Requirement Gathering with GenAI and Agentic AI: Why Most Organizations Still Can’t Prove the ROI
GenAI has transformed how requirements are created—faster than any other phase of the software lifecycle. Yet proving business impact remains elusive.
User stories can now be generated from meeting transcripts. Legacy Jira backlogs can be mined into epics. UX telemetry can be converted into behavioral requirements without a single workshop.
Yet, according to MIT Sloan’s 2024 research on AI adoption maturity, more than 70% of organizations investing in GenAI still cannot directly attribute business outcomes to that investment—with the largest attribution gap appearing in upstream activities like planning and analysis.
The paradox is hard to ignore: GenAI requirement gathering is accelerating, but the business impact remains frustratingly unclear. This is not a tooling problem. It is a measurement problem that existed long before GenAI arrived.
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Speed Is Visible. Value Is Not.
In theory, GenAI requirement gathering should be the easiest place to demonstrate GenAI ROI. Planning happens early, artifacts are tangible, and cycle times are measurable.
Yet in practice, requirement gathering remains one of the least instrumented phases of delivery. According to IEEE Software Engineering studies, ambiguity introduced at the requirement stage accounts for a disproportionate share of downstream rework—yet fewer than 15% of organizations track requirement quality as a first-class metric. GenAI Requirement Gathering highlights this long-standing blind spot.
GenAI didn’t create this blind spot—it exposed it.
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What We’re Seeing Across Enterprise Teams
The following insights reflect early findings from an ongoing pulse survey and structured assessments conducted across enterprise product and engineering teams. Final results will be published once the dataset is complete.
In our preliminary analysis of approximately 45–50 enterprise teams spanning fintech, healthcare, and software platforms, a consistent pattern is emerging:
~78% report that GenAI Requirement Gathering has accelerated requirement writing
Only ~23% can confirm that this speed translated into measurable delivery acceleration
~91% acknowledge they do not track requirement quality before and after GenAI adoption
The most frequently cited blocker to proving ROI was not model accuracy or tooling—but “no baseline to compare against. “
This gap—between perceived acceleration and provable impact—appears regardless of industry or delivery maturity. Speed is happening. Evidence is not.
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When Faster GenAI Requirement Gathering Doesn’t Change Outcomes
Many teams assume that planning speed will naturally cascade into delivery performance. But according to MIT Sloan research on productivity in knowledge work, localized efficiency gains rarely translate into system-level improvements unless the surrounding operating model changes as well.
In requirement gathering, this disconnect shows up in familiar ways:
Backlogs are generated faster, but sprint predictability remains flat
Refinement meetings shrink, but mid-sprint clarification resurfaces
Documentation looks cleaner, but downstream defects persist
The problem is subtle: GenAI improves the artifact without necessarily improving the intent. And intent is what delivery depends on.
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Developer Productivity: The Metric That Feels Right—but Misleads
Most GenAI ROI narratives begin with developers—and understandably so. Teams report less time spent writing stories, fewer clarification loops, and faster backlog readiness.
But MIT-led studies on AI and productivity caution against relying on self-reported efficiency gains without baseline normalization. Hard questions surface quickly:
Did story throughput actually increase—or just shift earlier in the lifecycle?
Did reclaimed time go toward innovation—or get absorbed by more work?
Are teams delivering faster—or simply planning faster?
Without disciplined measurement, productivity becomes perception. And perception doesn’t survive executive scrutiny.
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Quality Signals That Quietly Point Back to GenAI Requirement Gathering
When defects rise, teams look at code. When MTTR increases, they look at operations. When customers complain, they look at UX. Rarely do they look upstream—to requirement intent.
Yet according to IEEE and MIT-referenced defect origin studies, a significant percentage of production issues can be traced back to misunderstood or incomplete requirements.
GenAI-generated requirements are often syntactically clearer. That does not guarantee semantic alignment. This is where confidence quietly erodes.
