Assessing AI solution feasibility is a critical competency in today's technology-driven business environment. It refers to the ability to systematically evaluate whether an artificial intelligence solution is technically viable, operationally implementable, and likely to deliver meaningful business value for a specific organizational challenge. This multi-faceted skill combines technical literacy, business acumen, and strategic thinking to determine if an AI initiative is worth pursuing.

The importance of this competency cannot be overstated as organizations increasingly explore AI adoption. Professionals who excel at assessing AI solution feasibility can save companies millions in avoided failed implementations while identifying truly transformative opportunities. This skill manifests differently across roles – technical professionals need to evaluate model performance and data requirements, business leaders must assess ROI and organizational readiness, while product managers need to align AI capabilities with user needs. Regardless of role, this competency encompasses critical dimensions including technical evaluation, data readiness assessment, resource estimation, risk identification, and alignment with business objectives.

When interviewing candidates for roles requiring this competency, look for specific examples that demonstrate a structured approach to evaluation. The best candidates will show they can move beyond the hype cycle of AI to make grounded assessments based on evidence rather than assumptions. Listen for how candidates have balanced innovation with practical constraints, managed stakeholder expectations, and learned from both successful and unsuccessful AI initiatives. As highlighted in Yardstick's guide on behavioral interviewing, probing for details with targeted follow-up questions will reveal whether candidates have genuine experience or are simply reciting theoretical knowledge.

Interview Questions

Tell me about a time when you had to evaluate whether an AI solution was the right approach for a business problem.

Areas to Cover:

• How the candidate framed the business problem before considering solutions
• The evaluation process they used to assess AI vs. alternative approaches
• Specific criteria they established for determining feasibility
• How they balanced technical capabilities with business requirements
• Their approach to stakeholder management during the assessment
• The final recommendation and rationale behind it

Follow-Up Questions:

• What alternative solutions did you consider, and how did you compare them?
• How did you determine what success would look like if you implemented the AI solution?
• What were the biggest uncertainties in your assessment, and how did you address them?
• How did you communicate your findings to stakeholders with varying levels of technical understanding?

Describe a situation where you determined that an AI solution that seemed promising initially was actually not feasible or appropriate.

Areas to Cover:

• The initial excitement or pressure around the AI solution
• The red flags or concerns that emerged during assessment
• The specific analysis that led to the negative determination
• How the candidate managed stakeholder disappointment or pushback
• Alternative approaches they recommended instead
• Lessons learned from the experience

Follow-Up Questions:

• What specific factors led you to conclude the solution wasn't viable?
• How did you handle any pressure from stakeholders who were enthusiastic about the AI approach?
• What was most challenging about delivering the negative assessment?
• How did this experience influence your approach to future AI feasibility assessments?

Share an example of how you assessed the data requirements for an AI initiative and determined whether they could be met.

Areas to Cover:

• The candidate's process for identifying required data inputs
• Methods used to evaluate data quality, quantity, and accessibility
• How they addressed data gaps or quality issues
• Their assessment of data governance and compliance considerations
• Stakeholder involvement in the data assessment process
• How data readiness influenced the overall feasibility determination

Follow-Up Questions:

• What specific metrics or criteria did you use to evaluate data quality?
• What challenges did you encounter in accessing or preparing the necessary data?
• How did you balance ideal data requirements with practical constraints?
• What recommendations did you make regarding data collection or improvement to enable AI capabilities?

Tell me about your experience developing a business case for an AI solution.

Areas to Cover:

• The candidate's approach to quantifying potential benefits
• How they estimated implementation costs and timeline
• Methods used to assess both tangible and intangible returns
• Their process for calculating ROI and payback period
• Key stakeholders involved in business case development
• How they accounted for uncertainties and risks

Follow-Up Questions:

• What were the most challenging aspects of quantifying the benefits?
• How did you validate your assumptions about potential returns?
• How did you account for potential implementation delays or cost overruns?
• What feedback did you receive on your business case, and how did you incorporate it?

Describe a time when you had to assess the ethical implications of implementing an AI solution.

