Data.
Not opinion.

Sales Performance Management Market Study 2025

In this eighth edition of this report, the Dresner Advisory community of respondents scores sales performance management use, adoption, budgets, and overall success. We analyze these attributes by organization size, geography, and other attributes.

This year, the community scores the importance of four main areas of sales planning: 1) Features, 2) Integrations, 3) Technologies, 4) Relative importance of SPM initiatives compared to the general population

2025-10-15

Financial Consolidation, Close Management and Financial Reporting Market Study 2025

While FCCR can be considered a mature market, it benefits from new technologies. Agentic AI is transforming FCCR and similar enterprise applications by deploying autonomous agents that collect data, reconcile accounts, generate entries, and prepare reports. Unlike basic automation, these agents learn, make decisions, and adapt to context, which speeds up reconciliation and consolidation, boosts accuracy, strengthens compliance through real-time monitoring, and allows finance teams to concentrate on strategic tasks.

2025-09-30

AI, Data Science, and Machine Learning Market Study 2025

Artificial intelligence (AI), data science (DS), and machine learning (ML) are shifting from experimental to strategic enablers. Most organizations report AI is playing either a direct or supporting role in their business, with investment driven by the need to solve inefficiencies, experiment, and prepare for disruption. At the same time, AI governance remains inconsistent, with approaches ranging from centralized oversight to ad hoc or absent controls, reflecting an uneven path to maturity.

Despite modest current deployment, momentum is building. Larger organizations are sustaining long-term AI use, correlating with experimentation in generative and agentic approaches. Features such as outlier detection and model explainability remain essential, underscoring the need for transparency and trust. Overall, organizations are cautious but optimistic, advancing at different speeds toward more mature and pervasive use of AI, DS, and ML to drive business value. 

2025-09-04

Don’t Spend More on Data and Analytics, AI, or Digital Transformation Without Increasing Governance Investments

For most organizations, the gap between how often they invest more in the top areas of data and analytics and AI, and how frequently they increase spending on governance, represents a potential risk. Proper data governance provides the structure and processes necessary to ensure organizations can derive optimal value from their data. If an organization hasn’t emphasized strong governance while getting its data house “in order,” investments in high-priority areas like AI and data and analytics have a bigger chance of delivering less value than intended. We believe that organizations that fail to spend commensurately on data governance likely will see diminished returns from their investments in data and analytics, AI, and even digital transformation—especially if they are multiplying investments in these areas.

2025-10-27

Data Leaders Must Adapt BICC Staffing Levels, Skills, and Activities

The nature of business intelligence (BI)-related initiatives has evolved dramatically over time and continues to change. Different types of data sources, emerging paradigms for integrating and delivering data, new analytical models, and rapidly accelerating artificial intelligence (AI) constructs all introduce new requirements for skills and capabilities in the BI competency center (BICC; which some organizations call a data and analytics center of excellence, or similar). Data leaders that do not adapt their BICCs by modernizing skill sets and refocusing core activities risk falling behind on key trends dependent on optimal BI architectures and will struggle to support contemporary business requirements and use cases.

2025-10-14

Industrialize Enterprise Data to Really Take Advantage of Agentic AI

Agentic AI marks a turning point as profound as the shift from data centers to the cloud. What began as assistants—tools that helped people complete tasks—have become agents: autonomous digital labor that can plan, coordinate, and execute multistep workflows, end to end. For data leaders, this isn’t about incremental productivity gains; it’s about rethinking what can be achieved when machines don’t just help, but act.

2025-09-26