Data and AI Maturity are increasing overall, but it is getting harder to reach the highest levels of Maturity. In a recent survey (DAICAMA) of almost 1,200 companies worldwide – representing nine major industry clusters -, BCG found that while 95% of companies are striving to leverage AI for business value, the proportion of companies qualifying as the most mature in Data and AI declined from 13% in their 2021 survey to 8% today. As data and AI capabilities become more sophisticated, it becomes more difficult to develop and maintain high levels of Maturity. This is partially due to how Generative AI has changed the landscape and introduced new opportunities and challenges. This survey also had other top level findings:

• More companies are recognizing the interdependence of Data and AI capabilities to unlock the full value of data, and are focused on increasing both Data and AI Maturity
• The main difference between organizations with high vs low Data and AI Maturity is on the levels of maturity for 1/Ideation and prioritization, 2/Attracting and retaining top AI talent, 3/Establishing business-driven data governance, 4/Creating data ecosystems, and 5/Fostering a data-driven culture.
• Companies that are not setting realistic expectations for Data and AI Maturity end up with lower enthusiasm for AI transformation
• Companies with high Data and AI Maturity:
  • Have 4 times more use cases scaled and adopted across their business than those with low maturity.
  • For each use case they implement, the average financial impact is five times greater.

A Closer Look at Maturity Levels

The DAICAMA survey showed that traditional data-rich sectors like technology, finance, consumer and healthcare continue to lead, while industries such as automotive are making significant strides. However, the public sector lags behind, hindered by a lack of pressure to adopt digital solutions and a challenge in attracting the necessary talent.

Geographically, the U.S. maintains its status as the leader in data maturity, with Singapore as a close second. India, in particular, has seen a 23% increase, thanks in part to government and private-sector investments in digital infrastructure.

Setting Realistic Ambitions

While many companies aim to scale their AI initiatives within three years, historical data indicates that most will likely fall short of their ambitions. The average company has historically moved up just one maturity level every ten years. This slow pace of growth has surprised many executives, and this realization has led them to recalibrate their expectations, with the latest survey showing a trend toward setting more attainable goals.

Enhancing Maturity Across Capabilities

Companies are increasingly recognizing the importance of improving all capabilities that contribute to data and AI maturity. This holistic approach helps them avoid the pitfalls of focusing on isolated improvements. By addressing the interdependencies among capabilities, organizations are better positioned to unlock the full potential of their data assets.

Five Areas Where Leaders Excel

Despite the decline in the proportion of highly mature companies, those that do achieve top-tier status are significantly outpacing their peers in several key areas:

1. Ideation and Prioritization: Leading firms empower business leaders to innovate, focusing on transformative use cases rather than small, incremental improvements.
2. Talent Acquisition: These companies invest heavily in attracting and retaining top AI talent, ensuring their teams possess both technical skills and business acumen.
3. Business-Driven Data Governance: Top players implement data governance structures that address specific business needs, moving beyond mere compliance.
4. Data Ecosystems: By forming strategic partnerships, leading organizations create robust ecosystems that drive new value and competitive advantage.
5. Data-Driven Culture: They foster a culture that embraces data, using behavioral science to encourage adoption and track progress.

For organizations navigating the complexity of building their AI maturity, the first step is to assess their current maturity level for each needed capability. By identifying high-impact use cases and developing the necessary capabilities, companies can create a unified approach that accelerates their journey toward data and AI excellence. The road may be challenging, but with strategic planning and execution, the potential rewards are substantial. One Framework that can be used for identifying the needed capabilities, is AWS’ CAF-AI Framework.

Embracing the evolving nature of data and AI is no longer just a competitive advantage—it’s essential for survival in the modern business world.

The full report is here: https://www.bcg.com/publications/2024/leaders-in-data-ai-racing-away-from-pack