Data.
Not opinion.

AI and Agentic AI in 2026

== Co-Authors: Myles Suer and Brian Lett ==

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

The Office of Finance in 2026

The chief financial officer (CFO) steers the organization toward its strategic goals by enabling the financial backbone of the business. CFOs play a critical role in enterprise technology decisions, both by ensuring that finance uses appropriate current technologies and by helping guide internal IT investment across the business. This special report aims to assist the CFO with decision making by examining technologies used by finance organizations, with an in-depth focus on four areas: artificial intelligence (AI)—including generative AI and agentic AI—enterprise resource planning (ERP), enterprise performance management (EPM), and environmental, social and governance (ESG).

2026-07-15

Environmental, Social, and Governance Market Study 2026

This study shows that global ESG has retreated in 2026 from the progress it made in prior years, despite investor demand, regulatory pressure, and an increasing understanding of the connection between robust ESG performance and business success. Nevertheless, stakeholders’ insistence on precise and truthful disclosure has led to a growing emphasis on transparency in ESG reporting.

However, ESG faces many challenges and roadblocks, such as changing regulations and obtaining executive sponsorship for what many organizations consider optional. Successful navigation of these challenges depends on adopting a strategic approach and focusing on transparency, accurate data, and a commitment to measurable impact.

2026-07-09

Wisdom of Crowds Enterprise Performance Management Market Study

Enterprise performance management deployment rates are high, systems are mature, and the buying audience sees tremendous value and additional growth within and outside of their organizations in 2026, according to the Dresner Advisory Services Wisdom of Crowds® Enterprise Performance Management (EPM) Market Study.

Dresner's annual EPM Market Study provides a comprehensive assessment of usage and trends around EPM, systems that enable organizations to plan for the impact of various internal and external factors on its future performance and business outcomes.

The 12th annual study offers in-depth analyses on all aspects of the market, including current usage, deployment intentions, software longevity and importance, success with EPM, implementation and sourcing strategies, planning priorities, and the impact of artificial intelligence (AI) in EPM.

2026-06-30

AI, Data, and Analytics Governance Market Study

AI, data, and analytics governance initiatives support a wide range of use cases, according to the Dresner Advisory Services AI, Data, and Analytics Governance Market Study. Executive dashboards and KPI reporting, financial planning and analysis, and operational performance monitoring are the top three use cases, according to this year's survey.

AI, data, and analytics governance provides the oversight and operating framework of information-related decision rights, principles, policies, processes, people, and technologies that enables an organization to achieve its business objectives. Dresner's AI, Data, and Analytics Governance study, now in its fourth edition, analyzes end user deployment and sentiment around the governance, including its relative importance, business use cases, governance practices and policies, and solutions support.

2026-06-15

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

Many Organizations Face a Governance Gap

In his book “The 7 Habits of Highly Effective People,” Stephen Covey, inspired by a speech by former U.S. President Dwight Eisenhower, coined the term “Eisenhower Matrix” to describe a tool to help improve productivity, prioritization, and time management. Covey’s simple four-box square—which uses an x-axis with sections labeled “urgent” and “not urgent,” and a y-axis with sectors labeled “important” and “not important”—helps with prioritization by categorizing tasks by urgency and importance

This concept also is also relevant for data and analytics leadership: Things that are urgent and important should draw the focus and attention of data leaders, who should then allocate resources to address the identified priorities. Among the top three challenges that organizations most often consider urgent—data quality, data integration, and governance of AI, data, and analytics—our survey respondents also frequently view data quality and data integration as having the highest level of importance (see Dresner Advisory Services’ analysis of technologies and initiatives strategic to business intelligence (BI) in the 2026 Wisdom of Crowds® Business Intelligence Market Study). And when it comes to spending plans, our respondents report that the category of data and analytics (which includes data quality and data integration) shows the second-highest number of budget increases (behind AI).

2026-07-28