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Then check which applications depend on the same data or integrations. For the remaining improvements, weigh evidence of useful results against the full effort required. We suggest giving each application a concrete next step: improve it, investigate it or defer further work. The review sheet and a fictional example help you make that choice.", "citableAnswer": "MING recommends reviewing existing AI applications by addressing urgent operating problems, checking shared dependencies and comparing improvements by evidence of useful results and full effort. Each decision needs an owner, a verifiable outcome and a review date. This is a proposed method with a fictional example, not a validated ranking or measured savings.", "hookSourceIds": [], "author": { "name": "MING Labs", "role": "Editorial team" }, "evidenceNote": "Proposed method with a fictional example, informed by public NIST, AWS and Microsoft guidance. 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No completed repair or customer result." }, { "id": "S2", "title": "AI RMF Core — AI Risk Management Framework 1.0", "publisher": "NIST", "date": "2023; read 29 October 2026", "url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/", "supports": [ "GOVERN1.6: inventory according to risk priorities", "MANAGE1.1: assess intended purpose and whether development or deployment proceeds" ], "method": "Voluntary risk framework; not an ordered checklist or certification. Revision announced as in progress." }, { "id": "S3", "title": "Business perspective: The AI strategy in the age of AI", "publisher": "Amazon Web Services", "date": "Read 29 October 2026", "url": "https://docs.aws.amazon.com/whitepapers/latest/aws-caf-for-ai/business-perspective-the-ai-strategy-in-the-age-of-aiml.html", "supports": [ "Portfolio capabilities can depend on earlier capabilities", "Consider business results and existing solutions" ], "method": "Provider portfolio guidance, not validation of our example." }, { "id": "S4", "title": "Business plan for AI agents", "publisher": "Microsoft Learn", "date": "Read 29 October 2026", "url": "https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/business-strategy-plan", "supports": [ "Compare impact, feasibility and user desirability", "Do not advance a case with unclear safeguards" ], "method": "Provider planning guidance; no numerical score or outcome is adopted here." } ], "faqs": [ { "q": "Can I simply rank my AI applications by ROI?", "a": "Yes, when benefit and full costs have comparable foundations. Keep estimates and missing information visible. An unresolved approval or shared data source can determine what needs to happen before the most financially attractive improvement." }, { "q": "Should I merge applications that use the same data?", "a": "First check whether they do the same job. Sales and support can share data while needing different permissions, outputs and approvals. One source correction can make sense while both applications remain separate." }, { "q": "Should I retire an AI application that is rarely used?", "a": "Check whether low usage was actually observed and whether it fits the task. A monthly report is needed less often than a daily assistant. 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Use a review sheet and fictional example to weigh useful results, full effort and shared dependencies.\"\ncanonical: https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases\nlang: en\nlast-updated: 2026-10-29\n---\n\n# How do I prioritize my existing AI use cases?\n\n[Home](https://www.minglabs.com/) [Insights](https://www.minglabs.com/insights) [Answers](https://www.minglabs.com/insights/answers) How do I prioritize my existing AI use cases?\n\nAnswer\n\n\n\nStart with errors or missing approvals that need attention now. Then check which applications depend on the same data or integrations. For the remaining improvements, weigh evidence of useful results against the full effort required. We suggest giving each application a concrete next step: improve it, investigate it or defer further work. The review sheet and a fictional example help you make that choice.\n\nFictional example\n\n## One source can hold back two applications.\n\n**Outdated product export**\n\nBoth drafts use this source.\n\nSales draft\n\nSupport draft\n\nCheck the shared source, then test each application.\n\nIn this fictional example, both teams need the same source corrected. Plan that work once, then check each application separately.\n\nLast updated: October 2026 | Next review: November 2026 MING decision guide [Machine-readable record ](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases.json)\n\nBy MING Labs · Editorial team\n\nThe answer at a glance\n\n1. [What needs my attention now?](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#inventory)\n2. [Where do my applications depend on each other?](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#shared)\n3. [Which improvement should I take on next?](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#next)\n\n## What needs my attention now?