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Data & AI with balance-sheet impact

We untangle the most critical knots around data & AI.

Strategy, organisational development and technology from a single team — for automotive, manufacturing and data-intensive sectors, with a focus on P&L impact and fundamental transformation.

Four triggers — four answers
Field 01 / 04
Strategy & Transformation

Where the AI transformation really stands — and how it reaches the P&L.

◇When to bring us in
01

The supervisory board asks about the value contribution of AI and you cannot say ad hoc whether your portfolio contains the most valuable use cases.

02

AI initiatives run everywhere in the company, but not everyone pulls in the same direction. What’s missing is the target state everything aligns to.

03

You need an honest baseline assessment and, from it, a P&L-guided data & AI strategy, from portfolio through organisation to technology.

◆How we work

Where does your company really stand? Not in the number of pilot projects but in the readiness to change processes fundamentally and to anchor AI deep in the P&L.

For the first time, you see in black and white where you really stand across the dimensions of portfolio, organisation, data, technology, enablement and compliance. That transparent picture is the basis for every further decision.

Every process is assessed for suitability and economic P&L potential for AI deployment, supported by our Agentic Process Analyzer (APA), which cuts the analysis from weeks to days.

The result is a concrete target state and a roadmap, prioritised by value contribution, an investment plan that holds up in the boardroom.

01 · Weeks 1–4

360° assessment

Maturity level determined per dimension, AI potential quantified per process.

02 · Weeks 5–8

Target state

Value streams prioritised by P&L effect, target states defined per dimension.

03 · Weeks 9–12

Roadmap

Strategic roadmap and investment plan — holds up in the boardroom.

01

Maturity assessment

Baseline assessment across all dimensions with concrete areas for action.

02

Strategic target state

Concrete target states per value stream and organisational unit.

03

Prioritised roadmap

By business impact and feasibility — holds up in the boardroom.

◇What you have afterwards

  • Clarity in 12 weeks on which value streams hold the greatest P&L potential.

    Speed

  • A decision-making foundation for the executive board, robust, not opinion-driven.

    Decision readiness

  • Support through the transformation in concept development, enablement and consensus-building.

    Transformation
Field 02 / 04
Organisation & Operating Model

The organisation that delivers data & AI and the Operating Model, when agents join the workforce.

◇When to bring us in
01

The strategy is set and the portfolio is clear, but the organisation is not set up to execute the data & AI strategy effectively.

02

Agents are taking over their first processes, and suddenly it is unclear who decides, who is accountable and how your company will even work tomorrow.

03

On paper the change is described, the technology stands ready, but it never arrives in the organisation.

◆How we work

The right strategy doesn’t fail on the idea, but on the organisation that is meant to deliver it. A target state on paper changes nothing as long as structure, responsibility and people don’t move with it.

You get a clear picture of how data & AI need to be organised in your company: from responsibilities through governance to the question of what is central and what is decentralised. So that a clear strategy becomes an organisation able to act.

You see how your company works when agents take over a possibly large share of the processes: which roles emerge, which change, which disappear, who leads whom, and how results are measured when the output comes from hybrid teams.

You don’t just get the change described, you get it anchored. With an approach that brings leaders and teams along, builds consensus and makes sure that concept and technology become lived reality.

01 · Weeks 1–4

Process redesign

Value stream instead of activity chain, documented agent-ready.

02 · Weeks 5–8

Autonomy architecture

Autonomy per step (L0–L3): who decides, who controls, who is liable.

03 · Weeks 9–14

Operating Model

Roles, responsibilities, interfaces made binding.

01

AI-native process design

From the activity chain to the goal-driven value stream.

02

Autonomy architecture

A clear definition for every process step, who decides: human or machine.

03

Target operating model

Roles, responsibilities and interfaces for the AI-native organisation.

◇What you have afterwards

  • Target picture of the setup: responsibilities, governance, central/decentralised — able to act, not theoretical.

    Data and AI organisation

  • Roles, responsibilities, decision paths and KPIs for a company where agents are co-workers.

    Target operating model

  • From concept to lived reality: bring leadership along, build consensus, secure adoption.

    Transformation & enablement
Discover moreAgentic Enterprise Transformation
Field 03 / 04
Technology & Architecture

Artificial intelligence is no reason to forget 30 years of software-engineering experience.

