Joanna Leung

𝗧𝗛𝗘 𝗣𝗥𝗢𝗕𝗟𝗘𝗠
Mid-market companies have little tolerance for failed AI investments. Every team submits use-case ideas, while IT has a handful of developers and a fixed budget. Manual vetting takes weeks. Strong ideas stall. Weak ideas divert engineers from higher-margin work.

𝗛𝗬𝗣𝗢𝗧𝗛𝗘𝗦𝗜𝗦
If a self-service intake and financial triage framework sorts each AI idea before development starts, then business stakeholders will route proposals through it, as measured by the share of proposals triaged before engineering handoff.

𝗖𝗢𝗡𝗖𝗘𝗣𝗧
Gartner reports that about 50% of GenAI initiatives are abandoned after proof of concept (2026). Small teams cannot absorb this waste.

A self-service intake and financial triage framework protects engineering time and accelerates the AI roadmap. The framework sorts each idea into a custom build, a staff-enablement fix, or a rejection. Leaders see projected cost before development begins.

Discovery identified Total Cost of Ownership (TCO) blindness. Teams fund prototypes without costing API tokens, compute, and maintenance, and these costs often exceed the build cost. Simple automation ideas remain idle because IT has no standard process to return them to the business.

𝗣𝗔𝗜𝗡 𝗣𝗢𝗜𝗡𝗧𝗦

• Resource misallocation: small teams build for use cases that deliver small company value, resulting in missed AI investment ROI. Simple automation ideas remain idle because IT has no standard process to return them to the business, nor ways to enable non-technical staff to build.

• Total Cost of Ownership (TCO) blind spot: unplanned running costs (e.g. API tokens, compute, and maintenance) convert working tools into mid- to long term financial liabilities.

• Intake bottleneck: manual assessment of low-value use cases consumes weeks of the tech team’s time.

𝗦𝗢𝗟𝗨𝗧𝗜𝗢𝗡
A self-guided intake and triage tool. Teams submit use case details in a form. The framework scores the answers on three pillars. The logic is interface-agnostic, so it runs in any tool a client already uses.

3-Bucket Triage:

1. High ROI with custom tech routes to the engineering roadmap.
2. High ROI with low tech routes to staff enablement.
3. Low ROI is rejected at intake.

Capital Guardrail: sets the maximum build spend (High, Medium, Low) to cost-box and safeguard investment to meet the target ROI.

TCO Model: factors in long-term running cost (High, Medium, Low) before development starts.

𝗘𝗫𝗘𝗖𝗨𝗧𝗜𝗢𝗡
A business unit submits a short assessment form with the details the framework requires. The framework triages the use case into one of the three buckets. Then, the output recommends a next step, adapted to the organization’s own process.

For use cases that require custom tech, the project enters development with a hard budget cap ensuring ROI.

For use cases require low tech, staff are encouraged to build on their own. Supplemented by the AI Enablement initiative, executed in parallel, including AI Adoption training for employees, access to company-wide AI tools, an employee crowd-shared AI solution library, and regular office hours with an AI Enablement Specialist. The submitter solves the problem without requesting a custom build from IT.

𝗜𝗠𝗣𝗔𝗖𝗧

• Assessment Consistency: business and engineering score every idea on identical criteria, which removes opinion and politics from prioritization.
• Efficient resource allocation: protect finite, high-cost tech resources for the build of high ROI use cases.
• Speed to Market: empower a wider employee base to build for higher volume of low-tech-required use cases, accelerating AI adoption company-wide at scale
• Capital protection: Leaders see hidden TCO before committing spend, so low-yield ideas stop at intake.

𝗣𝗢𝗦𝗧-𝗟𝗔𝗨𝗡𝗖𝗛
A fully automated version of use case triage, built in the tool the client’s team already uses, with a form-based intake for use case details.

Role

AI Adoption Consultant

For

Mid-Market Companies

Date

April – May '26

Type

AIOps, AI Adoption, AI Governance

URL

www.pragmatico.ai/