04
AI
Agents, models, copilots, orchestration
- LLMs
- Domain agents
- RAG
- MCP & A2A
AI Acceleration
AI is no longer experimental.Most companies still cannot scale it.
A proven playbook for moving AI from pilot to production. Clarity and execution in four to six weeks, grounded in the integration, data, and governance work that makes AI real.
A proven method, three phases
- 01ExploreFind the value
- 02DesignShape the blueprint
- 03TransformShip it, govern it
Four to six weeks, then ready to scale
01 / The challenge
AI initiatives stall before they scale
- 01
Hidden costs
AI spend creeps in across teams, agents, and SaaS embeds, with no consolidated view of where value is created or wasted. Budgets overrun before anyone has a measurable answer to show for it.
- 02
Stuck in pilot mode
Demos work. Production does not. The leap from POC to enterprise-grade infrastructure, with the integration, data, and governance work that involves, is where the majority of projects die.
- 03
Unknown ROI
Without measurable KPIs and a tracked total cost of ownership, AI investments stay defensive, never a strategic line item. Leadership sees activity but cannot read the business case.
The bottleneck is not AI capability. It is visibility.
02 / Where we focus
We focus on the part that makes AI real
Models and demos are the visible part. The work that decides whether AI scales is the layer underneath: how systems expose data, how that data is governed, and how every AI interaction is observed and controlled.
FlorenceNext focuses on the foundations: integration, data readiness, and governance. The unglamorous work that turns a POC into a production AI capability.
- 03Our focus
API & Integration
How systems are exposed and governed
- API gateway
- AI gateway
- iPaaS
- Event bus
- 02Our focus
Data
Domains, ownership, freshness
- Warehouses
- Lakes
- Events
- Operational stores
- 01Our focus
Systems
The ground truth of the business
- CRM
- ERP
- Mainframe
- SaaS
- Custom apps
03 / The playbook
The AI Acceleration PlaybookA proven method
A repeatable mechanism to decide what to build, validate that it is feasible, and ship it without losing control. Three phases, designed to converge fast.
From kickoff to a delivered first agent in four to six weeks.
- 01Phase 01
Explore
Find the value. Validate the constraints.
Engage business and IT to identify high-value AI use cases. Assess data quality, integration readiness, security exposure, and technical feasibility against five standard dimensions. End with a prioritized backlog, not a wish list.
Jump to Explore - 02Phase 02
Design
Define a clear roadmap and a scalable architecture.
Translate the selected initiatives into functional and non-functional requirements, a logical target architecture for AI, data, and integration, and a sequenced implementation roadmap. Every AI project is built to scale, not just to demo.
Jump to Design - 03Phase 03
Transform
Implement, monitor, and govern. Measured by KPIs and TCO.
Deliver the first agents and the foundation underneath them. Three execution models for three engagement shapes. Consistent oversight, accountability, and observability across all of them.
Jump to Transform
04 / Explore
Phase 01
Business-driven, realistic, ready to scale
Where the value lives, where the constraints sit, and which initiatives are worth committing to. Two sprints, one shared map.
A. Discovery
Discovery Sprint
A hybrid bottom-up and top-down engagement that surfaces practical opportunities directly from the people who run the work, then validates them against function-level priorities. Four phases, adaptable to the size and maturity of the organization.
- 01
Leadership alignment
Working session with the leadership team or project sponsors. Build a shared understanding of AI opportunities in the business context, align on scope, and surface strategic constraints before engaging the wider organization.
- 02
AI Discovery Workshops
Two half-day workshops with a cross-functional group of operators. Function-homogeneous groups map their own repetitive, slow, or skill-bottlenecked tasks against the 6 AI Primitives, then position them on an impact / complexity matrix.
- 03
Functional deep-dives
Dedicated sessions with department heads, process owners, and where relevant the Digitalisation or IT lead. Validate which use cases are genuinely high-impact, surface system dependencies, and align with ongoing initiatives.
- 04
Leadership restitution
Closing session with leadership. Present the consolidated and prioritized use case map, agree quick wins and self-service opportunities, define next steps and the structure of the Decision Sprint.
Workshop framework
The 6 AI Primitives
A framework used in the workshops so non-technical participants can identify opportunities in their own operational context. The primitives cover the vast majority of where generative AI delivers real value today.
- 01Content Creation
- 02Automation
- 03Research
- 04Coding
- 05Data Analysis
- 06Ideation / Strategy
B. Decision
Decision Sprint
Each surviving use case is assessed against five standard dimensions. Consistency over depth: we are not building the architecture, we are narrowing the backlog.
Is this use case valuable AND feasible AND safe AND governable AND worth the spend?
- 01
Data feasibility
Required data domains, known system of record, availability, and freshness. Can the required data be accessed reliably without major rework?
