Cervino Strategy — Strategy consulting × AI

01 · The thesis — 2,400 m

Orders of magnitude, not gadgets.

Generative AI doesn't make organisations “slightly more productive”. It bends cost equations and makes entire markets contestable at any moment. Four shifts define the decade — and force the practice of strategy itself to modernise.

Software cost ÷ 10–100

Build is no longer a barrier

A credible prototype takes days; a demo application, less time than preparing a meeting — almost between two steering committees. Development costs drop by one to two orders of magnitude, and with them the protection they used to provide.

Growing autonomy

Entire tasks, executed end to end

Research, analysis, code, deliverables: the complexity of tasks executed end to end by agents is rising fast, with human oversight set at the right level.

Frictionless information

Asymmetries are eroding

Collecting, structuring and aggregating a market's information takes days, no longer months. Positions protected by information asymmetry become contestable — by you, or against you.

Accelerated iteration

Test & learn changes scale

Prototype, test with customers, correct: the full cycle compresses dramatically. Learning speed becomes the central competitive advantage.

And the practice of strategy must evolve with it.

Augmented framing

Thinking that integrates the new possibilities from the moment options are framed — not as an appendix to the plan.

Embedded test & learn

Structural hypotheses are tested along the way, prototypes in hand — no longer just on slides.

Modernised roadmap steering

Leaving 2010s practices behind: custom coordination tools, continuous visibility, documented trade-offs.

Decision materials that fit

Dashboards, demos, simulators: living materials rather than the frozen quarterly deck.

Friction-based barriers are disappearing. This is an executive-team matter — not just a CIO one.

02 · The method — 2,900 m

AI takes hold through practice, not theory.

We work with executive teams on their strategy, deals and transformation agendas, infusing them with AI-native methods and tools. It is by using AI on real matters — analysing, deciding, prototyping, steering — that its potential becomes clear and new practices take hold. And this matters most at the executive level: the way leadership teams work then spreads through the whole organisation.

I

The decision

The problem at hand gets solved — from strategic plan to transaction, from offer launch to operational initiative — with top-tier standards and modernised delivery.

Decide

II

The transformation

New ways of working take hold by working — not by training. The executive team experiments on its own matters; the practice then spreads.

Install

III

The instrumentation

The tools built during the engagement stay: agents, automations, dashboards, roadmap coordination tools — on your data, within your security framework.

Equip

StrategyMergers & acquisitionsOffer launches & innovationOperational leadership of strategic initiativesData & AI transformation… and the full executive agenda

Backed by the firm's strategy & deals DNA — the legitimacy to enter the real subjects — and by a network of experts and AI startups mobilised for each engagement.

See what it changes in practice — the ATLAS illustration →

03 · Use cases — 3,300 m

Making the impossible possible. Fast.

An engagement starts with an executive's question — “Does my plan still hold?”, “Where does AI actually change my economics?”, “Can we test this offer before launching a programme?”. Six illustrations, among many others — the field is open.

A custom database for a transaction

Build a proprietary database from public sources — registries, regulator, company websites — with every data point traceable. A real example: ATLAS, a mapping of French underwriting agencies, 99 players, 57 documented columns per company.

See the full illustration →

A new value proposition

Create an offer from what already exists: exposed APIs, monetised data, a proposition tested on the market with minimal build effort.

Build effort divided by 10

Designing new customer experiences

Redesign a journey end to end: digitised, prototyped, tested and iterated with real customers — ahead of the IT roadmap.

Quarters → weeks

Prospecting at scale

Targeting, personalisation, follow-ups: a sales pipeline industrialised by agents — without an army of SDRs.

Coverage multiplied, costs contained

Processes → best practice

The state of the art is already documented somewhere. AI aggregates it and aligns your processes with it — inefficiency is no longer a given.

Cycles sharply shortened

Test & learn ×10

From idea to market verdict: working prototype, user feedback and iteration in days. Innovation becomes a light — and inexpensive — exercise again.

Idea → first feedback in days

Observed on real cases — every context is scoped before any figure is committed.

04 · The terrain — 3,700 m

Three worlds. One conversation.

AI-native transformation fails when you only know one world. Institutions hold the assets and the constraints, scaleups the speed, tech the deep layers. Working with an executive team means moving across all three — and translating.

Institutions, from the inside

A decision there is not a slide: it is a journey — risk committee, compliance, shareholders, regulator. Knowing where a transformation gets through, and where it bogs down, lets you infuse AI without triggering the organisation's immune system.

Banking & payments · Insurance · Asset management · Private equity

Innovators, as the benchmark

At fintechs and scaleups, strategy reads in unit economics and iteration speed. Having worked with them — fundraisings, due diligence, product roadmaps — provides the reference for what “fast” really means, and what scales.

