2024 Harvard Business Review Analytic Services Report on the Age of AI

Harvard Business Review Analytic Services surveyed 362 professionals involved in AI decisions from around the world to learn more about how they’re approaching AI adoption — including challenges they face and strategies they’re testing to overcome them.

Find out how your AI investments and strategies compare and learn about critical next steps to leverage your enterprise data as a strategic asset.

Key Takeaways from Harvard
Business Review Analytic Services

Nearly two-thirds of respondents said that AI adoption is a mid-level or top strategic priority for their organizations, but only 10% of respondents feel their organizations are “completely ready” to adopt AI.


say AI adoption is a mid-level or top strategic priority


feel their organization is completely ready to adopt AI


do not agree their organization has the data foundation required for the new era of AI


say having high-quality data is very important for successfully adopting AI

Data leaders are considering several strategies and techniques, but 80% agree that having consumable data is a must for meaningful AI adoption.

Download the report for more industry benchmarks and the steps experts recommend to improve your enterprise-wide data management strategy for AI.

Scroll down to answer the survey questions and see how you compare.

North American Participants
0 %
LATAM Participants
0 %
EMEA Participants
0 %
APAC Participants
0 %

Hear From Your Industry Peers

Harvard Business Review Analytic Services surveyed 362 professionals from the Harvard Business Review audience across a variety of industries from around the globe and found that, while the majority of participants believe AI will be a major disruptor in their organization’s industry in the near future, fewer of them are confident that their organization is ready to successfully adopt AI.


make data decisions


influence data decisions


implement data decisions or prepare data


are end users of the organization's data

Building a Trusted Data Foundation

Having consumable data lies at the heart of AI readiness for the professionals HBR-AS surveyed. But what does that look like in practice?

Nearly all respondents (91%) agree that having a reliable data foundation is essential in order to successfully adopt AI. However, just over half (55%) agree their organization’s data foundation is reliable.

How Do You Compare?

To successfully adopt AI, how important or unimportant is it that an organization’s enterprise data foundation has the following attributes?


High quality data (accurate, standardized, non-duplicated data)


Comprehensive data management tools and processes


Clear data governance policies

Florin Rotar headshot

“You need a good data platform to be able to unify data and operate in the real world, where you are never going to have everything centralized.”

— Florin Rotar, Chief Artificial Intelligence Officer at Avanade

Overcoming Data Challenges

To address this issue, respondents indicated taking a multi-pronged approach. The vast majority are cleaning their data to make it more accurate and trustworthy, while others are focused on breaking down data silos and implementing master data management (MDM).

What You Should Know:

  • Having consumable data is imperative for any AI adoption initiative.
  • Adopting AI means walking a fine line between moving too fast and moving too slow.
  • Data governance and data fabrics are great tools to have, but they’re even better when paired with MDM.

What To Do:

  • Get your data consumption-ready by breaking down data silos and de-duplicating, matching, merging and standardizing it across your data sources.
  • Be ambitious yet cautious. Eagerness to move too quickly can cause serious damage — even more so than lagging can, in some cases.
  • Make the most of your data by using MDM in tandem with tools like data governance and data fabrics.

How Do You Compare?

Which of the following efforts is your organization working on over the next 12 months, if any, to address the data challenges it faces in adopting AI?

Cleaning data to improve its accuracy and reliability


Breaking down data silos and improving data access across the enterprise


Rolling out master data management

Download the report for more industry benchmarks and the steps experts recommend to improve your enterprise-wide data management strategy for AI.

“[Master data management] helps us unlock the potential value of the data by enabling different sorts of use cases — including AI use cases where we can start to predict the behavior of a consumer…We see MDM as a big value unlock for our business.”

— Brian Zenk, Vice President of Data Science at Nestlé Purina

Download the Full Report Today

AI adoption can seem daunting, but it doesn’t have to be that way. Learn how you can successfully implement AI across the enterprise to gain more powerful insights from AI-powered analytics and boost productivity across the board.

Download the full, free report from Harvard Business Review Analytic Services to learn:

  • How data professionals around the world view their levels of maturity for AI adoption
  • The biggest obstacles standing between organizations and AI adoption
  • Tactics and strategies organizations are taking today to become AI-ready
  • What steps experts from leading organizations recommend taking to be the most effective
  • Why MDM paired with data governance and data fabrics is a winning combination

Download Report


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MDM vs. MDS graphic
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