{"id":34486,"date":"2026-07-06T12:52:00","date_gmt":"2026-07-06T10:52:00","guid":{"rendered":"https:\/\/askme.it\/insights\/endless-pocs-and-roi-obsession-where-companies-get-ai-wrong\/"},"modified":"2026-03-26T12:23:15","modified_gmt":"2026-03-26T11:23:15","slug":"endless-pocs-and-roi-obsession-where-companies-get-ai-wrong","status":"publish","type":"insights","link":"https:\/\/askme.it\/en\/insights\/endless-pocs-and-roi-obsession-where-companies-get-ai-wrong\/","title":{"rendered":"Endless POCs and ROI Obsession: Where Companies Get AI Wrong"},"content":{"rendered":"
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The first six mistakes in GenAI adoption relate to the ideation and experimentation phase. The six that follow emerge when organizations try to bring solutions into production. This is where many companies stall, often without understanding why.<\/p>\n<\/section>\n

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Underestimating AI engineering<\/h2>\n

Moving from a working POC to a production solution requires engineering work that most organizations have not planned for. A documented case illustrates this curve clearly: a client built a knowledge base solution for less than $100,000, achieving a recall rate above 60%. Getting it to 80% cost another $200,000. Reaching 90% would have required $1 million, and another million would have yielded only one additional percentage point. Knowing where the diminishing returns boundary lies is an integral part of planning.<\/p>\n<\/section>\n

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Compliance that blocks everything<\/h2>\n

Many successful POCs get abandoned due to compliance issues around data security, privacy, and ethics. The problem is not compliance itself, which is necessary, but the lack of transparency in the process. When the AI team does not understand how compliance decisions are evaluated, frustration shuts down the initiative. What is needed is an AI board that synthesizes stakeholder priorities and evaluates use cases transparently based on cost, risk, and value.<\/p>\n<\/section>\n

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A one-size-fits-all approach to data<\/h2>\n

Traditional data management methods are not sufficient for AI in production. Each use case has different contexts and requirements for data quality, governance, and representativeness. An adaptive data infrastructure is needed, with rich contextual metadata and a continuous evaluation framework that monitors drift over time.<\/p>\n<\/section>\n

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Losing sight of aligned objectives<\/h2>\n

A system that works technically can still fail if the business does not understand what it is doing or does not trust the results. A telling example: a company used GenAI to predict employee resignations, identifying promotions and salary increases as the main risk factors. Top management faced an ethical dilemma: should promotions be managed based on attrition risk or performance? The use case was canceled. The lesson is that governance and transparency must be built in from the start, not bolted on afterward.<\/p>\n<\/section>\n

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Treating AI as a finished project<\/h2>\n

An AI project has a start date but no end date. Data preparation costs, model serving, testing, and infrastructure expenses continue even after launch. Many companies run out of budget not because they underestimated the initial project, but because they failed to plan for the operational phase. Managing AI as a product, with a dedicated product manager and an agile methodology, is the structural answer to this problem.<\/p>\n<\/section>\n

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Giving up too early<\/h2>\n

Production AI solutions degrade over time as data and business needs change. A company that had succeeded in the POC phase found itself struggling in production due to these drifts. Instead of abandoning the effort, it invested in LLMOps capabilities, including monitoring, evaluation, and observability, and managed to handle the dynamic nature of the requirements. Continuous monitoring is not optional: it is an integral part of any serious AI deployment.<\/p>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

Six technical and organizational mistakes in the GenAI engineering and production phase: from compliance that blocks everything to data management, to the mistake of treating AI as a finished project.<\/p>\n","protected":false},"featured_media":34488,"menu_order":0,"template":"","insights_category":[557],"insights_tags":[589,599,601,615,765],"class_list":["post-34486","insights","type-insights","status-publish","has-post-thumbnail","hentry","insights_category-ai-and-organization","insights_tags-ai-adoption","insights_tags-ai-engineering-en","insights_tags-ai-governance-en","insights_tags-ai-production","insights_tags-llmops-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34486","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights"}],"about":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/types\/insights"}],"version-history":[{"count":1,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34486\/revisions"}],"predecessor-version":[{"id":34487,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights\/34486\/revisions\/34487"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/media\/34488"}],"wp:attachment":[{"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/media?parent=34486"}],"wp:term":[{"taxonomy":"insights_category","embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights_category?post=34486"},{"taxonomy":"insights_tags","embeddable":true,"href":"https:\/\/askme.it\/en\/wp-json\/wp\/v2\/insights_tags?post=34486"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}