Generative AI services

Banks that convert relevant signals into timely, personalized offers consistently win wallet share. Agentic personalization—small, orchestrated AI “agents” that detect signals, fetch approved content, propose next-best actions, and escalate to humans under guardrails—lets banks scale one-to-one offers without exploding cost or audit risk.

Pharma commercial operations is at a pivotal inflection point, where the sheer volume and variety of field signals—ranging from HCP interactions and payer feedback to real-world evidence and patient-reported outcomes—unlock unprecedented opportunities to shape market access strategies in near-real time. This data deluge, fueled by digital tools and advanced analytics, empowers teams to anticipate payer hesitations, refine reimbursement narratives, and adapt launch plans dynamically. However, the true challenge lies in reliably converting these noisy, fragmented inputs into defensible, actionable market decisions. Without structured processes, signals risk drowning in silos, leading to delayed insights, inconsistent strategies, and missed windows for a competitive edge.

Deal desks sit at the critical junction of revenue generation and risk management in modern enterprises, serving as the nerve center where high-stakes decisions collide. Underwriters crunch numbers for viability, pricing analysts fine-tune margins to stay competitive, sales teams push for speed to close deals, and legal experts scrutinize for compliance pitfalls. Everyone jockeys for limited time: a quote must ship within the hour to beat competitors, yet exceptions—like custom policy carve-outs or non-standard terms—demand careful, multi-layered review to avoid legal exposure or financial hemorrhage. This tug-of-war creates a bottleneck, where urgency from sales clashes with caution from risk teams, often leaving deals in limbo.

Revenue operations (RevOps) is the business’ operating system for turning interest into invoices. But most RevOps teams still wrestle with three recurring frictions: slow handoffs between marketing and sales, forecasting blind spots, and revenue leakage between order and cash. Modern teams want the speed and precision of automation — without the governance headaches and forecast misses that early automation projects often introduce.

Pharma teams are already seeing what agentic AI can do: faster protocol reviews, automated literature triage, and near-instant signal summaries for safety and ops. But the joy of capability quickly collides with the reality of cost. Left unchecked, high-volume agentic pipelines — many small steps calling models repeatedly, plus expensive RAG lookups and multimodal transforms — can drive cloud spend through the roof.

When banks and insurers adopt agentic AI, the upside is clear: faster decisions, fewer manual handoffs, and measurable cost savings across underwriting, claims and fraud operations. But high-risk domains demand more than speed — they demand visible control. That’s where supervisor agents come in: runtime guardians that enforce policy, pause risky actions, collect reason-of-record, and make auditable decisions possible at scale.

You brought the machine to life: pilots impressed, stakeholders nodded, early KPIs looked promising. Then, after go-live, things started to fray. Tickets rose. Edge cases surfaced. Costs spiked. A few weeks later someone asked the inevitable question: “Why did we think this would be easy?”

This article explains how to design human–agent orchestration so automation accelerates outcomes without creating regulatory holes, reputational risk, or ethical lapses. It’s written for senior leaders (CIOs, CROs, heads of legal, and CX owners) who must decide which responsibilities remain human and how to manage that boundary in practice.

In the fast-evolving world of enterprise AI, “pilotitis” has become a dreaded syndrome—a pattern where promising AI initiatives fizzle out before delivering real value. Executives across industries share strikingly similar tales: a cross-functional team assembles a dazzling proof of concept (POC) that wows stakeholders with quick wins, perhaps automating a tedious workflow or generating insightful reports.

Shift from scattered documents to managed data products—structured, verified sources that let AI retrieve trustworthy information reliably across research, compliance, and decision workflows.