Even when you have a first-rate artificial intelligence solution, so much of your success with it comes down to the data you have to fuel it.

  • Do you have quality data to drive the AI solution?
  • Do you have the necessary depth of quality data to drive the AI solution?
  • Do you have all the necessary data consolidated to drive the AI solution?
  • Have you transformed the data so it’s interoperable?
  • Is the data available in a timely fashion?

We’ve found that those are a few of the most common reasons why AI solutions fail. However, there’s another major pitfall to look out for that won’t cause your AI solution to not work, but will cause it to underdeliver on its return on investment. That pitfall is the cost structure to access all the data your AI solution needs.

Related post: Why Your AI Solutions May Not Be Working as Expected

Timely & Low-Cost Data Access

The 4 C’s of data apply to AI as much, if not more, as any other use, especially as companies adopt agentic AI. So, in addition to the above, you want to make sure your data is Complete, Consistent, Correct, and Current.

Technically, current means on the same timeframe across sources, not necessarily that all the data is real-time and up-to-date. However, as the number of AI solutions proliferates across organizations, some of them will need frequent data refreshes, if not near real-time data.

Because of that growing reality, data access costs are becoming more critical to success with AI. Designed to offset vendors’ CPU usage expenses, those costs typically come in one of two forms:

  • API call costs. If your technology providers charge you per API call to retrieve your data from their system—many popular operational platforms charge fees for API calls to reduce the number of calls made against their systems—those costs can add up quickly and drastically undermine the ROI of your AI solutions.
  • Usage fees. Along with, or instead of, per-query charges, some vendors charge for data egress or extraction. This can come in the form of fees for storage and for moving data from their cloud to somewhere else.

Related on-demand webinar: How to Overcome Common AI Mistakes

Taking Inventory of Your Data Access Costs

As you prepare for a future where AI solutions are more involved with all aspects of your business operations, it’s critical to take an inventory of your data access charges across all of your applications. Do so with an eye on three different time horizons: 

  1. Current state: What’s your current exposure to data access charges? Is your current exposure constraining how you’d like to use AI and other solutions? Are you currently hitting your existing API or Usage caps on systems where critical data resides? Do you have the ability to pull the data from high cost systems periodically into a separate solution where you can leverage it for analytics or AI many times without additional costs?
  1. Near-term horizon: Given your AI plans for the next 12-18 months, how much are your data access needs likely to increase? Given the current data access charges associated with each of your platform providers, how much more will your increased data access needs cost you per year? 
  1. Long-term horizon: Project your increased data access needs over the next 5 years. Given that increase, what changes to your tech stack and data architecture do you need to make to lower your data access costs? Have you considered a centralized data platform with built in AI to meet your future requirements?

We see the long-term goal for companies to reduce overall data access costs while simultaneously providing access to more data, more often. To achieve this, look for tech stack components that are highly integrated, if not natively integrated by being part of the same system architecture. And for AI specifically, look for providers where AI is embedded directly in their applications rather than bolted on or sitting outside the app.

All of those characteristics are a part of Oracle’s current vision for its applications. For instance, our entire Oracle Fusion Cloud Application Suite is built on the same Oracle Cloud Infrastructure, making them seamlessly integrated. Oracle Data Platform and Oracle’s Customer Data Platform provide critical data hubs around which your apps work. And predictive AI, generative AI, and AI agents are embedded directly in our Fusion apps, reducing costs and increasing speed.

Regardless of the vendors you choose, paying close attention to data access costs will be important as you deploy more data-hungry AI solutions.

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