◇When to bring us in
01

Pilot projects are running, but few manage the step into production.

02

Nobody knows what the right architecture for Agentic AI actually looks like.

03

A lot has been built, but it does not scale, does not perform, does not answer reliably.

◆How we work

In agentic AI, the wheel is being reinvented en masse right now and in the process, core principles of software architecture that have held for 30 years are being violated. Just because it says "AI" on the label, they apply no less. They apply more.

We treat agentic AI as what it is: demanding enterprise software. Scalability, performance, security and reliability are not new problems, but for the most part solved disciplines. We apply them rather than reinventing them under the label "AI".

We can do this because we bring together three things that rarely come together: a deep understanding of data, experience with enterprise IT at scale and the craft of building excellent software, now in an agentic context.

And we build where it counts: not in sandboxes, but as a lighthouse system in your real environment, in production, with real governance, real data, real users. A lighthouse with digetiers is not a PoC.

01 · Weeks 1–6

Target architecture

Building blocks, standards, make-or-buy: decided, not open.

02 · Weeks 7–16

Lighthouse build

A system in production: real data, real users, real governance.

03 · Month 4+

Scaling

Expansion stack by stack on a validated foundation.

01

Enterprise target architecture

For the AI-native organisation. Global and per solution.

02

Make-or-buy decisions

For every architecture building block with clear reasoning.

03

Lighthouse in production

Not a sandbox, not a pilot but a running system, end to end.

◇What you have afterwards

  • An architecture that scales and performs, built on principles, not on hype.

    Resilience

  • A lighthouse system in production - Proof of Production, not Proof of Concept.

    Production readiness

  • Full control over your stack: no vendor dictates your architecture, no switch tears it down.

    Strategic freedom
Field 04 / 04
Knowledge Graph & Ontology

Knowledge Graphs that scale and do not get stuck in academic discussions.

◇When to bring us in
01

Your data lives in data products, but no agent can work with it reliably.

02

Every new use case starts from zero because the enterprise knowledge is not modelled.

03

You have tried this before, but the Knowledge Graph still does not scale.

◆How we work

Without a solid knowledge infrastructure, every AI application is a castle in the air. Enterprise knowledge must become machine-interpretable. So clear in its meaning that an AI agent can work with it without a human translator.

We know where Knowledge Graphs fail: too academic, never in production, does not scale. That is exactly where we come in. We master RDF and OWL for industrial use cases, not for theory.

What is rare is the combination: domain know-how, conceptual design, ontology development and technical implementation: from a single source. Pragmatic and methodically first-rate at the same time.

The ontology, and with it the enterprise knowledge, must not be bound to a single platform, and every agent must build on the same shared state.

01 · Weeks 1–4

Knowledge mapping

Domain ontology: what means what,
in plain language.

02 · Weeks 5–10

Knowledge Layer

A machine-interpretable layer, for every agent.

03 · Weeks 11–16

Integration

Your existing platforms connected, not replaced.

01

Data architecture

From Data Mesh to Lakehouse, matched to your maturity.

02

Knowledge Layer

Enterprise knowledge, machine-interpretable, as a callable service for AI agents.

03

Integration architecture

Not throwing everything away but connecting it intelligently instead.

◇What you have afterwards

  • A knowledge layer that makes your data platform smarter — not a parallel system.

    Knowledge leverage

  • Open standards, full portability — the knowledge belongs to your company, not the platform.

    Portability

  • One Knowledge Layer that every new agent builds on instantly — instead of starting from zero.

    Scaling effect
Discover mored.AP - Our Knowledge Layer solution
Client examples

A selection of our engagements.

Selected engagement examples from everyday practice. Where do you see yourself?

Organisation for vehicle data analysis.

DATA ANALYTICSOPERATING MODEL

Decentralised analytics initiatives produced contradictory insights. A unified, agile analytics organisation creates a reliable basis for decisions and measurable financial value.

Learn more

Enterprise IT Transformation.

Operating ModelAGILE / SAFe

Caught between digitalisation pressure and cost efficiency: an agile, product-oriented IT that drives the business instead of merely serving it.

Learn more

Data integration & virtualisation.

DATA PLATFORMCLOUD

End-to-end process control from a single system, without changing the legacy world.

Learn more
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