10 critical questions you need to answer to successfully implement industrial AI
Implementing AI in the industrial sector can be transformative. AI is not the end goal, but a means to achieve success. However, to ensure a successful and efficient implementation, it’s crucial to address several key questions. Here are 10 critical questions to guide your AI strategy.
1. What are your specific goals and objectives in implementing AI?
Before diving into AI implementation, it’s essential to define clear goals. Are you aiming to improve operational efficiency, reduce costs, enhance safety or optimize energy production? Understanding your objectives will help tailor the AI solutions to meet your specific needs and help guide the direction of AI development. By having clear goals and KPIs you can monitor and assess the success of AI and demonstrate its value.
2. How do we measure the success of our AI implementation?
Once you have set clear goals, you need to define some measurable key performance indicators (KPIs) to measure success of your AI initiatives. These could include metrics related to efficiency improvements, cost savings, safety enhancements and overall return on investment (ROI). Regularly track and analyse these KPIs to assess progress and make necessary adjustments, making sure you are moving towards your objectives.
3. What data do we need and how can we ensure its quality and availability?
Data is the backbone of AI. The garbage-in, garbage-out principle essentially dictates that the quality of AI is limited by the quality of the data. Based on the objective or use case, you need to determine what data is required. Data can be structured (such as sensor data, business process/transaction data, environmental data) or unstructured (such as reports, emails, notifications, etc. Ensuring data quality involves data validation and cleaning processes.
4. What AI technologies and tools are best suited for our needs?
Choosing the right AI technologies is crucial. Should you use rule-based systems, machine learning, deep learning, a large-language model or other generative AI, or a combination? Evaluate the platforms and software that will support your AI initiatives, considering factors such as scalability, ease of integration and vendor support.
5. What are the potential risks and how can we mitigate them?
AI implementation comes with risks, including cyber security concerns, data privacy issues and ethical considerations. Develop a comprehensive risk management plan to address these challenges and ensure the safe and ethical use of AI.
6. What is the cost-benefit analysis of implementing AI?
Conduct a thorough cost-benefit analysis to understand the financial implications of AI implementation. Consider both initial and ongoing costs versus the expected benefits and return on investment (ROI). This analysis will help justify the investment and guide budget allocation.
7. How do we ensure the scalability and flexibility of our AI solutions?
Your AI system should be able to handle increasing amounts of data and adapt to changing conditions. Scalability and flexibility are key to ensuring that your AI solutions can grow with your business and remain effective over time.
8. How do we make sure that employees can trust AI and benefit from it?
It is easy to focus solely on technology integration; however, the ‘person behind the technology’ is even more important. To be successful in AI implementation, you need buy-in from the team members who will use and operate it. You should build awareness of their needs and how AI could unlock more potential for them.
9. What skills and training do our employees need?
Upskilling your workforce is essential for the successful implementation of AI. Identify the skills and training required for employees to effectively use and manage AI technologies. Invest in training programmes to bridge any skill gaps.
10. What are the regulatory and compliance requirements?
Ensure that your AI implementation complies with industry standards and regulations. Stay informed about any specific regulatory requirements related to data privacy, security and ethical use of AI. Compliance is crucial to avoid legal issues and maintain trust with stakeholders.
Conclusion
Addressing these ten questions will help you develop a comprehensive and effective strategy for implementing AI in your energy company. By setting clear goals, ensuring data quality, choosing the right technologies, and managing risks, you can harness the power of AI to drive innovation and efficiency in your operations.
Need help with answering the questions?
With decades of experience within the digital domain, combined with more than 160 years of industry domain and critical infrastructure engineering expertise, we can help you ensure that AI applications in industrial settings are safe, reliable and compliant with relevant standards.
About the author:
Dr Abdillah Suyuthi is Head of Machine Learning services, supporting clients across various industries to develop and operate trustworthy and reliable machine learning solutions across multiple industry domains including maritime, oil & gas, energy, and railways, handling diverse data types including environmental, industrial, and business process data.
Learn more about Abdillah here.
2/28/2025 11:00:00 AM