Reveal 2026, 2025 & 2024 Analytics
50% of technology leaders say recruiting and retaining skilled tech workers is the biggest business challenge heading into 2026. [18]
57% of organizations say integrating AI into development workflows is their biggest software development challenge for 2026. [19]
49% of organizations cite security threats as a top software development concern. [20]
48% of organizations identify data privacy and regulatory compliance as major development challenges. [21]
77% of organizations plan to increase their use of AI in 2026. [22]
76% of organizations already use embedded analytics internally. [23]
84% of organizations expect their focus on business intelligence (BI) to increase in 2026. [24]
42% of organizations struggle with incorporating AI into their operations. [25]
36% of organizations report limited internal resources as a major constraint. [26]
25% of organizations cite economic cutbacks as a key pressure affecting software development. [27]
66% of organizations report productivity gains from incorporating AI into their workflows. [28]
63% of organizations say investing in skill development is a key driver of productivity gains. [29]
62% of organizations say embedding analytics or BI contributes to productivity gains. [30]
62% of organizations say automating repetitive tasks is a major contributor to productivity improvements. [31]
54% of organizations say global economic or geopolitical conditions are delaying product launches or expansion plans. [32]
43% of organizations say economic conditions are forcing them to reduce innovation budgets. [33]
36% of organizations say economic conditions are causing them to shift vendors or cloud providers. [34]
35% of organizations say economic pressure is leading them to change development team locations. [35]
32% of organizations expect global economic conditions to negatively impact their organization in 2026. [36]
46% of organizations plan to increase revenue as part of their 2026 expansion strategy. [37]
40% of organizations plan to adopt or purchase new technologies or applications in 2026. [38]
35% of organizations plan to expand into new markets in 2026. [39]
34% of organizations plan to build new applications in 2026. [40]
32% of organizations plan to take on new projects in 2026. [41]
32% of organizations plan to modernize existing applications in 2026. [42]
30% of organizations plan to hire additional staff in 2026. [43]
23% of organizations expect an increase in requests for proposals in 2026. [44]
20% of organizations plan to expand UX/UI capabilities in 2026. [45]
39% of organizations say making better business decisions is the top priority for embedded analytics and BI. [46]
36% say uncovering trends and patterns faster is a key embedded analytics priority. [47]
30% say boosting productivity is a primary goal of embedded analytics initiatives. [48]
30% say automating data analysis at scale is a core embedded analytics priority. [49]
51% of tech leaders identify security as their top software development challenge for 2025. [1]
45% feel AI code reliability is the biggest software development challenge for 2025, while 41% see data privacy at the top of the list. [2]
The main priority in 2025 is AI adoption, with 73% of tech leaders planning to expand the use of AI within organizations in the next year. [3]
55% find that AI deployment will be the biggest challenge they face. [4]
42% of tech leaders will incorporate or increase the use of AI to effectively utilize resources in 2025. [5]
Only 13% will use data to improve their decision making to utilize their resources more effectively. [6]
81% of tech leaders noticed a significant increase in interest in Business Intelligence or Embedded Analytics in 2024. [7]
81% of data analytics users use embedded analytics in 2025. [8]
47% of users utilize BI for productivity tracking. 42% – for trend analytics, 33% for decision-making, 31% for CRM. [9]
42% of users pinpoint struggling with tech resources as the main challenge in adopting embedded analytics. [10]
35% of users pinpoint shifting analytics needs as the main challenge in adopting embedded analytics. [11]
32% of users claim legacy infrastructure is the key barrier to embedded analytics adoption. 30% see cost justification as the main hurdle, while 29% claim it’s user adoption. [12]
For 20.2% of customers, the main reason for wanting and turning to embedded analytics is to make better decisions. [13]
39% of survey respondents say that their organizations is using embedded analytics to monitor and improve productivity. [14]
31.4% of survey respondents say that their organization are using embedded analytics to generate higher revenue. [15]
Understanding business problems is the top reason for using embedded analytics for 29.6% of survey respondents. [16]
The ability to make informed business decisions is the primary reason for 24.8% of survey respondents to use embedded analytics. [17]
Sources: Reveal Annual Survey Report: Top Software Development Challenges for 2026; Reveal Survey Report: Top Software Development Challenges for 2025; Reveal, From Adoption to Integration: Overcoming AI Deployment Challenges in 2025–2029; Reveal Embedded Analytics Survey Report 2024.
