Data.
Not opinion.

State of the Enterprise AI Platform Market in 2026

The Enterprise AI Platform (AIP) market has undergone a re-definition since mid-2025, including a flurry of enterprise AIP software vendor announcements in the first half of 2026. What began as a fragmented landscape of specialized tools consisting of vector databases, LLM APIs, agent frameworks, and semantic layers is rapidly becoming a competitive battlefield among 13 major enterprise software vendors to own the complete AIP stack. Every major vendor has announced agent orchestration capabilities, semantic/ontology products, and governance frameworks in the same 12-month window. The surface similarity of these announcements masks material strategic differences that will determine vendor winners and losers during the next five years.

2026-09-10

AI and Agentic AI in 2026

AI has captured the attention of CEOs and boards for its potential to boost productivity, drive real business transformation, and create new competitive advantages. Our latest research shows executives are most interested in using AI to (in order of priority): address specific business challenges, respond to industry disruption, and keep pace with competitors. Even so, in 2026, 31% of executives say their firms are only exploring the business potential of AI. By contrast, 14% say AI now represents a cornerstone that drives and shapes their business strategy, and another 49% believe AI plays an important supporting role in broader strategy.

Organizations are seeing concrete ROI from AI: 9% report returns of 51% or more, and 26% report returns between 24%-50%. Including the rest of the high-performer group, 58% of respondents report returns of 12% or higher. Looking ahead, we expect aggregate ROI to slow as the “easy” wins from generative AI, vibe coding, and simple agent tasks (what we call task automation) become harder to reap. The real gains going forward will come from agentic workflows and agentic systems, which link individual tasks into full workflows or end-to-end business processes.

2026-08-27

ERP in 2026

Despite its pervasiveness, enterprise resource planning (ERP) has long suffered from definitional challenges. Many ERP users do not understand it or even realize they are using it. Vendors struggle with ERP as a marketing term; instead, they describe themselves as developing and providing software solutions that integrate and manage core business processes across an organization.

Fundamentally, ERP is a suite of integrated software applications designed to standardize operations, automate tasks, and provide consolidated data across the finance, human resources, and supply chain management departments. ERP also has been evolving into a comprehensive operational and analytical platform that organizations leverage in digital transformation—for example, by building agentic artificial intelligence (AI) into it.

2026-08-11

Wisdom of Crowds® Financial Consolidation, Close Management and Financial Reporting Market Study 2026

This is the sixth year we have published a detailed market study for FCCR. It is part of our ongoing research into the broader enterprise performance management market and focuses more closely on performance management capabilities explicitly targeting the finance function.

While FCCR can be considered a mature market, it benefits from the introduction of new AI 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.

2026-09-24

AI Development Platforms Market Study 2026

As we start our 20th year as an independent, objective, and data-driven research organization, our commitment to providing insights into data, analytics, and performance management remains steadfast. Although this year’s report marks the 13th edition of our annual research into AI, DS, and ML, we have updated its focus to reflect these platforms’ evolution into broader AI development platforms. Whereas DS and ML focus on building and deploying models, AI development platforms build on that remit to support what has quickly become the full stack of modern AI: foundation models, agents, data integrations, and enterprise-grade control.

2026-09-18

ModelOps Market Study 2026

This fifth annual edition of our ModelOps Market Study coincides with the 19th anniversary of Dresner Advisory Services. Rapidly expanded use of AI, data science (DS), and ML makes ModelOps a critical discipline for enabling organizations to effectively manage models at scale—especially those entering into production. We expect ModelOps to maintain or increase in importance as organizations develop and manage more models, and as awareness of the business value of ModelOps workflows and platforms becomes widespread.

More organizations report models in production, and the number of deployments has also risen significantly year over year in 2026. However, many organizations continue to face challenges—especially for production models—such as determining which function (or functions) should ultimately be responsible for their oversight, how often models should be updated, and which ModelOps features to select and prioritize.

2026-09-18

Scaling Enterprise AI: Balancing Values and Risks; Aligning Platform and Organizational Readiness

Scaling enterprise artificial intelligence (AI) requires aligning AI platform capabilities and choices with organizational maturity, avoiding fragmentation of technologies, and developing and nurturing associated skills and competencies. On the one hand, agentic workflows are driving positive, measurable ROI. However, decentralized AI adoption creates integration complexity, semantic misalignment, and increases risk stemming from fragmented and incomplete governance. Success demands a unified framework for evaluating vendors across a complete platform stack and governance framework, prioritizing semantic grounding over commoditized orchestration.

Organizations must cultivate five core competencies: strategic alignment, robust data foundations, human-process integration, phased scaling, and proactive governance. A centralized AI Program Office ensures consistent oversight, perimeter definition, and kill-switch authority. By balancing rapid innovation with rigorous risk management, organizations can transition from isolated pilots to scalable AI systems driving value.

2026-09-10

BI Thrives Beyond Executives: To Maximize Business Value, Give the Power to the People

Most organizations started with business intelligence (BI) investments targeted at executives and managers and focused on providing them with analytic data that could help improve the business’s overall performance. These deployments continue to consume the majority of many organizations’ BI resources and attention. A significant number of data leaders, their teams, and business leadership believe that insights from BI are most impactful in the hands of executives and high-level managers—that is, those individuals empowered to make major strategic and tactical decisions and change the trajectory of the business.

Unfortunately, this view causes many organizations to forego the substantially greater value that comes from applying BI at critical decision points inside business processes spread across the enterprise and beyond. By embedding BI at these points of “action,” large numbers of individual contributors inside an organization—as well as customers and other external parties—can positively influence business outcomes, collectively “moving the needle” in significant ways relative to the organization’s strategic goals.

2026-08-25

‘Better Decision Making’ Is Vague and Weak; Target Precise Business Outcomes to Justify BI

Clear, specific, and quantifiable goals are key for successful BI initiatives. But most organizations investing in BI instead use the general goal of “better decision making” as a simplistic placeholder, standing in for more clearly articulated and tangible benefits. While BI often does result in better decision making, data leaders and their teams who use such a vague term to justify their BI programs to executives and business leaders are building those programs on unstable ground and unreliable support.

Better decision making is a “soft,” vague goal because it depends on subjective interpretation:

• Which decisions will be better and in what ways?
• How good or bad were those decisions before BI, and exactly how much better will BI make them?
• How does that improvement help the organization—does it have a positive impact on the business’ strategic goals? If so, to what degree?

2026-08-17