{"id":6929,"date":"2026-10-22T12:48:43","date_gmt":"2026-10-22T15:48:43","guid":{"rendered":"https:\/\/wearebigcheese.com\/sin-categorizar\/the-roi-of-getting-off-to-a-good-start-how-to-build-a-business-case-based-on-an-ai-assessment\/"},"modified":"2026-10-22T12:52:41","modified_gmt":"2026-10-22T15:52:41","slug":"the-roi-of-getting-off-to-a-good-start-how-to-build-a-business-case-based-on-an-ai-assessment","status":"publish","type":"post","link":"https:\/\/wearebigcheese.com\/en\/blog-en\/the-roi-of-getting-off-to-a-good-start-how-to-build-a-business-case-based-on-an-ai-assessment\/","title":{"rendered":"The ROI of Getting Off to a Good Start: How to Build a Business Case Based on an AI Assessment"},"content":{"rendered":"\n

The ROI of Getting Off to a Good Start: How to Build a Business Case Based on an AI Assessment<\/h2>\n\n

By Sebasti\u00e1n Grattarola \u2014 CTO of BigCheese, a Premier AWS Partner<\/em><\/p>\n\n

The conversation about artificial intelligence in organizations has taken a new turn. Two years ago, the question was, \u201cDoes this work?\u201d Today, almost no one doubts that it does; the question has changed, and it\u2019s a tougher one: \u201cWhere do I start, and how do I know it\u2019s worth it?\u201d<\/strong> That\u2019s the question leaders around the world are asking themselves<\/a>. <\/p>\n\n

\"\"<\/figure>\n\n

It\u2019s a business question, not a technical one. And yet most AI initiatives start on the technical side\u2014a model, a pilot, a test that someone saw in a demo\u2014without first answering how much the problem they want to solve costs or how much it would be worth to solve it. The predictable result: a lot of activity, little conviction, and a budget that no one dares to approve because no one put the numbers on the table. <\/p>\n\n

In the organizations we work with in Latin America\u2014fintech, retail, and payment providers\u2014the message is always the same: there\u2019s no shortage of enthusiasm for AI, but there\u2019s a lack of a business case that can turn that enthusiasm into an investment decision. That is exactly the purpose of an AI Assessment: measure before you build<\/strong>. <\/p>\n\n

What Does an AI Assessment Address?<\/h3>\n\n

An AI Assessment is a short, focused project that takes a vague “we want to use AI” and turns it into a prioritized portfolio of use cases, each with its own business case<\/strong>. The deliverable isn\u2019t a technical report\u2014it\u2019s the clarity needed to decide where to invest first, how much it will cost, and what you can expect to get in return. <\/p>\n\n

The focus, then, is not on the method (the phases, workshops, interviews) but on the output: an informed investment decision. This follows the same logic as AWS\u2019s Path-to-Value framework<\/a>, which maps out the journey of generative AI from idea to value in production. And at the heart of that decision is the ROI model. Below, I\u2019ll share the framework we used to build it, so you can apply the same logic to your own organization. <\/p>\n\n

Where Does the Return Come From: Five Dimensions of Value<\/h3>\n\n

The most common mistake when estimating the value of an AI agent is to focus solely on cost savings\u2014that is, how many person-hours are freed up. It\u2019s the easiest dimension to calculate, but it\u2019s rarely the most significant. A solid business case projects value across five dimensions<\/strong> (in line with the AWS Cloud Value Framework<\/a>, which measures the value of the cloud beyond mere cost savings): <\/p>\n\n

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  1. Cost savings and efficiency<\/strong> \u2014 person-hours freed up by tasks that agents can complete independently or more quickly, plus reduced costs associated with legacy tools, errors, rework, and SLA violations.<\/li>\n\n\n\n
  2. Revenue generation and growth<\/strong> \u2014 faster responses that drive sales recovery, improved conversion rates through personalized recommendations, new capabilities (24\/7, new channels), reduced churn, and the ability to scale volume without incurring a linear increase in costs.<\/li>\n\n\n\n
  3. Productivity and throughput<\/strong> \u2014 how many more transactions are processed in the same amount of time, how much resolution time is reduced, and how many hours are freed up for the team to focus on higher-value work.<\/li>\n\n\n\n
  4. Customer and Employee Experience<\/strong> \u2014 improved CSAT\/NPS, reduced customer effort, and, on the internal side, teams freed from tedious tasks, with the resulting impact on turnover.<\/li>\n\n\n\n
  5. Risk mitigation and compliance<\/strong> \u2014 consistent enforcement of policies, real-time detection of fraud and errors, traceability and auditability, and operational continuity that does not depend on specific individuals.<\/li>\n<\/ol>\n\n

    The key is to assess each dimension against a baseline of the current state<\/strong>. Without a credible baseline (how much the process costs today, how often it\u2019s run, how long it takes, how much is lost due to errors), ROI is a guessing game. That\u2019s why the model always starts by quantifying the present before projecting the future. <\/p>\n\n

    The Model, Step by Step<\/h3>\n\n

    Let’s look at how this framework applies to an illustrative case. The numbers that follow are examples, chosen to demonstrate the method<\/strong> (they do not correspond to an actual client), but the structure is the one we use in every engagement.<\/p>\n\n

    The scenario<\/strong>: An organization manually processes a high volume of incoming documents\u2014for example, verifying and extracting data from documents that arrive through multiple channels. Currently, a team reads, classifies, and uploads these documents by hand. <\/p>\n\n

    Step 1 \u2014 Baseline of the current state.<\/strong> Let\u2019s assume 4,000 documents per month, an average of 15 minutes per document, with an hourly labor cost of US$18. That amounts to 1,000 person-hours per month, about US$18,000 per month, or US$216,000 per year in processing costs alone. Added to that is an 8% rework rate due to data entry errors. <\/p>\n\n

    Step 2 \u2014 Value Projection (the five dimensions).<\/strong> A GenAI agent (built on Amazon Bedrock for extraction and classification, with guardrails and traceability built into the design) can autonomously handle a conservative percentage of the documents (say, 60%) and accelerate the rest. We project the value as follows: <\/p>\n\n