A new MIT SMR Connections report sponsored by ThoughtSpot, the
AI-Powered Analytics company, explores how early adopters of
generative AI 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 across North America,
Europe, and Asia-Pacific. Of the 1,000 global respondents, 67% are
already leveraging generative AI 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 generative AI 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 generative AI for analytics has already
placed over a third (37%) of early adopters far ahead of their
competitors.
The Path to Generative AI Success in
Analytics The success of generative AI 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 generative AI 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 generative AI.
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 generative AI 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 generative AI 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 for accuracy, 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
generative AI for analytics and those who aren’t is stark,” said
Cindi Howson, Chief Data Strategy Officer at ThoughtSpot. “With
generative AI, 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.”
To read the full report, visit
ThoughtSpot.com.
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 generative AI for
data and analytics.
About ThoughtSpot
ThoughtSpot is the AI-Powered Analytics company.
Our mission is to create a more fact-driven world with the easiest
to use analytics platform. With ThoughtSpot, anyone can leverage
natural language search powered by large language models to ask and
answer data questions with confidence. ThoughtSpot enables everyone
within an organization to limitlessly engage with live data in any
popular cloud data platform, making it easy to create and interact
with granular, hyper-personalized, and actionable insights.
Customers can take advantage of both ThoughtSpot’s web and mobile
applications to improve decision-making for every employee,
wherever and whenever decisions are made. With ThoughtSpot’s
low-code developer-friendly platform, ThoughtSpot Embedded,
customers can also embed AI-Powered Analytics to their products and
services, monetizing their data and engaging users to keep them
coming back for more. Organizations like T-Mobile, BT, Daimler,
CVS, Medtronic, Royal Bank of Canada, Nasdaq, OpenTable, Capital
One, Huel, and Comcast rely on ThoughtSpot to transform how their
employees and customers take advantage of data. Try ThoughtSpot
today and see for yourself.
PR Contact:
Lindsay NoonanDirector of Communications,
ThoughtSpotpress@thoughtspot.com
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