From Pilot → P&L: Rewire | Prove | Protect.
Forward-Deployed Leaders for High-Stakes AI
Most organizations can't scale AI ROI beyond pilots.
The problem isn't the technology. It's the operating model.
From operators who’ve shipped it - to leaders who must own it.
Close the Gaps: Value | Reality | Ownership
01
THE SCALING CRISIS
Why Most Organizations Can't Get AI to P&L
The latest data is clear: Most organizations see cost benefits at the use-case level, but fewer than 40% see enterprise-wide impact.
This isn’t a model problem.
It’s an operating model and leadership problem.
You've lived this pattern:
"Great strategy, couldn't operationalize it."
"Pilot worked in the lab, broke in the real workflow."
"Ready to launch, but no one owns the end-to-end outcomes or risk."
High performers do something different. They don’t just add AI. They:
-
redesign workflows with AI as the foundation
-
put executives who personally own the outcome
-
aim for transformation, not incremental efficiency gains
Most organizations are learning from the wrong sources:
-
Consultants give you frameworks, not operational reality.
-
Vendors give you pitches, not implementation truth.
-
Conferences give you edited case studies, not messy workflow reality.
-
Internal teams give you strategy and budget, but not deployment experience.
What's missing: operators who've shipped it where failure has consequences and tools that reveal how the work really happens before you deploy AI into it.
These gaps show up in three predictable ways:
01
The Value Gap
You optimize the task, but ignore the system.
The Trap: Leadership wants "AI ROI". Teams deliver a dozen pilots with nice local metrics - a 10% efficiency gain in one queue, a few minutes saved per case. On paper, everyone is winning.
The Reality: Finance can’t see it in the P&L. Backlogs, overtime, and complaints don’t move. No one in the business feels the change. The system constraint never moved, so nothing important changed.
The Failure Mode: You’re "doing AI" everywhere, but changing system performance nowhere. AI becomes a series of expensive experiments, not a business transformation.
02
The Reality Gap
You added a shiny UI on a broken workflow.
The Trap: On paper, your SOP has 5 clean steps. You buy AI based on the map. You assume the process is followed as written.
The Reality: The SOP is a fiction. We find that for every written rule, there are n "Shadow Rules" your team actually uses to get work done - the manual workaround, the legacy system check, the "ignore this field" habit, the "Maria knows how to fix this" edge case.
The Failure Mode: You build for the "Official Process" (the SOP) instead of the "Shadow Process" (the real work). The AI fails at the first edge case, staff bypass it, and you own an expensive prototype that no one uses.
03
The Ownership Gap
You have AI projects, but no one owns the system.
The Trap: It’s 3 AM. The AI breaks. A decision goes sideways. The Board asks: "Who is accountable?"
The Reality: Everyone owns a slice, but no one owns the whole.
-
Legal says "We advised on the policy."
-
Vendors say "Check the T&C."
-
Ops says: "We were never trained on this".
-
Governance lives in PDFs, not in day-to-day operations.
The Failure Mode: You stall launch because risk is undefined or you launch without a safety net, and a small error spirals into a crisis.
02
THE FORWARD-DEPLOYED DIFFERENCE
We don't just add AI. We fix the operating model.
You are stuck because of the Value, Reality, and Ownership gaps. Our job is to close those them.
We’re not consultants who write reports and leave. We are operators who have deployed AI where failure has consequences.
WE DO ONE THING DIFFERENTLY: We put a Forward-Deployed Leader (FDL) in the seat - your own fractional AI executive who owns value, workflow, and risk end-to-end and re-architects the operating model around AI.
---
How We Close the Gaps
1. Fixing the Value Gap - Design for System Flow
We don't chase "efficiency" in random silos. We design for how the whole process flows end-to-end.
The Reality: In any complex workflow, there are usually a small number of steps that set the pace for the entire chain.
-
If you use AI to speed up a step before that constraint, you just create a pile-up.
-
If you use AI to speed up a step after it, you create a vacuum.
The Fix: An FDL’s first move is to identify the steps that set the pace for the whole process you are trying to transform, then sequence AI deployments to relieve it. We don't layer AI onto broken processes; we automate the outcome to ensure flow, not friction.
2. Fixing the Reality Gap - The "Shadow Workflow" Audit
We treat your SOP as a hypothesis to break, not a requirement to build on.
The Reality: Documented processes are fiction. We don't just interview staff; we run an SOP Reality Lab. We stress-test your written rules against real historical cases to expose the "Shadow Workflow" - the dozen undocumented workarounds your staff actually use to close cases.
The Fix: Instead of accepting the “5-step process” on paper, we red-team your workflow. We redesign the workflow around these hidden variations so AI still works at 3 AM when volume spikes and exceptions stack up, not just on the happy path.
3. Fixing the Ownership Gap – Make Someone Own the System
We don't write 50-page policy & governance papers. We make sure someone is actually in charge.
The Reality: Governance usually lives in a PDF. When your system drifts, no one knows if and when they are allowed to intervene.
The Fix: Before any system goes live, an FDL assigns a Single Human Owner to every system, aligns Legal and Ops on the rules, and then builds a simple override so your frontline staff can pause the AI and rollback to a safe process in 30 seconds without asking for permission.
