AI Planning For Business
Archive clear roadmap for AI adoption
Here’s how to proceed with AI planning:
Assemble a Team
It’s now essential to form cross-functional teams, steered by designated leaders, to guarantee smooth cooperation within your organization. These teams, with their diverse skill sets, promote an integrated approach that spurs innovation and efficient resolution of problems. Encourage a culture of open dialogue and collective objectives for the fruition of the project. Additionally, appoint a change management advocate to strategize for upcoming transitions.
- Outcome: A dedicated team formed to lead and advocate for your project.
Data Identification
Dig deeper to pinpoint and evaluate the data quality required for your proposed AI/ML models. Prior to training your AI, Identify the data you need to gather and prepare it for ingestion by the AI/machine learning process. Your data engineers, whether in-house or contracted, must scrutinize the quality, quantity and structure of the data to ensure it meets the needs of your model’s objectives.
- Outcome: Dataset identified with assessed quality and scale
Technology Identification
During this step, your AI engineers will take one step deeper to cement the AI/ML model choices and the technologies that will propel your AI solutions forward. The AI engineers will delineate the project scope and settle on the most effective ML model, algorithms and toolkit to exploit your data and yield the desired business results.
- Outcome: Selected AI/ML models, algorithms and toolkit for implementation.
Finalize Project Plans
Start by making a detailed plan for your project. Break it down into clear tasks and deadlines. Allocate resources strategically to boost productivity and facilitate project execution. This precise planning with an emphasis to risk and change management is crucial for achieving success without squandering resources.
- Outcome: A project plan with sprints and/or phases
