91% of leaders say AI raises the stakes for data-driven decisions, 70% see insights trapped in unstructured data, and only 43% have data governance frameworks — highlighting why most organizations aren’t fully AI-ready.
Artificial intelligence is no longer experimental. According to Salesforce’s State of Data & Analytics Report (Second Edition), nearly every organization today uses at least one form of AI. Yet, widespread adoption has not translated into widespread readiness.
The report highlights a critical reality: AI success is being constrained not by ambition, but by data foundations. The report highlights that 91% of business leaders believe the rise of AI makes it more important to be data-driven. However, across industries, organizations are struggling with data trust, integration, governance, and accessibility. These gaps are now emerging as the biggest barriers to measurable AI-driven outcomes.
AI Adoption Is High. AI Readiness Is Not.
While most enterprises describe themselves as data-driven, many still struggle to translate data into business impact. Leaders increasingly recognize that AI raises the stakes. As AI systems become more autonomous and agent-driven, the quality, context, and governance of data become non-negotiable.
The report reveals a widening gap between AI intent and operational readiness. Organizations may deploy AI tools, but without reliable, unified, and contextual data, those tools fall short of delivering meaningful value.
Unstructured and Trapped Data: The Untapped AI Asset
The report reinforces a long-standing but unresolved challenge: most enterprise data is unstructured. Text documents, PDFs, scanned files, media assets, and legacy content continue to hold valuable insights, yet remain largely inaccessible to analytics and AI systems. 80%–90% of enterprise data is estimated to be unstructured. As companies seek to become more data-driven and improve AI capabilities, unstructured data’s trapped insights and untapped value are getting renewed attention. 70% of data and analytics leaders believe the most valuable insights for their organizations are trapped in unstructured data.
Compounding the issue is data fragmentation. Enterprises operate across hundreds of applications, with limited connectivity between systems. As a result, data remains trapped in silos, slowing AI initiatives and limiting cross-functional intelligence.
AI readiness increasingly depends on an organization’s ability to:
- Extract insights from unstructured content
- Integrate data without excessive duplication
- Make trusted data available where business users actually work
The Data Confidence Crisis Undermining AI
One of the report’s most striking findings is the lack of confidence organizations have in their own data. A significant portion of enterprise data is viewed as unreliable, incomplete, or poorly contextualized.
This lack of trust directly affects decision-making. Business leaders report delays in accessing insights, contradictory conclusions across teams, and, in many cases, decisions driven by intuition rather than evidence.
In an AI-driven environment, this becomes a structural risk. AI systems amplify the strengths and weaknesses of the data they consume. When data quality is inconsistent, AI outputs become harder to trust, explain, and operationalize.
Governance Is Lagging Behind AI Acceleration
As AI adoption accelerates, governance frameworks are struggling to keep pace. Many organizations lack consistent data governance policies across environments, and ethical AI guidelines remain underdeveloped. According to the Salesforce report, 88% of data and analytics leaders believe AI advances demand new data governance approaches, but only 43% have established formal data governance frameworks and policies.
This creates challenges around compliance, explainability, and security, particularly in regulated industries. Without scalable governance models, organizations risk undermining trust in AI systems before they reach maturity.
AI readiness, therefore, is not only a technical challenge, but also an organizational one.
Industry Snapshots: AI Readiness in Practice
Healthcare
Healthcare organizations face unique AI readiness challenges driven by language barriers, fragmented clinical data, and strict regulatory requirements. While AI holds promise for clinical decision support and operational efficiency, inconsistent data standards and limited data fluency slow adoption at scale.
Publishing and Content-Driven Industries
Publishing, media, and education organizations sit on vast volumes of unstructured content. The report highlights that these industries often struggle less with data volume and more with data activation. AI readiness here depends on structured content pipelines, metadata enrichment, and seamless content interoperability.
Enterprise Technology and Services‘
Technology-driven organizations place high importance on data quality and integration but still report gaps in harmonization and governance. Even in mature tech environments, AI initiatives stall when data strategies fail to align with business priorities.
Across industries, the pattern is consistent: AI readiness reflects data maturity, not industry size or digital ambition.
What the Report Makes Clear About AI Readiness
The findings point to a clear conclusion. AI-ready organizations are those that:
- Treat data as a governed, shared asset rather than a departmental byproduct
- Invest in integration strategies that reduce silos and duplication
- Prioritize data quality, context, and accessibility alongside AI models
- Embed governance and security into AI design from the outset
AI readiness is not a single initiative. It is an ongoing transformation of data, systems, and operating models.
Where Impelsys Fits In
The challenges outlined in the Salesforce report closely align with the areas where Impelsys delivers impact.
- Unstructured Data Activation: Impelsys helps organizations unlock value from content-heavy data sources through AI-enabled content processing, semantic enrichment, and intelligent extraction.
- Data Readiness for AI: From data engineering to validation and quality assurance, Impelsys supports enterprises in building reliable data foundations that AI systems can trust.
- Governance and Compliance: With strong experience in regulated industries such as healthcare and life sciences, Impelsys enables compliant, secure, and auditable AI-ready workflows.
- Content and Knowledge Platforms: Through platforms like moǹk, Impelsys supports scalable content delivery that aligns with modern AI and analytics needs.
By focusing on data confidence, content intelligence, and scalable foundations, Impelsys helps organizations move from AI experimentation to AI readiness.
Closing Thought
The Salesforce State of Data & Analytics Report sends a clear message. AI is already here, but readiness is still being built. Organizations that succeed in the AI era will not be those that adopt the most tools, but those that invest in trusted data, strong foundations, and responsible governance. AI readiness, ultimately, is a data transformation journey.
Authored by – Ravikiran SM and Sharada Bastia
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