New opportunity

AI Business Consultant Intern

Univers · Singapore

About this role

Univers is a platform for Physical AI that enables critical operations to perceive, understand, and act in real time. As an AI Business Consultant Intern, you will bridge the gap between complex business problems and AI-driven solutions. This is a business-led consulting role focused on structured problem solving, value analysis, and solution shaping rather than software engineering or AI research. Responsibilities: Business Problem Solving & Value Identification: Support client discovery through research, business analysis, and workshop participation. Break down business models, P&Ls, and KPIs to identify improvement opportunities. Develop financial models and quantitative business cases. AI Use-Case Translation & Solution Shaping: Translate business needs into defined AI use cases and roadmaps. Partner with technical teams to assess data readiness, feasibility, and business impact. Communicate AI capabilities, benefits, and trade-offs to business leaders. Client Communication & Value Delivery: Develop proposals, executive presentations, and workshop materials. Support pilots and proofs of value by defining KPIs and adoption plans. Coordinate deliverables across stakeholders. Qualifications: Currently pursuing a Bachelor's or Master's degree in Business, Economics, Finance, Operations, Engineering, or a related field. Demonstrated consulting-style problem solving and quantitative grounding. Practical AI fluency is required, including understanding machine learning, generative AI, LLMs, and agents. Proficiency in PowerPoint and Excel is essential. Exposure to SQL, Python, Power BI, or Tableau is useful. Strong written and spoken English is required; Mandarin is an advantage. Relevant experience in consulting, strategy, or business analytics is preferred.

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

Skills for this role

Business AnalysisConsultingAI StrategyData AnalysisFinancial ModelingPowerPointExcelSQLPythonPower BITableauProblem SolvingStakeholder Management

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

Understand the role

What matters most

Structured Problem Solving and Value Analysis
The role requires breaking down ambiguous business challenges into actionable components. You must demonstrate the ability to use issue trees and value-driver analysis to connect operational KPIs and P&Ls to specific AI-driven improvement opportunities.
AI-to-Business Translation
You must act as a bridge between technical teams and business stakeholders. This involves translating complex AI capabilities, such as LLMs and agents, into clear business language, assessing technical feasibility, and defining measurable outcomes for non-technical leaders.
Executive Communication and Deliverable Creation
A core expectation is the production of high-quality, client-ready materials including executive presentations, proposals, and workshop content. You must be able to synthesize complex data and discussions into concise, actionable recommendations.
Quantitative Business Modeling
You are expected to develop financial models and quantitative business cases to justify AI investments. Proficiency in Excel is essential for sizing value at stake and supporting evidence-based recommendations for clients.

Likely challenges to think through

Balancing Technical Feasibility with Business Ambition
A common challenge in AI consulting is managing client expectations regarding what AI can realistically achieve given data constraints. Prepare by practicing how to explain AI limitations, such as data readiness or model evaluation risks, without discouraging the business objective.
Navigating Cross-Functional Stakeholder Dynamics
You will coordinate between business, data, product, and engineering teams. The difficulty lies in aligning these groups with different priorities. Prepare by reflecting on experiences where you had to synthesize conflicting inputs into a unified project roadmap.
Rapid Context Switching Across Industries
Univers serves diverse sectors like energy, manufacturing, and logistics. You may need to quickly learn the operational nuances of a new industry. Prepare by demonstrating your research methodology for rapidly understanding unfamiliar business models and industry-specific pain points.

Practice your answers

Technical practice

Structuring an AI Business Case

What to coverExplain how you would use a value-driver tree to connect a client's operational pain point to a specific AI solution. Focus on identifying the key KPIs that would be impacted and how you would quantify the expected financial return.

Differentiating AI Approaches

What to coverDescribe the fundamental differences between traditional machine learning and generative AI/LLMs in a business context. Discuss when you might recommend one over the other based on data availability, accuracy requirements, and the nature of the task.

Assessing Data Readiness

What to coverWhen partnering with technical teams, what specific questions would you ask to determine if a client's data is sufficient for an AI pilot? Consider aspects like data quality, volume, accessibility, and historical relevance.

Communicating AI Trade-offs

What to coverHow would you explain the concept of 'model hallucination' or 'black-box' risks to a non-technical executive? Suggest how to frame these risks alongside the potential business benefits to ensure informed decision-making.

