Why I Am Not Chasing the AI Trend — and Why Our Results Show It

Picture of Michael Pelikan
Michael Pelikan

Co-Founder/CTO, Path2Response

I have been working in artificial intelligence since before most people in this industry started using the term. At NASA’s neurocomputing institute in the 1990s, I built neural networks. Real ones — not the marketing version of AI, the engineering version, where you are accountable to whether the system actually works.
So when I hear that a competitor has moved to a single generative AI model for data science, I am not impressed. I am concerned — for their clients.

The generative AI problem in statistical modeling

Let me be precise, because the term AI is being used to describe two very different things right now. There is true AI — statistical models, machine learning, expert systems. And there is generative AI — large language models that synthesize content based on patterns in training data.
AI is being used to describe two very different things... true AI = statistical models, machine learning, expert systems.
Generative AI is genuinely powerful for what it does. It is not what statistical modeling requires.
When I am building a model to identify who is likely to respond to a specific offer, the model needs to find real signals in real data. Generative AI will generate something that looks right. But it does not know that the offer is a seasonal promotion, that there is a 20% coupon on the cover, or what the macroeconomic environment looks like this mail season.
Humans know those things. Data scientists with domain expertise know those things. My job — and what we built this platform to do — is to give those humans better tools, not replace them with a system that generates plausible-sounding outputs.
My job is to give humans better tools, not replace them with a system that generates plausible-sounding outputs.
The companies going all-in on generative AI for modeling are going to see their results fall. Some already are.

What we built instead

Path2Response was built without the co-op playbook. I came from processing massive, complex data sets for airlines. I had never built a data cooperative, and I did not want to replicate how they had always worked.
We embrace data in its natural, unstructured form. No fixedwidth fields, no translation loss, no signal trimmed at the edges to fit a predefined structure. Data arrives intact, the models see it intact, and intact data processed at high recency is where performance comes from.
We run on elastic cloud infrastructure — hundreds of servers, thousands of nodes when a client needs it, scaled back down when they do not. Same-day and next-day model turnaround is standard. That speed enables iteration: run a model, review it, adjust it, run it again. The best results come from that loop, not from a single output handed back after a long wait.
And we let the model work. When clients and brokers overspecify — loading up pre-selects and forcing the model toward a predetermined answer — performance degrades. When we take those constraints back, apply the right variables, and let the model find its own signals, it consistently outperforms what human pre-selection would have produced. The model finds nuances that experienced people miss.
That requires human expertise at the front end to provide context — and then the discipline to step back.

Why it matters

Response indexes moving from the 70s to 130, 150, 165 in head-to-head tests. Prospect universes doubling while performance
stays above index. Direct mail retargeting delivering
incremental ROAS of 2x to 20x or more.

Response indexes moving from the 70s to 130, 150, 165 in head-to-head tests. Prospect universes doubling while performance stays above index.
Those results do not come from a generative AI model. They come from a platform built with the right philosophy, by people who understand what data actually is.
If your current data partner is heading in the generative AI direction, ask a simple question: can they show you the results?
Michael Pelikan is Co-Founder and Chief Technology Officer of Path2Response. Path2Response’s formal AI usage policy prohibits generative AI in production modeling or decisioning. Client data is never used to train models and is never shared with external AI systems. Reach out directly at inquiries@path2response.com to share your ideas and insights.