Measuring ROI in the Age of AI-Assisted Software Development

We are living through one of the most dramatic transformations in the history of software development. AI-assisted coding, LLM copilots, autonomous agents, and automated DevOps workflows are reshaping how engineering teams plan, create, release, and operate software.

Yet according to a recent MIT study, 95% of GenAI projects fail to deliver measurable ROI. Not because AI is ineffective — but because organizations skip the foundational steps required to generate and measure value. Below is a practical framework to navigate ROI in the GenAI era, before a single line of code is written.

1. Start With the Mission — Not the Tools

Most teams jump directly into using AI tools, chasing velocity or productivity stories. But ROI is not a tool-level metric. ROI is a mission-level outcome. Before adopting GenAI tools, engineering leaders should answer five grounding questions:

1. What problem are we solving?

2. What outcomes matter most? (speed, resilience, safety, customer value, cost savings)

3. What risks are acceptable for this mission?

4. Where in the software lifecycle should AI be applied — and where should it not?

5. How will success be measured in business terms?

Skipping this step is how organizations generate “AI-washed output”: high activity, unclear value, no measurable ROI.

2. Understand the Context — ROI Is Not Uniform

AI does not create value evenly across all types of work. Context defines how value shows up — and what ROI should measure. For example, is AI being applied within a new application or for legacy modernization?

In new builds, AI typically accelerates:

• Feature experimentation

• Time-to-market

• Cost-per-feature

• Developer scaffolding and reasoning

Here, the ROI lens is speed, innovation, and acceleration.

In legacy modernization, these ecosystems operate under constraints such as:

• Deep dependency chains

• Hidden/implicit business logic

• Technical debt

• High-risk changes

Here, ROI focuses on stability, resilience, maintainability, and cost avoidance.

Mission + Context = What ROI Should Actually Measure.

If the mission is innovation but the metrics focus on stability, ROI appears negative.
If the mission is stability but the metrics focus on speed, ROI looks disappointing. This mismatch is one reason the MIT study reports such a high failure rate.

3. Why 95% of GenAI Projects Fail — The MIT Study’s Real Insight

The MIT finding has raised alarm across leadership teams — but the root cause is somewhat straightforward: Organizations deploy AI without the prerequisites for value creation or measurement.

a. No mission alignment. AI is adopted because “competitors are doing it,” not because it supports a defined business goal. Teams lack:

• measurable KPIs

• governance frameworks

• success criteria

• lifecycle strategies

b. No context awareness. Executives assume AI = productivity, without distinguishing:

• new builds vs. legacy work

• risk posture vs. velocity goals

• cost avoidance vs. value creation

c. No Visibility into how AI changes the work. Companies measure output, not origin.
They track activity, not value.

d. Outdated metrics

Traditional software development metrics, such as story points, commits, LOC, surveys,  collapse under AI-assisted development. They cannot capture the influence, depth, or quality of AI involvement.

The MIT insight is not that AI fails. It’s that organizations lack the mission clarity, contextual framing, and measurement foundation required to see its value. This is where provenance becomes essential.

4. Provenance: The Missing Prerequisite for AI ROI

Organizations cannot measure AI ROI until they understand how the software was created — and that requires provenance, the origin record of code. Provenance reveals:

• Who or what wrote the code (human, LLM, agent, hybrid)

• What prompts, instructions, or context shaped it

• How developers validated or modified AI output

• What risks or anomalies were introduced at origin

• Whether code was duplicated, hallucinated, or drifted

• How origin patterns predict long-term cost, stability, or incidents

Once provenance is captured, new AI-native ROI metrics become possible — metrics specifically designed to fill the measurement gap the MIT study warns about.

Provenance is not the end of the ROI journey. It is the starting point that enables productivity, risk, cost, governance, and intelligence use cases.

5. Measuring ROI Is a Journey — Not a Single Metric

In an era where software is increasingly built by hybrid human–AI teams, organizations must adopt a modern ROI model that accounts for this shift. A practical journey looks like this:

Mission → Context → Provenance → Metrics → ROI → Intelligence

• Mission defines why we are building.

• Context determines what ROI should measure.

• Provenance reveals how work is actually created.

• Metrics replace assumptions with evidence.

• ROI becomes auditable, comparable, and repeatable.

• Intelligence becomes the competitive edge — guiding future planning, resource allocation, risk posture, and innovation velocity.

The MIT statistic that 95% of GenAI projects fail should not discourage teams.
It should motivate them to adopt provenance-driven intelligence systems that finally make ROI observable, measurable, governable, and improvable.