{"id":199,"date":"2020-09-16T15:04:48","date_gmt":"2020-09-16T15:04:48","guid":{"rendered":"https:\/\/kaaspi.com\/staging\/8740\/?page_id=199"},"modified":"2026-06-16T08:18:11","modified_gmt":"2026-06-16T08:18:11","slug":"ai-readiness-review","status":"publish","type":"page","link":"https:\/\/kaaspi.com\/staging\/8740\/services\/ai-readiness-review\/","title":{"rendered":"Practical AI readiness & Use Case Review"},"content":{"rendered":"\n
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AI Readiness<\/p>\n\n

Practical AI Readiness & Use Case Review<\/h1>\n\n

\n Helping firms identify practical AI use cases, assess knowledge and information readiness, and turn business problems into clear requirements for AI-enabled tools.\n <\/p>\n\n

\n Contact Richard<\/a>\n Back to services<\/a>\n <\/div>\n <\/div>\n\n
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Information architecture before artificial intelligence<\/h2>\n

\n AI tools depend on the quality, structure and governance of the knowledge they draw on. If the underlying information architecture is weak, AI adoption is more likely to expose confusion than remove it.\n <\/p>\n <\/div>\n\n <\/div>\n <\/section>\n\n

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When this review is useful<\/h2>\n\n

\n Many firms are under pressure to explore AI, but are not yet clear where it will create real value, which use cases are worth pursuing, or whether their knowledge and process foundations are ready.\n <\/p>\n\n

\n The challenge is rarely just the technology. Firms need to understand the business problem, the workflow, the information sources, the risks, the governance requirements and the practical adoption path.\n <\/p>\n\n

\n Kaaspi helps firms move from broad AI interest to clearer use cases, stronger foundations and practical requirements for AI-enabled work.\n <\/p>\n <\/div>\n <\/section>\n\n

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Typical issues<\/p>\n

Common problems this work can address<\/h2>\n <\/div>\n\n
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AI pilots are being considered before the business problem is clear<\/div>\n
Potential use cases exist, but have not been properly prioritised<\/div>\n
Documents are duplicated, outdated or poorly classified<\/div>\n
No clear view exists of which knowledge sources are authoritative<\/div>\n
Taxonomy, metadata and search are not strong enough to support reliable retrieval<\/div>\n
Permissions, confidentiality and information boundaries are unclear<\/div>\n <\/div>\n\n <\/div>\n <\/section>\n\n
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How Kaaspi helps<\/h2>\n

\n The review combines business analysis, knowledge architecture, process thinking and practical AI readiness assessment. The aim is to help firms decide what is worth doing, what needs to be fixed first, and what requirements should guide any AI-enabled work.\n <\/p>\n <\/div>\n\n

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1<\/div>\n

Identify practical use cases<\/h3>\n

\n Work with stakeholders to understand business problems, workflows and opportunities where AI-enabled tools may create practical value.\n <\/p>\n <\/article>\n\n

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2<\/div>\n

Assess readiness foundations<\/h3>\n

\n Review knowledge sources, documents, taxonomy, metadata, permissions, governance, ownership and process maturity.\n <\/p>\n <\/article>\n\n

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3<\/div>\n

Define requirements and next steps<\/h3>\n

\n Translate business needs into clearer requirements, adoption considerations and practical actions for internal teams, vendors or implementation partners.\n <\/p>\n <\/article>\n\n <\/div>\n\n <\/div>\n <\/section>\n\n

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Focused on the foundations that make AI useful<\/h2>\n\n

\n This is not a technical model evaluation or software implementation exercise.\n <\/p>\n\n

\n The focus is on whether the firm has clear use cases, reliable knowledge sources, usable information architecture, appropriate governance and practical workflows to support useful, safe and credible AI-enabled work.\n <\/p>\n <\/div>\n <\/section>\n\n

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Typical outputs<\/h2>\n

\n The aim is to give leaders, project owners and operational teams a clear view of where AI may help, what foundations need attention, and what should happen next.\n <\/p>\n <\/div>\n\n

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Practical AI readiness findings report<\/div>\n
Prioritised AI use case assessment<\/div>\n
Knowledge and document foundations review<\/div>\n
Taxonomy, metadata and search improvement priorities<\/div>\n
Governance, ownership and permissions observations<\/div>\n
Business requirements for AI-enabled tools<\/div>\n
Practical 90-day action plan<\/div>\n
Senior playback session<\/div>\n <\/div>\n\n <\/div>\n <\/section>\n\n
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Follow-on support<\/h2>\n

\n Where useful, Kaaspi can support use case workshops, knowledge architecture improvement, taxonomy and metadata design, process redesign, requirements definition, tool selection support, stakeholder engagement and implementation planning.\n <\/p>\n <\/div>\n\n

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Why Kaaspi<\/h2>\n

\n Kaaspi is led by Richard Tomlinson, a former Chief Knowledge Officer and senior knowledge architecture leader with experience across financial services, international law firms and professional services.\n <\/p>\n

\n Richard has advised on the evaluation of AI-enabled sentiment analysis tools for company earnings reports in an investment management environment, and led work to structure investment case content for more consistent analysis and future AI readiness.\n <\/p>\n <\/div>\n\n <\/div>\n <\/section>\n\n

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Business-led, not hype-led<\/h2>\n\n

\n Kaaspi does not start from the assumption that AI is the answer. The starting point is the business problem, the information environment, the workflow and the practical requirements.\n <\/p>\n\n

\n That approach helps firms avoid buying technology before they understand what they need it to do, what information it will rely on, and what needs to change for it to be adopted properly.\n <\/p>\n <\/div>\n <\/section>\n\n

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Start with a focused conversation<\/h2>\n\n

\n To discuss whether a Practical AI Readiness & Use Case Review would be useful for your firm,\n contact Richard at richard@kaaspi.com<\/a>.\n <\/p>\n\n Contact Richard<\/a>\n <\/div>\n <\/section>\n\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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