Preparing for Takeoff
Artificial Intelligence (AI) is set to become integral to almost every industry in the near future. To successfully introduce AI, leaders need a well-planned strategy. Taking a thoughtful approach like the one outlined in this series will help you drive the high levels of change adoption needed to make the most of your AI investment.
So far we discussed…
Building Knowledge: Setting up your initial foundation for AI by educating leadership and stakeholders, assessing your current infrastructure, and getting aligned on a unified strategy. (Read the article here)
Proving the Concept: Trying out your chosen AI tools within a portion of the organization by addressing a specific business challenge and gathering useful feedback. The purpose of this phase is to verify you can “Do AI” and gauge how open your team might be to adopting it. (Read the article here)
Next we’ll take what we learned from the Proof of Concept and prepare for the broader rollout by conducting a short “Pilot”. The purpose of the Pilot phase is to test-drive your broader rollout approach.
3. Pilot: Scale and Refine
• Expand to a Broader Segment: After a successful Proof of Concept, extend the AI solution to additional use cases or deploy it across a larger segment of your organization. This expansion should be strategic, targeting areas where the AI can deliver measurable value and improvements. You can lay the groundwork for change adoption by also seeking out “Change Champions” and users who most look forward to leveraging AI. They’ll be critical to helping their peers in the next phase.
• Build your Change Adoption Strategy: Assess your change adoption readiness and start outlining your change adoption strategy. Account for communication, learning, support and change sponsorship. Extend the AI Champions group as well.
• Monitor Key Performance Metrics: As the AI solution is applied more widely, closely track essential performance metrics such as accuracy, efficiency, user adoption, and overall impact. These metrics will help you assess the AI’s effectiveness and identify any areas that need attention.
• Fine-Tune the AI Model and Workflows: Based on the data collected and feedback received, update your initial AI Governance Model and start to make necessary adjustments to the AI model and related workflows. This iterative process is crucial to ensuring that the AI system is robust, reliable, and fully aligned with your organization’s needs.
In the next article, we’ll roll out your AI solution to all parts of the organization that could benefit. (Hint: this may not include everyone!)
-Steve
Reach out to theBigRocks if your team is considering a rollout of artificial intelligence tools like ChatGPT or Microsoft Copilot(c). We can help you walk through this model and plan for success.