Areas to Cover:

• The specific ethical concerns identified (bias, privacy, transparency, etc.)
• The candidate's process for evaluating ethical risks
• How they balanced ethical considerations with business objectives
• Stakeholders consulted during the ethical assessment
• Mitigation strategies they recommended
• How ethical considerations affected the final feasibility assessment

Follow-Up Questions:

• What framework or approach did you use to identify potential ethical issues?
• How did you address disagreements among stakeholders about ethical priorities?
• What specific safeguards or governance mechanisms did you recommend?
• How did this experience shape your approach to subsequent AI assessments?

Share an example of how you estimated the resources required for an AI implementation.

Areas to Cover:

• The candidate's methodology for resource estimation
• Specific categories of resources they considered (talent, technology, time, budget)
• How they accounted for uncertainty and complexity
• Their approach to identifying hidden or often overlooked costs
• The basis for their estimates (industry benchmarks, past projects, expert input)
• How resource requirements influenced feasibility determination

Follow-Up Questions:

• What was your process for identifying all the necessary resource categories?
• How accurate were your estimates in retrospect, and what would you do differently?
• How did you communicate resource requirements to stakeholders who might have had different expectations?
• What contingencies did you build into your resource plan?

Tell me about a situation where you had to assess whether your organization had the necessary capabilities to successfully implement an AI solution.

Areas to Cover:

• The candidate's approach to evaluating organizational readiness
• Specific capabilities they assessed (technical expertise, infrastructure, processes)
• Gaps they identified and how they were addressed
• Their evaluation of change management requirements
• Stakeholders involved in the capability assessment
• How capability considerations affected the overall feasibility determination

Follow-Up Questions:

• What were the most significant capability gaps you identified?
• How did you prioritize which capabilities needed to be developed first?
• What recommendations did you make for building the necessary capabilities?
• How did you balance the need to build capabilities with the pressure to move quickly?

Describe your experience conducting a pilot or proof of concept to assess AI solution feasibility.

Areas to Cover:

• The candidate's approach to designing the pilot scope and objectives
• How they determined success criteria and metrics
• Their process for selecting test cases or data
• The evaluation methodology they used
• How they managed stakeholder expectations during the pilot
• How pilot results informed the broader feasibility assessment

Follow-Up Questions:

• What was your rationale for the specific scope and scale of the pilot?
• What unexpected challenges emerged during the pilot, and how did you address them?
• How did you ensure the pilot was representative enough to inform full implementation decisions?
• What would you do differently in designing your next AI proof of concept?

Tell me about a time when you had to assess the integration requirements for an AI solution within an existing technology ecosystem.

Areas to Cover:

• The candidate's process for mapping integration points and dependencies
• How they evaluated compatibility with existing systems
• Technical challenges they identified and how they addressed them
• Their assessment of data flow and system performance impacts
• Stakeholders consulted during the integration assessment
• How integration considerations affected the overall feasibility determination

Follow-Up Questions:

• What were the most complex integration challenges you identified?
• How did you prioritize which integration issues to address first?
• What trade-offs did you consider between ideal integration and practical constraints?
• How did you communicate technical integration challenges to non-technical stakeholders?

Share an example of how you evaluated vendor claims about an AI solution's capabilities.

Areas to Cover:

• The candidate's approach to due diligence on vendor claims
• Specific methods used to validate capabilities (demos, references, testing)
• How they distinguished between current capabilities and roadmap promises
• Their assessment of vendor stability and support
• Red flags they identified in vendor representations
• How vendor evaluation influenced the overall feasibility assessment

Follow-Up Questions:

• What specific criteria did you use to evaluate vendor credibility?
• How did you test or validate the vendor's most important claims?
• What discrepancies did you find between vendor claims and actual capabilities?
• How did you manage internal expectations while conducting vendor due diligence?

Describe a situation where you had to assess the scalability of an AI solution beyond an initial use case.

Areas to Cover:

• The candidate's methodology for evaluating scalability
• Technical factors they considered (performance, architecture, data volumes)
• Organizational factors they assessed (expertise, processes, governance)
• Their approach to estimating scaling costs and timelines
• How scalability considerations influenced initial implementation decisions
• Trade-offs they identified between immediate needs and future scalability

Follow-Up Questions:

• What specific indicators did you look for to assess scalability potential?
• What technical or organizational constraints did you identify that might limit scaling?
• How did you balance the need for a successful initial implementation with longer-term scalability?
• What recommendations did you make to ensure the solution could scale effectively?

Tell me about a time when you had to assess the regulatory or compliance implications of an AI solution.

