Chapter 12: Discovering Use Cases

The AI Ops Interview: Unlocking High-Value Use Cases

As organizations progress through the three phases of AI Ops adoption—from initial experimentation to enterprise-wide integration—success hinges on one critical factor: choosing the right starting points. Throughout this book, we've examined the foundations of AI Operations (AI Ops)—what it is, why it matters, and how organizations can adopt a human-centric approach to AI integration. We've explored the cultural and organizational readiness required to embrace AI, the importance of AI literacy across the enterprise, and the structured approach to scaling AI adoption. Now it's time to tackle the most common question organizations face when beginning their AI Ops journey: Where do we start?

Finding the right use cases is essential for progressing through each phase of AI Ops adoption. Without a structured approach to discovering AI opportunities, organizations risk investing in AI for AI's sake—chasing hype rather than solving meaningful business problems. In this chapter, we'll explore how to systematically identify AI use cases that align with your current phase of adoption, engage employees to surface real pain points, and differentiate between practical applications and overhyped distractions. By the end, you'll have a clear framework for discovering and prioritizing AI initiatives that drive real impact and accelerate your progression through the AI Ops adoption journey.

Discovering the right AI use cases is not a passive process—it requires structured engagement, thoughtful questioning, and a deep understanding of business challenges. AI solutions often fail when they are applied in a vacuum, without first understanding the unique workflows, bottlenecks, and pain points that employees experience daily. That's where the AI Ops interview comes in.

The AI Ops interview is a structured approach that allows AI teams to act as detectives and consultants, uncovering valuable AI opportunities by engaging directly with employees. It's not about pushing AI for AI's sake; rather, it's about listening, identifying inefficiencies, and finding the areas where AI can deliver real, measurable impact. A well-executed interview provides actionable insights, reveals opportunities that might otherwise be overlooked, and helps align AI investments with actual business needs.

In this chapter, we'll break down the AI Ops interview process step by step, showing how to conduct effective conversations with employees, surface the best AI use cases, and ensure AI adoption is driven by real-world challenges—not hype or assumptions.

The AI Ops Interview Process

As discussed in Chapter 1: Why AI Operations, AI Ops requires a blend of detective work, consulting, operational expertise, systems thinking, and technical development. Rather than merely deploying AI solutions, AI Ops teams should act as internal consultants, meaning they engage with departments to assess pain points, identify inefficiencies, and provide tailored AI-driven recommendations rather than simply implementing technology for its own sake. They work closely with different departments to identify pain points and high-value AI opportunities. A structured interview process is key to achieving this.

Structuring the Interview

The AI Ops interview process follows a clear structure to build relationships and trust with stakeholders. This process consists of the following steps:

Building Rapport
- Begin the meeting by fostering a comfortable, open conversation.
- Ask general questions about the interviewee's role, their department, and their experiences working in the organization. For example, "What led you to this role?" or "What do you enjoy most about your current position?"
- These types of questions help build rapport and encourage open discussion.
Understanding AI Perceptions
- The first substantive question should be open-ended: "What are your thoughts on AI?"
- This helps gauge their level of understanding and sentiment toward AI, allowing the AI Ops professional to identify which persona from Chapter 2: Who This Book Is For they are speaking with (e.g., Energetic Emma, Skeptical Sam, Cautious Chris, etc.).
Defining the Department's Mission
- "What is the primary objective of your department?"
- "How do you measure success?"
- "What is the most common reason for failure?"
- "If you could wave a magic wand and change one thing about your work, what would it be?"
- These questions help establish key success metrics and identify where AI can provide the most value.
Understanding Critical Processes
- Ask the interviewee to walk through their department's critical tasks step by step.
- Utilize visual collaboration tools like Miro boards or whiteboards to map out workflows using shapes and connectors.
- Look for inefficiencies, bottlenecks, and repetitive tasks where AI could optimize performance. For example, manual data entry across multiple systems often leads to errors and inefficiencies, making it a prime candidate for AI-driven automation.
Exploring Potential Impact of AI Improvements
- "What would happen if we eliminated the most common points of failure?"
- "How would your team's productivity and job satisfaction improve if these challenges were resolved?"
- This step helps frame AI solutions in the context of tangible benefits for the interviewee's team.
Closing Without Recommendations
- The interview should not end with AI Ops making a recommendation.
- Instead, the AI Ops professional should take their notes, systematically document key insights, and categorize findings based on recurring themes and potential areas of impact. They should then compile these insights into a templated report that is delivered back to the interviewee.
- This report should include a summary of findings, key pain points identified, potential AI opportunities, and broader process improvement areas. The use of a consistent template will build familiarity over time and make it easier for departments to interpret and act upon recommendations.
- After finalizing the report, the AI Ops professional should reflect deeply and analyze the best AI (or even non-AI) solutions to propose later, ensuring that their recommendations are data-driven and aligned with organizational priorities.