\n\nConsider a fictional example: sales wants better proposal drafts, support wants more reliable product answers, and a third team wants better meeting notes. You need to decide where to spend your team’s limited time. That starts with understanding what goes wrong today and the result each team needs.\n\nWe suggest walking through a recent task with each application owner. Which results reach the person who needs them, what needs reworking, and where is an application acting beyond its approval? Bring urgent problems to the responsible person immediately. You can then compare the remaining improvements.[\\[S1\\]](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#source-s1) NIST also connects an AI inventory to risk priorities and the question of whether a system meets its intended purpose.[\\[S2\\]](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#source-s2)\n\n## Where do I start with my AI applications?\n\nWhen several teams need improvements at once, we can discuss their tasks and the next useful step with you.\n\n[Discuss my AI priorities](https://www.minglabs.com/contact?type=project&topic=ai-usecase-review)\n\nTell us what you have in mind. We will respond personally.\n\n## Real work reveals the benefit and the effort.\n\nUse this sheet to review the applications on a comparable basis. Record specific examples and the period you examined. Missing information gives you something to investigate before deciding.\n\n| My question for each application | What I need to record |\n| --- | --- |\n| What result does my team need? | The task, who uses its result and how the work would get done without this application. |\n| How well does it work today? | Results used, errors and corrections from a normal work cycle. |\n| What does it take to keep it running? | Fees and time spent on review, rework, maintenance and exceptions; mark estimates. |\n| Who can decide and intervene? | The responsible person, permitted actions and who takes over if it fails. |\n| What do other applications also depend on? | Shared sources, integrations, permissions or checks. |\n| What do I need to decide next? | One improvement or investigation, its owner, the time needed and the expected result. |\n\nIn our meeting-notes example, nobody has recorded correction time. Measure it during normal use before deciding to expand the tool. The license fee covers only part of the effort; our [operating-cost example](https://www.minglabs.com/insights/answers/what-does-it-cost-to-run-an-ai-agent-in-production) shows how to include people’s time. Choose comparable periods: a monthly report and a daily tool have different opportunities to demonstrate value.\n\n## One shared data source can hold back two improvements.\n\nSales and support find the same outdated product details in our example. Both applications read the same old export. Have that source checked before starting two separate projects for better prompts or new models. AWS’s AI portfolio guidance likewise recommends considering dependencies between initiatives.[\\[S3\\]](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#source-s3)\n\nIn this example, first agree how affected drafts will be checked or held back while the issue is investigated. Give the person maintaining the product export the correction task. Sales and support then test their own outputs. Those checks establish which errors are resolved and what work remains.\n\nPlan the shared source work once and the two application tests separately. If the same rework is removed, count that saving only once too. The applications themselves can remain separate: a proposal needs different approvals from a support reply.\n\n## Which improvement should I take on next?\n\nOnce urgent problems and dependencies are understood, compare the expected benefit with the full effort of making and running each improvement. Include whether your team can use it and has time for the work. Microsoft names impact, feasibility and user desirability as criteria for agent ideas.[\\[S4\\]](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases#source-s4) Keep the basis for each assessment beside it.\n\nOur fictional example leads to the following work plan:\n\n| Decision | Next step | How I can check the result |\n| --- | --- | --- |\n| Check the shared cause first | Contain affected drafts, investigate and correct the product export, then test both applications. | Both applications use the checked source version; each team reviews its new drafts. |\n| Measure the missing effort | Record correction time and useful meeting notes during normal use. | Effort and results can be compared with the team’s previous way of working. |\n| Defer the model change for now | Check which errors remain after the source review. A separately evidenced model problem may warrant an earlier test. | A specific remaining error gives the next test a purpose. |\n\nAgree an owner and a review date for each action. If one person owns several tasks, their time also needs an order of priority. If the review supports retiring an application, our [pause, repair and retirement guide](https://www.minglabs.com/insights/answers/when-to-shut-down-an-ai-agent) helps you plan the handback of its work.