How to Create and Host an AI Hackathon in 6 Weeks

In a sterile conference room in Sweden, 40-50 employees from the country's largest music licensing company (STIM) gathered for an impossible challenge: create functional AI solutions in just two days.

By the end of 48 hours, they had built three working AI systems, including an internal FAQ bot and sentiment analysis tools, with at least one ready for immediate production deployment.

AI hackathons deliver measurable business results where traditional training fails. In days (versus months), organizations can see a huge return on investment through rapid skill development, cross-functional collaboration, and production-ready solutions.

Hackathons are a tangible strategy for moving beyond circular AI discussions and toward structured implementation. Academic literature and educational research validate this approach, demonstrating hackathons' ability to break routines, encourage creativity and collaboration, stimulate experiential learning, and increase skillsets at scale. "You can't lose in a hackathon—whether you have five people or 500 people," says Motorola Solutions' Janet Carmody. "It always produces excitement, fun, and people getting together to geek out."

In this piece, I'll build off of my previous article on AI hackathons, detailing the rise and catalyzing factors of AI hackathons as a transformation strategy. Here, we'll dive into a practical approach for creating your first AI hackathon.

‍

‍Phase I: Pre-Planning (4-6 Weeks Before the Date)

1. Define Your Audience and Goals 

A framework from Devpost identifies three primary hackathon types: 

1. Internal hackathons focused on innovation and collaboration
2. Public hackathons targeting developer community engagement
3. Customer hackathons are designed to drive revenue and engagement 

Internal AI hackathons prove most effective for enterprise capability building, allowing controlled experimentation with business-relevant challenges. This is especially critical when working with internal-facing strategies, unreleased IP, etc.

2. Define Executive Stakeholder Roles 

Within an AI hackathon framework, who is doing what needs to be established early on. The Gates Foundation playbook suggests defining four critical roles and assigning stakeholders to them: 

1. Executive Sponsor for overall support and leadership
2. Program Manager for event details and execution
3. Coordinator for logistics and scheduling 
4. AI Consultants for technical guidance 

This structure ensures accountability while distributing workload effectively.

3. Select a Topic

Open-ended targets can lead to nebulous, vague results. Rather than open-ended AI exploration, focus on business-relevant categories that save time or money, grow revenue, or delight customers.

One recent internal AI hackathon I oversaw established clear and specific objectives early on: identifying gaps in a current go-to-market funnel, exploring AI solutions using existing tools, and designing practical automation solutions to improve lead quality and conversion rates.

4. Decide on Team Structures 

Pre-assigned groups work better than self-organizing teams, with optimal participation ranging from 20 to 75 participants. An implementation guide from Scoro recommends organizing participants into teams of five based on job function, encouraging creative team names to build engagement.

‍5. Define the Timeline and Budget

Internal AI hackathons typically require 2-4 weeks total planning time, compared to a typical 2-3 month runway for public events. Shorter timelines maintain momentum while allowing adequate preparation for resource gathering and use case development.

In terms of financial commitment, expect $7–15 per person for each meal covered, with venue costs ranging $10–30 per person in major cities. Toolkits like this one provide detailed budget tracking templates that factor in categories like swag, prizes, and technology platform costs.

6. Select a Platform

All-in-one platforms should offer registration, submission management, judging capabilities, and project galleries. For internal events, single sign-on access proves essential, while data security measures and SOC 2 compliance protect sensitive business information.

Phase II: Executing an AI Hackathon

Pre-Event (2 Weeks Before)

1. Develop a Use Case 

Each team lead must identify specific use cases and knowledge sources before the event begins. Some hackathon models recommend pre-assigning groups with designated leads responsible for selecting use cases and gathering resources in advance.

2. Provide the Right Resources 

Technical resources form the backbone of successful AI hackathons. Essential elements include:

• Access to large language models and AI platforms
• Example code and starter templates
• Relevant datasets and API documentation
• Prompt engineering resources and best practices
• Credits or free access to typically paid AI tools

The computing power required for AI development can be expensive, so providing credits or free access removes barriers to participation and ensures equitable competition.

3. Communicate Strategically 

Distribute starter kits containing event overview, key deadlines, getting-started instructions, and submission requirements. Establish technical support channels through Discord or Slack. Regular updates maintain engagement without overwhelming participants.

Day-of

1. Use a Structured Agenda

Here’s an example:

• Kickoff (15 minutes)
• Working Session (90 minutes)
• Show and Tell (50 minutes)
• Winner Selection (10 minutes).

This compressed timeline, by design, creates a sense of urgency while providing adequate development time.

2. Build Support into the Hackathon Infrastructure 

Implement mentor-to-team ratios of one pair per 2-3 teams. Include advisors for specific skills—design, security, infrastructure—and AI consultants for technical assistance. Diverse skill sets on a team prove essential for success.