- 02
Security and risk
Sensitive or regulated data, access control needs, blast radius of errors. Can this be implemented safely with acceptable risk?
- 03
Tools and platform readiness
Existing platforms that could support the use case, alignment with enterprise standards, need for new tooling. Can we deliver with what is already in place?
- 04
Governance and control
Ability to enforce access policies, observability requirements, need for centralized control. Can this be governed without introducing unmanaged risk?
- 05
Cost and effort
One-time setup, ongoing usage cost, vendor dependency. Is the expected cost proportional to the business value?
Outputs
What you walk away with
- AI capability and usage snapshot
- AI tools and model inventory, including shadow AI
- Data domains and systems-of-record map
- Integration capabilities and constraints baseline
- Prioritized use case backlog with feasibility assessment
- Executive summary, ready for sponsor sign-off
05 / Design
Phase 02
From decision to blueprint
The "what" has been decided in Explore. The "how" is now ready for delivery: requirements, architecture, technology selection, and a sequenced roadmap.
- 01Activity
Requirements Definition
Functional requirements aligned to business outcomes, plus non-functional requirements covering security, performance, scalability, and compliance. Assumptions and constraints made explicit.
- 02Activity
Technology and Option Evaluation
Architectural patterns and options assessed against the requirements. Build vs buy trade-offs called out. Alignment with existing platforms and enterprise standards verified.
- 03Activity
Logical Solution Architecture
End-to-end logical architecture covering AI components (models, agents, orchestration), data sources and access patterns, integration and exposure layers, control and governance points.
- 04Activity
Implementation Readiness
Implementation boundaries and sequencing, dependencies and prerequisites, validation that the design can realistically be delivered with the team and budget on the table.
Outputs
Deliverables
- Solution Architecture Document
- Functional and Non-Functional Requirements
- Technology and Vendor Selection Criteria
- Implementation Roadmap
06 / Transform
Phase 03
Three execution models, one delivery discipline
How we engage depends on how you want to deliver. The accountability, oversight, and governance stay constant across all three models.
- Model 01
Build
End-to-End Delivery
Design and implement the full solution end-to-end, from platform setup to AI capabilities and integrations.
Who it is for
- You want a single accountable delivery partner
- You prefer faster execution with minimal coordination overhead
- You are implementing well-scoped or initial AI initiatives
Our duties
- Platform and foundation setup (AI, data, integration, gateway)
- Development and configuration of AI agents and workflows
- Integration with enterprise systems and data sources
- Security, governance, and observability implementation
- Go-live, stabilization, and initial support
- Model 02
Coordinate
Co-Delivery Model
Deliver the solution through coordinated execution with your internal teams and technical partners.
Who it is for
- You have internal delivery capabilities or preferred partners
- You need to combine multiple skill sets
- You require shared ownership of implementation
Our duties
- Joint delivery planning and role definition
- Coordination of development activities across teams
- Architectural oversight and design alignment
- Quality assurance and integration validation
- Support through go-live and early operations
- Model 03
Partnership
Foundation and Governance Model
Build the core foundation, then govern and guide the continuous development of AI capabilities over time.
Who it is for
- You plan to scale AI initiatives across multiple teams
- You want to build internal delivery autonomy
- You need a long-term architectural and strategic partner
Our duties
- Implementation of the AI and integration foundation
- Definition of standards, patterns, and guardrails
- Governance of ongoing development initiatives
- Regular roadmap and progress reviews
- Advisory support for emerging opportunities and challenges
Discipline
Constant across all models
- Delivery governance and progress tracking
- Alignment with agreed architecture and standards
- Risk, dependency, and quality management
- Enablement of internal teams
Outputs
Deliverables
- A working AI solution or foundation in production
- Trained and enabled internal teams
- Documented standards, patterns, and decisions
- A roadmap for continued evolution
07 / AI readiness assessment
Pre-engagement
Before we run the playbook, we run the diagnosis
A short pre-engagement double-track that confirms whether the foundations can carry the AI program, or whether they need to be built first. Designed to expose blockers early, while they are still cheap to fix.
- Track A
Assess the integration landscape
Systems, data flows, and connections needed to support AI. Identifies gaps, bottlenecks, and opportunities across the current architecture, without descending into a full enterprise audit.
- Track B
Assess the business initiatives
Priorities, processes, and value drivers across the organization. Pinpoints where AI can enhance performance, efficiency, or decision-making in ways that align with the strategy already in motion.
Foundation for scalable AI
The two tracks converge into a single technical foundation: the systems, data access patterns, governance points, and business priorities required to make every downstream AI initiative strategically aligned and ROI-positive.
