The time to prepare for AI is now

How to ready your organization for AI

GenAI: trend, hype, obnoxiously boring writer, sure, but also a wake-up call that AI is ready to take on a lot more.

The tech exists now; it’s just waiting for the right use cases. All of which depend on access to data: reliable, accurate, well-understood data.

And if that doesn’t scare you, it should. There are a lot of ways for AI to go wrong:

Scuzzy datasets train AI models, making for garbage outputs
Your people use clean, deduped, accurate data to train a chatbot (yay!) but then the bot accidentally spills customer PII or your competitors use it to infer trade secrets
Your folks have a terrific use case idea but you can’t get data into their hands fast enough to support it

AI risk doesn’t mean you can afford to shut it down.

In fact, the worst case AI scenario might take some time to play out but it will happen if you don’t prepare: use cases will mature and competitors will start using AI to woo your customers, get new products to market faster and create efficiencies that let them undercut your prices.

It takes time to build a foundation for AI readiness, and the organizations that haven’t prepared will scramble to catch up.

But the organizations that build a foundation for AI and data governance now will accelerate every data use case and achieve faster, better AI use cases.

It starts with getting confident in the quality, accessibility and governance of your data. Enhancing data quality does more to improve AI than tinkering with models.⁶

46% of data leaders identified “data quality” as their greatest barrier to realizing GenAI potential⁵

⁵Is Your Company’s Data Ready for Generative AI?

⁶Solving the data-centric versus model-centric AI governance debate

So what do you need to do to prepare?

Make high-quality datasets available:

Automate ongoing reliability monitoring and tasks
Ensure lineage is clear
Provide business context so data scientists can understand what the data means

If you’re in a large or dispersed organization, provide a centralized place where data scientists can share and access AI use cases and understand their effectiveness, intended uses and governance parameters — let them stand on the shoulders of giants instead of wasting time recreating models their colleagues have already created and tested

Build use case review and policy application into your governance platform (automated workflows will make this easier and more consistent)

Ensure you can dynamically track the datasets for every use case, especially as the uses or data sensitivity evolve

Build in checks for external compliance and internal regulation

Make sure you can track every use case, model and how they’re used — automated traceability is best for regulations

Get ready to scale AI

AI’s serious potential comes with an equally serious set of challenges, from compliance concerns to delivering value to helping your people trust it.

The only way to safely scale AI is to systematize compliance and trust. Unifying governance for data and AI helps companies achieve oversight and traceability from end to end — every data input to AI output.7

Data Citizens Dialogues Podcast