that informs them.  

To make AI more effective and fair, it's important to use varied data that accurately represents the population you're serving and the context in which your decisions will be made. The world of AI is constantly learning from historical data to uncover hidden patterns. However, it's important to realize that the data being fed into the system can have a major impact on its output. Take the recent example of Amazon's AI system for hiring candidates, which was trained using resumes from a historically male-dominated applicant pool. As a result, the system began to favor male applicants, perpetuating the already-existing bias. To ensure a diverse and equitable hiring process, it's crucial to carefully examine and address any skewed training data.  

When it comes to data for AI, there's more to consider than just the information itself. One important factor is whether there's enough properly labeled data available. This allows the AI algorithm to learn from both positive and negative examples. Known as "supervised learning," labeled data relies on humans classifying the data correctly. However, there's also "unsupervised learning," where AI can learn from patterns and correlations in the data without external help. A great example of this is grouping customers by their purchasing behavior without any explicit instruction. So, having enough labeled data is crucial for effective AI, but there are other ways for machines to learn too. 

Are you sitting on a goldmine of useful data, but struggling to digitally store and manage it? It's a common problem that can seriously hinder AI adoption. After all, AI can only digest digital information, not paper records. If you haven't switched to a digital format, now is the time to do so. Another important consideration is your master and reference data. Do you have a master data management strategy in place? This is a vital component for successful AI implementation, as it impacts all data processes. In short, if you want to fuel AI, you need to master your data first. 

How’s Your Data Infrastructure? 

With an overwhelming amount of data at our fingertips, accessibility is key. And not just any accessibility, but real-time access. Two exciting trends are emerging to meet this need. Firstly, AI is being embedded into edge computing, allowing for deep learning to occur right at the edge. Secondly, infrastructure for analytics is rapidly expanding, with cloud storage, data lakes, and distributed architecture paving the way for lightning-fast access to valuable data. 

As you dive into the world of AI, you'll want to consider which frameworks and tools to utilize beyond just data pipelines. There's a wealth of open-source options to choose from, and the AI vendor landscape is rapidly expanding. One exciting trend is the integration of data science and AI tools with cloud storage, with offerings like Amazon's SageMaker leading the way. If you're thinking of relying on Amazon, Google, or Microsoft as vendors, that's fine – but if you're considering building your own AI platform, you'll need to think about how these tools will fit into your existing infrastructure. And don't forget to consider whether you have the necessary expertise in-house or how quickly you can upskill your team or consider alternative methods. 

To succeed, you’ll need to ensure that your systems are equipped to handle not just initial use cases, but also future ones. This means having a dynamic data infrastructure that can support real-time decision-making and integrate seamlessly with our operational systems. The key question to ask is whether our infrastructure can scale up and adapt to meet these evolving requirements. So, let's roll up our sleeves and dive into the necessary investments and changes needed to build a robust AI-powered architecture! 

Data Integration 

If you want to harness the full power of AI and drive real change in your business, it's not enough to simply adopt the technology. AI-enabled insights need to be integrated into your existing systems and workflows in order to make a tangible impact. This means bridging the gap between your data scientists and your front-line decision makers and ensuring that AI insights are delivered in a way that informs and improves workflows. If you've already gone down the analytics or data science route, you'll know that the same principles apply here – it's all about the last mile and making sure that insights are actionable and integrated into your workflows. 

Many businesses struggle with implementing successful analytics strategies, and one common reason for this is the lack of easy integration into daily workflows and systems. Too often, employees must pause their work, switch to a separate tool, and consult reports or dashboards before continuing. This interruption can lead to frustration and decreased productivity. To ensure success in implementing AI, it's important to consider how to seamlessly embed analytics at the point of decision making, avoiding unnecessary interruptions and frustrations. Don't let a lack of integration set your organization up for less than stellar outcomes. 

Don’t Forget About Data Governance 

When it comes to AI governance, it's crucial to have a plan in place to ensure that the technology is being used ethically and responsibly. While there are many dimensions to AI governance, we'll focus on the technical side of things for now. This includes elements like data governance (making sure the right data is being used), algorithmic accountability (ensuring that algorithms don't unfairly discriminate), and more. As you delve deeper into AI governance, consider three key components: data, models, and decisions. 

Data lineage 

Do you truly understand the journey of the data used in your AI applications? When it's flowing in real-time, such as from social media or internal systems, have you considered its origin, purpose, and legal responsibilities? Knowing the answers to these questions is critical for safeguarding customer privacy, maintaining regulatory compliance, and ensuring ethical AI practices. 

Model history 

As AI systems evolve and become more accurate, it's important to ensure that the right model is being used when it comes to decision-making for individuals. This is where predictive model management and governance come in - tools and frameworks exist to manage this process. With the right systems in place, AI can be used effectively while ensuring that the well-being, health, life, liberty, and prosperity of those affected are properly considered. 

Decision rationale 

Do you want to be able to confidently explain the rationale behind a decision that was made? Consider the importance of having solid data to back up your choices. For example, think about the Métis inmate case and how crucial it was to have accurate data in order to make an informed decision. Don't underestimate the value of having reliable data to support your decisions. 

Accountability and Authority 

As artificial intelligence (AI) becomes more prevalent, it's important to consider how humans and AI will interact. If humans are still involved in decision-making alongside AI, it raises questions about accountability, ownership, and autonomy. Who holds which rights and responsibilities? What happens if there's a problem? And, perhaps most importantly, are humans equipped to make ethical decisions assisted by AI? These are complex issues that require careful consideration as we move towards a more AI-driven future. By taking the time to understand and address these concerns, we can create a future where AI works in harmony with humans to improve our lives. 

Artificial intelligence (AI) is more than just a buzzword. It requires a high level of maturity in data management, enterprise architecture, infrastructure, and governance for success. Luckily, if you've already started building a foundation for enterprise analytics, you're on the right track. But if you're just starting or need to improve, we recommend checking out Info-Tech's resources. Using AI can also be a powerful tool to accelerate funding, resources, and project plans for your data-related initiatives. However, keep in mind that data is the backbone of AI and a strong infrastructure and data management practices are essential. Neglecting governance and the implications of AI decision-making can also lead to unnecessary risks. So don't underestimate the importance of having a solid foundation before diving into AI. 

Getting Started 

Below are some action items to help you get started on a successful AI journey:  

• Take stock of the current maturity level of your data management practices. Based on the AI cases being considered, understand where your gaps may be. 
• Be sure to have a rich and steady supply of high-quality data in which you can create a robust data strategy. This will also help with future AI applications including which additional data could give you a competitive advantage or help sustain it. 

• Establish a data governance framework. 
• Document analytics and AI value chains for your organization. This will help you understand how to both deliver and embed AI (and analytics) into decision workflows and translate insights into actions and impact. 
• Begin the conversation about AI governance framework within your association.  

Wrapping IT Up 

When it comes to AI, it's important to go beyond just brainstorming cool ways it could help your business. Instead, take a closer look at your data. Do you have enough of it? What information is missing that would be helpful? Consider how long it might take to gather the missing pieces, and if it's worth collecting yourself or seeking it out from another source. Also, take a moment to evaluate the quality of the information you already have. Does the data need to be cleaned, enriched or otherwise revised for AI to properly interpret it? Though it can be a lot of work, taking these steps is crucial for achieving successful results. 

Are you curious about how AI can benefit your association or non-profit? Let us be your go-to resource for leveraging the power of Artificial Intelligence (AI). Contact us today to plan your personalized AI Workshop!