08 / Maturity ladder
From Shadow AI to Core
Where every organization we work with starts, and where we take them. Five steps, sequenced so the foundation work and the first agents progress in parallel, not in series.
Shadow AICore
- 1
Stabilize
Get visibility on what is already running, retire what is unsafe.
- Inventory shadow AI tools
- Retire unsafe automations
- 2
Foundation
Stand up the gateway. Wrap the first APIs. Audit every call.
- Stand up an AI gateway
- Wrap five to ten APIs with MCP
- Enforce policy and audit per call
- 3
Acceleration
Identify the first high-value use cases and ship the first domain agent.
- Identify priority AI use cases
- Add domain knowledge base and RAG
- Launch the first domain agent
- 4
Orchestrate
Connect agents to each other. Keep humans in the loop where it matters.
- Event-driven agent handoffs
- Human-in-the-loop approvals
- 5
Scale and Govern
Replicate the proven pattern across the organization, on policy packs you trust.
- Replicate the working pattern
- Roll out policy packs org-wide
Twelve to eighteen months on average, depending on scope and platform readiness
09 / Proof
What we have already shipped
Four engagements, each picking up at a different point in the playbook. Anonymized at client request.
- ExploreCase 01
Engineering and construction, Europe
AI Discovery Sprint, 60+ workshop participants
A structured opportunity exploration program engaging the entire organization through a hybrid bottom-up and top-down process. The team identified, mapped, and prioritized use cases across every function, then defined a concrete operational roadmap for implementation.
- 60+ cross-functional workshop participants
- Use cases mapped to the 6 AI Primitives
- Impact / complexity matrix with quick wins isolated
- Top 3 to 5 business cases with value and resource estimates
- A 90-day AI roadmap presented to leadership
- DesignCase 02
Trade association, Italy
AI Matching System for public grants and incentives
An intelligent automatic matching system between available public grants and the trade association’s member companies. Designed to reduce manual research and outreach while increasing the relevance of identified opportunities for each business.
- Member company profiles built from CRM, enriched with ATECO codes and geography
- Automated grant ingestion from central and local institutional sources
- Scoring engine combining structured filters and semantic analysis
- Web dashboard ranking grants by compatibility score
- Daily refresh, deadline management, alerting
- TransformCase 03
Global utility, Europe
AI Open Innovation Strategic Radar
A structured open innovation initiative. After a Discovery phase that mapped and prioritized AI use cases within the Innovation team, we built an AI-powered matching agent that evaluates startup proposals against the company’s strategic needs and surfaces a structured scorecard.
- Multi-dimensional scoring (strategic fit, technology, scalability, regulatory, maturity)
- Institutional memory across historical pilots and evaluations
- Gap analysis and guided questions for every startup proposal
- Embedded in the Innovation team’s existing scouting workflow
- TransformCase 04
Construction and infrastructure, Italy
AI Procurement Copilot for contract drafting
An AI-powered contract drafting assistant for the procurement department. Automates the generation of supplier and subcontractor contracts from structured supplier data, vendor lists, quotes, and historical interaction data.
- Standard and slim subcontracting agreements
- Standard and slim supply-and-installation agreements
- Unified drafting workflow across supplier, vendor, and commercial data
- Scales with the customer’s ongoing cloud document management transition
10 / How we deliver
Where technology meets strategy
FlorenceNext runs the technical foundation. Sirocco runs the business strategy and the program. The combined practice delivers end-to-end, with no handoff gap between the two.
FlorenceNext
Technology- 01
Advanced Technology Expertise
Deep technical know-how across modern architectures and platforms: MuleSoft, Salesforce, Kong, and the AI ecosystem around them.
- 02
Agnostic Integration
A flexible integration approach that connects across any system or technology stack, without committing the customer to a single vendor.
- 03
AI Enablement
Help client organizations adopt, integrate, and scale AI solutions effectively across the platforms they already trust.
- 04
Scalable Solutions
Adaptable, future-ready systems that grow with the business and the AI roadmap, not against them.
Sirocco
Strategy- 01
Business Analysis Excellence
Strong capability in mapping processes and translating business needs into delivery-ready requirements.
- 02
Strategic Consulting
Guides organizations through digital transformation with clarity, sequencing, and stakeholder alignment.
- 03
SAFe Methodology Mastery
Applies agile frameworks at scale to ensure alignment, speed, and predictable delivery quality across large programs.
- 04
Value-Driven Approach
Every initiative is tied to a measurable business outcome, with KPIs and TCO tracked from kickoff onwards.
The result
End-to-end solutions, with no gap between business intent and technical execution.
Let's accelerateBook a Discovery Sprint
Ready to move past pilot mode?
Bring us your AI ambitions and your integration estate. We will show you what is realistic in four to six weeks, backed by a Discovery Sprint that engages your business and IT teams from day one.