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Agentic AI: When Acceleration in Requirement Gathering Introduces a New Risk
Agentic AI systems don’t just assist—they infer, reason, and decide. They synthesize legacy tickets, stakeholder feedback, UX logs, and domain rules to produce requirements that are internally consistent and highly confident.
According to MIT-affiliated research on AI governance, increased autonomy introduces a specific risk: plausible wrongness that humans detect too late.
In requirement gathering, this manifests as:
Stories that are logically sound but strategically misaligned
Acceptance criteria optimized for system logic, not real-world behavior
Reduced debate, as GenAI Requirement Gathering, because outputs appear “complete”
The failure mode is not obvious error. It is silent misalignment at scale.
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The Unspoken Organizational Friction
Power Dynamics Shift
Business stakeholders feel “summarized”—their nuance compressed. Product managers feel relief—but also exposure. Engineers feel detached from intent, no longer co-creators. Leaders sense momentum but struggle to justify further investment.
The Confidence Gap
According to organizational psychology research frequently cited by MIT Sloan, when teams experience speed without proof of impact, cognitive dissonance sets in. That dissonance doesn’t lead to adoption—it leads to skepticism, then quiet abandonment.
This is how promising GenAI initiatives stall—not because they failed, but because trust never fully formed.
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Failure Scenarios We See Repeatedly
These are not hypotheticals. They are patterns observed across real implementations.
Scenario 1: The Fintech Trap
A fintech organization used GenAI to generate compliance requirements. Stories were produced ~60% faster. In UAT, compliance reviewers identified 12 domain-specific gaps the AI missed. Rework took longer than writing requirements manually.
They accelerated the wrong thing.
Scenario 2: The Medical Device Deadlock
A healthcare device manufacturer used GenAI for clinical requirements. Artifacts were cleaner. Formatting was consistent. During surgeon validation, feedback was blunt: “This captures what we do—but not why we do it this way.“
Regulatory submission was delayed by three months. The intent was missing.
Scenario 3: The Enterprise Mirage
A large financial institution reported ~40% faster backlog refinement. Leadership celebrated. Sprint velocity stayed flat. Defects increased by ~2%. The bottleneck moved downstream.
Progress became an illusion.
How to Recognize If You’re in the Measurement Trap
You may be experiencing the GenAI measurement blind spot if:
Story throughput increased, but sprint predictability did not
Backlog refinement time dropped, but defect escape rates stayed flat
Planning feels faster, but customer or business metrics haven’t moved
Leadership believes GenAI is working—but can’t articulate what changed
GenAI was deployed before measurement design was discussed
If three or more apply, you’re not behind—you’re in the gap. And this is where the real conversation starts.
Where We Might Be Wrong
Some argue this perspective is overly cautious—that GenAI’s value will become obvious as organizations mature their measurement infrastructure. They may be right.
The risk is not that GenAI is overhyped. The risk is that organizations are waiting for proof while measuring the wrong things. History suggests that value doesn’t emerge automatically—it is designed for.
The Real Conversation Starts Here
GenAI Requirement Gathering has not failed. Measurement strategy failed before GenAI arrived.
You can’t bolt governance onto speed. You have to design for measurement before acceleration.
In conversations with dozens of transformation teams across regulated and product-led organizations, one pattern repeats: Teams that scale GenAI responsibly didn’t start with tools.
They started with a question: “How will we know this worked?”
Everything else flows from that.
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Ready to Move Beyond Speed and Measure Real AI ROI?
Don’t let perceived acceleration mask silent misalignment. Connect with our strategy team to design a robust GenAI measurement framework for your organization.
Author’s Profile
Dipal Patel
VP Marketing & Research, V2Solutions Dipal Patel is a strategist and innovator at the intersection of AI, requirement engineering, and business growth. With two decades of global experience spanning product strategy, business analysis, and marketing leadership, he has pioneered agentic AI applications and custom GPT solutions that transform how businesses capture requirements and scale operations. Currently serving as VP of Marketing & Research at V2Solutions, Dipal specializes in blending competitive intelligence with automation to accelerate revenue growth. He is passionate about shaping the future of AI-enabled business practices and has also authored two fiction books.