Enterprise AI isn’t just for large enterprise organizations. In this Guide, you will learn how small data teams can use AI to supercharge their data strategy, whether you are focused on creating operational efficiency through automation or would like to better understand your customer with AI-powered insights.
Could you use a quick primer on AI terms and technologies? We've distilled the central terms in AI today to set the stage for a deeper conversation of the possibilities for adding AI to your data strategy.
How much does it cost to get started with AI? As with all conversations about new technology, "it depends." But we've broken down the main cost centers in AI adoption, including use case and tools.
The best way to find ROI in your AI strategy is to pick a use case that aligns with your current business objectives. We've outlined use cases that span your organization, like preventing fraud, providing proactive customer service, and getting rid of cumbersome reporting tasks so your team can turn their attention to high value work.
The great news about AI adoption? The same work you are doing to build a data culture at your organization will help you get ready for implementing an AI strategy. Learn about essential positions to develop on your data team, and focus areas for AI readiness.
Josh Nash, Camden National Bank’s SVP and Director of IT, shares how Camden National Bank is preparing for AI applications that will improve operational efficiency and add to the bank’s data governance and customer initiatives.
In this Guide, you’ll also hear from industry experts at the Financial Brand, Snowflake, AWS, Qlik and Passerelle, who share tips and strategies for AI readiness and implementation that impact revenue and meet business objectives.
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