In today’s fast-evolving landscape, Generative AI is proving to be a game-changer across industries, from energy to aerospace. But as powerful as AI may be, achieving widespread adoption and tangible business outcomes still poses significant challenges.
For Capgemini, AWS, and their innovation consultancy partner Onova, hackathons have proven to be a powerful accelerator for AI adoption, skill development, and innovation. By embracing a hackathon-led growth model, Capgemini and AWS recently embarked on a groundbreaking Global Gen AI Hackathon, focusing on targeted, industry-specific use cases that can drive real-world value.
Victor Li, CEO of Onova, explains this approach as “going from zero to use case to end solution,” a journey that happens at remarkable speed within the hackathon framework. In our recent webinar, we dived deeper into the business objectives, key results and strategies that made this hackathon a success.
Be clear on your goals — A focus on employee upskilling and generating real-world use cases
Capgemini set two clear goals for this hackathon: equipping employees with Gen AI skills and knowledge on AWS and developing AI solutions relevant to priority industries. “We had two main objectives,” shared Genevieve Chamard, who led the hackathon from Capgemini’s side. “One was to educate and upskill our employees globally on the latest Gen AI tools. The second was to create industry-specific solutions that would allow our consultants to walk the talk in industries like financial services, energy, automotive, and aerospace and defense.”
1. Employee Upskilling on Gen AI Tools:
The hackathon facilitated a massive, structured upskilling initiative, allowing employees to earn over 2,000 badges in various Gen AI technologies, such as Amazon Bedrock and Amazon Q. “Our employees are now fully equipped to leverage these tools in real-world scenarios, placing us ahead of the curve among global consulting firms,” Genevieve noted. By encouraging employees to interact directly with these tools, the hackathon bridged the gap between theoretical knowledge and hands-on expertise.
2. Building Industry-Focused Use Cases:
Unlike many hackathons where participants have free rein, this event was tightly scoped to align with Capgemini’s client and industry goals. “By focusing on industry-specific use cases, we avoided generic solutions and instead developed tailored MVPs that directly address the challenges of our target industries,” Genevieve explained. As a result, Capgemini’s industry leaders gained a portfolio of ready-to-deploy AI solutions, developed within just two weeks, that can now be further refined and brought to market.
Key Results: Impressive Scale and Tangible ROI
The scale and outcomes of the hackathon were impressive. With 1,600+ participants from over 40 countries, Capgemini and AWS engaged over 170 teams, supported by nearly 50 mentors and judges. By the end of the hackathon, 62 fully developed solutions were submitted, resulting in numerous viable MVPs, including one winning solution selected to showcase at AWS’s upcoming re:Invent conference. “The hackathon accelerated our ability to build assets,” shared Genevieve, “achieving in two weeks what would have otherwise taken months or even a year.”
Another critical outcome was the shortened time required to transform hackathon MVPs into client-ready solutions. “In just a few weeks, some solutions will be ready for client demonstrations at Reinvent,” a testament to the efficiency fostered by this hackathon. The hackathon’s framework proved invaluable in reducing development timelines, allowing Capgemini to deploy targeted, functional AI prototypes into industry applications with unprecedented speed.
Mentors also emphasized the high level of solution readiness. Niko Borodachuk, an AWS Solutions Architect, remarked, “What we saw in this hackathon was a fully packaged solution, including a front end. It was ready to showcase to clients and went beyond just technical functionality.” This depth of readiness meant that, rather than presenting just prototypes, the hackathon participants created polished, client-ready solutions—remarkable considering the time constraints.
Low Attrition Rates and High Engagement: A Key Indicator of Success
Typically, virtual hackathons face high dropout rates. This hackathon, however, maintained engagement levels two to three times higher than average, reflecting the strong commitment of participants. The unique combination of a structured curriculum, relevant industry focus, and dedicated mentorship played a significant role in this success. “Keeping people motivated for two weeks while balancing full-time work was a challenge,” shared Victor Li. “The participants’ dedication and our partners’ support made all the difference.”
