Better together: AI literacy and data literacy go hand in hand

As innovations throughout the customer experience tech stack continue to make AI-enabled CX a reality, many organizations are rapidly approaching a pivotal phase in their AI maturity journey.  

Up until now, the AI adoption movement has focused on just that — adoption. Most organizations have taken this time to experiment, establish pilot projects, and generally explore the utility AI can have within their existing customer experience ecosystem. However, this philosophy is quickly giving way to a new phase of AI adoption – one that focuses on taking these learnings and turning them into holistic AI strategies that will help guide future AI investments and directly influence CX outcomes.  

As part of this recalibration, many organizations have focused on the “people” element of AI adoption. In other words, they’re attempting to answer the question: “How do we help our people become more AI literate so they can identify and apply AI solutions more effectively and with better results?” 

For those who were around in the early days of the internet, think of the first time you used a search engine. At its inception, the Yahoo! search engine was pitched as access to anything – all information at your fingertips. While this was technically true, there was a level of expertise and understanding about how the search bar worked that was vital to finding what you were looking for. You needed to know how to use “and,” quotes, and other techniques to ensure you got as close as you could to your desired results. Call it search literacy if you will.  

While this is obviously a basic (and dated) analogy, it paints a picture of the gap in literacy that still must be bridged when it comes to AI. Midway through 2024, AI literacy has become a trending topic across the AI landscape as efforts ramp up to start building that bridge.  

As a concept, AI literacy refers to the ability to understand what AI is, where to use it, and how to create or manipulate it responsibly. Like many others, we’ve made it a priority to emphasize AI literacy at TTEC Digital (maybe you’ve seen our AI certification badges across LinkedIn in recent weeks).  

There’s no doubt this is important work.  

But in the rush to spread AI literacy and sharpen AI strategy, many organizations are missing the other half of the equation: Data literacy. 

According to Gartner, data literacy refers to “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use-case application and resulting value.” 

Ultimately, data literacy is what will separate OK AI strategies from truly transformational ones. No matter the AI use case, unstructured data is the fuel. Content, conversation, and customer data create the foundation that will propel your AI strategies forward, but only when guided by the bumpers of data governance policies, classification techniques, and others.  

To borrow from our earlier analogy, what good is knowing how the search bar works if the search results are all junk anyway? As organizations continue to prioritize AI literacy in the coming months, it will be vital that they couple these efforts with data literacy efforts and investments too.  

The two form a flywheel. As you increase your organization’s data literacy, it will help bolster your ability to recognize and apply AI to new areas and communicate those to the teams and individuals who will use them. Meanwhile, higher AI literacy drives better use cases and applications for AI, which in turn can help produce more valuable data. 

So, the question becomes: “How can organizations be sure to emphasize data literacy alongside AI literacy?” 

Here are three tactics to get you started:  

1. Establish executive champions for data literacy.  AI benefitted from widespread word-of-mouth, which helped it quickly climb to the top of every leadership committee’s priority list. More foundational data strategies haven’t had this same luxury. As a result, it will require a conscious effort from leadership teams to establish executive champions who will promote data literacy from the top down. By obtaining leadership buy-in and providing consistent reinforcement of data directives, organizations can be sure their data literacy doesn’t become siloed in the IT or analytics department – where it may not be used to drive your core business strategy.  
2. Incentivize data-driven decision making. Encourage key employees and roles to establish concrete and measurable goals related to using data in decision-making. Over time, expand these incentives to include more individuals and more data-oriented goals. As for how to incentivize these behaviors? Try tying performance bonuses to insights or create awards programs that recognize and reward individuals who exhibit superior results due to strategic data integration.  
3. Focus your data literacy efforts where they can have the biggest impact. This might seem like a no-brainer, but seeking to drive data literacy and maturity far and wide across the organization all at once is a recipe for failure. Start by evolving key programs and strategies where potential data insights can have the greatest impact. Examples of this might include adding data visualization training to leadership programs; offering introductory data analysis courses during onboarding to the marketing, sales, and service teams who work directly adjacent to valuable sources of data; or establishing new processes that include regular data reviews and workshops.  

Ultimately, the best way to jumpstart your data literacy strategy is to pick a sustainable approach. The unique org structures, technology maturity, and data resources in every organization should dictate how, when, and where you choose to focus your efforts. When it comes to building a strong customer experience in the age of AI, taking the time to invest in deeper data literacy now is a strategy that will set you up for big dividends down the road as AI becomes an even larger part of truly differentiated customer experiences.