By now, virtually every business has heard of AI, and most realize that effective data management is a key ingredient for using AI effectively.
Many companies also have mature AI and data management tools and processes in place.
But the world of AI and data management is changing quickly, and being at the forefront today doesn’t necessarily mean your business is primed for success tomorrow. To get (or remain) ahead of the curve, you need to know what’s coming next.
To provide some guidance, here’s a look at five AI and data management trends that forward-thinking businesses will adopt in 2025. These predictions reflect my experience leading Indicium, a data services company that helps other businesses take full advantage of their data to improve operations and decision-making.
1. Focus on finding tracking and optimizing the ROI of AI
As we enter 2025, the typical business has moved from the AI talking and planning stages into the implementation stage. Companies already have AI solutions in place.
Going forward, the challenge will become ensuring that AI investments actually deliver value. That’s why a key AI business trend in 2025 will likely be tracking the ROI of AI services and finding those that deliver the biggest boost to the business.
Simply having AI in place will no longer be enough to remain competitive; a key practice for business success will be determining which AI investments are the most valuable – and, by extension, which turn out not to deliver the ROI that organizations hoped for, which will inevitably be the case for some investments.
2. Connect AI models to business data
When businesses first began moving to take advantage of generative AI technology a couple of years ago, many focused on implementing “off the shelf” solutions that were pretrained on generic data, and that could accomplish generic tasks like populating word processor documents or presentation slides.
That made sense at the time because connecting AI models to custom data is complicated, and many companies didn’t have the data infrastructure, data quality or data management tools in place to train models extensively on their own data. So they settled for more basic solutions.
Today, however, models trained on generic data are no longer enough to deliver a competitive edge. Businesses must also be able to connect models to their own business data so that the models can understand their unique business context and offer solutions tailored to it.
Some companies may go a step further by training their own models using custom data, too – although that practice is likely to become common only for larger businesses with particularly complex and specialized AI needs.
Either way, expect 2025 to be defined in part by efforts to connect models to business data in ways that weren’t important during earlier stages of AI adoption, when “off the shelf” tools sufficed.
3. Align data with business needs
In most companies, data management is a task that falls to technical personnel. But it shouldn’t be technical teams alone who are capable of working with data. Every business unit – from engineering, to accounting, to sales and marketing and beyond – should be able to leverage data to assist in decision-making and to help automate processes.
To this end, businesses in 2025 should seek ways to align data with diverse business needs and use cases. Tools (such as no-code analytics solutions) exist to help with this process, but tools alone won’t solve the challenge. Businesses also need to establish methodologies that allow them to transform and organize their data in ways that make it available to diverse stakeholders.
4. Simplify data access for non-technical stakeholders
Along similar lines, deriving the greatest value from data requires everyone in the business – including those without technical skills – to be capable of interacting with data.
Here as well, the technology to “democratize” data access in this way exists. For example, generative AI and natural language processing tools make it possible for anyone to ask detailed questions about a data set and receive answers. You don’t need to be able to write SQL queries to interact with data. Likewise, data mesh can help to simplify access to data for diverse stakeholders within a business.
However, these approaches to data access only work if businesses have the data management processes in place to ensure that every stakeholder can find the data they need, and that the data is of sufficient quality to support their use cases. So, democratizing data is not just a matter of deploying new types of data analysis and reporting tooling; it’s also about doubling-down on data management and quality.
For these reasons, expect to see businesses increasingly investing in new approaches to data analysis and management in 2025 as they seek to place the power of data in the hands of all of their employees.
5. Build an AI-friendly organizational culture
When it comes to AI and data management, having the right tools and processes in place will only get your business so far. You must also establish an organizational culture that primes stakeholders to take full advantage of data.
Doing so requires educating users about which types of AI solutions are available to them, when they should use them and what their limitations are. It also entails building awareness of the foundations on which AI capabilities rest – like data management processes – and how the decisions employees make about collecting, formatting and storing data impact a company’s ability to use AI effectively.
There has been some talk to date about building an “AI culture,” but the concept has had a limited impact. Expect this to change in 2025 as more and more companies recognize that deploying AI tools isn’t enough to guarantee an effective AI transformation, and that they must also invest in cultural change.
6. Embrace all types of AI
The bulk of the buzz surrounding AI over the past couple of years has centered on generative AI, the type of technology behind solutions like ChatGPT and GitHub Copilot. But increasingly, businesses are recognizing that other types of AI – like predictive and descriptive AI – have valuable roles to play, too. These types of AI still account for the majority of AI tools and services in use in modern businesses.
For this reason, I believe that 2025 will be a year when we see renewed focus on AI investments of all types. Generative AI will remain a key part of the picture, but the hype surrounding genAI has peaked, and organizations are recognizing that they need to consider the possibilities offered by all types of AI – including AI solutions that may feel “older” because they are not as buzzworthy as genAI – if they want to thrive.
Conclusion
To summarize, 2025 is poised to be the year when AI, and the data management processes that undergird it, transforms from buzz to value. To maximize that value, businesses should embrace trends and strategies like tracking the ROI of AI investments, placing sophisticated AI tools in the hands of everyone in the organization and developing AI-friendly company cultures.
Going forward, simply using AI in some form, or having data management practices in place that enable basic use of AI, won’t be enough. Remaining at the forefront of the AI revolution requires continued innovation based on trends like those I’ve identified above.
About the author
Matheus Dellagnelo is the co-founder and CEO of Indicium, an AI and data consultancy. Indicium is a global data services company that provides end-to-end solutions for every stage of the data lifecycle, from strategy to execution. Backed by a $40MM investment, we aim to become the #1 modern data service company in the Americas.
Featured image: Adobe Stock
