New opportunity

AI Advisor

Accenture · Bengaluru, India

About this role

Accenture is hiring an AI Advisor for its Bengaluru office. The role focuses on evaluating, prioritizing and designing AI use cases that address business problems and opportunities, improve processes, deliver measurable value and support AI adoption across the organization.

Responsibilities

Work with stakeholders to identify the data and technology requirements for AI use cases and design effective, sustainable solutions.
Serve as a subject-matter expert, manage team performance, make team decisions and contribute to decisions across multiple teams.
Solve problems within the immediate team and across teams; facilitate workshops and brainstorming sessions to develop AI solutions.
Mentor junior team members in AI technologies and methodologies.

Requirements

Minimum 5 years of experience; the posting also specifies a minimum of 5 years in Generative AI and lists an experience range of 5–10 years.
Proficiency in Generative AI, a strong understanding of AI model development and deployment, experience analyzing and interpreting data to inform use-case design, and familiarity with business process improvement methodologies.
15 years of full-time education.

Preferred skills: Python, large language models and AWS AI Services.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AIPythonLarge Language Models (LLMs)AWS AI ServicesAI model developmentAI model deploymentData analysisBusiness process improvementStakeholder managementTeam leadershipWorkshop facilitationMentoring

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Prioritize business-led AI use cases
The role evaluates, prioritizes and designs AI use cases for specific business problems and opportunities, using data analysis to inform design.
Design feasible, sustainable solutions
Work with stakeholders to identify data and technology requirements. The posting calls for Generative AI proficiency and a strong understanding of AI model development and deployment.
Deliver measurable process value
Use cases should simplify or improve business processes, produce measurable outcomes and support scaled AI adoption.
Lead decisions and develop others
The advisor is expected to act as a subject-matter expert, manage team performance, make team decisions, contribute across teams, facilitate workshops and mentor junior colleagues.

Likely challenges to think through

Value versus feasibility
A likely difficulty is choosing a high-value use case when its data or technology requirements limit what can be delivered. Prepare a clear way to compare expected business value with available data, implementation needs and measurable outcomes.
Immediate improvements versus sustained adoption
A solution may improve one process without being practical to deploy or adopt more widely. Prepare to explain how model development, deployment and process changes would affect the sustainability of a proposed use case.
Team decisions versus cross-team alignment
The role combines responsibility for team decisions with contributions to decisions across multiple teams. Prepare an example of how you would use a workshop to surface differing requirements, reach a decision and communicate its rationale.

Practice your answers

Technical practice

How would you prioritize several proposed AI use cases?

What to coverSuggested preparation: Explain how you would compare business value, feasibility, data readiness, implementation effort and risks with stakeholders. Describe what evidence would justify advancing one use case before another.

How would you determine whether a business problem is a good fit for Generative AI?

What to coverSuggested preparation: Define the task and desired outcome, then compare a Generative AI approach with a simpler process or technology change. Address the quality, cost and oversight trade-offs that could change your recommendation.

What would you examine in the available data before designing an AI use case?

What to coverSuggested preparation: Discuss relevance, quality, accessibility and gaps in the data, and how analysis would inform the design. Explain how you would communicate a data limitation that makes the proposed use case less feasible.

How would you turn stakeholder needs into data and technology requirements?

What to coverSuggested preparation: Describe how you would clarify the business workflow, users, desired outcome and constraints before selecting a solution approach. Explain how you would resolve conflicting requirements without assuming a particular tool is necessary.

How would you develop and evaluate a Generative AI solution for a defined use case?

What to coverSuggested preparation: Outline an iterative path from a baseline and representative test cases to evaluation against the business task. If discussing large language models, a listed good-to-have skill, explain how you would assess output quality, failure cases and the need for human review.

What would you consider when moving an AI model from development to deployment?

What to coverSuggested preparation: Cover testing, integration with the process, operational ownership and how performance would be checked after release. Discuss the trade-off between a quick rollout and a solution that remains effective and sustainable.

How would you troubleshoot an AI use case that is not improving the target process?

What to coverSuggested preparation: Separate possible causes such as a poorly defined problem, unsuitable data, model behaviour or workflow friction. Explain what evidence you would gather before changing the solution or reconsidering the use case.

How would you measure whether an AI use case delivers business value?

What to coverSuggested preparation: Define a baseline and a small set of measures tied to the process being improved, then explain how you would check outcomes after adoption. Distinguish model performance from measurable changes in the business process.

What would make an AI solution easier to adopt across multiple teams?

What to coverSuggested preparation: Discuss reusable design decisions, stakeholder ownership, training needs and feedback from teams using the solution. Explain how you would balance consistency across teams with differences in their workflows.

How might you use Python or AWS AI Services when assessing a use case?

