Our approach

The value lies in the 70%.

Only a fraction of the value of AI comes from the models. The rest is people, processes and change. We invest where the value actually is: the 10-20-70 rule, made concrete.

That is why upskilling, process redesign and change management are not add-ons for us. They are the work itself.

Scalero's 6-4-4 method

Six domains. Four disciplines. Four levels.

The method in three numbers. Six domains describe what has to change. Four disciplines make sure the change actually happens. Four levels of upskilling let your people carry it.

Transformation domains

What changes.

Strategic Foundation, Organization & Culture, Operational Reinvention, Technology & Data Backbone, Customer & Product Reinvention and Trust & Governance.

See the six domains ↓

Disciplines in the acceleration layer

What makes it happen.

Change management, communication, value measurement and continuous evolution, across all six domains.

See the four disciplines ↓

Levels of upskilling

What makes it last.

Leadership, all employees, depth by function and AI champions, in parallel and not in sequence.

See the four levels ↓

6-4-4 describes what we do. Delivery runs in five phases, from Diagnose to Sustain.

01Strategic Foundation
02Organization & Culture
03Operational Reinvention
04Technology & Data Backbone
05Customer & Product Reinvention
06Trust & Governance

Nothing stands on its own, and that is the whole point.

DeliveryDiagnose›Design›Pilot›Scale›Sustain

6 · What we cover

The six transformation domains.

Each domain has its own content areas and concrete deliverables. Click a domain to open it.

01 · WhyStrategic Foundation

Establish the company's own AI First position and path. Before anything else, leadership answers one question: what does AI First mean for us?

AI-first vision & ambition: Define your interpretation: operational, product or structural AI First, or a hybrid. Turn it into a stated position over a 3–5 year horizon.
Strategic positioning: Market and competitor analysis, the threat from AI natives. Where AI is defense and where it is offense.
Investment thesis & roadmap: A multi-year capital and resource plan, sequenced across horizons 1, 2 and 3.
Leadership alignment: Workshops and 1:1 coaching for the executive team and the board, so that everyone tells the same story.
AI-first narrative: The internal and external story that ties all initiatives together. Without it, implementation becomes ad hoc.

Typical deliverablesAI First Strategy Document · Investment Roadmap · Executive Alignment Charter · Narrative Playbook

02 · WhoOrganization & Culture

Shape the organization that can deliver AI First in practice. People do what they are measured on, so structure, culture and incentives have to change at the same pace as the technology.

Operating model & structure: Redesign of units, decision rights and reporting lines for AI-driven work. Agile pods, new roles (AI Product Manager, AI Ops Lead, Prompt Engineer).
Culture & mindset shift: From AI fear and AI hype to a pragmatic culture of experimentation. Psychological safety to try, fail and learn.
Upskilling: The central capability lever. See its own section further down.
Talent strategy: Three parallel tracks: recruitment, development and redeployment as tasks change.
Performance & incentives: AI use as part of performance reviews, new KPIs, structures that reward AI-driven productivity.
Leadership model: A shift from managing tasks to managing outcomes.

Typical deliverablesTarget Operating Model · Cultural Transformation Plan · AI Fluency Curriculum · Champions Program · Reskilling Pathways · Talent Strategy · Performance Framework · Leadership Charter

03 · HowOperational Reinvention

Rebuild how the work actually gets done. This is where most of the value of AI is created or lost.

Process mapping & value analysis: A systematic inventory of the core processes, prioritized by value, AI suitability and ease of change. Builds the use case portfolio.
AI-native process design: Redesign from the ground up. Rule of thumb: if the process looks the same after AI is introduced, you have done it wrong.
Agentic workflow design: Workflows where AI agents carry out entire chains of tasks, with human↔agent handoff and escalation logic.
Human–AI collaboration models: Which tasks are “Centaur” (clear division of work) and which are “Cyborg” (intertwined), adapted by function.
New ways of working: AI-first meetings, documentation and decision-making that become everyday practice.
Use case portfolio management: A stage-gate model that moves initiatives from experiment to scale.

Typical deliverablesProcess Portfolio Map · AI-Native Process Designs · Agent Workflow Library · Ways-of-Working Playbook · Use Case Pipeline

04 · WhatTechnology & Data Backbone

Build the infrastructure that makes AI First possible. AI without a data foundation becomes theater, and a data foundation without AI architecture becomes a museum.

Data strategy & data foundation: Data quality, master data, metadata management. The shift from project data to product data, where datasets are treated as long-lived assets.
Semantic layer & knowledge infrastructure: Ontologies, knowledge graphs, vector stores. The layer that makes your data usable for AI.
AI & agent architecture: Model strategy (foundation models, fine-tuning, RAG), agent platform, orchestration. Designed for scalability and replaceability.
Platform & tool strategy: Build/buy/partner decisions. Getting the tool sprawl under control: Copilot, ChatGPT Enterprise and industry-specific solutions coordinated instead of running side by side.
Integration & interoperability: Connections to ERP, CRM and production systems. API strategy and MCP/agent interoperability.
Security & data protection: Identity management, data isolation, protection against prompt injection, audit trails. Built in from the start.
MLOps & AgentOps: Running models and agents in production: monitoring, versioning, continuous improvement.

