A version of this article first appeared on eidebailly.com.
AI presents incredible opportunities for organizations, from enhancing efficiency and personalizing the customer experience to allowing for more in-depth forecasting and modeling. However, AI systems require large amounts of data for operation and training. This creates challenges for organizations related to their data quality, security, and availability. Addressing these challenges is crucial, as the reliability of AI is directly related to the quality of the data it uses.
Poor data quality, inadequate security, and governance issues can hamper the most ambitious AI strategies. Implementing AI in your organization requires a strategic plan focusing on your data and understanding where AI will make the biggest impact.
Maximizing the Value of AI
AI is not one-size-fits-all. To maximize its value, you must first understand your objectives and how AI can help you meet them. Begin by conducting a thorough assessment of your business processes to identify areas where AI can create efficiencies or uncover new opportunities. For instance, automating repetitive tasks can free up your team for more strategic work, while AI can uncover valuable insights from customer data to guide decision-making.
Navigating AI Challenges
Eide Bailly recently asked organizations which obstacle they find most challenging for fully harnessing AI:
To navigate these challenges and apply AI strategically, organizations must prioritize their data. Get your data ready to leverage AI tools by following these steps:
- Consolidate and model your data: Ensure all business-critical data is unified in one place. For example, merge customer, financial, and operational data to gain a complete view.
- Create a single version of the truth: Ensure consistent, synchronized data across the organization to eliminate discrepancies and support uniform decision-making.
- Pick your platform(s): Select scalable AI and data platforms that meet your organization’s needs. This doesn’t always mean implementing new technology; many systems have built-in AI.
- Eliminate data silos: Breaking down barriers between departments and systems can help uncover hidden insights. For instance, integrating marketing and sales data can enhance customer targeting efforts.
- Enrich your internal data with external data sources: Enhance internal datasets with relevant external data to improve AI insights. For example, enrich shipping data with weather forecasts to optimize delivery routes.
- Identify your highest quality or differentiating data: Focus on refining and leveraging high-quality, unique data. For example, use proprietary customer behavior data to improve product recommendations and boost subscription renewals.
Security
Securing structured and unstructured data has never been more important. Not only is it vital for your organization’s security, but industries like healthcare and financial services have developed regulatory requirements around AI usage. You must understand the tools you’re using and the tools your people have access to. The key to success lies in educating your employees on how to use AI tools safely and effectively.
Governance
A governance framework ensures that your technology meets your organization’s needs. This requires input and cross-functional collaboration from all areas of your organization. To create a scalable governance framework, you must understand where you are now and where you want to go. Regular reviews and updates ensure that your data governance and AI practices keep pace with technological advancements and changing business needs.
Preparing for AI Exploration
Whether you are utilizing AI in existing applications or looking to deploy new technology, you must have a sound strategy in place. Take Eide Bailly’s AI Readiness Assessment to gauge your digital maturity and discover resources that will help you get your organization ready for the future of technology.
