From AI interest
to business value.

Imagine an executive deciding whether to fund a multimillion-dollar AI programme. A 50-page proposal can describe the opportunity. A three-day hackathon can let the people doing the work try the idea on a real example and show the executive what happened.

The business case for an enterprise hackathon depends on what that test establishes. Is the problem important to the client? Can the team make the approach work with approved data? Who would own and fund the next stage?

Questions the report examines

• How can evidence from a prototype lead to a funded consulting engagement?
• When does a guided challenge suit the objective, and when do teams need an open innovation format?
• What happens in each of the four phases from preparation to a pilot decision?
• What did DHI Group and AWS build, and how did Snowflake and Accenture judge the business value of submissions?

A working example reduces some of the uncertainty in a large investment decision. A sponsor can watch frontline users test the prototype on an approved sample of company data, see which assumptions hold, and decide whether a pilot justifies the expense. Four pieces of work contribute to that decision.

• Frame the problem. Identify one workflow, measure the difficulty users experience, and name an executive sponsor. Discovery may reveal process changes that would help alongside the technology.
• Build a working prototype. Test whether the proposed design works with representative data and cloud infrastructure. Document which architectural assumptions held up and which need further testing.
• Validate it with users. Ask frontline employees to try realistic tasks and show where the workflow breaks down. Their feedback identifies design changes, training needs and barriers to adoption.
• Make the business case. Estimate the potential return, identify gaps in legacy systems, and scope the integration and delivery work a pilot would require.

The report expresses its commercial hypothesis as consulting revenue = validated problem + working proof + named owner + funded next step. In practice, the first three give a sponsor grounds to consider the fourth. Fees depend on the work the client actually commissions.

Schedule the decision before the build begins. Each prototype should leave the review with a business owner, a primary metric, a review date and a proposed scope for a limited pilot. The sponsor can then fund that scope, request further evidence or close the case.

Start with the organisation’s objective. CloudLabs describes two delivery formats. The AWS Hackathon Acceleration Package gives a separate example of how to organise the work around an enterprise use case.

Challenge-based hackathons. These one- or two-day events use guided scenarios, instructions and automated validation. They suit skills development, developer productivity and quick technical checks. Examples include GitHub Copilot adoption and Azure OpenHack challenges.

Open innovation hackathons. Cross-functional teams design their own solutions to a broader challenge, such as modernising an application or integrating generative AI. Programmes can run from one week to one month. A jury evaluates the teams’ work and selects ideas for further development. CloudLabs’ comparison of the two formats.

The report identifies four operational phases.

1. Preparation and ideation. Define the business problem, secure an executive sponsor and agree the judging criteria. Brief participants, register them and form teams.
2. Enablement. Run targeted workshops and tool training before the event. Check access in advance so teams can begin building on the first day.
3. Build and collaboration. Provide isolated cloud sandboxes, approved data and an agreed resource allowance, such as Azure credits or AWS resources. Arrange technical support for the duration of the build, including overnight coverage for a 24-hour event, and put subject-matter experts within reach of teams.
4. Evaluation and the path to production. Use a published scoring matrix, recognise strong submissions and hold the sponsor review. For selected work, identify the security, scaling and integration tasks that stand between the prototype and production.

DHI Group and AWS

DHI Group wanted to accelerate development of generative AI features for its talent acquisition business. Using the AWS Hackathon Acceleration Package, it brought together 20 people in three cross-functional teams for a three-day build. Senior DHI leaders evaluated the prototypes on the final day.

The winning team connected ClearanceJobs with AgileATS. Its prototype let a recruiter ask, in ordinary language, for security-cleared software engineers with relevant GitHub experience. An agent running on Amazon Bedrock AgentCore used the Model Context Protocol (MCP) to call search and profile tools. AWS Lambda functions retrieved candidate records and enriched them with GitHub profile data.

AWS and DHI report that the teams built working features in three days that they would typically expect to take more than three months. They validated the architecture during the event and agreed on further hardening and integration. After the event, Kiro usage across DHI’s product and engineering teams increased by 84%. AWS and DHI’s architecture and results.

Snowflake and Accenture

The partners organised a virtual hackathon with an in-person finale in Bangalore. Teams could tackle vehicle quality, telecom network operations or retail challenges using tools such as Snowflake Cortex AI, Python and Streamlit. The published rubric made the relative importance of each criterion explicit.

The two examples address different decisions. DHI leaders could inspect a working recruiter workflow and commit resources to taking it further. Snowflake and Accenture gave teams and judges a common way to compare the merits of their submissions. Snowflake’s programme and official judging rubric.

An enterprise hackathon can compress technical exploration into a short build and give a client evidence for a funding decision. Getting from that decision to a rollout takes an owner, a budget and a delivery team.

• Evidence. Ask users to try the prototype on representative data and capture the results a sponsor will need to judge feasibility and value.
• Commercial follow-through. Connect the validated problem and working proof to a named owner and an explicitly funded next step.
• Format. Use a one- or two-day guided challenge for skills validation. Give an open innovation team one week to one month when it needs to develop and test a solution.
• Readiness. Prepare the people and cloud environment in advance, include users and technical specialists on the team, and bring decision-makers into the review to choose what proceeds.