Mumbai (Sept. 15, 2026) : A new SAS report with research insights by
IDC uncovers what’s powering the organizations winning the race to profit from
their AI investments: embracing trustworthy AI measures. Organizations applying
trustworthy AI practices were 15 times more likely to report strong return on
investment (ROI) from their AI projects.
As identified in the second annual Data and AI Impact Report: The New Economics of Trust, organizations with the strongest governance,
data quality and auditability practices – a comparatively small market segment
– consistently outperformed peers, reporting at least double the ROI from AI
deployments. Fewer than one in 20 trustworthy AI ‘laggard’ organizations
reported the same.
“When AI works, it’s incredibly impactful,” said Bryan Harris, CTO at
SAS. “However, it is well documented that state-of-the-art agents can have
error rates that exceed 25% on complex tasks – which is unacceptable in
high-stakes decision-making. In order to achieve accuracy and repeatability,
organizations must embed domain expertise into agentic workflows, while keeping
people at the center of governance and oversight. Organizations that do this
successfully will close the trust gap and gain a competitive advantage in the
market with AI.”
Noshin Kagalwalla, Vice President – Public Sector, APAC & Managing
Director, SAS India, said, “The findings reflect a broader shift among
organizations in India toward strengthening the foundations required to support
trustworthy and explainable AI. With over 90% of surveyed organizations
planning to increase AI investments, establishing trust in AI systems will be
critical to realizing lasting business value.”
“As AI becomes more autonomous, organizations face a new challenge:
maintaining confidence in systems people don't fully understand,” said Chris
Marshall, Vice President at IDC. “Our findings show that stronger oversight,
explainability, accountability and data foundations are becoming prerequisites
for scaling AI successfully.”
In India, organizations reported year-on-year improvements across
measures of AI trustworthiness, while also highlighting the continued need to
strengthen explainability, accountability and data quality as adoption expands.
Among surveyed organizations, the financial services and technology sectors
demonstrated notable progress across measures of maturity and infrastructure.
Investment intentions among surveyed organizations in India remain
strong, with 91.8% expecting AI spending to increase over the next 12 months.
Notably, the proportion planning a significant increase of more than 20% rose
from 5% in 2025 to 27.3% in 2026, reflecting continued confidence in AI as a
strategic business priority.
The report’s findings span three themes:
AI that can't explain itself is a major business liability
Researchers found that at many organizations, employees are
increasingly hesitant to rely on systems that may or may not be able to offer
correct output or explain how AI arrived at a final decision. As AI gains
autonomy, this liability grows, making explainability crucial for success.
The report also explored a major hurdle to success in AI adoption:
when employees' lack of trust in AI decisions leads them to override and make
manual corrections. This only perpetuates the AI trustworthiness deficit, and
can cost organizations time, productivity and profitability. When AI decision-making
is only as good as the data it’s based on, building a strong data foundation
becomes pivotal for organizations looking to reduce override rates.
Among surveyed organizations in India, insufficient explanation behind
AI-generated recommendations and inadequate context were cited as the most
common reasons employees override AI outputs. The results suggest that as AI
systems become more capable, the ability to explain decisions and provide
business context remains critical to user confidence and adoption.
Key findings:
● 97.2%
of users override AI-generated recommendations in at least some cases.
● The
number one reason employees decided to override AI, regardless of whether its
output was considered correct, was when the AI could not provide an explanation
behind its decision.
● Trust
falls from 76% for generative AI to 66% for agentic AI, highlighting growing
concerns as AI systems gain more autonomy.
Trustworthy AI practices drive business success
The report exposes a widening ROI divide between organizations that
prioritize trustworthy AI practices and those that do not. The findings suggest
organizations gaining the most value from AI are not necessarily deploying
different technologies but instead managing AI differently.
Key findings:
● Organizations
investing in trustworthy AI measures are 15 times more likely to report strong
or high ROI on their AI projects (62% vs. 4%).
● Organizations
with the strongest trustworthy AI practices realize 1.85 times greater gains
across 13 different business outcomes, including revenue growth, cost savings
and customer experience.
● 85%
of these AI leaders with trustworthy practices are increasing their investment
in this area by more than 10% this year, actively widening the performance gap.
Too many organizations are losing time and money to weak data
foundations
Most organizations are deploying AI on severely underdeveloped or
outdated data and data infrastructure. Without a strong data foundation to
support crucial transparency and explainability, organizations struggle to
govern AI effectively and realize value.
The findings also point to the growing importance of data quality as
organizations scale AI adoption. In India, Data Quality & Governance
emerged as one of the most important factors for 69.4% of surveyed
organizations, reflecting a broader shift toward strengthening the foundations
required to support trustworthy and explainable AI.
Key findings:
● Only
17.5% of enterprises have a fully optimized data infrastructure mature enough
for the demands of agentic AI, which negatively impacts performance.
● Organizations
with an optimized data foundation are four times more likely to expect strong
ROI from AI projects, and six times more likely to mandate the data quality and
explainability controls necessary to build trust.
Take a deeper dive
The findings are based on a global survey of 2,699 decision-makers
with knowledge of or influence over their company's data and AI initiatives.
The survey was conducted across 28 countries and four focus industries:
banking, insurance, life sciences and the public sector. The report highlights
industry use cases and findings that demonstrate how leaders in each of these
industries around the globe are approaching AI.
Key findings:
● Banking
leaders are going beyond compliance, treating robust AI governance as a
competitive advantage and operational necessity – 85% of AI leader banks have
established governance frameworks, compared to just 29% of laggards.
● Forty-one
percent of public sector leaders are increasing trustworthy AI investment by
more than 20% in the year ahead, which is as fast as the most ambitious
organizations across any industry.
● 23%
of life sciences organizations have scaled AI company-wide – the highest of any
industry.
Explore study findings and access the full report here.
What makes AI trustworthy?
Trustworthy AI is
artificial intelligence designed to be reliable, fair, secure, up to regulatory
standards, and able to clearly show how it arrived at a decision. Users and
decision-makers at all levels within an organization must be able to hold an AI
system to a pre-determined chain of accountability for incorrect or missing AI
output. Any AI system must also be governed and proven to be in compliance with
clear rules.
What makes an organization a trustworthy AI leader?
Within the study, organizations were scored out of 100 against five
dimensions of trustworthy AI. The report’s trustworthy AI leaders were
organizations with an average total score of 80 or higher.
Each organization was scored across the following five trustworthy AI
criteria.
- Data quality and
governance.
- Model governance and
oversight.
- Explainability and
fairness.
- Responsible AI policy.
- Audit and
accountability.
SAS is a global leader in data and AI, helping organizations make
confident decisions with AI they can trust. For decades, SAS has set the
standard for delivering software that drives meaningful impact, incorporating
deep industry expertise, transparency and governance. SAS gives you THE POWER
TO KNOW®.
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Editorial Contacts:
Kunal
Aman, SAS
kunal.aman@sas.com
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