AI × BUSINESS
Explore AI in Business
AI can help a support team answer questions, a founder compare competitors, or an operations team plan stock. Each use needs a clear decision and a way to check the output.
Read a customer-support study, examine its productivity measure, and consider two research projects using public sources.
Select a step to see what happens.
Customer question
Define the business question
Choose a specific customer need or market question. Record which decision the research is meant to inform.
Research notes
Compare evidence from named sources
An assistant can organize competitor information or customer-support material. Dates, definitions, and source quality affect the comparison.
Founder decision
Separate an observation from an assumption
Check the underlying sources before drawing a conclusion. A plausible market narrative is not evidence that customers will buy.
Illustrative business-research workflow. Assumptions remain visible.
THE BASICS
The business question comes first
A student planning a bike-repair service needs to understand the customers, competing shops, and the amount of work a small team could handle. Those questions determine which information to collect and what a useful analysis should explain.
Classification sorts records into categories, such as delivery questions and refund questions. Forecasting estimates a future quantity from past observations. Generative AI produces material such as a draft comparison or explanation. A tool-using agent connects steps in a workflow. Choose the method according to the task and the evidence available.[3][6]
For competitor research, use the same comparison criteria for every business, link each fact to its source, and record missing information. For a forecast, decide how later observations will be used to check the estimate. Both projects should leave a record another person can inspect.
APPLICATIONS
Which business tasks can AI support?
Follow the information through each task: the input, the model’s contribution, and the person responsible for checking the result.
Published workplace study
Helping a support worker find a useful response
A customer describes a problem in everyday language, while the worker needs relevant product guidance.
- The material
- A customer conversation and the support system's available information.
- AI’s role
- In one studied deployment, a generative assistant suggested responses to human customer-support workers.
- The human decision
- The worker remained responsible for the conversation. A student can examine whether a suggested response addresses the question and fits the documented policy.
The limitEvidence from one employer and one tool does not establish the same result for another support team, product, or population.[1]
Public-source market research; proposed AI-assisted workflow
Organizing evidence about a possible market
An appealing idea may overlook existing alternatives, the reachable audience, or the difference between interest and demand.
- The material
- Dated public competitor pages and relevant demographic or economic tables. The SBA distinguishes existing-source research from asking customers directly.
- AI’s role
- A proposed assistant could extract comparable facts from a selected source collection and organize questions that still need customer research.
- The human decision
- Choose a meaningful comparison and open the original sources. Census Business Builder provides selected demographic and economic information for researching places and business types.
The limitA geographic population total is not a count of willing customers. An AI-generated customer persona is not evidence from an interview.[4][5][6]
Documented forecasting method
Forecasting demand to inform a planning decision
Ordering too much can leave unsold stock; ordering too little can leave customers waiting. A planner needs more than a guess about next week's quantity.
- The material
- Time-stamped observations, such as product demand, with consistent units and recording intervals.
- AI’s role
- Amazon's DeepAR documentation describes learning from related time series to forecast future values. The model learns from observed sequences to produce forecasts.
- The human decision
- Compare the forecast with a simple baseline and consider storage, lead times, and the cost of different errors before choosing an order.
The limitA forecast is not an instruction to purchase stock. AWS recommends withholding future observations during evaluation; the same documentation notes that simpler methods may work better with limited data.[3]
CASE STUDY
What did a customer-support study actually measure?
A productivity headline becomes more useful when you name its unit and setting.
The final 2025 Quarterly Journal of Economics article Generative AI at Work studied a staggered assistant rollout involving 5,172 support workers at a business-software company. Its headline outcome was successfully resolved issues per hour. The authors reported an average increase of about 15%, with effects differing across workers.[1]
Resolved issues per hour measures throughput. To interpret that measure, ask what counts as a successful resolution and which quality checks were used. A result for one support task needs a fresh evaluation before it informs a different job.
An HBS Institute summary describes benefits and failures on different consultant tasks. That comparison makes task selection part of the analysis: identify which work the tool was asked to do and how its output was judged.[2]
Follow the process Open a step to see what happens.
Name the task
What work is the person trying to complete?
Define success
Which unit, comparison, and quality checks matter?
Keep the boundary
Which other claims would need different evidence?
