95% of enterprise Gen AI pilots deliver zero P&L impact. We build the 5%.

Operator-grade deployment for Telco, Data Center, and FMCG. Workflow first. Model second.

The problem
95%

Pilots with no measurable P&L impact (MIT, Aug 2025).

2.8x

Outperformance of firms that redesign workflows (McKinsey QuantumBlack).

67 / 33

Vendor-bought vs internally-built success rate (percent).

The bottleneck is not the model. It is the work around the model.
Diagnostic

The Four-Gate Test

Enter one use case. Answer four gates. Get a verdict and a pattern recommendation.

Gate 1
Value
Is there a P&L owner accountable for capturing the value?
Failure mode: Pilots funded by a generic innovation budget.
Gate 2
Task Fit
Is the task inside today's AI frontier, and can outputs be verified cheaply?
Failure mode: Verification cost exceeds task value.
Gate 3
System Fit
Is the data accessible, clean, and legally permissioned, and does the workflow exist?
Failure mode: The workflow IS the project.
Gate 4
Strategic Horizon
In 18 months, when inference costs have fallen 10x and competitors use the same vendor, what is left that we own?
Failure mode: Funded as strategic; actually defensive.
One follow-up
Does this task need human judgment to frame and validate, or to focus on prioritized cases at the end?
Sector lanes · Telco

The frontier is no longer in the network. It is in how the network explains itself.

Use case 1 · Funnel

Intelligent tier routing across customer ops

Reasoning models cost 5–20x more per task. Routing alone reduces customer-ops LLM spend 40–60%.

Use case 2 · Sandwich

Outage triage and dispatch

47.8% ticket reduction; 85% OSP referral reduction in comparable deployments.

Use case 3 · Sandwich

Lowest-quintile agent productivity copilot

+36% productivity for the bottom quintile; ~0% for the top. Compression, not replacement.

The Value Ceiling

Ambition without capability destroys value.

Move the slider to set today's capability.

Current capability5/10

Adapted from Mindspan Labs, AI Transformation Flywheel, Wharton Gen AI for Business, May 2026.

How we work

The operator approach

4–6 contained 120-day pilots

Redesign the work, not the tools. Each pilot paired with a named line-manager owner. Sandwich or Funnel chosen, not defaulted.

Buy before build, default

67% vs 33% success rate. We pick vendors and integrate. We do not rebuild what already exists.

Brakes that let you go faster

AI Bill of Materials per app. Sandbox-first development. Train-don't-ban. Structural oversight, not human-attention oversight.

Discipline

What we will not do

We will not run a pilot without a named P&L owner.
We will not deploy an agent without an explicit failure-mode mapping.
We will not architect for a single vendor; the open-weight gap is now 3–6 months, not 12.
We will not lead with cost cuts. ROI comes from augmentation, not headcount reduction.
This site's analytical frame draws on the May 2026 Wharton Executive Program "Generative AI for Business" (Tambe, Puntoni), Mindspan Labs' AI Transformation Flywheel, McKinsey QuantumBlack workflow-redesign analysis, and the McKinsey "Automation Curve in Agentic Commerce" (Jan 2026). All quantified claims are sourced; specific deployment benchmarks are from comparable operator engagements.
Contact

Request a 30-minute operator review.