Hackathon-Led Growth: Why Hackathons Work for Generative AI Adoption
The hackathon wasn’t just about delivering a final product; it was an immersive learning experience for employees and a highly effective mechanism for organizational change. Here are some factors that contributed to its success:
1. Structured Upskilling Through Hands-On Learning
Prior to the hackathon, Capgemini employees participated in a prerequisite curriculum, which helped drive completion rates for Gen AI training badges. AWS Partner Solution Architect Venky Hosur commented on this, saying, “We saw a three-digit percentage increase in badge completion month over month.” This foundation in core skills allowed participants to tackle challenging use cases with confidence.
2. A Collaborative Model: Playing to Each Partner’s Strengths
One of the hackathon’s unique features was its collaborative approach. Onova managed logistical coordination, allowing AWS to focus on technical workshops and Capgemini to drive industry-focused solutions. “This partnership allowed each of us to focus on what we do best,” said Venky. “Onova handled the heavy lifting of coordinating everyone, while we could focus on training and technical support.” This model allowed the hackathon to operate at a larger scale without sacrificing quality.
3. High-Quality Mentorship
The hackathon attracted expert mentors who provided deep technical guidance to participants. “Participants weren’t just working with general support but had access to experts who could help them navigate complex, industry-specific AI challenges,” Niko explained, “The level of engagement and the focus on quality mentorship helped participants achieve more polished, higher-value solutions.”
Success Stories and Standout Solutions
The hackathon yielded several noteworthy projects, each addressing complex problems in high-stakes industries:
1. Energy Sector: AI-Powered Vegetation Management
The winning solution, which will be showcased at re:Invent, uses AI to monitor vegetation growth around power lines, a critical application in energy infrastructure. As Genevieve noted, “This team demonstrated creativity and technical skill, developing a viable solution that will be ready for clients in a matter of weeks.”
2. Financial Services: Data-Driven Trading Advice
In financial services, one team developed an AI tool for automated trading advice, harnessing vast datasets to improve brokerage decisions. “This solution has the potential to change how we approach investment decisions,” Genevieve observed, highlighting its impact potential.
3. Automotive: Regulatory Compliance Automation
Another impressive project developed a solution for automotive manufacturers to automate regulatory compliance across different countries, pulling data from various sources to ensure vehicles meet international standards. This application aims to improve both efficiency and compliance accuracy.
Looking Forward: Lessons and Recommendations for Future Hackathons
From scoping the use cases to leveraging dedicated mentorship, this hackathon offers valuable lessons for organizations looking to adopt a hackathon-led growth strategy. Here are some key takeaways for C-suite executives and innovation leaders:
1. Define Clear Objectives and Scope Early
As Venky emphasized, “Tight scoping helped keep everyone aligned on outcomes.” Focusing on specific industries and use cases led to more relevant, actionable solutions.
2. Secure Leadership Support and Cross-Functional Involvement
Leadership support was crucial to the hackathon’s success. Niko highlighted the importance of this support, saying, “Identifying key stakeholders early on is essential for an event of this scale.” Having leaders actively involved, from defining use cases to mentoring, elevated the hackathon’s quality and impact.
3. Utilize a Partner Model to Amplify Expertise
The hackathon’s success was also driven by a three-way partnership model that played to each partner’s strengths. This approach allowed Capgemini, AWS, and Onova to collectively create an experience that was both immersive and effective. As Victor explained, “The right collaboration model is crucial to bringing AI to life at scale.”
The Future of Hackathon-Led Growth in AI Adoption
Capgemini’s large-scale virtual hackathons serve as a blueprint for organizations looking to accelerate AI adoption and innovation at scale. By focusing on actionable outcomes, structured upskilling, and collaborative partnership models, hackathons are emerging as a powerful strategy for in-house corporate innovation and generating fully packaged solutions in just a matter of weeks. As Capgemini and AWS look ahead, plans are already underway to host similar hackathons in 2025, potentially expanding to new industries and further enhancing the collaborative model. “We have only scratched the surface of what hackathons can achieve,” Genevieve reflected. “This hackathon was an impressive catalyst, and we’re excited about where it can take us.” For leaders in high-impact sectors, the message is clear: Hackathon-led growth offers a fast-track path to innovation and competitive advantage in the era of Generative AI.
Watch the Full Webinar Below:
Interested in learning more? Watch the full webinar and connect with our partners to discuss how a large-scale corporate hackathon could drive generative AI innovation in your organization.