Fintech & insurtech · Scaleups · B2B SaaS · AI startups

Tech, from prompt to infrastructure

From prompt to infrastructure, through models and tools: beneath every AI promise sits a full chain — data, systems, platformisation. In-house experience at a European cloud provider and technology due diligence make it possible to separate what actually ships from what merely sounds good.

Cloud & data · Platformisation · Architecture · Product & tech organisations

05 · References — 4,000 m

Examples of engagements.

The firm advises on C-suite topics — strategy, growth, deals, transformation — across financial services, fintech and tech: established groups, investment funds, scaleups. Two examples, detailed below, give a feel for the work.

Transformation · Payments

Building a payments business unit at the heart of a banking group

For a leading banking group — creation of a merchant payments business unit: acquiring, issuing, data, value-added services. Central coordination role: cross-functional trade-offs across business, product, data, operations, risk and compliance; leadership of the data workstream — use cases, technical foundation, roadmap; preparing and securing executive-committee decisions.

What it illustrates — working at the centre of the decision system, aligning functions, and giving leadership real instruments to steer with.

Strategy · Insurance

Building the growth plan of an international health insurer

For a leading international health insurer — construction of the strategic growth plan: diagnostic of positions by market, identification and prioritisation of growth levers, financial trajectory in support, working directly with senior management.

What it illustrates — the firm's strategy DNA: setting an ambition, making clear choices, committing senior management to a trajectory.

Two examples among others — detailed references on request.

The team

Strategy core team

Senior consultants from top-tier strategy consulting and in-house strategy leadership. Board-level standards: plans, deals, transformation.

AI Lab

AI engineers, data scientists, product leaders and former tech entrepreneurs. Built during the engagement: agents, tools, prototypes, instrumentation.

Network, per engagement

Specialised experts and AI startups mobilised by subject. The right player at the right time, with no search or intermediation cost.

06 · Under the hood — 4,200 m

Technical depth, in the background.

Nobody hires Cervino for its knowledge of MCP servers — but that knowledge is what makes the practice possible. Daily command of the chain, from interface to infrastructure, and of its real limits.

Models: the frontier keeps moving

Reasoning models, massive context windows, open-weight closing in on proprietary: the landscape redraws itself every quarter. The useful skill is no longer naming “the best model”, but continuously arbitrating quality, cost, latency and confidentiality — use case by use case.

Development: a change of nature

Cursor, Codex, Claude Code: the most advanced teams no longer write code — they direct agents that do. One person ships a website, a mobile app, an API or a design end to end. UI itself becomes an available skill.

Autonomous agents: the 2026 milestone

Claude Cowork and its connectors marked a milestone: agents plugged into company systems, able to run entire workflows. With skills, connectors and MCP servers, the scope of what can be delegated expands every month.

No-code: prototyping democratised

No-code and low-code platforms — workflow automation, visual databases, app builders — have moved the internal-build frontier: a simple business tool no longer justifies an IT project. Testing an idea now costs less than debating it.

Data × algorithms × LLMs

The real gains come from combinations: structured data + LLMs, classical algorithms + agents, retrieval + reasoning. You don't gain percentage points of productivity — you change operating system.

A frontier that moves every month

Models, protocols, tools: a flood of innovations every month, and a field of possibility constantly pushed back. Continuous watch and experimentation are part of the job — not a sideline.

Anatomy of a mission agent — market pre-scanexcerpt from a real specification

Modelarbitrated per task — extraction: light model, structured outputs · synthesis: frontier model, long context

Temperature0 for factual extraction — raised only for ideation

Tools · MCPcompany registry · regulator's register · published accounts · scoped website reading

Instructiondefensive — never invent, mark “n/a” when proof is missing, one source per cell

Guardrailshuman review before any spend · verification on third-party sources only · divergences displayed

Evalshand-built control set · non-regression before any model change

Outputstructured base + standalone interface + export — rebuilt from a single command

The real agent behind the ATLAS illustration — the rest is shown in a demo.

Security & sovereignty: a matter of context.

Fifteen years in the most regulated environments — banking, insurance, payments — teach one thing: each context calls for its own approach. What can run through a hosted model, what requires strict partitioning, what must stay inside your own systems. We help draw those lines — GDPR, data security, sovereignty, usage governance — before any deployment, with your risk and compliance teams.

GDPRData securitySovereigntyPartitioningUsage governance

07 · Summit — 4,478 m

Have a challenge in mind? Let's talk.

A direct read: what AI changes for your P&L, where to start, what is mature — and what is not.

Book a meeting by email

EN / FR · contact@cervinostrategy.com