AI Strategy

Client 

Category

IBM

AI Strategy

Overcoming barriers to AI adoption

By realizing that a sharp fracture between technology and business is hampering organizations’ fulfillment of the AI potential, I developed a systematic method that relies on strategy to weave AI into the business dynamics.


Enterprises don’t have a process to identify and articulate problems, secure baseline metrics to define what successful AI projects look like, and map AI initiatives to business results. With this framework, I enable customers to start adopting AI by developing a strategy supported by KPIs showing the return on investment of AI initiatives.

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The AI Strategy method resulted in the development of IBM Enterprise Design Thinking for Data and AI framework: designed as a toolbox of more than 50 exercises addressing business, data and AI and AI ethics issues.

Supporting Business Strategy with AI

The AI Strategy framework uses human needs as a lens to identify and define AI initiatives that support the broader corporate strategy.
With this method, I enable both business and technical stakeholders to align business needs with AI intents to build a prioritized portfolio of AI initiatives.

Business Impact

With the AI Strategy framework, I contributed to changing IBM’s go-to-market strategy by allowing the company to better enter the data and AI market and increase sales pipeline opportunities.


I worked on business development strategies to grow the portfolio of IBM’s clients by partnering with clients’ MD, sellers and, services teams.
I generated long-term leads by prospecting and working with clients.


I expanded the human-centered AI strategy practice across IBM software and services as part of my mission to contribute to IBM’s cultural transformation in data and AI practices and leadership.

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Customer Value

With the AI Strategy framework, I enable customers to look at the organization holistically to: quantify the total value of the AI portfolio, track the value of the portfolio realization, move 2X faster from strategy to execution, gain a common language to develop better communication among both business and technical stakeholders to align on business intents.

Human-Centered Design practice acting as a strategic tool

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. 

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Recognizing data patterns in complexity

The framework comprises six phases: Explore, ideate, visualize, assess, define, and plan. It brings teams from ideation to prioritization of AI initiatives by adopting a human-centered focus.
 

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Years of relentless experimentation

Since I joined IBM in 2019, I have experimented with and researched how to give shape to data in a strategic way.

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

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A two years cross-collaboration with the IBM Design Program Office

By sparking an intense collaboration with the IBM Design Program Office, I had the opportunity to work with IBM's Distinguish Designer for AI to innovate the mainstream IBM Design Thinking framework and create a new one, that was named IBM Enterprise Design Thinking for Data and AI.