Skip to main content

Build high-performance organizations through data-driven insights

Industry Insights: CIO vision 2025 — bridging the gap between BI and AI


Powering the data and AI–driven financial institution of the future

Financial services institutions are ready to revamp their business intelligence (BI) and leverage AI to increase efficiency and spark innovation, but data foundations increasingly become the bottleneck.

As this survey report by MIT Technology Review Insights shows, AI and data management are essential pillars of enterprise success. In fact, 94% of CIOs surveyed are already using AI in lines of businesses, and more than half expect AI use to be widespread or critical in their IT, finance, product development, marketing, sales and other functions by 2025.

Of the 14 industries surveyed, AI investment will be strongest in financial services. Financial institutions are expected to see the highest investment growth in data management and infrastructure, although AI leaders were most numerous among retail/consumer goods and automotive/manufacturing companies. So far, the top three investment priorities have been on talent and skills development, improving data quality, and ML and BI infrastructure. 

For most organizations, however, becoming AI-led is a work in progress. Since S&P Global (a financial information and analytics company) acquired an AI solutions provider in 2018, “AI, machine learning, and natural language processing have become embedded in everything that we do,” says Swamy Kocherlakota, S&P Global’s Chief Information Officer, “[but] we’re spending a lot of time trying to work out how we can apply our AI, machine learning and NLP models at scale.” Scaling AI involves improving data management, including data processing speeds, governance and quality.

Similarly, insurers such as Tokio Marine face the constraint of scaling Al. For example, one challenge is rendering historical data in the company’s legacy systems "fully Al-friendly" and properly integrating external data into its Al models. “Just as critical is overcoming the cultural challenges involved,” says Masashi Namatame, Group Chief Digital Officer and Managing Executive Officer at Tokio Marine. “In order to become Al-driven, we need to change the mentality of our entire business.”

Finally, data security is also a priority with financial services: CIOs reveal they plan to increase spending on security improvement by an average of 101% over the next three years.

Ultimately, through effective data management, financial institutions can transform the way they operate with more personalized customer experiences, modern risk and compliance, optimized data sharing, and more productive employees.

Case Study

Tokio Marine: Striving to become Al-driven

Insurers of all types now routinely use Al models to drive underwriting, streamline claims processing and accelerate claims adjudication, protect against insurance fraud, and improve risk forecasting, for example. Tokio Marine — Japan’s oldest insurance company, which has done business since 1879 — has been applying advanced uses of AI, particularly in its auto insurance business, says Namatame: “To assess collision damages, the company uses an Al-based computer vision solution to analyze photos from accident scenes.” Comparing these with what he describes as “thousands or even millions” of photos of past analogous incidents, the model produces liability assessments of the parties involved and projects anticipated repair costs. Al has also provided the company with tangible benefits in online sales — especially in personalized product recommendations and contract writing, according to Namatame. 

Use cases currently in development include the analysis of data from in-car drive recorders, which monitor driver actions and behaviors. Read the full case study. 


“Applying AI as broadly, as aggressively and as enthusiastically as possible. No part of our business should be untouched by it.”

—Masashi Namatame, Group Chief Digital Officer, Managing Executive Officer, Tokio Marine
s&p global

“AI, machine learning and natural language processing have become embedded in everything that we do.”

—Swamy Kocherlakota, Chief Information Officer, S&P Global

How to future-proof

The research points to these key attributes to instill in your data and technology foundations: openness, multicloud, democratization. An open and unified platform like the Databricks Lakehouse Platform makes it possible to scale AI efficiently — and ultimately, create business value.