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Our research agenda reflects the hottest, most relevant topics and pains points in the data, BI and analytics space. See what we have in store for the upcoming year:
Start: May 2026
Publication: Q4 2026
Region: Global
As one of the largest business software companies SAP touches many business transactions and generates massive amounts of useful data. Over 325,000 customers therefore seek for solutions to manage, analyze and use this data.
Start: November 2025
Planned Publication: November 2026
Region: Global
Authors: Dr. Christian Fuchs, Craig Schiff and Kelley Lynn Kassa
BARC’s and BPM Partners’ joint research report will provide an up-to-date unique global view of the CPM market. Our comprehensive study combines data from two of the major surveys in this space: BARC’s The Planning Survey 26 and BPM Partners’ 2026 BPM Pulse.
The report reflects on the trends currently shaping the CPM market. We ask more than 1,000 users, consultants and vendors for their views on the most important trends in planning, budgeting and forecasting, financial consolidation and supplementary topics. Numerous respondents reveal a comprehensive picture of regional, company and industry-specific differences and deliver an up-to-date perspective of the market.
The report provides detailed information on:
By purchasing the marketing rights, you will:
Start: Summer 2026
Planned Publication: November 2026
Region: Global
BARC’s research report gives you an up-to-date, global view of the data and AI market. As the world’s largest survey of its kind, it brings together the perspectives of professionals across industries, company sizes, and regions, helping to separate the trends that are here to stay from those that are just passing hype.
We ask more than 1,500 users, consultants, and vendors for their personal rating of the most important trending topics, from data security and privacy, data quality management, and data & AI governance to data sovereignty, generative AI, and agentic AI. Numerous respondents reveal a comprehensive picture of regional, company, and industry-specific differences and deliver an up-to-date perspective of the market, including time-series comparisons that show how trends have developed over the years.
The report provides detailed information on:
By purchasing the marketing rights, you will:
Publication: December 2, 2026 (The field phase for this study has officially concluded. You can still secure our remaining sponsorship packages to leverage the final data and generate leads.)
As SAP advances its data strategy with innovations like the Business Data Cloud, a new chapter in enterprise data management has begun: rich with promise, but also new complexity. For many of SAP’s 325,000+ customers, the challenge is no longer about data availability, but about clarity: how to best organize, access, and analyze their SAP and non-SAP data to drive business outcomes and prepare for AI.
This study uncovers how organizations are adapting to the evolving SAP data universe, where they see the biggest barriers and opportunities, and how they plan to invest to turn data modernization into a competitive advantage.
Core questions:
Start: H2 2026
Publication: 2026 / 2027
Region: Global Research
Authors: Christian Fuchs, Stefan Sexl
The financial close and consolidation process is a cornerstone of the finance function and is becoming increasingly important for many organizations, both from a regulatory and a managerial perspective. For decades, finance teams have struggled with manual, time-consuming, and error-prone Excel-based processes and fragmented systems. In an era of increasing volatility, shorter decision cycles, and the need to integrate comprehensive data from multiple ERPs, business units, and reporting domains, many organizations are now planning to invest in modern consolidation and group accounting software platforms.
At the same time, the advent of artificial intelligence (AI) and advanced automation through AI agents provides an opportunity to streamline these processes and is fundamentally reshaping the role of the finance professional. New, AI-enabled capabilities promise to further automate labor-intensive tasks, improve data quality, and turn the close and consolidation cycle into a more continuous, insight-driven process.
This survey will explore the key challenges, expected benefits, and investment plans for adopting these technologies. We will examine:
The survey will also investigate integration requirements for consolidation and group accounting processes. For CFOs and other finance leaders, the survey will provide a roadmap for navigating this transformation by offering insights into best practices, highlighting the strategic directives of leading organizations, and provide a vision for the future of the finance function.
Start: January 2027
Publication: May 2027
Region: Global Research
Authors: Dr. Carsten Bange, Timm Grosser
As data and AI enter core business processes, sovereignty decisions affect how organizations manage risk, provider dependencies, and innovation. The challenge is to translate strategic intent into workable governance and architecture. Building on Data Sovereignty 2026, this global BARC survey tracks relevance, drivers, funding, maturity, implementation measures, barriers, and innovation impact over time. Recurring questions preserve the data sovereignty baseline, while new research examines how leaders consciously choose an appropriate level of data and AI sovereignty along a continuum. The focus is on implementation capability today: how organizations put their chosen level into practice and demonstrate that it delivers the intended control. Readers gain a benchmark of organizational progress and a practical view of the decisions, resources, and capabilities needed to close the gap between sovereignty ambitions and operational reality.
