50+ Questions to Build Your AI Strategy Around

Artificial Intelligence (AI) has the potential to revolutionise businesses by driving efficiency, innovation, and competitive advantage. However, to truly harness its power, organisations must carefully strategise their AI initiatives. Asking the right questions is a crucial step in this process. Below, we present essential questions to consider when formulating your AI strategy, along with a detailed rationale for each set of questions and pointers on who should be involved in answering them.

Defining Your "Why"

Before diving into specific questions, it's crucial to define the "why" behind your AI strategy. Understanding your core objectives will prevent the creation of vanity projects, ensure value creation, and align investments with business needs. This outcome-focused approach will help avoid investments in solutions that the business doesn't need and ensure that your AI strategy is aligned with key business outcomes.

Let's use that as a logical starting point from which to formulate our 50+ strategic AI questions....

Understanding Your Business Objectives

1. What are our primary business goals?

2. How can AI help us achieve these goals?

3. What specific problems are we trying to solve with AI?

4. What are our key performance indicators (KPIs) for success?

5. Which business functions could benefit most from AI?

6. How does AI fit into our long-term vision and strategy?

7. What are our competitors doing with AI?

8. What unique value can AI bring to our customers?

Rationale: Clear business objectives ensure that AI initiatives are purposeful and aligned with broader organisational goals, driving tangible outcomes. By understanding how AI can specifically address these goals, you can prioritise projects that offer the most value.

Who to involve: Senior leadership, strategic planners, department heads, and customer experience teams should be involved in answering these questions to ensure alignment with overall business strategy.

Assessing Current Capabilities

9. What is our current level of AI maturity?

10. Do we have the necessary data infrastructure in place?

11. What existing AI and machine learning tools are we using?

12. Do we have skilled personnel to manage and deploy AI projects?

13. What gaps exist in our current capabilities?

14. How do we measure the success of our current AI initiatives?

15. What is our budget for AI projects?

Rationale: Understanding your starting point helps in identifying gaps and areas that need improvement, ensuring a smoother AI implementation process. This assessment will also guide the allocation of resources and training needs.

Who to involve: IT leaders, data scientists, AI specialists, HR for training and development needs, and finance teams for budget considerations.

Data Management

16. What types of data do we currently collect?

17. Is our data clean, structured, and ready for AI applications?

18. Do we have adequate data governance policies?

19. How do we ensure data privacy and security?

20. How can we enhance our data collection processes?

21. What is the quality of our current data?

22. How frequently is our data updated?

23. Who owns the data within our organisation?

Rationale: Data is the backbone of AI. Proper data management ensures the reliability and effectiveness of AI models. Clean, structured, and well-governed data is essential for accurate AI predictions and insights.

Who to involve: Data management teams, data scientists, IT security, and legal teams to ensure compliance with data privacy regulations.

Identifying Use Cases

24. What are the most impactful AI use cases for our business?

25. How can AI improve our customer service?

26. Can AI help us optimise our supply chain?

27. How can AI assist in predictive maintenance?

28. Can AI enhance our marketing strategies?

29. What internal processes can be automated using AI?

30. How can AI improve our product development cycle?

31. What are potential quick wins with AI for our organisation?

Rationale: Identifying the right use cases ensures that AI efforts are focused on areas with the highest potential impact. This approach helps in prioritising projects that align with strategic business goals and deliver measurable benefits.

Who to involve: Business unit leaders, process owners, data scientists, and customer service teams to identify practical and impactful use cases.

Technology and Tools

32. What AI technologies and tools should we adopt?

33. Should we build or buy our AI solutions?

34. What are the best platforms for deploying our AI models?

35. How do we ensure our AI tools integrate well with existing systems?

36. What are the scalability requirements for our AI solutions?

37. How do we keep up with the latest AI advancements?

38. What cloud services will support our AI needs?

Rationale: Choosing the right technology stack is crucial for the efficient deployment and scalability of AI solutions. This ensures that the AI tools can handle current and future demands while integrating seamlessly with existing systems.

Who to involve: IT leaders, data scientists, software engineers, and procurement teams to evaluate and select the most suitable technologies.

Talent and Training

39. Do we have the necessary in-house expertise for AI projects?

40. What training programmes are needed to upskill our team?

41. Should we hire new talent or collaborate with external experts?

42. How do we foster a culture of innovation and experimentation?

43. What are the best practices for managing AI teams?

Rationale: Skilled personnel are essential for the successful implementation and management of AI initiatives. Continuous training and fostering a culture of innovation will keep the team updated with the latest advancements and best practices.

Who to involve: HR, learning and development teams, department heads, and external AI consultants for training and recruitment strategies.

Ethical and Legal Considerations

44. What ethical guidelines will govern our AI projects?

45. How do we ensure our AI models are unbiased and fair?

46. What are the legal implications of our AI use cases?

47. How do we maintain transparency in our AI operations?

48. What is our protocol for handling AI-related incidents?

49. How do we comply with relevant data protection laws?

Rationale: Addressing ethical and legal considerations helps build trust and ensures compliance with regulations. This is crucial for maintaining a positive reputation and avoiding legal pitfalls.

Who to involve: Legal teams, compliance officers, data ethics committees, and AI developers to establish and enforce ethical guidelines.

Measuring Success

50. How will we measure the success of our AI initiatives?

51. What metrics will we use to track performance?

52. How often will we review and adjust our AI strategy?

53. What feedback mechanisms are in place to learn from our AI deployments?

54. How do we ensure continuous improvement of our AI systems?

Rationale: Regularly measuring and reviewing AI performance ensures that the strategy remains effective and aligned with business goals. Continuous feedback and improvement processes are essential for long-term success.

Who to involve: Data analysts, performance managers, business intelligence teams, and strategic planners to monitor and evaluate AI projects.

Conclusion

Developing an effective AI strategy requires a thoughtful and comprehensive approach. By asking the right questions, involving the right stakeholders, and focusing on defining the "why," you can ensure that your AI initiatives are aligned with your business objectives, well-supported by data and technology, ethically sound, and continually optimised for success. At WeBuild-AI, we believe that a well-crafted AI strategy can transform your organisation, driving innovation, efficiency, and competitive advantage in today’s digital landscape.

Embrace the power of AI with confidence and clarity. Start by asking the right questions, and let those answers guide your journey to AI excellence.