\n\nBasis for this decision guide\n\nThe example is fictional. The tables present our proposed approach, informed by public guidance, rather than measured customer results or a validated ranking. The right priority depends on your evidence, obligations and available people.\n\nFor your next review, choose two applications that compete for the same people’s time or depend on the same data. Fill in the sheet with their owners and agree the first action whose result you can check at the next meeting.\n\nCitable short version\n\nMING recommends reviewing existing AI applications by addressing urgent operating problems, checking shared dependencies and comparing improvements by evidence of useful results and full effort. Each decision needs an owner, a verifiable outcome and a review date. This is a proposed method with a fictional example, not a validated ranking or measured savings.\n\nProposed method with a fictional example, informed by public NIST, AWS and Microsoft guidance. No measured customer results.\n\n## What would help my team move forward?\n\nBring the applications you are choosing between. We can discuss what needs clarifying first and which improvement you can take on next.\n\n[Discuss my AI priorities](https://www.minglabs.com/contact?type=project&topic=ai-usecase-review)\n\nTell us what you have in mind. We will respond personally.\n\n[Read the retirement decision guide ](https://www.minglabs.com/insights/answers/when-to-shut-down-an-ai-agent)\n\n## Sources\n\n\\[S1\\]\n\nDependency-aware review sheet and invented product-export example MING Labs · 29 October 2026 Supports: Connect application decisions to shared work and separate acceptance evidence, Record unknowns, ownership and a next review Original proposed method; fictional applications and decisions. No completed repair or customer result.\n\n\\[S2\\]\n\n[AI RMF Core — AI Risk Management Framework 1.0](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/) NIST · 2023; read 29 October 2026 Supports: GOVERN1.6: inventory according to risk priorities, MANAGE1.1: assess intended purpose and whether development or deployment proceeds Voluntary risk framework; not an ordered checklist or certification. Revision announced as in progress.\n\n\\[S3\\]\n\n[Business perspective: The AI strategy in the age of AI](https://docs.aws.amazon.com/whitepapers/latest/aws-caf-for-ai/business-perspective-the-ai-strategy-in-the-age-of-aiml.html) Amazon Web Services · Read 29 October 2026 Supports: Portfolio capabilities can depend on earlier capabilities, Consider business results and existing solutions Provider portfolio guidance, not validation of our example.\n\n\\[S4\\]\n\n[Business plan for AI agents](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/ai-agents/business-strategy-plan) Microsoft Learn · Read 29 October 2026 Supports: Compare impact, feasibility and user desirability, Do not advance a case with unclear safeguards Provider planning guidance; no numerical score or outcome is adopted here.\n\n## Frequently asked questions\n\nCan I simply rank my AI applications by ROI?\n\nYes, when benefit and full costs have comparable foundations. Keep estimates and missing information visible. An unresolved approval or shared data source can determine what needs to happen before the most financially attractive improvement.\n\nShould I merge applications that use the same data?\n\nFirst check whether they do the same job. Sales and support can share data while needing different permissions, outputs and approvals. One source correction can make sense while both applications remain separate.\n\nShould I retire an AI application that is rarely used?\n\nCheck whether low usage was actually observed and whether it fits the task. A monthly report is needed less often than a daily assistant. If retirement is justified, arrange for unfinished work and dependent processes to be taken over before removing access or stopping services.\n\n## Quick Answers\n\n[What is a hybrid organisation of people and AI agents?](https://www.minglabs.com/insights/concepts/what-is-hybrid-organisation)\n\n## Available actions\n\n- **Copy answer for my AI**: copies the [AI briefing](https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/insights/answers/how-do-you-prioritize-existing-ai-use-cases\nMachine-readable index: https://www.minglabs.com/llms.txt\n" }