VP Strategy & Operations · Enterprise AI · London
I build the commercial and operating machine that turns AI capability into enterprise adoption.
I'm Shelly Luciano, VP Strategy & Operations at Leah, a SoftBank and Insight Partners-backed enterprise AI company. Five years, three promotions, and the operating systems behind its move from CLM to AI-first. I write about the Execution Gap: why AI companies win the demo and lose at adoption.
3
Promotions in five years, Director to VP, at a SoftBank and Insight Partners-backed AI company
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Direct reports led across EMEA and LATAM
40+
AI trials run end to end with real client data
200+
Enterprise clients advised across the AI GTM lifecycle
The Execution Gap
AI companies don't lose at the demo. They lose at adoption. The work that closes that gap is GTM readiness, customer enablement, renewal architecture and operating cadence. That is what I build.
What I do
One operating thesis, three surfaces.
01
Scaling Operating Systems
The commercial infrastructure a fast-growing AI company runs on: operating cadence, forecasting and reporting, decision rights between product, GTM and leadership, and the account and lifecycle model everything else inherits from. The unglamorous work that turns growth into something repeatable.
02
Agentic Operating Models
Redesigning end-to-end enterprise workflows around AI agents. Not a tool implementation, an operating model redesign, where agents take real work in support, service and customer operations and the system around them is rebuilt to match. The current build is a read layer across the whole post-signature book: it analyses, prioritises and surfaces, and stops before it acts. That was a design choice, not a limitation.
03
AI Commercialisation
Turning AI capability into enterprise customer adoption: GTM readiness, enablement, demos, trials, customer education, and the feedback loops between product, GTM and leadership that decide whether a deal renews.
Beyond the boardroom
The same operator's lens, on the questions that sit underneath the technology.
AI is being deployed faster than most organisations can absorb it. I write about what enterprise AI adoption actually requires, what changes when knowledge is abundant, and what an honest standard for 'real-world value' from AI looks like.
Read my point of viewOn the record
From the podcast.
A short clip on what enterprise AI adoption actually requires. Particularly why the gap between capability and adoption is the work that decides whether AI companies grow or stall.
Insights