Unit Economics of Agentic Workflows: The Time-to-Production Paradox

Deploying AI Workflows in Production = Hard

Everybody’s selling the dream:“AI will save your team months of effort!”But here’s the paradox: the faster AI is supposed to save time, the longer it actually takes to get into production.

Welcome to the Time-to-Production Paradox.

The Brutal Enterprise Reality

Let’s talk numbers:

• 88% of AI pilots never reach production(InformationWeek,CIO).
• For the tiny minority thatdo? Enterprises spend16+ weeks just to get one workflow live(KPMG,NextGov).

Break it down:

• Pilot / POV:2–6 weeks
• Secure Integrations & Env Hardening:4–8 weeks
• Policy & Governance Layer:4–6 weeks
• Observability & Runbooks:3–6 weeks
• Rollback, SLA, Scaling:3–5 weeks

Total: Four months of work — and no guarantee the workflow ever sees the light of production.

This is the Time-to-Production Paradox: the more enterprises chase “value” from AI, the longer and messier the path to actually achieving it.

Real-World Friction

Why does this happen? Because reality bites:

• Platform engineering bottlenecks:pilots die when workflows don’t fit existing pipelines or deployment infra.
• Security & compliance overhead:every call needs audit trails, approvals, and hardening before prod.
• Data & MLOps gaps:scaling pilots breaks when pipelines, rollback, and observability aren’t ready.
• Organizational misalignment:Atlassian found AI saves engineers ~10 hours a week, but silos steal six of them back.

The paradox builds: more time invested → higher value required to justify → even less chance of production.

Let’s Talk About the Field

Step onto the field and just look around:

• ServiceNow’s “AI updates a record.” That’s not AI in production — that’s Clippy with a badge.
• Langraph’s “agent emails a reminder.” Groundbreaking stuff… in 2003. Outlook called, it wants its feature back.
• CrewAI’s “Slack ping.” Wow. Truly game-changing. No engineer in history haseverautomated a Slack message. Next up: teaching AI how to set your OOO reply.
• Four months of enterprise rollout. Imagine telling your CIO:“Yes, we spent a quarter’s budget so our AI can update a Salesforce field.”That’s like bragging you trained for a marathon and then jogged to the mailbox.

Meanwhile, the workflows that actually matter CVE remediation, compliance enforcement, cost control are left sitting on the bench like the kid nobody picked for dodgeball.

This is the state of the field: everyone flexing toy demos while pretending they’re production-grade.

The Workflows That Actually Matter

TakeCVE remediation as a real-world example. When a new vulnerability drops, a “real” workflow needs to:

• Scan environments for exposure across fleets.
• Pull affected assets from vulnerability scanners.
• Cross-reference package versions and OS builds.
• Spin up Terraform changes or Ansible playbooks to patch infra.
• Restart services, roll workloads safely, and confirm rollback strategies.
• Open Jira tickets, update Slack/Teams war rooms, notify stakeholders.
• Feed results back into observability and compliance logs for auditors.

That’s 60+ steps, 200+ tool calls, not a three-step toy flow. And it’s business-critical because every hour wasted is a bigger attack surface.

Kubiya: The Paradox Killer

Here’s where Kubiya flips the rules:

• 1 session → production.
• 3 sessions (avg) → full-stack workflows live.
• 60+ steps, 200+ tool calls → deployed same day.

Instead of spending 16 weeks knitting together approvals, you’re closing CVEs and patching systems in real time.

Closing Thought

The industry has shrugged and accepted the16+ week cycle as “normal.” Analysts, consultants, vendors all nodding like this is gravity.

But Kubiya proves it’s not.

With Kubiya: From four months→ to one session From pilot purgatory→ to production reality From Cancún ceramics class→ to enterprise-grade CVE remediation

That’s the real escape from the Time-to-Production Paradox.