IPPE Tech Talk: Creating an AI Implementation Plan

Al is making Its way Into food production plants. But the real question is, what can it do for your business? In this session from the IPPE 2025 Tech Talk series, Tim Stork, PPO Senior Solutions Engineering Manager,  breaks down why having a solid Al implementation plan is crucial. He’ll share the key elements to include, what to measure, and how to stay on track to achieve your goals in this rapidly evolving space.

Key Points:

As artificial intelligence (AI) becomes increasingly integrated into business operations, having a robust implementation plan is essential for maximizing its value and mitigating risks. Here’s a comprehensive guide based on key insights to help you build an effective AI implementation plan.

Why You Need an AI Implementation Plan
Even if you believe your facility isn’t using AI, chances are it’s already embedded in some processes or equipment. Without a strategic plan, businesses risk falling behind competitors who are leveraging AI for efficiency and growth. A well-crafted plan ensures you can:

• • Maximize ROI: Avoid wasted investments by setting clear goals and success metrics.

• • Mitigate Risks: Address potential operational, cybersecurity, and ethical risks proactively.

• • Stay Competitive: Keep pace with industry trends and innovations.

Key Components of an AI Implementation Plan

1. 1. Define Clear Goals and Objectives:
      • • Identify the specific value AI will bring to your operations.
      • • Ensure alignment among stakeholders to avoid conflicting expectations.

1. 1. Scope and Technical Planning
      • • Determine how AI will integrate with current processes and technologies.
      • • Assess data inputs, outputs, and machine learning models.
      • • Plan for data management and cybersecurity measures.

1. 1. People and Culture:
      • • Identify key stakeholders across departments.
      • • Foster open communication to manage change and reduce resistance.
      • • Address potential workforce challenges and training needs.

1. 1. Risk Assessment and Mitigation:
      • • Conduct risk analysis (e.g., FMEA) to identify operational, technical, and human-related risks.
      • • Reassess risks throughout the project lifecycle to adapt to new challenges.

1. 1. Measuring Success:
      • • Establish Key Performance Indicators (KPIs) to track progress.
      • • Regularly review data to evaluate if the project is meeting its objectives.
      • • Communicate results transparently to stakeholders to ensure alignment.

Iterative Implementation for Continuous Improvement
Whether you’re deploying third-party solutions or developing custom AI applications, start small. Implement a pilot project, measure its outcomes, learn from the results, and iterate. This lean feedback loop allows you to:

• • Address major risks early.

• • Refine processes based on real-world data.

• • Build confidence among stakeholders through demonstrable successes.

Effective Communication: The Glue for AI Success
Clear, consistent communication is critical throughout the AI implementation journey. Regular updates help manage expectations, reduce misinformation, and foster a culture of collaboration. Sharing both successes and challenges openly ensures continuous improvement and stakeholder buy-in.

Final Thoughts
Implementing AI is more than just a technological upgrade; it’s a strategic shift that impacts operations, culture, and business outcomes. By following a structured, people-centric approach and focusing on continuous learning and communication, organizations can harness the full potential of AI for long-term success.

Did you miss our other Tech Talk video? Watch it here > How Artificial Intelligence and Machine Learning Can Improve Food Safety