Typical deliverablesData Strategy · Reference Architecture · Platform Decisions · Integration Blueprint · Security Framework · Ops Model

05 · OutcomeCustomer & Product Reinvention

Make AI First visible where it counts, in the value to the customer. The companies that truly become AI First redefine what they offer the market.

AI-native product design: Products where AI is the primary interface or the core function. Conversational interfaces, predictive personalization, autonomous functionality.
Customer lifecycle redesign: The whole customer lifecycle rethought with AI at the center: discovery, sales, delivery, support and relationship.
New business models: Outcome-based pricing made possible by AI measurement, usage-based models, AI-as-a-service, entirely new revenue streams.
Service & support transformation: AI-first customer service with a clear human-in-the-loop design.
Brand & trust: How AI use is communicated externally. Trust signals, transparency, “AI inside” positioning. Managing the B2C risk.
Innovation & new markets: Opportunities that did not exist before AI: adjacent markets, new customer segments, new geographies.

Typical deliverablesProduct Reinvention Roadmap · Customer Journey Redesigns · New Business Model Canvases · Brand & Communications Strategy · Innovation Pipeline

06 · SafeguardsTrust & Governance

Build in accountability from day one. Delayed governance costs twice: first in incidents, then in retroactive work.

AI Governance Framework: Roles and responsibilities (AI Council, Model Owners, Risk Officers), policies, review processes, model registry.
Regulatory compliance: EU AI Act roadmap (risk classification, documentation, deadlines), the EU Digital Operational Resilience Act (DORA) for the financial sector, industry-specific regulation, GDPR implications.
Risk management: AI-specific risks: hallucinations, bias, model drift, prompt injection, data leakage, vendor dependencies. Risk register and mitigation plans.
Ethical framework: Principles and edge cases (surveillance, decisions that affect individuals, automated recommendations). Ethics committee and escalation paths.
Trust & safety: What AI may do autonomously, what requires human approval, how traceability is created.
Third-party governance: Management of AI vendors: due diligence, contract clauses, continuity, exit strategies.

Typical deliverablesGovernance Framework · Compliance Roadmap · Risk Register · Ethical Code · Trust & Safety Playbook · Vendor Management Model

4 · What ties it together

The acceleration layer, what ties it together.

Four disciplines that run across all six domains. They decide whether the transformation actually happens.

Change management

A structured method for moving people through the transformation. Not one-off training but a continuous program: awareness → understanding → capability → adoption → embedding. Executive sponsorship, change agents across the organization, interventions tailored to each employee group.

Communication

Internal and external communication, coordinated. Internally: regular, transparent, two-way. Externally: deliberate positioning and managing the risk of being seen as replacing people. A crisis management plan for AI incidents.

Value measurement & tracking

A system that follows value from initiative to result. Outcome measures (shorter lead times, quality, ARR growth), not activity measures such as the number of AI users. Reported to the board quarterly.

Continuous evolution

The field moves faster than traditional transformations. Structures for ongoing horizon scanning, vendor evaluation, regulatory updates and strategic recalibration. AI First is not a goal but a state that has to be maintained.

4 · The central capability lever

Upskilling, at four levels.

Upskilling is the single most direct lever in the entire transformation. Most of the value of AI flows through people, and upskilling decides whether yours can actually deliver it. Companies that skip the broad investment get stuck at the experiment stage. The four levels run in parallel, not in sequence.

Illustration, not measured data.

Breadth: Leadership & boardBreadth: All employeesBreadth: By functionBreadth: Selected 5–10%

Level 1

Leadership capability

Leadership & board

AI strategy, governance, ethics and investment decisions. 1:1 coaching and executive workshops. Leadership cannot lead what it does not understand.

Level 2

General AI fluency

All employees

What AI can do, what it cannot do and how to use it safely. Baseline training, scenario exercises and policy. Everyone uses AI day to day, without creating risk.

Level 3

Role-specific depth

By function

Each function gets the right depth. Prompt libraries, use case training and coaching on real work tasks.

Level 4

AI Champions & power users

Selected 5–10%

Key people who drive adoption in their teams. Intensive training, their own network and a mandate to shape tools and policy. They pass the skills on.

Typical deliverables AI Fluency Curriculum (4 levels) · Champions Program · Learning Infrastructure · Reskilling Pathways

The honest version

A 40% improvement in one task does not automatically become 40% on the bottom line. Value leaks at the seams between tasks, teams and systems, and most pilots that stay pilots deliver nothing. Returns require transformation, and transformation requires method. That is exactly what we do.