Governance stops being a document and becomes part of how people work.
---
The Proof
These aren’t hypotheticals. We often get the call after the first failure.
-
We’ve audited “successful” pilots that claimed efficiency wins but created massive backlogs downstream.
-
We’ve mapped the messy, real workflow variations the strategy team didn’t know existed.
-
We’ve been in the room when “Who owns this risk?” stopped a $2M launch indefinitely.
03
HOW TO WORK WITH US
Two Paths to enterprise-wide impact
-
Most organizations need one of four things:
-
Leadership to own orchestrate AI → P&L and close all three gaps.
-
Reality checks to make sure AI survives the real workflow.
-
Clear rules so people know how to act at 3 AM.
-
Assurance before launching something that touches real lives.
Start where you are.
01
Forward Deployed Leader (Fractional Executive)
Need ongoing AI leadership? Redesign your operations around AI.
Problem solved: Closes the Value, Reality, and Ownership gaps together.
Outcome: You get an AI leader who owns your operating model around AI - what you do, how it fits real work, and who is responsible when it fails or succeeds. Their job is to move you from "we have pilots" to "we have live systems changing real outcomes".
Best for: Organizations ready to deploy and scale in the next 6-12 months that need a captain to run the whole journey.
02
Shadow Workflow Audit (Ops Reality Check)
Need to know if your process is actually ready for AI?
Problem solved: Stops you from automating a process that doesn't exist. Closes the Value & Reality gaps.
Outcome: we deliver a Divergence Report. We take your SOPs and stress-test them against your actual case data to show you exactly where your written rules fail. It answers "Can we safely put AI into this workflow, and what has to change in the process and rules before we do?"
Best for: Leaders 3–6 months from a major decision (RFP, build vs buy, vendor renewal, expansion) who need grounded evidence.
03
AI Policy to Playbook
Buried in policies no one uses? Turn 50-page docs into "3 AM" rules.
Problem solved: Prevents "nobody knew what to do" moments. Closes the Ownership gap.
Outcome: We turn abstract AI and data policy into a short rulebook staff can follow under pressure: what the system may do, what must stay human, when to escalate, and what to record. Clear, instructions: "When X happens, do Y. If the model does Z, stop it and call this person."
Best for: Leaders with good policies on paper but inconsistent behavior across teams or frontline staff.
04
AI Deployment Assurance
Worried about a launch touching real people? Get a safety-check.
Problem solved: the "it worked in the lab, so we shipped it" mistake. Closes the Reality & Ownership gaps.
Outcome: We stress-test your planned launch for reliability in messy conditions, likely failure modes, monitoring and alerting, how to pause or roll back safely, and who is accountable when something looks wrong. You get a clear view of whether the system is ready for real people and what must change if it isn’t.
Best for: High-stakes organizations approaching Go-Live who need a credible "safety check".
START ANYWHERE
Need ongoing leadership? Embed a Fractional FDL to orchestrate the transformation.
Need a targeted fix? Start with a Sprint, Playbook, or Assurance Audit to close a specific gap before you scale.
04
THE SIX LAWS OF AI RESILIENCE
We don't ship until we answer six questions.
Every engagement is stress-tested against the failure modes that break AI in production. If we can’t answer these, we don’t launch.
01
Ownership: When the system makes a mistake, who owns the harm? (Not "who fixes the code," but who accepts the risk?)
02
Override: Can a frontline worker intervene or kill the process in under 30 seconds without asking for permission?
03
Reliability: Does the system hold up under peak load, messy data, and the 17 workflow variations we mapped?
04
Feedback: How fast do you find out that the system is making the wrong calls in the real world?
05
Drift: How quickly can you spot when performance quietly degrades - and correct it?
06
Proof: Can you demonstrate fairness, safety, and security to a regulator or auditor on demand?
05
WHO WE SERVE
We serve leaders in public sector, healthcare, and pharma + life sciences - environments where operational excellence, public trust, and regulatory scrutiny are non-negotiable.
We work where workflows are messier than any process document suggests, where governance requirements make casual experimentation impossible, and where failure has consequences beyond lost revenue.
01
Public Sector
Agency and emergency services leaders deploying AI where public trust, equity, and accountability can't be compromised.
02
Healthcare
Clinical and operational leaders deploying AI where patient safety, clinical efficacy, and regulatory compliance define success.
03
Pharma & Life Sciences
R&D, regulatory, and commercial teams using AI across discovery, trials, and market access - where evidence, auditability, and risk management are as important as speed.
06
Contact Us
Ready to Partner with Us?
Contact us today.
Trusted AI Leadership
Shreya Amin
Former Chief AI Officer, New York State
17+ Years in High-Stakes Data & AI
Building data / AI products and systems where failure isn’t theoretical - it’s operational. We turn complexity into reliable, auditable systems leaders can ship and scale.
“AI leadership isn’t about prediction - it’s about accountability when it fails. You can outsource innovation, not accountability.”
Risk & Readiness
Defensible go/no-go decisions that withstand audit, scrutiny, and crisis.
Operational Reality
Handoffs, overrides, and rollback — proven under real-world pressure.
Executive Outcomes
High-ROI decisions. Faster time-to-value. Accountable operations.