Bridging the Gap: AI Ambitions vs. Business Preparedness Revealed
In today's rapidly changing technological landscape, the drive towards artificial intelligence (AI) is more pronounced than ever. However, a recent study from the Harvard Business Review Analytic Services, sponsored by Hyland, sheds light on a significant disparity between the aspirations surrounding AI and the current state of readiness within businesses. This report, titled "Bridging the Readiness Gap to the Agentic Enterprise," reveals that while companies are keenly aware of the transformative potential of AI, many lack the foundational operational structures required to harness this technology effectively.
The study indicates that a staggering 94% of surveyed business leaders recognize the critical importance of integrating data, processes, and applications for successful AI implementation. Yet, only 27% feel that these key elements are adequately connected within their organizations today. The necessity for this integration stems from the fact that various forms of information, especially unstructured data—such as emails, PDFs, images, and videos—represent a vast reservoir of insights yet to be fully leveraged. While 65% of participants reported having at least some level of preparedness for utilizing structured data in AI, only 39% claimed the same for unstructured data, which constitutes the bulk of operational information in many firms.
Interestingly, the primary challenge is not a lack of data. Most organizations sit on an abundance of information that remains locked within unstructured formats across various repositories and workflows. The report emphasizes that without addressing the foundational elements of governance, access, and workflow execution, merely deploying new AI tools will not suffice. As Jitesh S. Ghai, CEO of Hyland, aptly notes, the focus must shift from merely accessing models to operationalizing AI in a reliable, governed, and contextually relevant manner.
This gap is not just an operational hurdle; it's an opportunity for substantial innovation. The transition to what Ghai terms an 'agentic enterprise' hinges on embedding AI into existing workflows that depend on content, data, and controls already in place. The report identifies several critical obstacles that continually hinder organizations from adopting and scaling AI. Significant issues include data silos, security and privacy concerns, formatting challenges, inadequate data management and governance, and insufficient or unclear data strategies. Alarmingly, only 10% of companies highlight a lack of data as their primary concern, suggesting that the root problem lies in preparation, accessibility, and trust rather than sheer data volume.
Moreover, the study reveals that many businesses have yet to incorporate AI into their everyday operations fully. Among those engaging with AI—whether actively using, testing, or piloting the technology—39% reported that most AI-based workflows still rely on standalone tools. Only 12% have successfully integrated AI directly into their workflow processes. Less than half of respondents (45%) believe that their AI initiatives yield the anticipated outcomes, indicating a pressing need for a pragmatic approach to AI readiness.
As organizations pivot towards more advanced and agentic forms of AI, the expectations have considerably heightened—not just in regards to technology, but also about the flow of information, governance of decisions, and measurement of value. According to Amy Machado, a senior research lead at IDC, companies that prioritize modernizing their content infrastructure and integrate intelligence into real workflows will be positioned to turn their AI ambitions into lasting impacts.
The report outlines several priority areas for organizations aspiring to bridge the readiness gap:
1. Prioritize Data Preparation: Especially for unstructured data that remains poorly optimized for AI use.
2. Modernize Content Platforms: To reduce fragmentation and enhance access, governance, and reusability.
3. Integrate AI into Workflows: Rather than relying solely on disconnected, standalone tools.
4. Align Leadership on Shared Governance: Establishing common definitions and accountability for successful AI practices.
5. Measure Success Holistically: Focusing on adoption, quality, and business outcomes, rather than merely speed to deployment.
This comprehensive study serves as a vital reminder for organizations to reflect on their approach to AI within their operations. For further insights regarding Hyland's offerings and platform solutions, interested parties are encouraged to visit Hyland.com. The findings showcased in the report are derived from surveys conducted in December 2025, incorporating feedback from 325 respondents across various business sizes and sectors, indicating a widespread need for actionable strategies in AI readiness.
With clearer understanding and focused efforts, companies can transform their ambitions into tangible and sustainable impacts through intelligent automation and AI integration.
The study indicates that a staggering 94% of surveyed business leaders recognize the critical importance of integrating data, processes, and applications for successful AI implementation. Yet, only 27% feel that these key elements are adequately connected within their organizations today. The necessity for this integration stems from the fact that various forms of information, especially unstructured data—such as emails, PDFs, images, and videos—represent a vast reservoir of insights yet to be fully leveraged. While 65% of participants reported having at least some level of preparedness for utilizing structured data in AI, only 39% claimed the same for unstructured data, which constitutes the bulk of operational information in many firms.
Interestingly, the primary challenge is not a lack of data. Most organizations sit on an abundance of information that remains locked within unstructured formats across various repositories and workflows. The report emphasizes that without addressing the foundational elements of governance, access, and workflow execution, merely deploying new AI tools will not suffice. As Jitesh S. Ghai, CEO of Hyland, aptly notes, the focus must shift from merely accessing models to operationalizing AI in a reliable, governed, and contextually relevant manner.
This gap is not just an operational hurdle; it's an opportunity for substantial innovation. The transition to what Ghai terms an 'agentic enterprise' hinges on embedding AI into existing workflows that depend on content, data, and controls already in place. The report identifies several critical obstacles that continually hinder organizations from adopting and scaling AI. Significant issues include data silos, security and privacy concerns, formatting challenges, inadequate data management and governance, and insufficient or unclear data strategies. Alarmingly, only 10% of companies highlight a lack of data as their primary concern, suggesting that the root problem lies in preparation, accessibility, and trust rather than sheer data volume.
Moreover, the study reveals that many businesses have yet to incorporate AI into their everyday operations fully. Among those engaging with AI—whether actively using, testing, or piloting the technology—39% reported that most AI-based workflows still rely on standalone tools. Only 12% have successfully integrated AI directly into their workflow processes. Less than half of respondents (45%) believe that their AI initiatives yield the anticipated outcomes, indicating a pressing need for a pragmatic approach to AI readiness.
As organizations pivot towards more advanced and agentic forms of AI, the expectations have considerably heightened—not just in regards to technology, but also about the flow of information, governance of decisions, and measurement of value. According to Amy Machado, a senior research lead at IDC, companies that prioritize modernizing their content infrastructure and integrate intelligence into real workflows will be positioned to turn their AI ambitions into lasting impacts.
The report outlines several priority areas for organizations aspiring to bridge the readiness gap:
1. Prioritize Data Preparation: Especially for unstructured data that remains poorly optimized for AI use.
2. Modernize Content Platforms: To reduce fragmentation and enhance access, governance, and reusability.
3. Integrate AI into Workflows: Rather than relying solely on disconnected, standalone tools.
4. Align Leadership on Shared Governance: Establishing common definitions and accountability for successful AI practices.
5. Measure Success Holistically: Focusing on adoption, quality, and business outcomes, rather than merely speed to deployment.
This comprehensive study serves as a vital reminder for organizations to reflect on their approach to AI within their operations. For further insights regarding Hyland's offerings and platform solutions, interested parties are encouraged to visit Hyland.com. The findings showcased in the report are derived from surveys conducted in December 2025, incorporating feedback from 325 respondents across various business sizes and sectors, indicating a widespread need for actionable strategies in AI readiness.
With clearer understanding and focused efforts, companies can transform their ambitions into tangible and sustainable impacts through intelligent automation and AI integration.
Topics Business Technology）
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