{"id":2473,"date":"2026-07-23T13:39:57","date_gmt":"2026-07-23T13:39:57","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=2473"},"modified":"2026-07-23T13:39:57","modified_gmt":"2026-07-23T13:39:57","slug":"harnessing-unstructured-data-for-agentic-ai-a-3-stage-maturity-model","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/harnessing-unstructured-data-for-agentic-ai-a-3-stage-maturity-model\/","title":{"rendered":"Harnessing Unstructured Data for Agentic AI: A 3-Stage Maturity Model"},"content":{"rendered":"<p><i><span data-contrast=\"auto\">Sponsored by Ohalo<\/span><\/i><span data-ccp-props=\"{}\"> .<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">Everyone knows context is king in the world of agentic AI. What needs more attention is the pivotal role of unstructured data in building the context that really differentiates your organization. Most AI adopters remain immature in this area. They struggle to organize and consume their proprietary documents, emails, and images.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">But, where to begin? This blog proposes a three-stage maturity model that helps data and AI teams understand their baseline, then establish and optimize data management processes. Our model spans the discovery, refinement, access, validation, and tracing of unstructured data objects. We inform this model with new findings from our BARC Report, <\/span><a href=\"https:\/\/hs.ohalo.co\/barc-report?utm_campaign=44864391-BARC%20Report&amp;utm_source=barc.com&amp;utm_medium=referral\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\"><span data-contrast=\"none\">Harnessing Unstructured Data for AI Innovation<\/span><\/a><span data-contrast=\"auto\">, coauthored by Merv Adrian.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Maturity Model for Unstructured Data Management<\/span><\/b><span data-ccp-props=\"{&quot;335551550&quot;:2,&quot;335551620&quot;:2}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;335551550&quot;:2,&quot;335551620&quot;:2}\"> <img loading=\"lazy\" decoding=\"async\" class=\"wp-image-2474 aligncenter\" src=\"https:\/\/solutionsreview.com\/thought-leaders\/files\/2026\/07\/oie_8YsNRl2Tkdqg.png\" alt=\"\" width=\"500\" height=\"309\" srcset=\"https:\/\/solutionsreview.com\/thought-leaders\/files\/2026\/07\/oie_8YsNRl2Tkdqg.png 426w, https:\/\/solutionsreview.com\/thought-leaders\/files\/2026\/07\/oie_8YsNRl2Tkdqg-300x185.png 300w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/span><\/p>\n<p style=\"text-align: center;\"><i>Our maturity model spans the discovery, refinement, access, validation, and tracing of unstructured data objects<\/i><\/p>\n<p><b><span data-contrast=\"auto\">Stage 1: Baseline<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">The \u201cbaseline\u201d maturity level, where most AI adopters stand today, is sobering. These organizations have raw, duplicative, and often stale inputs that sprawl across complex and distributed environments. They cannot find all the unstructured data they need, and have no consistent practices for classifying, indexing, and assigning ownership of what they do find. They lack controls for data quality, lineage controls, and policy enforcement. AI teams consume these inputs on an ad-hoc basis with no monitoring, validation, or tracing. We estimate that about 50 percent to 70 percent of organizations remain in this baseline maturity level.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">71 percent do not fully know where all their relevant unstructured data resides<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">64 percent cannot consistently enforce access and usage policies <\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">53 percent have not implemented lineage tools<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p style=\"text-align: center;\"><strong>50 to 70 percent of organizations have just a baseline maturity for managing unstructured data.<\/strong><\/p>\n<p><span data-contrast=\"auto\">What does this look like in practice?<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">Picture a regional insurance carrier, CoverMe, that processes claims through a cloud-based portal. Unstructured objects such as adjuster notes, customer emails, scanned medical records, and third-party inspection reports land in a mix of cloud storage buckets and SharePoint sites with no consistent tagging or taxonomy.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">When CoverMe\u2019s AI team launches an agentic assistant for claims triage, the agents piece together inputs and retrieval sources on an ad-hoc basis. Their embedded GenAI and ML models lack the necessary framework to cite consistent trusted sources or follow consistent logic. The agents burn unnecessary tokens, straining budget limits, and force claims adjusters to spend extra time fact-checking their outputs. This agentic AI initiative falls short of its ROI goals.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Stage 2: Established<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">The \u201cestablished\u201d maturity level shows meaningful improvement over the baseline. Organizations at this level scan, index, and catalog a portion of unstructured objects that business owners and AI experts identify for initial projects. They format and filter the AI inputs within these \u201cgolden\u201d sources and start to implement governance constraints such as role-based access controls and lineage views that link model inputs to outputs. They also train stakeholders to follow formal processes for curating legal documents, customer records, and so on.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: center;\"><b>Organizations with established maturity levels organize and consume a portion of unstructured objects to support initial AI projects.<\/b><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">For example, suppose a regional hospital chain, StayWell, embarks on an AI initiative to streamline discharges and improve patient outcomes in its cardiology unit. Its data and AI teams catalog cardiology examination reports, physician notes, discharge summaries, and patient histories. They tag them with metadata that captures document type, originating department, and primary care provider.