New research by MIT SMR Connections, sponsored by ThoughtSpot, highlights the imperative of adopting GenAI generative AI for analytics.
A new MIT SMR Connections report sponsored by ThoughtSpot, the AI-Powered Analytics company, explores how early adopters of GenAI for analytics are gaining a significant competitive edge, with many already experiencing positive results, and a majority seeing significant returns from their investments.
The report drew responses from 1,000 business and data leaders in multiple industry sectors including North America.
Of the 1,000 global respondents, 67% are already leveraging GenAI for an analytics use case – with 26% planning to and 7% evaluating its use.
Early Adopters Set New Competitive Benchmarks
According to the report, the early adopters find that the technology’s ability to accelerate data-driven decision-making is a key benefit of implementation (44%), alongside its ability to improve products and services (44%) closely followed by how the technology can improve the quality of business insights (42%).
As the early adopters advance the number and scale of their deployments, they expect the competitive gap to widen between themselves and those only in planning mode. Over a third of early adopters (35%) project major revenue boosts, including almost half (48%) expecting a 100% return on investment (ROI) within three years, and over one in ten (12%) expecting their ROI to exceed 300% within the same period. The findings underscore the business imperative for GenAI in analytics and the technology’s potential to deliver unprecedented business value.
The Impact on Business Decision-Making
Making swift, data-backed decisions is an important competitive advantage. It enables business leaders to quickly analyze complex datasets, forecast trends and simulate scenarios, enhancing their ability to anticipate and plan for market changes with significant accuracy. According to the report, the implementation of GenAI for analytics has already placed over a third (37%) of early adopters far ahead of their competitors.
The Path to GenAI Success in Analytics
The success of GenAI implementation hinges on key factors: evolving skill sets, fostering strong collaboration between business and data teams, and selecting the right tools to support business strategy.
To achieve this, organizations must establish clear and consistent communication between their business and data teams – ensuring alignment on a common execution strategy.
The majority of early adopters (75%) report strong partnerships and a centralized strategy, putting them in an advantageous position. In contrast, less than half (47%) of planners – companies that haven’t yet adopted GenAI but expect to do so – have achieved similar alignment, underscoring the competitive edge that early adopters gain through prioritizing collaboration across the organization.
Both early adopters and planners recognize the importance of key technical skills for creating or customizing generative AI solutions – with data modeling cited as the most critical (49%) by all respondents.
However, a notable difference emerges in their prioritization of natural language processing (NLP): many (41%) of the early adopters view NLP as a top priority, compared to just a limited share (28%) of planners. This divergence suggests that early adopters, having already deployed the technology, understand NLP’s potential to accelerate data-driven decision-making, positioning them to better attract and develop talent for effective use of GenAI.
The findings of the report also highlight the value of choosing the right tools and collaborating with external experts to enhance business outcomes.
Over half (52%) of successful early adopters are leveraging third-party GenAI tools for analytics, compared with a smaller share (32%) of planners. By relying on strategic partnerships and external expertise, early adopters are optimizing their resources, while minimizing the time and effort required from their internal teams as they scale their deployments.
Finally, as GenAI is a new technology, experts interviewed in the report emphasize that it’s important to keep humans in the loop to monitor its output and make necessary changes. Having humans review AI-generated content provides opportunities to correct the technology’s mistakes and avoid problematic use cases or unintended biases. In turn, this helps train the models, promotes trust and enables humans to correct errors or misinterpretations closer to the source.
Thoughts from the Top
“For decades, data has been locked away in the hands of the expert analysts, and the wider industry has had a $100 billion price to pay for this annually. Now, the gap between those who are adopting GenAI for analytics and those who aren’t is stark,” said Cindi Howson, Chief Data Strategy Officer, ThoughtSpot.
“With GenAI, organizations have the opportunity to deliver a data strategy more focused on business outcomes that delivers unprecedented value. Yet, success isn’t guaranteed. It’s a fast-evolving era, I encourage organizations to leverage lessons from early adopters that includes both technology and people considerations.”
Methodology
MIT SMR Connections conducted a global online survey, sponsored by ThoughtSpot, that drew responses from 1,000 data and business leaders from companies of various sizes in a broad range of industries and locations. Kadence International fielded the survey in the spring of 2024. The data was examined based on respondents’ roles, geographical locations, company size and other factors. To provide a rich context for discussion of the quantitative research results, MIT SMR Connections interviewed several authors, academics, consultants and industry practitioners. These individuals provided insight into current trends and future priorities about the use of GenAI for data and analytics.
