RSM Survey Reveals Gaps in AI Integration and Data Infrastructure
With 67% of companies integrating AI, yet only 36% achieving core embedding, the RSM survey underscores the vital importance of robust data governance. Leaders must act to fortify their data foundations to leverage AI for scalable growth.
Key Facts
- 67% of organizations use AI, but only 36% embed it in core processes, revealing scalability gaps.
- 44% of AI spending focuses on data infrastructure, indicating a shift towards foundational readiness.
- 47% of moderate AI success is due to integration issues, exposing vulnerabilities in data governance.
- 98% believe AI will transform tax functions, highlighting the need for robust data foundations.
- Strong data foundations improve decision-making speed and compliance, driving sustainable growth.
Summary
Recent findings from the RSM Middle Market AI Survey 2026 reveal a critical gap in the integration of artificial intelligence (AI) within organizations. While 67% of respondents report having integrated AI into their operations, only 36% have successfully embedded it across core processes. This disparity highlights a significant challenge: the ability to scale AI initiatives effectively hinges not just on technology but on the quality and governance of underlying data. For business leaders, this signals an urgent need to reassess data foundations to ensure they are robust enough to support AI-driven growth.
The survey indicates that data quality is the primary barrier to AI deployment, with over one-third of respondents citing it as a key inhibitor. Other challenges include security and privacy concerns, legacy system integration, and gaps in talent. These issues expose the limitations of current data infrastructures, which often fail to support the complexities of enterprise-wide AI applications. As organizations transition from pilot programs to broader implementations, weaknesses in data governance and integration become increasingly apparent, leading to operational inefficiencies and inconsistent decision-making.
RSM's recent webinar, "Data Foundations: The Operating Layer Behind AI, Analytics and Scalable Growth," emphasized the necessity of a strong data foundation. This foundation is not merely a technical asset; it is an operational capability that fosters trust, speed, and risk management across the enterprise. As AI initiatives expand, they often reveal foundational weaknesses that can stymie progress. For instance, customer records may differ across platforms, leading to manual reconciliation and delayed decision-making. Such fragmentation can stall AI initiatives, highlighting the need for a cohesive data strategy.
The 2026 survey further illustrates the challenges organizations face. Among those reporting limited success with AI pilots, 47% cited integration issues as the leading cause of failure. This points to a broader systemic problem: inconsistent definitions, disconnected workflows, and fragmented ownership hinder the effective deployment of AI. Without addressing these foundational issues, organizations risk maintaining a cycle of pilot success that fails to translate into enterprise value.
Investment trends reflect this growing recognition of the importance of data foundations. The survey reveals that 44% of organizations allocate the majority of their AI budgets to data platforms and infrastructure. This shift indicates that leaders are beginning to view data initiatives not just as technology upgrades but as essential components of overall business performance. A strong data foundation enables organizations to leverage AI effectively, reducing ambiguity and increasing confidence in outputs.
As AI becomes more embedded in operational processes, the demand for trust in data will intensify. The implications are particularly pronounced in functions like tax, where accurate and traceable information is critical. The survey indicates that 98% of respondents believe AI will significantly impact tax functions in the next few years, with nearly half expecting a shift from compliance execution to strategic planning. This underscores the need for organizations to modernize their data practices to enhance compliance, reporting accuracy, and overall enterprise performance.
The future landscape for AI, analytics, and automation will depend less on the latest technological advancements and more on the strength of the data foundations that support them. Organizations that prioritize the development of a cohesive data strategy, emphasizing governance, integration, and quality, will be better positioned to scale their AI initiatives. By investing in these foundational elements, businesses can foster an environment conducive to sustainable growth and operational excellence.
In this evolving market, executives must recognize that the path to scalable AI and enhanced operational performance lies in strengthening their data foundations. Those who act decisively to integrate governance, data quality, and strategic alignment into their operations will not only enhance their AI capabilities but also drive long-term business success.
Entities Mentioned
Companies
Technologies
People
Key Concepts
Definitions
- data foundation
- A data foundation is an operating capability that determines how consistently an organization can use information to make decisions, manage risk, and create value.
- AI scalability
- AI scalability refers to the ability of an organization to expand AI initiatives from pilot projects to full integration across core business processes.
- data governance
- Data governance establishes accountability, ownership, and decision rights over data management within an organization.
- integration challenges
- Integration challenges occur when systems and processes within an organization do not connect effectively, leading to inconsistent data and workflows.
- tax transformation
- Tax transformation involves modernizing tax operations to improve compliance, reporting accuracy, and strategic planning through better data management.
Use Cases
- →AI deployment in business processes
- →tax compliance and reporting
- →automated decision making
- →data-driven strategic planning
- →improving operational efficiency
- →enhancing reporting accuracy
Frequently Asked Questions
What are the main barriers to scaling AI in organizations?
The primary barriers include data quality issues, integration challenges, security and privacy concerns, and gaps in talent or skills. Addressing these challenges is crucial for successful AI deployment.
How does data governance impact AI initiatives?
Effective data governance helps clarify responsibilities and aligns oversight with risk management, which can reduce rework and facilitate smoother AI deployment across the organization.
Why is a strong data foundation necessary for AI?
A strong data foundation ensures that information is trustworthy, accessible, and consistently applied across decisions and processes, which is essential for the successful scaling of AI initiatives.
What role does integration play in data management?
Integration connects various systems and business processes, allowing for consistent data usage and reducing the likelihood of fragmented information that can hinder operational efficiency.
How can organizations prepare for scalable AI adoption?
Organizations can prepare by investing in their data foundations, focusing on data quality, governance, integration, and architecture to create a reliable environment for AI and analytics.