3. Use a Framework for Evaluation 

One framework commonly used is the “Three I's": 

1. Idea quality
2. Implementation excellence 
3. Impact measurement 

This framework helps judges focus on practical business value rather than purely technical complexity.

Places Hackathons Stumble (And How to Avoid Them)

Insufficient Support

Problem: Participants frequently get stuck without adequate guidance, leading to incomplete projects and frustrated teams. Additionally, Low executive engagement results in poor participation rates and limited organizational impact from AI hackathon initiatives.

Solution: Implement comprehensive support structures including mentor-to-team ratios, subject matter experts, and clear real-time communication channels. This extends beyond technical help—successful AI hackathons require energy management and team engagement. Involve executives as judges, problem statement contributors, mentors, and promotion partners throughout the process. Make them visible stakeholders who actively participate rather than passive observers. Executive involvement signals organizational commitment to AI innovation and encourages broader employee participation.

Vague Goals

Problem: Many AI hackathons fail because they're positioned as generic opportunities to "do AI" rather than solve specific business challenges.

Solution: Establish clear, measurable objectives as guardrails during planning and execution. Well-defined goals help teams focus on viable solutions that align with organizational priorities.

Research shows organizations track skill development, cross-functional teamwork, and long-term project implementation as key success metrics. This can be very specific to the type of hackathon and its aims.

Format Selection Paralysis

Problem: Virtual versus in-person versus hybrid format decisions create accessibility and engagement tensions that can paralyze planning.

Solution: Adopt hybrid approaches that combine virtual preparation phases with in-person execution events. Research on hybrid hackathons shows this format "allows scaling an event by engaging global participants virtually while partially retaining the benefits of face-to-face collaboration." Some organizations anchor their hybrid AI hackathons around existing conferences or company events to maximize attendance and energy.

Unclear Continuity Planning

Problem: Excellent AI hackathon projects often die without clear implementation pathways or ongoing support structures after the event.

Solution: Build follow-up mechanisms into initial planning rather than treating them as afterthoughts. Janet Carmody, Head of Culture at Motorola Solutions, explains their approach: "We have a 'Hack-on' space in Devpost where teams can continue on with their projects, have oversight by some of our senior leadership teams, and get further resources." Dedicated development spaces and clear next-step identification processes maintain momentum and integrate innovations into company culture.

Signs an AI Hackathon Succeeded (and Next Steps)

Immediate Outcomes

Track everything: participant numbers, viable projects created, and cross-functional collaboration achieved during the event. These baseline metrics provide quantitative evidence of reach and engagement.

Long-term Impact

Organizations measuring hackathon success focus on projects moving to production, enhanced AI literacy across the organization, and cultural shifts toward experimentation. Trey Spyropoulos from Toyota North America shares a tangible example: "Toyota was able to announce a new feature in one of our vehicles that was the winner of our first-ever hackathon." 

Additional success indicators include prompt libraries that can be leveraged by extended teams and improved over time, increased cross-functional collaboration beyond the event, and employee confidence in using AI tools for daily work challenges.

Building on Your Success

Start with a Pilot Approach

Target a single department first that has clear, measurable pain points where AI could provide immediate value. Aim for 30–50 participants to create energy while maintaining manageable logistics and ensuring adequate mentor-to-participant ratios. Focus on generating 5-10 practical use cases per hackathon. Using a smaller scale, you can more easily refine your process and build internal case studies that demonstrate concrete value.

Document and Scale

Capture detailed lessons learned from each hackathon: what worked, what didn't, participant feedback, and specific business outcomes achieved. Track quantitative metrics like participation rates, project completion rates, and post-hackathon implementation success alongside qualitative insights about collaboration and skill development. Scale gradually based on measurable success rather than enthusiasm alone; expand to additional departments only after demonstrating clear ROI.

Establish Recurring Capability

Position AI hackathons as recurring capability-building tools rather than one-time events, establishing a regular cadence. Janet Carmody notes the importance of ongoing efforts: "For the return on investment in our products, we have ongoing efforts for the rest of the year." Create dedicated infrastructure, including permanent "hack-on" spaces where teams can continue developing promising projects with ongoing executive oversight and resource allocation.

Build Momentum for Broader Adoption

When employees see colleagues creating functional AI solutions in days, theoretical AI strategy discussions transform into practical implementation planning. Leverage hackathon participants as internal AI champions who can evangelize tools, techniques, and approaches they've learned to their broader teams.

Structured execution and systematic follow-through is key. In the example at the top of this piece, STIM's 48-hour investment yielded three production-ready AI solutions precisely because they approached the challenge with clear objectives, adequate support, and executive commitment rather than treating it as a casual team-building exercise.

The framework exists. The evidence supports its effectiveness. The opportunity cost of delayed AI adoption grows daily as competitors implement these capabilities. Organizations that establish AI hackathons as recurring capability-building tools create momentum, higher ROI on AI investments, and long-term impact. And now is the perfect time to get started.

‍