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Consultant · AI strategy & transformation

Between the decisionand the delivery.

I'm Edwin Helet. I built payment systems for European banks before training in strategy at Emlyon and CentraleSupélec. Now, I would like to advise companies on their AI and data transformations — from strategic framing through to what actually runs in production.

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Location
Paris, France
Available for engagements and CDI
From September 2026
Portrait d'Edwin Helet
01Positioning

A path built from both ends.

Strategy wasn't what drew me in at first — the engineering was, a childhood ambition to build useful things. Working on real systems, under regulatory and delivery pressure, is what shifted my ambition: from "designing" to "understanding the needs technology has to answer". The specialised master's formalised that shift.

  1. 01

    Technical depth

    I worked inside a genuinely constrained environment: SEPA and card payments, thousands of daily transactions, European regulation setting the deadlines. You don't improvise there. That experience is what lets you know, before a strategic decision is made, what will actually hold downstream.

  2. 02

    Business judgement

    A hybrid profile isn't about ticking two boxes. It's about drawing a direct line between a business stake and the technological lever that answers it — and above all about choosing between levers when every one of them looks defensible on paper.

  3. 03

    Learning through repetition

    A dozen technical and business projects across engineering school, the specialised master's and entrepreneurship. Alacasa and CityZN took me across the entire value chain, from market positioning down to the database.

By the numbers
microservices in a cloud payments ecosystem
50+microservices in a cloud payments ecosystem
automated end-to-end tests
200+automated end-to-end tests
SAFe program increments, 8 teams, 3 countries
6SAFe program increments, 8 teams, 3 countries
European banks supported
2European banks supported
prizes won at an innovation challenge
3/4prizes won at an innovation challenge

Figures drawn from my time at SBS and the Emlyon innovation challenge.

02Expertise

Three arenas, one value chain.

A transformation is never decided on a single segment. Strategy without delivery stays an intention; delivery without strategy produces tools nobody uses. Every skill below carries its origin — expertise without provenance is just a claim.

01

Engineering & data

Designing, building and hardening what has to run in production. This is the ground where a recommendation is proven — or falls apart.

  • Distributed and cloud architectures

    Built at : SBS — ecosystem of over 50 microservices

  • Automation and software quality

    Built at : SBS — over 200 end-to-end tests

  • GDPR compliance and open banking

    Built at : SBS — European payment flows

  • On-device AI and local processing

    Built at : Nemoris — models running on the device

  • Artificial intelligence, computer vision, statistics

    Built at : EPF — digital specialisation

02

Strategy & markets

Reading a market, choosing between options that all look defensible on paper, and turning a finding into a business model that holds.

  • Market research, segmentation, competitive analysis

    Built at : Alacasa — 50-page business analysis

  • Business model and value proposition

    Built at : CityZN — validated with public and private actors

  • Business cases and AI-augmented models

    Built at : Specialised master's, Emlyon × CentraleSupélec

  • Management control and performance management

    Built at : Specialised master's — core courses

  • Competitive intelligence and geoeconomics

    Built at : Specialised master's, CFVG exchange (Vietnam)

03

Delivery & transformation

Framing it, bringing teams along, and seeing it through. This is where most transformations are lost — and rarely for technical reasons.

  • Agile at scale: SAFe, Scrum, V-model

    Built at : SBS — 6 program increments, 8 teams, 3 countries

  • KPI-driven steering

    Built at : SBS — over 100 user stories; specialised master's

  • Field validation and stakeholder interviews

    Built at : CityZN — Métropole de Lyon, HERE Technologies

  • Framing AI use cases

    Built at : SBS — regulatory mapping; Alacasa — matching

  • Product roadmap and technical trade-offs

    Built at : Alacasa — CTO & co-founder

03Background

From engineering to strategy, without leaving the ground.

