From AI Hype to AI ROI: Part II

Part 2: Enabling Employees

Building the Path from Awareness to Applied Learning

This is the second article in a three-part series on how enterprise leaders can accelerate AI adoption with practical, IT-aligned approaches that connect real use cases to measurable business value.

Online AI training is now common across the enterprise, but training alone rarely gets people over the adoption hump. What matters is turning awareness into applied practice for knowledge workers across the business.

One effective way to close that gap is through hackathon-style working sessions. Traditionally these have been more technical and product-oriented. AI changes that. Leaders can use these formats to help their orgs across marketing, HR, legal, finance, supply chain, sales, and other functions get curious, try approved tools, and start applying them to real use cases and challenges.

AI lowers the barrier to building and expands who can participate in experimentation. When employees feel supported using approved tools, adoption rises much faster.

If you want your teams to move the business forward, you need more than education: hands-on practice and business relevance are also imperative. Below is a simple framework for using hackathon-style programs to build adoption in broader enterprise environments.

Hackathons for Knowledge Workers

Hackathon goals: Use these programs to help teams learn new tools, build simple workflow thinking, and solve real problems together. Design cross-functional groups around specific business challenges so people learn the tools while improving how work gets done.

I consistently find that when I intentionally form cross-functional teams around a shared challenge or goal, my organizations operate more efficiently both within and across functions.

Target audience and required resources: These programs work best when they include adjacent functions, the right tools, coaches and mentors (this is where FDEs may be helpful, ensure they educating vs locking-in) and enough structure to produce something useful. When those pieces are in place, the format scales well.

• Tooling and tech: Start with tools your company already approves, pilots, or has available in sandbox environments.
• IT alignment and participation: Work with IT early. Even one IT point person can help reduce shadow IT, answer practical questions, and keep experimentation tied to company standards.
• Hands-on mentors and teachers: Bring in patient, practical mentors who know the tools and can teach others. Internal experts from IT, sales engineering, security, or data teams are often the right people to start with.

Use real business use cases: The format matters less than the use case. Design these programs around specific business problems, learning goals, and outcomes you want to improve. Build in feedback loops so successful ideas can turn into better processes, stronger guardrails, and clearer governance.

• Technology adoption: Help knowledge workers across business functions understand newer tools, what problems they solve, and how to use them.
• Innovation: Create internal offerings and develop stronger features or products for the market.
• Culture and employee morale: Help employees see new possibilities in technology through hands-on experience, broaden what they believe is possible, and improve how they solve challenges with (IT approved) tooling.
• Co-innovation: Work with customers or partners to co-create AI workflows, services, solutions, or products. This category is best suited initially to power users and technical teams rather than internal business functions; as knowledge workers build out skills and understanding of AI tooling & workflows, this type of hackathon across internal functions can help streamline business functions and operations, reducing OPEX.

Actions to ensure success: This should not happen in a silo. Use the bullets below as a practical starting point to improve alignment and increase the odds of success across your broader organization.

• Start simple. For teams that are new to AI, focus on basic tool familiarity and a small number of practical goals.
• Work with IT to understand what tools are approved, piloted, or under consideration.
• Teach teams why shadow IT creates long-term risk, not just short-term speed.
• Bring in adjacent functions when the problem requires it so teams learn how requirements, tradeoffs, and workflows connect.
• Choose use cases that solve real business problems and can be measured after the session.

Measure what matters

Metrics: Leaders need to close the loop on ROI. Define the problem up front, decide how success will be measured, and compare the outcome against cost, time saved, quality, and business value. Ensure you are lockstep with your finance teams so ROI can be mapped up and downstream for financial reporting.

Postmortem considerations: Did the teams solve the stated problem? What worked, what did not, and what is worth scaling? Turn the answers into better guardrails, better education, and better operating practices. 

Key Takeaways

If you want broader AI adoption, make it practical. Give teams approved tools, real business use cases, enough support to learn by doing, and clear measures of success. Leaders do not need a giant program to get started. They need focused, useful activity tied to the work. Utilize best practices summarized in this guide here to help.