Adopting Artificial Intelligence and Large Language Models (LLMs) within an organization is not a single technical deployment, but a progressive transformation involving strategy, processes, people, and governance. A structured roadmap is essential to reduce risk, ensure alignment with business objectives, and maximize long-term value.
Alice Data Science supports organizations through a phased, controlled, and measurable adoption roadmap, designed to transform AI from experimentation into a sustainable enterprise capability.
Phase 1 – Strategic Assessment and Readiness
The first phase focuses on understanding the organization’s current state and defining realistic objectives.
Key activities include:
- Assessment of business processes, decision points, and pain areas
- Evaluation of data availability, quality, and governance
- Identification of high-impact and low-risk AI use cases
- Definition of success criteria and key performance indicators (KPIs)
Outcome: a clear, shared vision of where AI can create value and how success will be measured.
Phase 2 – Use Case Design and Pilot Projects
In this phase, selected use cases are translated into controlled pilot projects, avoiding premature large-scale deployment.
Key activities include:
- Detailed functional and technical design
- Selection of appropriate AI models (ML, recommendation systems, LLMs)
- Definition of Human-in-the-Loop and governance mechanisms
- Development of proof-of-concept or minimum viable solutions
Outcome: validated use cases with measurable results and controlled risk.
Phase 3 – Data, Model, and Process Integration
Once pilot results are validated, AI solutions are integrated into existing enterprise processes.
Key activities include:
- Integration with enterprise systems and data sources
- Definition of workflows and decision-support interfaces
- Implementation of monitoring, logging, and auditability mechanisms
- Alignment with security, compliance, and IP protection requirements
Outcome: AI solutions embedded into real operational contexts.
Phase 4 – Training and Organizational Enablement
Technology alone is insufficient without people who can use it correctly and responsibly.
Key activities include:
- Role-based training programs (executive, managerial, operational, technical)
- Guidelines for correct AI and LLM usage
- Development of internal competencies and centers of expertise
- Support for cultural adoption and change management
Outcome: informed and competent personnel, capable of extracting value from AI systems.
Phase 5 – Scaling, Governance, and Continuous Improvement
In the final phase, AI becomes a scalable and governed enterprise capability.
Key activities include:
- Extension of successful use cases across the organization
- Continuous performance monitoring and KPI tracking
- Model updates, retraining, and prompt refinement
- Ongoing governance, risk management, and compliance oversight
Outcome: a mature, sustainable AI ecosystem aligned with long-term strategy.
A Controlled Path to Sustainable Value
This roadmap ensures that AI and LLM adoption progresses from experimentation to institutionalization, minimizing risk while maximizing impact. Each phase builds on validated results from the previous one, enabling organizations to maintain control, transparency, and accountability at every step.
Alice Data Science acts as a strategic and technical partner throughout the entire roadmap, supporting enterprises in transforming AI into a durable competitive advantage.
Alice Data Science (Milano Italy) – Intelligent Data - Smarter Decisions 