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">StayWell implements an internal agentic assistant that reviews all this information and generates simple but comprehensive patient recommendations about self-care, symptom management, and future visits. This helps the discharge nurse better educate the patient at checkout, which reduces follow-on patient inquiries by 30 percent and unnecessary ER visits by 15 percent.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Stage 3: Optimized<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">The \u201coptimized\u201d maturity level opens the way for much broader gains. These AI adopters lay a similar foundation as the \u201cestablished\u201d camp, but they do so across most or all business units. And their data and AI teams go further to build the context that drives competitive advantage. They catalog, format, filter, and enrich relevant unstructured data across the enterprise. This gives them a standardized, comprehensive view of agentic AI inputs and enables them to correlate wide-ranging data points.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">Optimized organizations also tighten governance with user-, row-, and column-level access controls; continuous auditing; and real-time observability and remediation of data quality issues. Authorized users can trace detailed lineage and source versioning to inspect the quality of agentic outputs.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">Picture a global factory equipment manufacturer, Cog, that has scaled its unstructured data foundation across business functions, from engineering and procurement to field service. Its data and AI teams have cataloged, tagged, and enriched millions of unstructured objects enterprise-wide. This creates detailed, governed views of equipment schematics, supplier contracts, technician service reports, and warranty claims. <\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">Cog&#8217;s agentic assistants correlate field failure patterns with engineering specifications and supplier records across product lines, enabling the company to predict equipment failures weeks in advance and cut warranty costs by 22 percent year over year. They deliver these results while operating within strict governance guardrails to ensure compliance with global regulatory requirements.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Putting Things In Context<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p style=\"text-align: justify;\"><span data-contrast=\"auto\">Legendary programmer Alan Kay has observed that \u201ccontext is worth 80 IQ points.\u201d But AI agents cannot get all that context from structured tables alone. Their human governors must modernize how they discover, refine, access, validate, and trace rich unstructured inputs in order to boost agentic IQ and deliver on stratospheric market expectations. To learn more about a solution that can help, be sure to check out <\/span><a href=\"https:\/\/www.ohalo.co\/platform\" class=\"external\" rel=\"nofollow\"><span data-contrast=\"none\">Data X-Ray<\/span><\/a><span data-contrast=\"auto\"> from Ohalo.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sponsored by Ohalo . Everyone knows context is king in the world of agentic AI. What needs more attention is the pivotal role of unstructured data in building the context that really differentiates your organization. Most AI adopters remain immature in this area. They struggle to organize and consume their proprietary documents, emails, and images.\u00a0 [&hellip;]<\/p>\n","protected":false},"author":1334,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Harnessing Unstructured Data for Agentic AI: A 3-Stage Maturity Model<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Harnessing Unstructured Data for Agentic AI: A 3-Stage Maturity Model\" \/>\n<meta property=\"og:description\" content=\"Sponsored by Ohalo . Everyone knows context is king in the world of agentic AI. What needs more attention is the pivotal role of unstructured data in building the context that really differentiates your organization. Most AI adopters remain immature in this area. 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For 30 years Kevin has deciphered what technology means to practitioners, as an industry analyst, instructor, marketer, services leader, and tech journalist. A frequent public speaker and co-author of two books about data management, Kevin most loves teaching data and AI leaders about evolving strategies, tools and techniques to capitalize on the value of data.\",\"sameAs\":[\"https:\/\/www.linkedin.com\/in\/kevinpetrietech\/\"],\"url\":\"https:\/\/solutionsreview.com\/thought-leaders\/author\/kevin-petrie\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Harnessing Unstructured Data for Agentic AI: A 3-Stage Maturity Model","robots":{"index":"noindex","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"og_locale":"en_US","og_type":"article","og_title":"Harnessing Unstructured Data for Agentic AI: A 3-Stage Maturity Model","og_description":"Sponsored by Ohalo . 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For 30 years Kevin has deciphered what technology means to practitioners, as an industry analyst, instructor, marketer, services leader, and tech journalist. A frequent public speaker and co-author of two books about data management, Kevin most loves teaching data and AI leaders about evolving strategies, tools and techniques to capitalize on the value of data.","sameAs":["https:\/\/www.linkedin.com\/in\/kevinpetrietech\/"],"url":"https:\/\/solutionsreview.com\/thought-leaders\/author\/kevin-petrie\/"}]}},"_links":{"self":[{"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/posts\/2473"}],"collection":[{"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/users\/1334"}],"replies":[{"embeddable":true,"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/comments?post=2473"}],"version-history":[{"count":0,"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/posts\/2473\/revisions"}],"wp:attachment":[{"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/media?parent=2473"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/categories?post=2473"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/solutionsreview.com\/thought-leaders\/wp-json\/wp\/v2\/tags?post=2473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}