This survey will examine:
Start: Q1/2027
Publication: Q2/2027
Region: Global Research
Authors: Shawn Rodgers
Companies are moving agentic AI from sandboxes into production. As agents gain access to enterprise data, tools, and business processes, model performance alone is no longer enough. Weak data foundations, fragmented architectures, unclear ownership, and governance designed for conventional AI can limit progress. As the successor to BARC’s Lessons From the Leading Edge study, this global survey will repeat selected questions and compare results over time to show how adoption, priorities, obstacles, and practices are changing.
It also tracks industry-specific use cases, including industrial AI, and examines how earlier lessons on data quality, cost, and delivery structures evolve as AI becomes more autonomous. The study identifies the foundations, operating models, and controls that distinguish leaders: agent identity, orchestration, runtime governance, observability, context engineering, and cost management. Readers can benchmark readiness and prioritize the steps required for productive agentic AI. It asks where autonomy creates value and where human control remains necessary.
Start: Q1 2027
Publication: Q2 2027
Region: Global Research
Authors: Kevin Petrie
Preparing and Delivering Data for AI: Fueling the Agentic Enterprise
Adoption Maturity, Production Challenges and Best Practices
The breakneck pace of innovation forces AI adopters to get serious about foundational aspects of data management. But they struggle to keep up with dynamic AI projects, proliferating data sources and rising governance challenges.
This BARC study takes the pulse on data management and measures how much AI adopters have matured since our first survey two years ago. We benchmark leading adopters against followers on data quality, lineage, privacy, security and readiness of structured and unstructured data. To separate durable trends from passing hype, we identify two-year shifts in use cases, tooling and skills.
Data, AI, IT and business leaders should read this report to learn:
Start: January 2027
Publication: July 2027
Region: Global Research
Authors: Dr. Carsten Bange and Robert Tischler
AI, changing user expectations, and modern data platforms are changing the market for BI & analytics. Organizations need faster, more reliable decision support, yet many users still face fragmented information, dashboard-heavy workflows, and limited trust in automated results. Conversational analytics, embedded insights, AI assistants, and agentic workflows offer new ways to deliver information and support action, but their practical value remains unclear. This BARC survey examines how organizations use these capabilities, where they produce tangible benefits, what blocks wider adoption, and which investments are planned. It compares user needs with organizational priorities and identifies the practices, technology foundations, and governance requirements that distinguish more advanced adopters. Readers can benchmark whether their organization is leading, following, or falling behind and see which tasks, capabilities, and investments should come next.
Core questions:
Start: Q2 2027
Publication: Q3 2027
Region: Global Research
Authors: Kevin Petrie
As organizations move agentic AI from experiments to production workflows, context becomes the gating factor. For AI to succeed, data teams must derive and govern this context across their sprawling environments. Those that get it right enable their agents to make informed decisions, take safe actions and create value.
This topical survey assesses the state of context engineering for agentic AI. We measure how organizations define, organize, govern, and operationalize context across projects and use cases. And we study the leaders whose mature, governed AI programs hold lessons for the rest of the market. Our findings will help data, AI, IT, and business leaders build a practical path from isolated projects to sustainable innovation.
Readers will learn:
Start: Q3 2027
Publication: Q4 2027
Region: Global Research
Authors: Kevin Petrie, Timm Grosser
Enterprise AI is now embedded in business processes, but governance maturity still lags behind adoption. Many organizations have policies and human approval in place, yet enforcement, continuous monitoring, and automated response remain limited. Building on BARC’s The State of Data and AI Governance, this global follow-up study tracks how governance structures, responsibilities, technologies, and controls are developing over time. It examines how leaders translate policies into enforceable controls, where they automate governance, and how they connect CISO, CDO, CAIO, and architecture teams. The study adds a focused assessment of enterprise AI security, including identity and access, shadow AI, runtime protection, and incident response. Readers can benchmark their maturity, identify gaps between oversight and enforcement, and learn which practices help leaders govern and secure AI without slowing productive use.
Start: Q2/2027
Publication: Q4 2027
Region: Global Research
Authors: Dr. Christian Fuchs and Robert Tischler
Volatility has become the norm in planning, rather than the exception. Many organizations recognize that they need to transform their planning processes, and they see AI as the most important lever for planning more efficiently. Meanwhile, vendors are promising AI agents, predictive forecasts, anomaly detection, and self-adjusting plans. In practice, however, many teams are still working on the basics: data, skills, processes, and responsibilities. The question of how AI can support achieving these efficiency objectives remains unanswered.
This BARC survey examines which planning tasks are measurably improved by AI, from data preparation and scenario analysis to commentary and reporting. It also analyzes how much autonomy successful organizations grant AI and how far they trust its results. Additionally, it examines which responsibilities organizations have defined for data, processes, and AI output. Building on previous BARC evaluations, the survey provides an updated view of the market by analyzing the progress made since then. The survey looks at what sets successful organizations apart, including their skills, data foundation, governance, and technology support. Readers will gain an independent, empirical benchmark of where AI is helpful for planning under uncertainty and where it is not.
Core questions:
