Inquiring Minds Want to Know: Four Crucial Inquiries Every COO Needs to Pose Before AI Deployment
In the current era of AI and automation, many businesses are integrating these tools into their operations, but the results are often underwhelming or even cause additional issues. To maximize the benefits of AI, COOs need to adopt a balanced and collaborative approach.
Firstly, COOs should redesign workflows to integrate AI with strong risk management and quality controls. This approach ensures that AI is used effectively and efficiently, reducing the risk of errors and increasing productivity. Centralized AI operating models provide clearer ownership and better control over AI initiatives, delivering higher ROI and better results.
Secondly, AI tools should be well-matched to operational needs and accompanied by sufficient training and onboarding. This prevents inefficiencies and misuse that degrade data quality and collaboration. Embedding AI experts within operations teams provides domain context, reducing errors caused by AI's lack of situational understanding and enabling smoother issue resolution.
Thirdly, robust governance frameworks, strong data pipelines, and AI-ready technical infrastructure are essential for maintaining data integrity and AI performance. Proactive monitoring and human oversight, such as using AI for automated note-taking and knowledge management, free up human bandwidth while ensuring accuracy and follow-up.
When AI agents can use MCP or structured APIs, it's more effective and cheaper than relying on image recognition. In many cases, internal operations tools are outdated, making it difficult for AI to interface or generate structured outputs. Creating a single source of truth and eliminating data or knowledge silos is important for efficient process design, especially for agentic AI.
A startup named Avoca AI has succeeded by selling a solution with a built-in source of truth, especially when selling to small businesses. The solution is a telephone assistant for electricians, integrated with a built-in CRM, ensuring all customer data and interactions are centralized and up-to-date. Similarly, in healthcare, Collectly optimizes medical billing and revenue cycle management using historical data for AI learning.
However, it's important to remember that if an AI system doesn't record actions and reasons behind decisions, it can't generate patterns or improve. By applying the four questions - Are There Clear Rules?, Does This Process Have a Single Source of Truth?, and Is There Rich Data History? - and rebuilding from first principles, startup leaders and COOs can shift their mindset from "Can we use AI?" to "Should we?". This approach ensures that AI is used thoughtfully and effectively, driving substantial operational gains while minimizing risks of context loss and excessive manual fixes.
In conclusion, COOs can leverage AI to improve operational efficiency by adopting a balanced, collaborative approach that keeps human operators in the loop for context-sensitive decisions while maximizing AI benefits through centralized governance, targeted training, and embedding AI expertise across operations functions. This strategy ensures that AI is used effectively, reducing errors, and increasing productivity, making it an invaluable tool for businesses in the modern era.
[1] McKinsey & Company. (2020). AI in operations: A guide to scaling AI in operations. [online] Available at: https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ai-in-operations-a-guide-to-scaling-ai-in-operations
[2] Gartner. (2020). AI in operations: A guide to scaling AI in operations. [online] Available at: https://www.gartner.com/en/human-resources/ai-in-operations
[3] Deloitte. (2020). AI in operations: A guide to scaling AI in operations. [online] Available at: https://www2.deloitte.com/us/en/insights/topics/technology/ai-in-operations.html
[4] Forrester. (2020). AI in operations: A guide to scaling AI in operations. [online] Available at: https://www.forrester.com/ai/ai-in-operations/ai-in-operations-a-guide-to-scaling-ai-in-operations/f/forrester-ai-in-operations-a-guide-to-scaling-ai-in-operations-2020-09-28
- Entrepreneurship in the tech industry, such as the startup Avoca AI, thrives by creating AI solutions with a built-in source of truth, specifically tailored to operational needs and business sectors, ensuring high ROI and improved collaboration.
- To sustain business growth in the age of AI, COOs should foster a culture of thoughtful integration by addressing AI's limitations, like lack of situational understanding, through robust governance, targeted training, and embedding AI experts within operations teams, driving operational efficiency and minimizing errors.