AI Ops professionals should also surface broader process improvement opportunities, acting as advocates for change even in areas AI may not directly address.

By following this structured approach, AI Ops teams can ensure they extract valuable insights while fostering buy-in from stakeholders. The next section (8.1.2) will explore how AI Ops professionals can engage broader teams to further surface pain points and develop a well-rounded perspective on AI opportunities.

The Power of Visual Mapping in AI Ops Interviews

Incorporating visual mapping into the AI Ops interview process is a crucial technique that enhances understanding and collaboration between the AI Ops professional and the interviewee. By creating a shared visual representation—whether on a whiteboard, Miro board, or even a simple sketch on paper—both parties ensure they are aligned in their comprehension of workflows, inefficiencies, and opportunities for AI intervention.

This collaborative mapping serves as a mutual reference point, facilitating clearer communication and reducing the likelihood of misunderstandings. It also becomes a time-saving asset for post-interview analysis and planning, ensuring that AI initiatives are grounded in accurate, well-documented insights.

The Role of Visual Mapping in Consulting & Business Strategy

The use of visual mapping is a well-established best practice in consulting and problem-solving interviews, where it helps professionals structure discussions, break down complex ideas, and guide strategic decision-making.

Consulting firms frequently rely on visual mapping during case interviews, encouraging candidates to sketch their thought process to identify key insights and structure their recommendations [1].
Case interview frameworks from top consulting firms emphasize that effective visual representation improves clarity and solution accuracy, making it easier for both the consultant and the client to remain on the same page [2].
In design thinking and user experience research, consultancies use visual mapping to align stakeholders, document pain points, and streamline workflow redesigns—techniques that directly apply to AI Ops interviews as well [3].

Why Visual Mapping is Crucial in AI Ops Interviews

Ensures Alignment on Process Details
Employees often describe workflows verbally, which can lead to gaps in understanding. A live, mutually built process map ensures that both the AI Ops team and the interviewee have a shared, accurate representation of the workflow.
Reveals Hidden Inefficiencies
Mapping a process visually helps surface bottlenecks, redundancies, and gaps that might not be obvious in a verbal discussion. By externalizing the workflow, inefficiencies often become visually apparent, providing clear starting points for AI solutions.
Accelerates Post-Interview Analysis
Instead of reconstructing an interview from notes alone, the AI Ops team can refer to the mapped process, saving hours in documentation, aligning internal teams, and ensuring no critical insights are lost.
Improves Stakeholder Buy-In
When AI Ops teams present findings to leadership or cross-functional teams, a clear process map helps illustrate why AI solutions are needed and how they fit into existing workflows, making it easier to gain support for AI initiatives.

How to Implement Visual Mapping in an AI Ops Interview

Start Simple – As the employee describes their workflow, begin sketching basic steps—boxes for tasks, arrows for transitions, and annotations for key pain points.
Encourage Real-Time Feedback – Check in with the employee: "Does this accurately represent your process?" or "Am I missing any key steps?"
Iterate as You Go – Adjust the diagram based on new insights that emerge during the discussion.
Use Digital Collaboration Tools When Possible – If working remotely, platforms like Miro, Lucidchart, or even Google Jamboard can create interactive process maps that allow employees to contribute in real-time.
Save and Distribute the Final Version – After the interview, refine and standardize the map so it can be referenced in the follow-up report, ensuring continuity between discovery and execution.

Proven Success in Consulting & Business Strategy

The benefits of visual mapping in AI Ops are reinforced by its widespread use in business consulting and strategic planning:

Consulting firms rely on process mapping to structure case interviews—it helps candidates and consultants break down business problems logically and communicate solutions effectively [1].
In business strategy, visual frameworks like flowcharts, journey maps, and service blueprints are used to enhance clarity, making problem-solving more efficient and data-driven [2].
Studies show that companies using visual collaboration tools experience faster decision-making and greater team alignment, reinforcing the value of real-time mapping in AI discovery [3].

Visual mapping is not just a documentation tool—it's a discovery tool. By externalizing the employee's thought process into a structured diagram, the AI Ops professional can surface unspoken challenges, inefficiencies, and areas where AI can have the biggest impact. Miscommunication in early AI discovery sessions can lead to wasted effort, misplaced priorities, and incorrect assumptions about the real problem AI is meant to solve—mapping a process visually mitigates this risk.

