Why AI Research Often Fails to Deliver ROI—and How to Fix It

🔧 Execution, Not Insight Quality, Is the Real ROI Bottleneck

Many teams can now generate high-quality market research quickly. The persistent problem is the “last mile”: translating insights into cross-functional action, attributing impact, and defending ROI to leadership. This study documents how Happioh designed a standardized consulting pilot that turns Atypica.AI reports into decision-grade implementation plans with measurable KPI lift—then packages the approach into a repeatable product called Pilot-in-a-Box.

🧭 Research plan: deciding whether “implementation” is a single service or three distinct jobs with different success metrics.

This research plan determined whether “implement our Atypica report” is one generic request—or a set of distinct Jobs-to-be-Done with different KPI definitions, decision-makers, and proof requirements.

Using JTBD interviews, we mapped five personas (e-commerce owner, B2B marketers, CMO, product manager) to the business progress they were actually trying to make—then used Lean Startup principles to design an MVP pilot that can be tested, measured, and iterated.

The core decision: design one universal pilot, or create standardized scopes aligned to different jobs. The interviews made the answer clear: one-size-fits-all fails on both execution and attribution.

🔍 AI research: using Atypica outputs as “insight inputs” to build standardized scopes, playbooks, and measurement logic.

AI research here is not about generating new market insights—it is about structuring implementation so insights reliably create outcomes. In this context, a “successful implementation program” is defined as one that (1) converts insight into actions a team can execute within weeks, and (2) produces KPI lift that can be credibly attributed.

Atypica’s report outputs were treated as modular inputs to three repeatable pilot scopes:

E-commerce Growth Pilot: translate insights into platform levers (ads, PDPs, pricing, listings) with clear A/B logic.
KPIs: CVR, AOV, CAC, ROAS.
B2B GTM Accelerator: convert insights into messaging, targeting, nurture, and sales enablement that improves funnel efficiency.
KPIs: MQL→SQL, sales cycle length, lead quality, CAC.
Product Innovation Sprint: convert consumer/trend insights into product briefs and alignment artifacts that reduce time-to-market.
KPIs: time-to-market, sell-through/feature adoption, conversion, reviews.

The insight was operational: clients don’t pay for “better understanding”; they pay for KPI movement with defensible attribution. That requirement shaped everything downstream—timeline, workflows, and deliverables.

🗣️ AI interview: capturing what clients need to believe before they commit budget—and designing the pilot to produce that proof.

AI interview functions as an implementation-design layer: translating stakeholder anxieties (attribution, resourcing, cross-team alignment) into concrete pilot mechanics that reduce perceived risk.

From five persona interviews, three recurring constraints emerged and were converted into pilot requirements:

“Tell me which levers to pull.” (E-commerce)
Outcome: weekly action plans tied to platform changes, not strategy-only recommendations.
“Help me translate insights across teams.” (B2B marketing/CMO)
Outcome: packaged messaging frameworks + sales talk tracks + exec-ready ROI narrative.
“Turn abstract insights into concrete specs.” (Product)
Outcome: insight-to-brief workshops and cross-functional alignment checkpoints.

To resolve “attribution hell,” the pilot standardizes control-group logic wherever possible: A/B tests for landing pages and ads, controlled messaging experiments, or pre/post baselines with clearly defined exposure windows. This is also why the engagement was fixed at six weeks: long enough to observe signal, short enough to remain an MVP.

These interviews ensure that the pilot is designed to produce belief—not just results, at the moment budget decisions are made.

✅ Final Takeaway

Overall, this Pilot-in-a-Box positions Happioh as the missing execution layer that turns Atypica’s AI research into a single, CFO-defensible decision-grade report: what changed, what moved, what it’s worth, and whether to scale.
The standardized menu of pilot scopes, the six-week playbook, and the ROI measurement template collectively solve the core adoption barrier: proving that insight implementation reliably generates outcomes.

If the first 3–5 pilots consistently hit a minimum 3x ROI threshold, the program becomes scalable—not as bespoke consulting, but as a productized engagement system that makes “insights-to-action” repeatable.

👉Learn more at https://atypica.ai