Artificial intelligence is becoming the strategic priority of all businesses in 2025. However, a major international study published by Precisely And the Center for Applied AI and Business Analytics ofthe LeBow College of Business from Drexel University reveals an alarming paradox: the majority of organizations are simply not ready to exploit AI because their data is sorely lacking in integrity.
The study”2025 Outlook: Data Integrity Trends and Insights“, based on the responses of more than 550 data professionals around the world, highlights a growing gap between digital ambitions and the reality on the ground: insufficient quality, flawed governance, and a shortage of specialized skills. The most worrisome observation remains the massive lack of confidence in the data itself.
While 76% of businesses say they want to base their decisions on data, the report reveals that 67% don't completely trust the data they use - a significant deterioration compared to the 55% recorded in 2023.
This statistic highlights a worrying trend: the more businesses rely on data for strategic decisions, the more they become aware of the fundamental weaknesses in their data ecosystems.
The research highlights several major obstacles in adopting AI:
These statistics reveal that data governance programs are no longer limited to regulatory compliance. They now form the backbone of any successful AI strategy, making it possible to map the data ecosystem, manage access, trace data history, and identify sensitive information.
The study also highlights that 60% of companies consider the lack of internal AI skills to be a major obstacle to their initiatives. This shortage does not only concern AI specialists, but extends to profiles capable of structuring, cleaning, and enriching data - fundamental skills that have become rare and highly sought after in the job market.
As technology advances at breakneck speed, teams struggle to keep up, creating a dangerous imbalance between ambition and execution capabilities.
Faced with these challenges, businesses have clearly identified their priorities:
The causes of quality problems are multiple: lack of automation tools, exponential data volumes, inconsistencies in formats or definitions between systems. The consequences are, for their part, very concrete: difficulties in integrating data, decisions based on erroneous information, and delays in the deployment of AI projects.
The study also highlights an encouraging trend: the rise of data enrichment (+5 points in one year) and spatial analysis (+8 points).
Organizations increasingly understand that accumulating more data is not enough to generate relevant insights. It is necessary to enrich them with context, intelligently cross sources, and automate their preparation to make them truly usable by AI systems.
The conclusion is clear: investing in AI without investing in data quality and governance means building a strategy on unstable foundations. The value of AI depends not only on the sophistication of its algorithms, but above all on the reliability and richness of the data on which it is based.
To fully exploit the potential of artificial intelligence in 2025, companies must first establish solid foundations:
It is precisely with this in mind that we have developed our platforms at Strat37:
As this study shows, before even talking about artificial intelligence, organizations need to focus on the fundamental integrity of their data. This is the prerequisite for any successful digital transformation in 2025.
Sources: “2025 Outlook: Data Integrity Trends and Insights” study conducted by Precisely and the Center for Applied AI and Business Analytics at LeBow College of Business (Drexel University), among more than 550 data and analytics professionals globally.
Do you want to assess the maturity of your data for AI? Contact our experts for a personalized diagnosis.
.png){kind=logo}
.png){kind=logo}