As we transition to the next section, we will explore how AI Ops professionals can expand their insights beyond initial interviews by engaging with broader teams and validating early findings.

Expanding Insights Beyond Initial Interviews

Once initial AI opportunities are identified through structured interviews, the next step is engaging broader teams to refine these insights and surface additional pain points. Sometimes, the interview process alone provides enough clarity to proceed, but in other cases, expanding the scope is necessary to get a fuller picture of organizational challenges. AI Ops professionals must create an inclusive environment where employees across different roles feel empowered to contribute their perspectives.

Key Strategies for Team Engagement

Facilitated Workshops: Organize structured workshops that bring together employees from various departments. These sessions should encourage open discussions about daily challenges, inefficiencies, and opportunities for AI-driven improvements.
Anonymous Feedback Channels: Create digital suggestion boxes or anonymous surveys where employees can share process pain points without fear of judgment or political consequences.
Role-Specific Pain Point Mapping: Recognize that different teams experience different challenges. A marketing team's bottlenecks will differ from those in supply chain management. AI Ops teams should tailor their discussions to ensure relevance and validate insights gathered during initial interviews.
Cross-Functional Collaboration: AI solutions often impact multiple departments. Engaging representatives from IT, operations, HR, and other key areas ensures that solutions are designed holistically rather than in silos. This step is especially crucial when initial interviews highlight dependencies between teams.
Demonstration of AI Potential: Employees may struggle to envision how AI can help them. Providing real-world case studies or small-scale AI prototypes can inspire confidence and encourage meaningful participation. Expanding on interview findings through these demonstrations can reveal additional use cases and refine solution designs.

By applying these techniques, AI Ops professionals can build a more comprehensive understanding of the real issues employees face, ensuring that AI solutions address actual business needs rather than assumed ones. In cases where interviews alone do not provide sufficient clarity, engaging teams more broadly ensures a well-rounded perspective and helps uncover additional opportunities for AI-driven improvements.

Differentiating Between AI Hype and Practicality

With an abundance of AI-related buzzwords and exaggerated claims in the market, organizations must develop a critical approach to assessing AI opportunities. The excitement surrounding AI has led to a flood of solutions—some offering genuine innovation, while others rely on marketing hype rather than measurable impact.

It is crucial to distinguish between solutions that provide real value and those that are driven by industry trends or vendor pressure. Without a structured approach, organizations risk investing in AI for AI's sake, leading to wasted resources, ineffective implementations, and disillusionment among employees.

The best AI initiatives begin with a well-defined business problem, not with a technology looking for a use case. By focusing on real-world bottlenecks, operational inefficiencies, and high-impact areas, organizations can ensure that AI investments are aligned with tangible business outcomes rather than speculative promises.

This section will provide a framework for cutting through the noise, helping organizations assess AI opportunities with pragmatism and precision, ensuring that every AI deployment contributes to sustainable business value.

Methods for Differentiating AI Hype from Reality

Assessing Feasibility: Many AI solutions sound impressive but require infrastructure, data quality, or expertise beyond what the organization possesses. AI Ops professionals should evaluate whether the technology can be realistically implemented within existing constraints.
Understanding True Business Impact: Just because an AI tool is sophisticated doesn't mean it is useful. Any AI initiative should have clear, measurable business outcomes, such as increased efficiency, cost savings, or improved decision-making.
Pilot Testing Over Vendor Claims: AI vendors often make sweeping claims about their products. Instead of taking them at face value, organizations should conduct controlled pilot projects to verify results before committing to large-scale implementation. Insights from expanded team engagement can help ensure pilot projects are structured around real business problems rather than hypothetical benefits.
Identifying Overengineered Solutions: Sometimes, simpler process improvements or automation tools may solve a problem just as effectively as AI. Organizations should evaluate whether AI is truly needed or if a more straightforward solution suffices. Findings from initial interviews and expanded engagement efforts can help determine the necessity of AI in specific areas.
Long-Term Viability: The AI landscape evolves rapidly. AI Ops professionals should assess whether a proposed solution is built on sustainable technology or if it risks becoming obsolete in the near future.

By maintaining a disciplined approach to evaluating AI use cases, organizations can ensure they invest in AI solutions that drive tangible value rather than chasing trends. AI Ops professionals must act as the bridge between technological advancements and practical business applications, ensuring that AI adoption remains strategic and sustainable.

Avoiding AI for AI's Sake

With the excitement surrounding AI, there is often a rush to apply it to every problem, regardless of whether it is the best solution. While AI has immense potential, it is not always the right answer. By taking an operations-first approach, organizations can avoid the trap of using AI just for the sake of it and instead focus on practical solutions that truly improve workflows and outcomes.

