Building a defensible ROI model for AI initiatives

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.View full definition → (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:

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.View full definition → 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.)

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

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

Step 4: Put it together

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.View full definition → 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.View full definition → regardless of the spreadsheet.

Realistic expectations for 2026

Research summarization is one of the most mature generative AI use cases in the sector, and vendors across the US and Europe now offer it, often embedded in existing research and data platforms. That maturity means the technology risk is low, but it also means the differentiation is low: the ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → comes from your workflow discipline, not the model itself.

Regulators are watching. In the EU, the AI Act (in phased application through 2026 and beyond) requires transparency and risk management for many AI systems, and firms must be able to explain and document AI-assisted processes. In the US, the SEC has scrutinized "AI washing" (overstating AI capabilities to clients or investors). 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.View full definition → model, with documented assumptions and monitoring, is also a compliance asset.

Key Takeaways

• Hours saved are not value; redeployed hours are. Always apply a conservative redeployment haircut and value time at fully loaded cost.
• The license is the small number. Data licensing, integration, and ongoing monitoring typically exceed the software cost; model them explicitly.
• Make the base case clear without the fuzzy benefits. Treat coverage expansion as upside, so your core case survives a skeptical review.
• Stress-test and track adoption. A model that survives a halved redeployment rate, and a tool that is actually used, are what turn a claim into a defensible number.
• Label every figure as an estimate with a date, and keep a human in the loop. It protects both your ROIROIReturn on Investment: the ratio of net profit to the cost of an investment. A 300% ROI means each dollar invested returns $3.View full definition → credibility and your regulatory standing.