Defining Success Metrics for Pilots

What to coverIf a client wants to implement an AI agent to improve operational uptime, what metrics would you propose to measure the success of a proof of value? Distinguish between technical performance metrics and business outcome KPIs.

Prioritizing AI Use Cases

What to coverGiven a list of potential AI initiatives, what framework would you use to prioritize them? Address factors such as business impact, technical feasibility, and implementation complexity.

Data Analysis for Strategy

What to coverDescribe a scenario where you would use tools like Excel or SQL to validate a business hypothesis. Explain how you ensure your analysis remains focused on actionable insights rather than just descriptive reporting.

Human-in-the-loop Requirements

What to coverWhy is human oversight critical in Physical AI applications? Discuss how you would design a workflow that balances autonomous AI action with the need for human control at critical decision points.

Behavioural practice

Handling Ambiguous Problems

What to coverDescribe a time you were faced with an unstructured or ambiguous problem. Explain the steps you took to break it down, form a hypothesis, and reach a logical conclusion.

Stakeholder Communication

What to coverProvide an example of how you translated a complex technical concept into simple, persuasive language for a non-technical audience. What was the outcome of that communication?

Collaborating Across Functions

What to coverTell me about a time you had to coordinate work between different teams with competing priorities. How did you manage dependencies and ensure the project stayed on track?

Learning New Domains

What to coverGive an example of a time you had to quickly learn about a new industry or technical topic to complete a project. What resources did you use and how did you apply that knowledge effectively?

Sharpen your knowledge

Topics to prepare

Structured Problem Solving
The role requires breaking down ambiguous business challenges into actionable components. Exercise: Practice creating issue trees and hypothesis-driven frameworks for a hypothetical industrial efficiency problem, such as reducing downtime in a manufacturing plant. This is essential for the core responsibility of identifying value pools and improvement opportunities.
AI Use-Case Translation
You must bridge the gap between business needs and technical AI capabilities. Exercise: Select a common business process (e.g., supply chain forecasting) and map it to specific AI approaches like predictive modeling or generative agents, defining the necessary data inputs and success KPIs. This prepares you to collaborate effectively with technical teams.
Quantitative Business Case Development
You will need to build financial models to justify AI investments. Exercise: Create a simple ROI model for an AI implementation, accounting for implementation costs, expected efficiency gains, and risk factors. This aligns with the requirement to develop quantitative business cases and value-sharing models.

Skills to revise

Executive Presentation Design
The role demands translating complex analysis into clear, executive-ready materials. Exercise: Take a complex technical topic and condense it into a 5-slide PowerPoint deck that focuses on business impact, trade-offs, and strategic recommendations. This is critical for client communication and value delivery.
Data-Driven Analysis in Excel
You must be comfortable working with operational data to support recommendations. Exercise: Use a sample dataset to perform a trend analysis or variance check on KPIs, then visualize the findings to support a business hypothesis. This is a core requirement for testing hypotheses and sizing value at stake.

Your preparation checklist

Develop a structured AI use-case framework
Create a sample presentation or document that identifies a specific business problem in an asset-intensive industry (e.g., energy or manufacturing) and maps it to an AI solution. Include the business objective, data requirements, potential AI approach (e.g., predictive maintenance or LLM-based agent), and success metrics.
Prepare a quantitative business case model
Build an Excel model that demonstrates how you would estimate the ROI of an AI implementation. Include assumptions for cost savings or efficiency gains, potential implementation costs, and a clear calculation of the value at stake to show your ability to connect technical solutions to P&L impacts.
Refine your AI fluency narrative
Prepare to explain the trade-offs between different AI approaches, such as when to use generative AI versus traditional machine learning. Focus on your ability to communicate these technical concepts to non-technical stakeholders, emphasizing risks, limitations, and human-in-the-loop requirements.
Curate a portfolio of consulting-style deliverables
Gather examples of your past work, such as case competition decks, strategy reports, or data analysis projects. Ensure these materials highlight your ability to structure ambiguous problems, synthesize complex information into executive-ready PowerPoint slides, and provide actionable recommendations.
Formulate strategic questions for the interview
Prepare thoughtful questions about how Univers balances technical feasibility with client business goals. Examples include asking how the team assesses data readiness for new clients or how they manage stakeholder expectations during the proof-of-value phase of an AI project.

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