We live in an attention economy. Customers aren’t short on options — they’re short on time. Products, shows, songs, apps: the supply is endless, but attention is scarce. In that world, every friction point is costly.
But CRM, Data Science, and Product teams still run their playbooks in silos: CRM blasts campaigns, Data builds models, Product ships features. And customers don’t know that those silos exist—they only know that the experience they’re having is fragmented.
And into that fragmented landscape comes one of the most hyped topics in our space: AI Decisioning.
The Ceiling of AI Decisioning
AI Decisioning is often pitched as the magic brain: a standalone solution that decides who gets what message and when. But on its own, decisioning is too weak.
What it really does is enhance rule-based systems. It can make journey-builder logic a little smarter, but the outcome is still the same: spaghetti flows, rigid funnels, and a ceiling on customer engagement. And this ceiling will come crashing down — we hear it day in, day out. At best, decisioning adds complexity. At worst, it entrenches old models.
This is the legacy of Software 1.0 — apps built like digital stores, where users bend to the product’s fixed aisles and funnels. Decisioning alone just decorates those aisles with a bit more personalization. It doesn’t change the structure.
The Real Shift: From Campaigns to Conversations
The real turning point is Software 2.0 and Agentic AI. Here, the architecture itself reconfigures. The blocks of an app like content, layout, and interventions are no longer static. They move and adapt in real time around each user.
That’s the purpose of Agentic Infrastructure: to turn products into adaptive conversations, not static catalogs.
Every surface counts. Push, in-app, email, homepage — they’re not external campaigns, they’re part of the product.
Interventions, not blasts. No more “send a cart abandonment message after three days.” Agents experiment, observe, and learn when and how to nudge.
Continuous personalization. The experience evolves with every action. The product doesn’t wait for users to declare intent. Instead, it helps them discover.
Customers don’t live in “channels.” They live in one continuous experience. Agentic Infrastructure treats it that way.
Under the Hood
Agentic Infrastructure doesn’t just run on simple decision trees. Under the hood, it draws on methods like contextual bandit reasoning, causal inference, and multi-objective learning — approaches that allow agents to experiment, optimize, and adapt in real time. Don’t believe it? Show it to your data science team. They’ll be delighted to see it’s not just another LLM or off-the-shelf ML model.
The Ceiling of AI Decisioning
AI Decisioning is often pitched as the magic brain: a standalone solution that decides who gets what message and when. But on its own, decisioning is too weak.
What it really does is enhance rule-based systems. It can make journey-builder logic a little smarter, but the outcome is still the same: spaghetti flows, rigid funnels, and a ceiling on customer engagement. And this ceiling will come crashing down — we hear it day in, day out. At best, decisioning adds complexity. At worst, it entrenches old models.
This is the legacy of Software 1.0 — apps built like digital stores, where users bend to the product’s fixed aisles and funnels. Decisioning alone just decorates those aisles with a bit more personalization. It doesn’t change the structure.