The concept of avoiding AI misuse ties back to Chapter 1, where we examined the rapid surge in AI adoption across businesses. 72% of companies have adopted AI in at least one function, yet only 54% of AI pilot projects successfully transition into full-scale production. This highlights a critical issue: many AI implementations are driven by hype rather than business needs, leading to abandoned projects and wasted resources. Chapter 6 discussed how AI Operations mitigates this problem by ensuring AI adoption aligns with real-world challenges rather than speculative use cases.

Key Considerations to Avoid Unnecessary AI Implementation

To prevent AI from becoming an overcomplicated, underutilized initiative, organizations must carefully evaluate its necessity in each scenario. AI is rarely a total replacement for operational efficiency—it should enhance it.

Process Improvement First: Before implementing AI, organizations should assess whether a simpler process improvement could resolve the issue. Some inefficiencies can be eliminated through basic workflow optimizations, such as implementing Kanban boards, automating approvals, or reconfiguring team workflows—none of which require AI.
Education and Training:
Many AI implementations fail because the problem is not technological but educational. Instead of introducing AI, organizations may need to upskill employees, improving their proficiency with existing systems. For example, in Chapter 4, we explored AI literacy initiatives, ensuring employees can maximize AI capabilities without creating unnecessary complexity.
Hiring or Role Adjustments:
If workloads are unmanageable or specialized expertise is lacking, hiring additional staff or restructuring roles may provide a more effective long-term solution than an AI-driven approach. AI rarely replaces expertise—it's more likely to be successful augmenting it.
Verifying True Need for AI:
Organizations should ask: Would this problem still exist if we improved our current processes? If the answer is no, then AI is not the right solution. AI should be used when automation is required to handle scale, complexity, or speed that cannot be achieved through manual workflows.
Balancing Technology with Human Judgment:
AI should enhance decision-making but not replace human oversight. In Chapter 5, we explored how AI amplifies employee capabilities rather than substituting critical thinking, creativity, or empathy. AI should function as a collaborator, not a decision-maker, especially in areas where nuanced judgment is required.

AI Hype vs. Practical AI: A Reality Check

The push for AI solutions is often fueled by marketing-driven narratives rather than practical business needs. AI vendors frequently overpromise, and companies end up investing in AI before fully validating its feasibility. A structured approach is needed to differentiate AI hype from real business impact.

1. Assessing Feasibility

Many AI solutions require infrastructure, data quality, or expertise beyond what an organization possesses. A feasibility assessment should determine:

Does the company have structured, high-quality data to support the AI model?
Is AI integration compatible with existing IT systems, or does it require extensive rework?
Does the company have the AI Ops talent to maintain and optimize the solution post-deployment?

2. Understanding True Business Impact

Just because an AI tool is sophisticated doesn't mean it is useful. Every AI initiative should have clear, measurable business outcomes, such as:

Increased efficiency (e.g., automating repetitive tasks in HR or customer service).
Cost savings (e.g., reducing operational expenses through AI-driven forecasting).
Improved decision-making (e.g., AI-powered analytics to optimize supply chain logistics).

3. Pilot Testing Over Vendor Claims

AI vendors often make sweeping claims about their products. Rather than taking them at face value, companies should conduct controlled pilot projects before committing to full-scale implementation. Insights from cross-functional engagement (discussed in Chapter 8) can help ensure AI pilots are aligned with real business problems rather than theoretical benefits.

4. Identifying Overengineered Solutions

Sometimes, simpler automation tools can solve a problem just as effectively as AI. Organizations should evaluate whether AI is truly needed or if a more straightforward solution—such as low-code automation platforms or RPA (Robotic Process Automation)—can achieve the same results at a lower cost.

5. Long-Term Viability

The AI landscape evolves rapidly. AI Ops professionals must assess whether a proposed solution is built on sustainable technology or risks becoming obsolete in the near future. AI solutions that require constant retraining, rely on proprietary models, or lack vendor support could pose long-term adoption risks.

The AI Ops Role: Ensuring AI is Deployed for the Right Reasons

AI Ops professionals must act as the bridge between technological advancements and real business applications. Their role is to evaluate, prioritize, and implement AI solutions that solve actual business problems, not just follow industry trends.

By maintaining a disciplined approach to AI adoption, organizations can ensure they invest in AI solutions that drive tangible value rather than chasing trends. AI Ops professionals must act as filters, ensuring AI adoption remains strategic, sustainable, and aligned with core business objectives.