3 Questions to Ask Before Starting Your AI Process Transformation

Over the past two years, I've witnessed a common pattern with companies rushing to implement AI. Eager executives, inspired by headlines about ChatGPT or competitors' AI announcements, kickstart ambitious initiatives without first answering the most fundamental question: "Which processes should we actually transform with AI?"

This might seem obvious, but it's consistently overlooked. As someone who has guided dozens of organizations through successful AI implementations—and helped recover several that were failing—I've discovered three essential questions that dramatically increase your chances of success.

Question 1: Is your process complex enough to benefit from AI, but structured enough to be modeled?

Not all process complexity is created equal when it comes to AI potential. The ideal candidates exist in what I call the "Goldilocks zone" of complexity—processes with enough complexity to benefit from AI capabilities but enough structure to be effectively modelled.

A financial services client initially wanted to use AI to improve their strategic planning process. During the assessment, client discovered the process was too unstructured and judgment-based for effective AI augmentation. The client redirected their focus to credit underwriting—a process with complex decision rules, numerous variables, and clear patterns that could be identified from historical data. The result? Their approval time dropped from 5 days to just 7 hours while improving decision quality.

Look for processes with these characteristics:

• Multiple decision points with defined inputs
• Numerous variables that influence outcomes
• Explicit or implicit decision rules
• Patterns that human experts recognize with experience
• A balance of rules and judgment

Quick assessment: Can experienced team members explain how decisions are made in the process, but struggle to document all the nuances and considerations that influence their judgment? If yes, you might have an ideal candidate.

Question 2: Do you have enough relevant data to train AI effectively?

AI systems learn from data. Without sufficient high-quality, relevant historical data, even the most sophisticated algorithms will struggle to deliver value.

A healthcare organization was determined to implement AI-powered diagnosis assistance but discovered they lacked sufficient labelled data showing how different presentations led to final diagnoses. Rather than abandoning AI entirely, they pivoted to patient scheduling optimization—a process with years of detailed historical data about appointment types, durations, and outcomes. Within months, they reduced no-shows by 31% and increased provider utilization by 24%.

Evaluate your data readiness by considering:

• Volume: Typically thousands of historical examples
• Relevance: Data directly related to the decisions being made
• Quality: Accuracy, completeness, and consistency
• Availability: Accessible in digital formats
• Variety: Multiple data types that provide context

Quick assessment: Can you easily access the last 1,000 instances of this process being performed, including the inputs, decisions, and outcomes? If not, you likely have a data challenge to address before proceeding.

Question 3: Will the potential impact justify the implementation effort?

Even technically feasible AI implementations can fail if they don't deliver meaningful business value relative to the effort required. The highest-value opportunities typically share certain characteristics that multiply their impact.

A manufacturer client was initially focused on using AI to optimize their marketing content creation. While feasible, the impact assessment revealed this would deliver minimal business value. They redirected their efforts to their quality inspection process—a high-volume activity performed thousands of times daily where even small improvements would multiply across the operation. Their AI-powered visual inspection system now detects 42% more defects while reducing inspection time by 58%, directly improving both quality outcomes and production capacity.

Look for these impact multipliers:

• High-volume processes where small improvements scale
• Direct connection to revenue or cost drivers
• Customer-facing processes affecting satisfaction and loyalty
• Risk-sensitive activities where improved decisions have outsized value
• Competitive differentiation opportunities visible to the market

Quick assessment: If you calculate a 20% improvement in this process, would the financial impact be immediately significant to your business? If not, look for higher-impact opportunities.

Finding Your AI Process Sweet Spot

The truly transformative AI opportunities exist where these three questions intersect positively. I recommend a systematic approach to finding these sweet spots:

1. Create a comprehensive inventory of your key business processes

Rate each process on:

• Complexity characteristics (suitable for AI modeling)
• Data availability and quality
• Potential business impact
• Implementation feasibility
• Strategic alignment

2. Prioritise processes scoring highly across all dimensions

3. Conduct in-depth assessment of your top 3-5 candidates before committing resources

The organizations which achieve the greatest success with AI don't just implement the most exciting or advanced algorithms—they apply the right AI capabilities to the right processes to solve real business problems.

What process in your organization might be in the AI sweet spot?

I'd love to hear your thoughts in the comments.

With 20+ years of experience in enterprise transformation, I have guided companies, across industries, through the journey from traditional setups to forward looking, results oriented business models. I specialize in helping organizations build the correct strategic foundations for building successful and sustainable enterprises.

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References:

1. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
2. Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, 97(4), 62-73.
3. Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can't do (yet) for your business. McKinsey Quarterly.
4. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review.
5. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).