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Build high-performance organizations through data-driven insights

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


Executive Summary

CxOs and boards recognize that their organizations’ ability to generate data-driven insights — often in real time — is of the highest strategic importance.

To become data-driven, companies are deploying increasingly advanced cloud-based technologies. What these tools deliver, however, will be of limited value without abundant, high-quality and easily accessible data. MIT Technology Review surveyed 600 CIOs from 18 countries across 14 industries — including retail, finance, media, healthcare and manufacturing —and interviewed C-level executives from Procter & Gamble, Johnson & Johnson, Virgin Australia, Tokio Marine, Cummins, CNH Industrial, Freshworks, S&P Global, Marks & Spencer and more.

Among this study’s objectives have been to compare the AI ambitions of different industry sectors and to determine the extent to which industry approaches to removing impediments to AI development vary. Three areas of difference stand out:


1/ Retailers and manufacturers are more ambitious about becoming AI-driven

Within the leader group — where AI is expected to be critical in at least five of the seven core functions (product development, IT, supply chain, marketing, sales, finance and HR) by 2025 — respondent companies from the retail/consumer goods and automotive/manufacturing sectors are the most prominent. Life sciences and healthcare organizations are also well represented (refer to figure 1).

2/ Data constraints on AI development loom especially large in three industries.

Among real estate and construction respondents, almost double the percentage of those in the overall sample (58% vs. 30%) cite limitations of existing data technologies as an impediment to AI development.

Some 40% of automotive/manufacturing respondents say the same – with 82% of executives (compared with 72% overall) stating that data-related problems are more likely than other factors to jeopardize the achievement of their future AI goals. That belief is shared by 80% of financial sector executives and 78% of those in real estate and construction firms.


3/ Financial services providers display the strongest investment growth intentions.

Expected spending growth on data capabilities in the financial sector dwarfs that of others across several line items. For example, investment to bolster data governance will increase by 74% between now and 2025, according to financial industry respondents, compared with 52% for the sample as a whole. The analogous differential relating to existing data and AI platforms is 61% vs. 42% — and for new platforms, 58% vs. 40%. Spending growth in these areas by retail/consumer goods and automotive/manufacturing firms will also exceed the sample average.


94% of CIOs say they are already using AI in lines of business, and more than half expect AI to be widespread by 2025

Currently, IT (67%) and finance (54%) are the two core functions leading in AI adoption. By 2025, IT (71%), supply chain (68%) and product development (67%) expect to adopt AI widely.


By 2025, CIOs expect revenue boost to be the most tangible benefit gained from AI

Overall, the ranking of benefits brought about by AI use is expected to change significantly between now and 2025.


AI and data strategies are intertwined

78% of CIOs say scaling AI to create business value is the top priority of their enterprise data strategy, and 96% of AI leaders agree.


Almost every AI leader is seeking to unify the data platform for analytics and AI

72% of CIOs say that data challenges are the biggest factor jeopardizing AI success. 68% of CIOs and 99% of AI leaders say platform unification is crucial.


Multicloud and open standards are integral to AI success

72% of CIOs are using multicloud and 92% of AI leaders believe it ensures strategic flexibility.

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.