What to coverSuggested preparation: Treat these as optional tools, since the posting lists them as good-to-have skills. Describe a relevant analysis or prototype you could use to test feasibility, and explain how you would decide whether the tool adds value.

Behavioural practice

Tell me about a time you aligned stakeholders on a technology approach.

What to coverSuggested preparation: Use a real situation, your actions to clarify differing needs and the resulting decision or outcome. Focus on how you translated business needs into a workable approach.

Describe a consequential decision you made while leading a team.

What to coverSuggested preparation: Give the situation, the options you weighed, the decision you made and its result. Explain how you managed team performance or accountability without overstating your individual contribution.

Tell me about a problem you helped resolve across multiple teams.

What to coverSuggested preparation: Set out the shared problem, what you did to coordinate the teams and the result. Distinguish your own actions from decisions made jointly.

Describe a workshop you facilitated to develop or refine a solution.

What to coverSuggested preparation: Explain the situation, how you structured participation and evaluated ideas, and what resulted. An example involving an AI use case is relevant if you have one, but the posting does not specify a workshop format.

Tell me about a time you mentored a junior team member.

What to coverSuggested preparation: Describe the learning need, the support or feedback you provided and the observable result. If relevant, connect the example to AI technologies or methodologies rather than claiming an outcome you cannot substantiate.

Sharpen your knowledge

Topics to prepare

Generative AI use-case fit
Must-have: Generative AI proficiency. For a hypothetical business process, compare a generative AI use case with a simpler alternative; explain where each creates value and what could make the AI approach unsuitable. This prepares for evaluating and designing use cases.
Use-case prioritization and value
Revise by ranking three hypothetical AI use cases using business impact, feasibility and measurable outcomes. The role explicitly calls for prioritizing use cases and delivering measurable value.
Data and technology requirements
For one hypothetical use case, list the data needed, questions to resolve with stakeholders and technology constraints to investigate. The posting emphasizes identifying underlying data and technology requirements.
Model development and deployment
Sketch the stages from use-case design through model development, deployment and ongoing evaluation; identify a decision to make at each stage. A strong understanding of these processes is explicitly requested.
Process improvement and scaled adoption
Map a hypothetical current process and proposed AI-assisted process, then identify a measurable improvement and an adoption obstacle. The posting calls for familiarity with process improvement methodologies and solutions that support scaled AI adoption.
Cross-team decision-making and mentoring
Work through a hypothetical disagreement between teams about an AI use case: define the decision, trade-offs and how you would explain the outcome to a junior colleague. The role includes team decisions, cross-team problem-solving and mentoring.

Skills to revise

Data analysis for use-case design
Practice interpreting a small hypothetical process dataset: identify a bottleneck, note data limitations and state how the findings would affect an AI use-case decision. The posting explicitly requests experience analyzing and interpreting data.
Workshop facilitation
Rehearse a short workshop plan that moves stakeholders from a business problem to candidate AI use cases, required data and next decisions. Facilitating workshops and brainstorming sessions is an explicit responsibility.
Python prototyping
Good-to-have, not required: use Python to inspect a sample dataset and summarize a process metric that could inform a use case. Python is listed as a preferred skill.
LLM solution comparison
Good-to-have, not required: draft and test two approaches to a hypothetical LLM task, then compare outputs against stated business criteria. Large Language Models are listed as a preferred skill.
AWS AI Services assessment
Good-to-have, not required: outline how you would assess whether an AWS AI Service fits a hypothetical use case, including data and deployment questions to resolve. AWS AI Services are listed as a preferred skill; the posting names no particular service.

Your preparation checklist

Build a use-case prioritization brief
The role evaluates and prioritizes AI use cases for measurable business value. Prepare a one-page example comparing two possible use cases by business problem, process impact, data needs and proposed success measures.
Prepare a Generative AI project walkthrough
The posting requires Generative AI proficiency. If you have a relevant project, outline your own decisions, stakeholder input, results and lessons learned; distinguish measured outcomes from estimates.
Practice a solution-design exercise
The role identifies data and technology requirements and calls for understanding of model development and deployment. Sketch how you would design, test and sustain one use case, including data dependencies and how value would be measured.
Show how analysis shaped a decision
The posting calls for data analysis and interpretation to inform use-case design. Prepare an example showing what you examined, what you concluded and how it changed a design or priority.
Collect leadership and facilitation examples
The role includes team decisions, cross-team problem-solving, workshops and mentoring. Prepare concise examples that clarify your contribution, the decision reached and the outcome; use only work you actually performed.
Organize evidence for stated requirements
The posting specifies at least five years of Generative AI experience and 15 years of full-time education. Prepare an accurate work timeline and education details so you can address those requirements directly.
Draft questions for the employer
Ask how AI use cases are prioritized, which stakeholders define data requirements, how value and scaled adoption are measured, and where the AI Advisor makes team decisions.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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