The excitement of bringing AI into your organization is peaking right about now. The C-suite is clamoring for activity as all your competitors are sprinting to gain the AI advantage. AI can be a game-changer and the growth and intelligence in this area will continue to expand for the foreseeable future. You don’t want to be left behind, stagnant, as your peers race ahead.
But is it feasible within your environment? The biggest challenge is whether your data is ready to support this effort. You don’t want to find this out after you’ve invested in the latest AI project that can’t be executed. Switching to my infomercial voice: Are you still in a legacy environment with Data locked away within your business systems? Have you modernized your environment to expose the critical data to feed the apps, BI, and AI your business needs? Is your data of high enough quality to provide accurate information to the AI engine? Do you have a data catalog and metadata to support the project’s demand for the right data on time? If not, call McLean Forrester!
Okay, enough of the infomercial, this is serious business. Data is the required fuel for any AI capability. You must ensure your enterprise, your environment, is prepared to support your exciting AI effort. Stale data, low-quality data, missing data, and inaccessible data will all contribute to the crash and burn of the AI project everyone in your organization is excited about. You don’t want that embarrassment. So, what to do?
Let’s start with the concept of your AI project. That AI project will have defined data needs. You don’t have to boil your data ocean all at once. We can start where you are and iterate to success, but the initial steps are to identify the data requirements of the re-imagined business process revolutionized by AI. Those data requirements then drive the process of developing architectures and data models within customer journey maps to ensure the right data is marked for improvement, exposure, and consumption. Through a collaborative and cost-conscious approach leveraging the right mix of data lifecycle, quality, curation, and other data “fit for function” activities lead us to improve the data to meet the project needs. Similarly, modernizing your enterprise capabilities to share trusted data will be a cornerstone element of your AI project’s success.
Yes, it can be dauntingly complex to successfully implement an exciting new AI project but covering your (data) bases (pun intended) is a necessary and foundational step that is often overlooked.
Let McLean Forrester help you in your journey to AI project success. We have experience in AI development and deeply understand the criticality of high-quality data as the underpinning foundation to make it successful.
