What is the role of AI in data’s future–and present?

How AI has – and will – continue to affect data management, privacy, activation, and analytics

With so many developments in the fields of both machine learning and generative AI, companies are confident in AI’s ability to improve processes and outcomes–especially when it comes to data. They just don’t quite know how to implement it yet. 

The result? Some promising leads, optimistic, if not fully fleshed out, plans, encouraging use cases, plenty of theoretical possibilities, and a lot of trial and error.

Putting theory into practice

While we don’t know the exact forms in which AI will be utilized, we do know AI is far from reaching its marketing potential. According to eMarketer, about half of CMOs in North America plan to use AI more in strategy, creative, and content development in the next year. 

In particular, marketers last year saw potential for genAI’s use in copywriting, data analysis, and market research. However, though 59% of the marketers surveyed saw potential for genAI’s use in copywriting, only 26% have actually implemented it for that purpose. 

For data analysis, marketers see genAI’s potential at 53%, but actual use is only up to 39% presently. Market research sees a similar discrepancy, with genAI’s potential perceived by 48% of marketers, yet with only 35% of marketers actually implementing it for that purpose. This gap between “potential” and actual use will definitely narrow in coming years–after all, about one-fourth of people are predicted to use genAI at work in 2025. A future where AI becomes vital to marketing business and the day to day of advertising is inevitable. But how will it be used, exactly? 

When it comes to data, AI is already trending toward a future of more effective and faster data management and analysis. Automation in data integration and classification, and insight as well as analysis for strategic decision-making is not just around the corner–it’s solidly in view. More sophisticated data analysis techniques using technologies like image recognition and natural language processing are arriving faster than we think.

Where AI is heading

We see AI use ramping up not only in creative and content development, but also, necessarily, in strategy. In a future gearing toward one-to-one personalization, greater analysis–especially of unstructured or nebulous data like visual content (say, social media posts)–will lead to greater applications for AI in creative strategy as well. 

From the first steps of data gathering to the process of analysis, activation and synthesis to the final product–using generative AI to create one-to-one personalization in marketing efforts–AI will affect the whole network of marketing.

Data management

According to Dimension Market Research, the global AI data management market size is expected to reach $34.7 billion this year, yet is expected to increase over sevenfold over the next decade, to $260.3 billion by 2033. The CAGR is expected to be around 25% from 2024 to 2033.

Data management is improved by AI through a number of processes, including automated data integration and cleaning, especially as certain types of data become quantifiable–customer reviews and images, for example.

Data analysis

Using algorithms and machine learning to gather insights from data has long been key to making marketing decisions–but now, it’s getting more sophisticated. Deep learning algorithms allow for better image recognition, and natural language processing can unlock insights from unstructured data–which is the majority of data altogether (examples include data from social media, audio, and video). This can lead to better sentiment analysis–and what’s more, AI can supercharge predictive analytics and forecasting. 

Data activation

Taking raw data and using it to extract insights and then make informed decisions on how to use that data will more and more be privy to the influence of AI. AI will be able to present actionable insights–and make suggestions for marketers to consider–at a far greater level–and the insights gathered will be far more valuable.

Data privacy

One of the biggest fears surrounding AI is how to ensure privacy. About half of marketers–53%–rated data security and privacy as a top barrier to AI adoption. Privacy regulations will likely be behind as governmental bodies struggle to keep up with the pace of AI. On the flipside, building privacy and security into AI from the ground up–will likely be the future of ensuring a privacy-forward approach.

A future–and present–for AI-enhanced data processes

Even CEOs and their management teams seem to disagree on the perfect uses for machine learning and genAI. While over half of CEOs see genAI as conducive to design, making it one of their top three use cases, less than a third of their management teams agree. While both agree on genAI’s potential for personalized marketing, managers are more hopeful about AI’s potential in the field of predictive analytics than their leaders, with a 24% percent gap between the two. 

As far as data goes, AI has a lot it can do, and many places where it can shine. It’s up to marketers and their teams to experiment with usage and find the applications that work best for the technology – while keeping privacy and security in mind … of course.