Mastering AI Readiness by Starting With Your Data Strategy

Author: Beverly Wright

AI is top-of-mind for companies in every industry right now. Many organizations see the possibilities in AI for streamlining processes, automating tasks and decisions, improving and personalizing customer interactions, and much more. As much as someone may want to jump straight into the nitty-gritty of implementing a new AI capability, in reality, successful AI implementations stem from strong data foundations. Before your company starts using AI, you’ll need an assessment including points like supportive people to make it happy, solid processes in place, technology platforms for enablement, and of course, good quality data, to ensure your AI endeavors function well. 


It all starts with having a solid data strategy. Your data strategy is the top item since it’s your ticket to avoiding bumps in the data road and ensuring your AI journey aligns with your company’s goals. Building an effective data strategy first sets you up with excellent data assets to help reduce your chances of the age-old GIGO (garbage in-garbage out) issues, especially since one major objective of AI involves allowing scalable and often larger, broader, and more impactful results. 


Let’s unpack the unsung champion of a successful AI journey and better understand how a killer data strategy can set you up for AI success.


What does it mean to be AI-ready?   

Becoming AI-ready requires ensuring a number of different elements are in sync. This includes your people, processes, technology, and data assets. 


First, your data itself requires readiness – to reduce hidden problems, messiness in results and solutions, and help with the criticality of ethical considerations. Your data should reflect the heart of the constructs it represents in a way that’s clean, accessible, governed, as unbiased as possible, and well-managed. Poorly managed data assets likely result in faulty AI implementations and unreliable, ineffective, or inefficient results. In worst-case scenarios, a misled data strategy may even point your AI solutions in more disastrously impactful ways. 


Processes matter too, whether you’re sticking to the scientific method or another reliable framework for testing your new AI projects, initiatives, and solutions. Having a solid process can bring structure, provide ethics, reduce hiccups, and make sure everyone’s on the same page and rowing in the right direction. Process alignment can help AI solutions implement more and allow for measurement of the success of your AI implementations.


Then there’s the tech piece: systems and tools should sync up seamlessly with the use cases you’re tackling. You’ll need your systems to work with your AI tools and your data to work effectively at every stage. This proves particularly challenging with duplicative technology, outdated or legacy systems in place, or other technology not aligned with your data strategy or overall company goals. As much as possible, leaders from the data, technology, and AI teams need to collaborate and focus on a unified vision for AI. 


Now, onto the trickiest part – culture. Being AI-ready isn’t just about having a method in place; it’s about fostering a culture that’s open and willing to embrace change. Your culture needs to be thirsty for automation, efficiency, and improvement. If your team is not on board to make AI a success at your organization, you will face continuous challenges making AI work for your company.  


Here’s the deal: when your company aligns data with people, processes, and technology, it creates a solid foundation for exploring AI. But when you’re missing any of these pieces, initiatives will likely lack the right fuel, running them will feel like a headache, and integrating AI into the big picture most likely won’t happen. 


However, when all these elements work in harmony, it’s like having a dream team for your AI efforts, with data strategy serving as the MVP, and ensuring you’re not just doing AI for AI’s sake, but fully rocking the innovation game.