Areas to Cover:

• The candidate's process for identifying relevant regulations or standards
• How they evaluated compliance requirements and risks
• Their approach to assessing potential regulatory changes
• Stakeholders consulted during the compliance assessment
• Mitigation strategies they recommended
• How compliance considerations affected the overall feasibility determination

Follow-Up Questions:

• What resources or expertise did you leverage to understand the regulatory landscape?
• How did you handle uncertainties about regulatory interpretation or future changes?
• What specific compliance risks concerned you most, and why?
• How did you balance compliance requirements with business objectives?

Share an example of how you evaluated change management requirements for an AI implementation.

Areas to Cover:

• The candidate's approach to assessing organizational impact
• How they identified affected stakeholders and processes
• Their evaluation of potential resistance points
• Methods used to estimate change management resources and timeline
• How they incorporated change readiness into the feasibility assessment
• Recommendations they made to support successful change

Follow-Up Questions:

• What indicators did you use to assess the organization's readiness for change?
• How did you identify potential sources of resistance?
• What change management strategies did you recommend, and why?
• How did change management considerations influence your overall feasibility assessment?

Describe a time when you had to re-evaluate the feasibility of an AI initiative after it had already begun.

Areas to Cover:

• The circumstances that triggered the re-evaluation
• The candidate's approach to gathering new information
• How they assessed progress against original feasibility criteria
• Their process for making the continue/pivot/stop recommendation
• How they managed stakeholder expectations during the re-evaluation
• Lessons learned from the experience

Follow-Up Questions:

• What early warning signs indicated that re-evaluation was necessary?
• How did you balance sunk costs against future investment considerations?
• What was most challenging about communicating the need for re-evaluation?
• How did this experience change your approach to initial feasibility assessments?

Tell me about a situation where you successfully championed an AI solution despite initial skepticism about its feasibility.

Areas to Cover:

• The nature of the initial skepticism or concerns
• The candidate's approach to addressing specific feasibility questions
• Evidence or analysis they gathered to support their assessment
• How they built stakeholder confidence in the solution
• Their process for de-risking the implementation
• Key success factors that validated their feasibility assessment

Follow-Up Questions:

• What specific concerns did stakeholders have about feasibility, and how did you address each one?
• What evidence or analysis was most persuasive in changing minds?
• How did you balance confidence in your assessment with acknowledgment of risks?
• What about your approach to implementation helped ensure the solution succeeded?

Frequently Asked Questions

How should I adapt these questions for candidates with limited AI-specific experience?

For candidates with limited AI experience, focus on their approach to evaluating complex technology solutions in general. Listen for transferable skills like critical thinking, stakeholder management, and business-technology alignment. You can modify questions to ask about "technology solutions" broadly, then use follow-up questions to explore how they would extend their approach to AI-specific considerations.

What's the ideal number of these questions to include in an interview?

Rather than trying to cover many questions superficially, select 3-4 questions most relevant to your specific role and organization, then use follow-up questions to probe deeper. This approach provides richer insights than rushing through more questions. For senior roles, you might dedicate an entire interview to this competency; for junior roles, combine a few questions with other competency areas.

How can I tell if a candidate is sharing genuine experience versus theoretical knowledge?

Authentic responses include specific details, challenges faced, and lessons learned rather than idealized processes. Use follow-up questions to probe for details that someone without real experience wouldn't know – ask about surprising findings, stakeholder reactions, or specific metrics used. Listen for nuanced perspectives that acknowledge trade-offs and complexities rather than textbook answers.

Should I prioritize technical or business evaluation skills when assessing candidates?

The balance depends on the specific role. For technical positions like ML engineers, emphasize technical evaluation capabilities while ensuring basic business understanding. For business roles like product managers, focus on business case development and stakeholder management while confirming sufficient technical literacy. For leadership positions, look for the ability to bridge technical and business considerations and make sound decisions with incomplete information.

How can I use these questions to assess a candidate's ability to avoid AI hype?

Listen for evidence that candidates have questioned assumptions, conducted rigorous analysis, and made decisions based on evidence rather than trends. Strong candidates will describe instances where they pushed back against unwarranted enthusiasm, requested additional validation, or recommended against AI solutions when simpler approaches would suffice. Their examples should demonstrate healthy skepticism balanced with openness to innovation.

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