A minority of organizations are already extracting value from AI today, while a majority struggle with misaligned expectations and poor trust in data.
While the potential benefits of AI are enormous, a recent report by Capgemini shows that only a small fraction of organizations are taking full advantage of the technology’s capabilities. Capgemini surveyed 500 technology executives and 504 business managers for the study.
The report shows that adoption of AI among organizations varies widely. Only a small percentage of organizations, referred to as Data Masters, effectively utilize AI to achieve significant business results. These organizations use AI not only for data analysis, but also for predictive and prescriptive decisions that give them a competitive advantage.
However, such implementations are the exception: barely 16 percent of surveyed organizations fall under the Data Master label. The majority of organizations lag behind in AI adoption.
Reactive vs. proactive
Many organizations are still in the early stages of AI adoption, and their decision-making processes remain largely reactive. That means, according to Capgemini, that they focus primarily on descriptive (what happened) and diagnostic (why did it happen) analyses.
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The hurdles for AI: lack of trust, low data quality and varying expectations
Only 23 percent of organizations use predictive analytics (what might happen), and even fewer (18 percent) use prescriptive approaches (recommendations to improve outcomes). Moreover, only eight percent of organizations use autonomous or self-optimizing approaches, where AI systems help make decisions to achieve specific goals.
Challenges in adopting AI
A significant barrier to effective AI adoption is data quality. Many organizations struggle with poor data quality, which undermines the accuracy and reliability of AI models.
In addition, there is a trust gap between business and technical executives regarding the reliability of data, which further hinders AI adoption. Only 20 percent of business executives say they fully trust the data they receive, while 62 percent of technical executives believe their business users trust the data.
The report also highlights a lack of alignment between data/AI strategies and overall business strategies in many organizations. 38 percent of business executives feel their organization’s data/AI strategy is aligned with their business strategy, compared with 56 percent of technical executives. This discrepancy leads to AI initiatives that do not deliver the expected business value.
Transformative role for AI
AI can help organizations realize significant improvements in operational efficiency and productivity. According to the report, data masters achieve an average of 19 percent higher operational efficiency than their counterparts.
Data masters, for example, are using AI to increase customer engagement through personalized interactions and improved customer service. AI models can analyze customer preferences and predict which products or services are likely to appeal, allowing companies to optimize marketing strategies and increase customer satisfaction. The report shows that data masters achieve a 22 percent reduction in customer turnover, which is 87 percent more than other organizations.
The road to better adoption
To effectively integrate AI, organizations must adopt a strategic approach that aligns with their business goals, addresses data quality issues, and fosters a culture of data-driven decision-making. The report recommends that organizations establish an AI and analytics Center of Excellence (CoE) to coordinate and direct AI activities, foster innovation, and help become a truly data-driven enterprise.