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AI ROI Audit Service

A boutique consulting service that conducts a 2-week on-site or remote audit of a company's current AI tool spend, actual usage patterns, and business outcomes. The auditor interviews stakeholders, maps tools to business problems, identifies redundancy and abandonment, then delivers a prioritized roadmap with specific tools to keep/drop/add and expected ROI per tool based on industry benchmarks and the company's actual workflow.

SERVICE

13 weeks • 70% confidence

Value Proposition

Most companies have 5-12 AI tools active but only 2-3 are actually driving measurable value; the rest are abandoned or duplicative. This service cuts through the noise by auditing real usage and ROI, then delivers a 12-month roadmap that typically saves 30-50% of AI spend while increasing actual adoption and measurable output (faster reports, fewer errors, etc.). Unlike generic consulting, it's outcome-focused: you pay for a roadmap tied to specific business metrics, not hours.

Target Audience

Mid-market companies (50-500 employees) in professional services, manufacturing, finance, and healthcare that have spent $50k-$500k on AI tools in the past 18 months and have no clear adoption strategy.

Key Features

  • Stakeholder interviews (dev, ops, finance, end-users) to map actual tool usage vs. stated goals
  • Spend analysis: which tools are actually being used, which are abandoned, which duplicate functionality
  • ROI calculation per tool based on time saved, error reduction, or revenue impact (e.g., sales acceleration)
  • And more, with full implementation detail...

Tech Stack

Spreadsheet/database (Airtable or Notion) for tool comparison and spend tracking Video conferencing (Zoom) for remote interviews Simple project tracker (Monday.com or Asana) to manage audit workflow Google Slides/Docs for roadmap deliverable
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Original Problem

Businesses struggle to determine which AI tools to invest in after the hype cycle deflates

Companies are confused about AI adoption strategy as the initial hype fades and they face a fragmented market of free tier tools versus expensive enterprise solutions. Decision-makers lack clear frameworks to evaluate which AI investments will actually deliver ROI versus which are commoditized or overhyped, leading to wasted budgets and missed opportunities.

Score: 52.5%

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