Aug 28, 2025
The Data Barrier in AI Research
When organizations explore AI-driven research, one concern consistently arises:
“How much of our data do we need to hand over? A single survey, or everything we’ve ever collected?”
The hesitation is understandable. Research data is sensitive, distributed across functions, and often subject to strict compliance. Releasing it all at once feels risky and, in most organizations, nearly impossible.
Yet this assumption creates an unnecessary barrier. Companies assume they need a “big data dump” to benefit from synthetic panels. In reality, the opposite is true: the most successful pilots start with minimal inputs and expand gradually.
The Myth of the Data Dump
Traditional thinking suggests that AI models require vast archives years of segmentation surveys, complete CRM datasets, and full campaign tracking. But this approach leads to:
Delays: Projects stall in compliance and IT bottlenecks.
Overload: Teams struggle to prioritize which data to release.
Mistrust: Stakeholders worry about privacy and misuse.
At Lakmoos, we have found the inverse: you can unlock immediate value with a handful of existing surveys and topline metrics. Our models already come with a behavioral baseline built on millions of public data points (demographics, lifestyle, media use). That foundation allows companies to start with very little, and layer in more only when it makes sense.
Progressive Enrichment: A Phased Approach
We call this method progressive enrichment: a structured way to build trust and expand impact. Instead of demanding all available data on day one, we scale in stages:
Phase 1: Start Small (0–3 months)
Input: 1–2 historical surveys (≈1,000–2,000 respondents each), plus 1–2 campaign KPIs.
Output: Instant simulations, ability to add new questions to existing surveys, empowerment of small teams.
Phase 2: Grow (3–12 months)
Input: Tracking data from 2–3 campaigns per quarter, aggregated CRM metrics.
Output: Predictive “what-if” scenarios for tariffs, bundles, and competitor moves; segment-level insights.
Phase 3: Scale (12+ months)
Input: Broader streams such as 6–8 ad tracking studies per year and aggregated app analytics.
Output: Company-wide foresight: cross-country comparisons, dynamic pricing models, and long-term brand alignment.
The message to executives is simple: start light, expand deliberately, and match investment to business value.
Organizations are shifting away from the belief that AI requires massive, all-at-once data integrations. The emerging practice is progressive enrichment, starting with the smallest viable dataset, proving value quickly, and expanding data use only when business impact is clear. Progressive enrichment is not just a technical approach. It is a cultural shift: it reduces compliance barriers, empowers smaller teams, and builds organizational trust in AI by showing measurable results early. Just as agile transformed software and lean reshaped startups, progressive enrichment is redefining how enterprises adopt AI in research.
Three Progressive Uses of AI Panels
Progressive enrichment is not only about how much data you share, but also what you can do with it as maturity grows.We see three distinct uses emerge:
Talk to Your Data
Instead of static reports, past surveys become interactive. Teams can interrogate existing findings, explore scenarios, and make forgotten insights actionable again.Augment Research
When stakeholders realize too late that they “forgot to ask” a question, AI panels can simulate the additional answers. This eliminates costly re-fielding and extends the value of every study.Simulate Surveys
Once trust is established, organizations run entire studies synthetically: ad testing, churn modelling, or concept validation. What once took weeks of fieldwork is replaced by instant simulations.
Together, these uses create a continuum of value: from tactical agility to strategic foresight. The trajectory is clear:
Tactical Stage: Extending surveys, testing creative concepts, answering “forgotten” questions.
Operational Stage: Running predictive models on pricing, churn, or loyalty.
Strategic Stage: Enabling executives to simulate market scenarios and align cross-country strategies.
Importantly, each model is client-specific and privacy-safe. Data never crosses organizational boundaries, ensuring compliance without limiting insight.
Most organizations already sit on years of valuable but underused research. Old segmentation studies, campaign trackers, and one-off surveys often get archived once the original project closes. Progressive enrichment treats these datasets not as static reports but as raw material for new simulations.
By upcycling existing research, companies can extend its life: forgotten surveys become the foundation for new questions, historic campaign results inform predictive models, and past segmentations evolve into interactive personas. Instead of constantly fielding new studies, leaders can unlock hidden value from what they already have.
The Leadership Imperative
Executives often overestimate the data required to benefit from AI-driven research. This slows adoption and limits competitive advantage. The imperative is to reframe the question.
Instead of asking, “Do we have enough data to start?” leaders should ask: “Which dataset can unlock the first 10% of value and how do we expand from there?”
By adopting a progressive enrichment mindset, companies reduce barriers, accelerate pilots, and scale only when impact is proven. The organizations that thrive in the AI era will not be those who hoard data, but those who learn to deploy it strategically, step by step.
Lakmoos synthetic panels prove that you don’t need a data overhaul to unlock the value of AI. You need a starting point, a safe framework, and the courage to begin small. From there, the path to strategic foresight is not a leap, but a series of deliberate steps.
