Unlocking AI Value: Lessons Learned

As businesses across industries race to embrace artificial intelligence (AI), the pressing challenge lies in finding practical, value-driving applications. Fabio Giraldo, the U.S. Head of Advanced Analytics at Mindshare, has navigated this journey firsthand with the development of Synapse — an AI platform designed to deliver measurable business outcomes for clients.

In a recent webinar, Fabio shared invaluable insights gleaned from building Synapse, a global predictive modeling solution that has elevated Mindshare's applied analytics practice. His experience offers a roadmap for tech leaders seeking to unlock the true potential of AI within their organizations.

Identifying Monetizable Use Cases

At the core of Fabio's approach is a keen focus on use cases that can be monetized by providing differentiated value to clients. "One of the big questions is, is it a product we can sell to a client? If it's an application that is going to provide value for the client, it is reasonable to think that for the right price, they're going to be willing to pay for it," he emphasized.

Synapse's development centered on applications like marketing performance analysis, natural language processing for ad copy optimization, creative analytics, and synthetic data simulations — areas where AI could directly impact business outcomes for Mindshare's clients.

Fabio recommends evaluating potential use cases through two lenses: internal efficiency/quality gains, and client-facing monetization opportunities. The sweet spot? Applications enhancing processes while creating new revenue streams.

Assessing Organizational Readiness

However, identifying valuable use cases is just the first step. Fabio underscored the importance of assessing an organization's readiness for AI adoption, including data maturity, AI literacy, openness to cultural change, and risk tolerance.  

1. AI literacy and data maturity: "You need data governance and pipelines feeding your models. Don't overlook this foundational enabler."
2. Cultural openness: “Excitement about AI doesn't translate into openness to transforming processes." Leaders must address this proactively through stakeholder conversations.
3. Risk tolerance: Adopting AI means ceding some decision-making to complex models. Understand the organization's risk appetite upfront.

A thorough readiness assessment surfaces capability gaps to be addressed for effective change management.

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Prioritizing Data Readiness  

Data readiness emerged as a critical enabler for successful AI deployment. "Data governance, having data pipelines ready that you can feed into your AI models, is going to be important," Fabio advised. Key areas of focus include:  

• Data governance: Ensuring data quality, consistency and accessibility

• Pipeline infrastructure: Robust pipelines to integrate, process and serve data  

• Analytics capabilities: In-house skills or partners to extract actionable insights  

Robust data pipelines and governance frameworks lay the foundation for AI models to deliver accurate and relevant insights.

Leveraging Borderless Talent

Another key learning from Fabio's experience was the value of leveraging borderless talent to accelerate AI initiatives. "Working with [Andela] has been very productive for us," he shared. "Borderless talent has helped us, for one, to scale up or down based on the demand that we have for these different applications."

Benefits of this model include:  

• Sourcing niche skills: Rapidly accessing expertise in emerging technologies

• Staffing flexibility: Seamlessly scaling teams up/down based on project needs  

• Faster delivery: Reducing typical hiring delays to drive innovation velocity  

• Cost management: Optimizing resources without compromising quality

As demand for AI/ML skills outstrips supply, the ability to tap into global talent becomes a competitive advantage.

A Journey, Not a Destination

Ultimately, Fabio's insights underscore that AI adoption is a journey, and progress lies in laying the foundational building blocks. "Getting the building blocks in place is progress towards implementing AI," he stated.  

As tech leaders navigate this journey, Fabio's experience with Synapse offers a guiding framework: identify monetizable, high-value use cases; assess organizational readiness; prioritize data readiness; and leverage the agility of borderless talent to drive innovation and deliver tangible business value.

See how Andela can help connect you to borderless talent, fluent in today’s AI skills.