Building a defensible ROI model for AI initiatives

# Building a defensible ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → model for AI initiatives

A mid-sized equity research desk deploys an AI tool that summarizes earnings calls and broker notes. Six months in, the head of research tells the CFO the tool "saves us a ton of time." The CFO asks: how much, exactly, and did it pay for itself? Nobody has the number. This lesson gives you that number, and the discipline to defend it.

Why most AI ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → models are wrong

Most AI business cases fail one of two ways. They overcount benefits (counting hours "saved" that nobody redeploys to anything valuable), or they undercount costs (ignoring the data cleanup, the integration, and the ongoing maintenance that dwarfs the software license).

A defensible model does three things:

• Ties every benefit to a measurable, redeployable unit (analyst hours, coverage names, error rates).
• Includes the hidden burden: data preparation, integration, model monitoring, and licensing renewals.
• States assumptions as estimates with dates, not facts.

We will build one for a research summarization deployment, the single most common generative AI use case in asset and wealth management as of early 2026.

The use case: research summarization

The tool ingests earnings-call transcripts, sell-side research (notes from investment banks), and regulatory filings, then produces structured summaries: thesis, key numbers, changes versus prior quarter, and risk flags. Analysts read the summary instead of the 40-page source.

This is a strong AI fit because the task is high-volume, text-heavy, and tolerant of a human review step. It is a weak fit for anything where a hallucinationhallucinationA hallucination is when an AI model generates output that is fluent and confident but factually wrong, fabricated, or unsupported by its source data.Voir la définition complète → (a confident but false AI output) goes straight into a client deliverable unchecked.

Step 1: Quantify analyst time saved

Start with the benefit everyone claims but few measure.

Assumptions (illustrative, treat as estimates you must validate with your own timesheet data):

• 12 analysts
• Each processes 20 research documents per week
• Reading and note-taking without the tool: 40 minutes per document
• With AI summary plus human review: 15 minutes per document
• Time saved: 25 minutes per document

Worked calculation:

Docs per week       = 12 analysts x 20 docs      = 240 docs/week
Minutes saved/doc   = 25
Weekly minutes saved= 240 x 25                    = 6,000 min = 100 hours
Annual (46 work wks)= 100 x 46                     = 4,600 hours

Now the trap. Hours saved are worthless unless redeployed. If your analysts fill the freed time with more of the same low-value reading, ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → is near zero. The value appears only when those 4,600 hours produce something: more names covered, deeper diligence, faster turnaround.

Redeployment haircut: assume only 60% of saved hours convert to productive output. That leaves 2,760 effective hours. Be conservative here. A model that assumes 100% conversion is not defensible.

To value the hours, use a fully loaded cost (salary plus benefits plus overhead), not base salary. If a research analyst's fully loaded cost is, say, 200,000 USD per year over roughly 1,840 productive hours, that is about 109 USD per hour. (This is an illustrative figure; use your own compensation data.)

Value of redeployed time = 2,760 hours x 109 USD = ~300,840 USD/year

Step 2: Value coverage expansion

The more strategic benefit. Freed capacity lets the desk cover more securities without new hires.

Say the redeployed 2,760 hours let the team add coverage of 15 additional small-cap names it previously ignored. The value here is not a time figure; it is an optionality and revenue figure. Coverage expansion can support new product (a small-cap strategy), better client service, or fewer blind spots in an existing portfolio.

Be honest: this benefit is real but harder to quantify, and you should present it as a range or as a strategic option, not a precise dollar figure. Padding it destroys credibility. A defensible model might say: "Coverage expansion enables a small-cap sleeve; if it attracts even a modest mandate, incremental fee revenue would exceed total project cost. We treat this as upside, not base case."

Step 3: The hidden data and maintenance burden

This is where naive models collapse. The software subscription is often the smallest cost.

Data acquisition and preparation

The AI needs clean, licensed, well-structured inputs. Earnings transcripts and filings are cheap or free. Sell-side research is licensed and expensive, and reusing it inside an AI tool may breach the provider's terms. Check redistribution and machine-processing rights before you build.

Data preparation (cleaning, deduplication, tagging by ticker and date) is real engineering work. For a first deployment, budget it as a meaningful one-time cost plus ongoing upkeep.

Integration

The tool must connect to your document store, your order management or research management system, and your entitlement controls (who is allowed to see what). Integration is usually the largest line item in year one.

Ongoing model monitoring

Generative models drift. Vendors update them. A summary format that worked in Q1 may degrade after a model version change. You need a monitoring process: periodic sampling of outputs, an accuracy scorecard, and a human owner. This is a recurring labor cost, not a one-off.

For a solid framework on evaluating and monitoring these systems, see the NIST AI Risk Management Framework, a free, widely used US government resource on trustworthy AI.

Illustrative annual cost stack

Software license (12 seats)        =  60,000 USD/yr   (estimate)
Data licensing / redistribution    =  40,000 USD/yr   (estimate)
Integration (yr 1, amortized 3 yr) =  50,000 USD/yr
Monitoring + review labor          =  45,000 USD/yr
Total annual cost                  = 195,000 USD/yr

Every figure above is illustrative. The point is the shape: license is under a third of total cost.

Step 4: Put it together

Base-case annual benefit (time)    = 300,840 USD
Annual cost                        = 195,000 USD
Net annual benefit                 = 105,840 USD
Simple payback (yr 1 incl setup)   = costs recovered within ~year 1-2

Coverage expansion sits on top as upside, not base case. A net positive base case that does not even rely on the fuzzy coverage benefit is exactly what makes the model defensible to a skeptical CFO.

🎬 [VIDEO: "How to Build an AI ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → Model" - youtube.com - a concise walkthrough of structuring costs, benefits, and payback for enterprise AI projects]

Step 5: Stress-test the model

A model you cannot break is a model nobody believes. Attack your own assumptions:

• Halve the redeployment rate (60% to 30%). Does the base case still clear zero? If not, your case rests entirely on coverage expansion, which is fine only if you say so plainly.
• Double the monitoring cost. Early deployments almost always underestimate this.
• Add a model-failure scenario. What happens if a summary drops a material risk flag and an analyst relies on it? Build in the review step and count its cost. In asset management the human-in-the-loop is not optional; it is a control.

Metrics that make it honest

Track a small set of outcome metrics, not vanity usage stats:

• Summary accuracy rate (sampled and human-scored)
• Analyst adoption (percent actually using summaries, not just logged in)
• Hours redeployed to named higher-value tasks
• Coverage names added

Adoption is the silent killer. A tool with brilliant output and 20% adoption has a negative ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.Voir la définition complète → regardless of the spreadsheet.