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AI Strategy

Over my journey at IBM, I created a framework for designing an enterprise-wide AI Strategy.

Being a data journalist working in a tech company such as IBM in a team of data scientists led me to explore and create new practices at the intersection of AI, strategy, and data storytelling.


Understanding the need of IBM’s clients for a way to track their conversation around data and AI over design thinking workshops, brought me to experiment and eventually create new tools and techniques that innovated the mainstream practices of design thinking by combining it with the power of data design.


Thanks to the collaboration with some IBM’s senior business leaders and distinguished designers, today the tools and practices that I created have originated a novel and iconic framework for data and AI Startegy named: IBM Enterprise Design Thinking for Data and AI. This framework is radically changing how IBM client-facing teams help enterprises in their AI transformation by fostering a data-first mindset to business problems.

Selected Clients:


ABC Bank 


Duomo Milan



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Today AI Strategy is Business Strategy. 


To me data is more than databases, lines of code, or numbers: data is the representation of a company’s business and its AI maturity. That’s why over my journey at IBM I used my expertise in data journalism and data design to help companies give shape to their data over design thinking sessions so to foster a data-driven mindset that makes data more human approachable. By adopting data as a lens to uncover overlooked opportunities in companies’ businesses, teams can identify a well-defined set of AI use cases and start crafting their AI strategy.


By giving shape to data and understanding what data they need to solve people’s problems and what’s the status of that data, companies can tell their own story by connecting all the insights they discover under a single overarching vision defining a long-term AI Strategy. 

To me data is more than databases, lines of code, or numbers: data is the representation of a company’s business and its AI maturity.

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Many of today’s challenges in implementing AI are grounded in teams of data scientists and developers rushing to implement data and AI projects without defining what success looks like to their organization. By not clearly defining their business purpose, technical teams set ambitious goals and deliver solutions that aren't ready to provide any tangible business value. To help with this dynamic, I created a new framework that bridges the gap between strategy and execution. This framework it's used to discover and solve problems by using a data-driven thinking while keeping the focus on human needs and ethics. This framework for AI Strategy helps teams shape data to visualize how it's being collected and organized. In turn, teams can then employ data to act as a catalyst for their AI-based business transformation and align  around the purpose of implementing AI in the fabric of the business.


The framework is composed of four main sections:


Set intents: it starts with setting intents to discover and articulate a team’s business needs by uncovering new data and AI business opportunities.


Identify: Then the team identifies the use cases and selects the data and the AI solutions needed to address them. They will identify what data they have access to and understand how it is being collected.

Evaluate: The data is evaluated to implement Data Ops by assessing the status of the data available using the Ladder to AI

Plan: The last section is all about planning: the team transfers the business intents into a technical implementation plan with concrete actions. This helps tactically connect strategy to real execution work.


The framework is delivered in two sessions:


Strategy Workshop
The first session is the strategy workshop. This runs over 1-2 days and is primarily attended by C-Suite executives and key stakeholders.  They define a well-articulated intent for the solutions to be implemented to drive whole-team alignment.


Technical Workshop
The second session is the Technical Workshop. The business intents are transferred to a technical team composed of data scientists, developers and designers that will make sure to embed the business requirements in their solution. The technical team ends the workshop with a clear blueprint of the technical implementation.