  1. 2025 — 2026

    Specialised Master's — International Business Development & Strategy

    Emlyon Business School × CentraleSupélec

    Lyon · Paris

    Strategy, management control, performance management, B2B marketing, negotiation, competitive intelligence, geoeconomics, international law. Academic exchange at CFVG, Hanoi and Ho Chi Minh City.

  2. 2023 — present

    CTO & co-founder

    Alacasa SAS

    France

    Technology, product and AI for a platform that inverts the real-estate brokerage model: index qualified demand rather than listings.

  3. 2024 — 2025

    Software design engineer

    SBS (formerly Sopra Banking Software)

    Courbevoie, France

    Digital transformation for two European banks — ASN Bank and Argenta — across SEPA payments and Visa/Mastercard cards. 40 sprints, 6 SAFe program increments, an ecosystem of over 50 microservices.

  4. 2022

    Vibratory imaging research

    Université de Sherbrooke

    Sherbrooke, Canada

    Five months of experimental research: trials across dozens of materials, validation prototypes, data analysis and technical recommendations.

  5. 2019 — 2024

    Engineering degree — digital specialisation

    EPF Engineering School

    Cachan, France

    Generalist training then specialisation: artificial intelligence, cybersecurity, computer vision, DevOps, cryptography, statistics, design thinking.

04Work

Five contexts, one method.

Each case study follows the structure I apply on engagements: context, diagnosis, levers, trajectory, results. The format is part of the argument.

Also

Consulting project for Onepoint (2023–2024): analysis of an infrastructure availability measurement problem using Raspberry Pi micro-sensors.

05Perspective

What I think about AI in the enterprise.

AI is a considerable asset for those who learn to harness it and turn it into business and organisational gain. But it raises a handful of questions few organisations treat seriously — and those questions are what actually decide whether a deployment succeeds.

01

The human question comes in three forms, not one

When AI reaches a team, the impact is never uniform. There is substitution: the task disappears — that calls for a redeployment plan, not a technical project. There is role transformation: the task remains but changes nature — an advisor facing a customer already informed by digital channels is no longer doing the same job, and that requires training and a shift in posture. And there is augmentation: the role doesn't change, it simply becomes more effective. Conflating the three means getting the support plan wrong.

02

Governance is not a constraint bolted on afterwards

The AI Act classifies systems and imposes, depending on the case, human oversight or human validation. In parallel, organisations are moving towards more sovereign solutions — European or proprietary — to limit the risk of their data leaking or being exploited. These are design parameters, not boxes to tick at the end of a project.

03

Explainability determines adoption

An AI system's choices and outputs have to be explainable. That is a regulatory requirement in regulated sectors. More importantly, it is the condition for business teams to trust the tool — and actually use it, which remains the only criterion that counts.

04

Three drifts worth watching

Over-application: deploying AI on processes that never needed it. Over-consumption: an energy cost that ends up contradicting the company's own sustainability commitments. Over-replacement: replacing people without reducing costs, merely relocating them. The limit of AI will not be its ability to replace humans, but its cost relative to them.

→Typical Mission

How I would approach a client problem.

  1. 01

    Context

    Restate the facts given by the client, without interpretation.

  2. 02

    Diagnosis

    Identify root causes across at least two dimensions — technical/data and organisational/human. One strong hypothesis rather than a list of leads.

  3. 03

    Levers

    Two or three at most. For each: a concrete action, and the business benefit stated before the technical detail.

  4. 04

    Trajectory

    Sequence it over time: framing, targeted pilot, broader rollout, continuous steering.

  5. 05

    Risks & KPIs

    Two or three indicators per lever, explicitly tied to what they measure. Three to five risks, always including an adoption risk and, in regulated sectors, a compliance risk.

06Contact

Let's talk.

I am based between Paris and Lyon, available for consulting engagements in AI strategy and organisational transformation. The simplest route is to email me directly.

[email protected]
Download CV
Location
Paris, France
Phone
+33 6 84 47 71 91
LinkedIn
linkedin.com/in/edwin-helet
Languages
French · English (TOEIC 895) · Spanish (B1)