Gartner Analytics
47% of sales operations and RevOps leaders list data integration across different systems and platforms as a top data-quality challenge. [1]
40% report inaccurate data stemming from user input as an issue. [2]
By 2026, more than 80% of software vendors will have embedded GenAI capabilities in their products. [3]
By 2025, context-driven analytics and AI models will replace 60% of existing models built on traditional data. [4]
74% of CDAOs report that executive leadership has confidence in their D&A function, yet only 49% have established business outcome-driven metrics that allow stakeholders to track D&A value. [5]
23% of CDAOs take lead in ownership of Gen AI. [6]
82% of Gartner D&A survey respondents say they can identify the data assets needed for new D&A projects. [7]
80% commonly share a data asset across more than one use case. [8]
Only 46% of users have value-oriented KPIs for D&A governance. [9]
By 2025, synthetic data and transfer learning will reduce the volume of real data needed for AI by more than 50%. [10]
By 2026, 75% of CDAOs who fail to make organization-wide influence and measurably impact their top priority will be assimilated into technology functions. [11]
Through 2025, at least 30% of GenAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. [12]
90% of current analytics content consumers will become content creators enabled by AI-powered tools offered by their BI solutions by the end of 2025. [13]
60% of organizations will fail to realize the value of their AI analytics use cases with augmented analytics solutions due to incohesive data governance frameworks. [14]
79% of corporate strategists see AI and analytics as critical to their success. [15]
50% of strategic planning and execution activities could be partially or fully automated; currently, only 15% are. [16]
Only 20% of strategists reported using AI-related tools, such as machine learning or natural language processing, for their function. [17]
94% of the respondents reported using third-party APIs. [18]
By 2025, for organizations to remain competitive, analytical and soft skills will be the most sought-after skills in the data and analytics talent market. [19]
Gartner predicts that by 2025, 95% of decisions that currently use data will be at least partially automated. [20]
Gartner surveyed 400 finance executives and found the most selected combination of value and technology was self-service data and analytics as a driver of employee productivity, with 49% of respondents indicating this perception of the technology. [21]
Gartner survey reveals 80% of executives think automation can be applied to any business decision. [22]
Sources: Gartner Sales Analytics; Gartner Product Development & GenAI; Gartner AI for Data Analytics; Gartner Data Analytics Summit 2024; Gartner Data Trends; Gartner Data Governance; Gartner Corporate Strategists Survey 2023; Gartner Emerging Tech Trends; Gartner Finance Executives Survey 2022; Gartner Automation in Business Decision Survey 2022.
Cloudtalk.io Analytics
According to Gartner, only 29% of organizations can evaluate data fast enough to stay on top of their game. Cloudtalk.io (citing Gartner)
Source: Cloudtalk.io (citing Gartner)
Forrester Analytics
40% of highly regulated enterprises will combine data and AI governance in 2025. [1]
The other 80% still rely on the 20% for data sourcing, data discovery, data integration, building metrics and KPIs, running analytics, and delivering insights. [2]
Embedded analytics may be the key to empowering over half of all non-tech decision-makers to utilize data-driven insights. [3]
Data and analytics decision-makers who say that their firms have advanced insights-driven business capabilities are 8.5 times more likely than those at firms at the beginner stage to report that their firm’s annual revenue grew by 20% or more. [4]
61% of organizations still use four or more business intelligence platforms, which means that analysts and insights professionals are constantly task- and context-switching, losing as much as 40% of their productivity. [5]
40% of data and analytics decision-makers surveyed by Forrester in 2023 indicated that the most important scenario for AI was to streamline IT processes via AI-driven automation and decision-making. [6]
In Forrester’s Marketing Survey, 2023, B2B respondents cite a lack of trust in the quality of data supporting analysis (40%), insufficient understanding by their teams (39%), and too many unconnected data sources (38%) as the top obstacles to executing measurement and analytics. [7]
Sources: Forrester Predictions 2025: Artificial Intelligence; Forrester Bring Data to the Other 80% of Business Intelligence Users; Forrester Is Your B2B Organization Insights-Driven?; Forrester The Key to Insights-Driven Decisions is Curiosity Velocity; Forrester Outcomes Drive Your Data Architecture Strategy; Forrester Leverage Data in Your Sales Strategy to Win 2023.
Fortune Business Insights Analytics
The Embedded Analytics Market is expected to reach $55.54 billion by 2030. [1]
Poor-quality data can cause enterprises and organizations approximately USD 12.9 million in losses every year. [2]
In 2022, IT and Telecommunications were the biggest end-users of Embedded Analytics, with 27.4%. [3]
Source: Fortune Business Insights, Embedded Analytics Market Report 2023–2030.
Verified Market Research Analytics
The Embedded Analytics Market size was valued at USD 54.95 Billion in 2024. [1]
The Embedded Analytics Market Size is expected to swell to 149 Billion dollars by 2031, growing at a CAGR of 14.65%. [2]
Source: Verified Market Research, Global Embedded Analytics Market Size and Forecast.
McKinsey Analytics
65% of respondents report that their organizations are regularly using GenAI. [1]
AI adoption has surged to 72% in 2024, a massive change from the 50% adoption in previous years. [2]
Companies already see 20% of their earnings before interest and taxes (EBIT) contributed by artificial intelligence (AI). [3]
Data and analytics could create value worth between $9.5 trillion and $15.4 trillion a year if embedded at scale. [4]
Only a small fraction of the value that could be unlocked by advanced-analytics approaches has been unlocked—as little as 10% in some sectors. [5]
Companies may be squandering as much as 70% of their data-cleansing efforts. [6]
More than half of all data lakes are not fit for purpose. [7]
Sources: McKinsey The State of AI 2024; McKinsey The Data-Driven Enterprise of 2025; McKinsey Accelerating Data and Analytics Transformations in the Public Sector; McKinsey Ten Red Flags Signaling Your Analytics Program Will Fail.