In a productivity claim, record the task, tool, participants, comparison, and measured outcome. Those details determine whether the finding is relevant to your question.[1][2]
SKILLS & STUDY
What to learn first
- Understand the customer and decision
- Learn the difference between a need, a preference, a buying decision, and a guess about an audience. Define what your analysis could change.
- Read tables and check arithmetic
- Use spreadsheets, percentages, units, and consistent dates. Write down a calculation so someone else can reproduce it without trusting the AI's explanation.
- Handle sources with care
- Keep the original link, observation date, and definition next to each fact. Separate a company's claim about itself from independent evidence and from your own interpretation.
- Match the technical scope to your starting point
- A source comparison can begin with careful reading and a spreadsheet. Automating retrieval needs programming and data handling. Training a forecasting model adds statistics, enough suitable data, and a defensible evaluation.
PROJECT IDEAS
Projects that test a business assumption
Choose a question, inspect the requirements, and keep a brief for later. Each proposal includes a way to check the work.
Save an idea in this browser or copy its brief to discuss with a mentor.
No account neededA competitor comparison you can defend
How do three public alternatives differ for one clearly defined customer need?
- Intended output
- A dated evidence table and a short memo separating observed features from your interpretation.
Skills, sources and checks
- Starting knowledge
- Careful reading, spreadsheet organization, and a consistent comparison rubric. Coding is optional for the initial analysis.
- Material to consider
- Public product, service, pricing, and policy pages. Preserve source links and note when a detail is unavailable.
- How to judge the result
- Check every factual cell against its source; use matching product tiers and dates; ask another reader to reproduce a sample of the comparisons.
A public-source comparison using stated criteria. Customer interviews and private company metrics are outside this version.
A focused market-entry question
What public evidence would help assess one service idea in a defined place?
- Intended output
- A research brief with a geographic definition, relevant public indicators, a competitor map, and unanswered customer questions.
Skills, sources and checks
- Starting knowledge
- Basic percentages, reading data definitions, and understanding why a broad population estimate differs from a reachable market.
- Material to consider
- Census Business Builder reports, public local information, and dated business websites. Keep years and geographic boundaries visible.
- How to judge the result
- Confirm the origin and definition of each measure. Check calculations and show how a stated assumption changes your interpretation.
Desktop research for one service and location. Customer demand remains a question for later direct research.
The project ideas use public or synthetic material. Keep customer records and personal information out of an educational prototype; business decisions still need someone accountable for the assumptions and consequences.
MENTOR SUPPORT
Develop a market-research agent
The STEMtiq Agentic AI Scholars Program has a market-intelligence pathway covering retrieval, calculation tools, explicit assumptions, and conclusions prepared for human review. A competitor comparison could provide the question and source collection for that workflow.
Discuss the source collection, comparison criteria, and evaluation with a mentor. The shared curriculum focuses on agent building; forecasting and specialist marketing methods have separate prerequisites.
5 weeks · 25 live hours · Grades 9-12
COMMON QUESTIONS
What students ask
How can AI help investigate a business idea?
It can help organize a defined source collection, extract candidate facts, or draft questions. Decide what evidence would change your view first. Then verify the underlying pages and keep uncertain or missing information visible. The report should explain what the evidence supports and what further research would change the decision.[4][6]
Do I need coding before exploring business AI?
You can begin by reading sources, designing a comparison, and checking spreadsheet calculations. Coding becomes useful when you want repeatable extraction or a tool-connected workflow. Training a model is a different technical step and needs appropriate data and evaluation knowledge.
Can a model tell me what customers really want?
A generated persona can suggest questions, but it is not a customer interview. Public datasets and direct research answer different questions. State which kind of evidence you have and what remains unknown.[4][5]
What should I check in an AI productivity claim?
Ask what task was measured, which people used which tool, what the comparison was, and how the outcome was defined. Faster work can still be unsuitable work. Look for evidence about quality and the conditions where the result might fail.[2]
Is a business project the same as an investment project?
No. A business project might investigate customer needs, competitors, or operations. This guide's ideas focus on those questions. Predicting asset returns and making personal investment decisions raise different questions and are outside these project scopes.
Discuss a business research project.
Prepare one customer or operations question and a few public sources. A mentor can help choose a manageable workflow and checks for its output.
Sources & further reading
- [1]
- [2]
- [3]Amazon Web Services: DeepAR forecasting algorithm: methods and evaluation (Undated technical documentation)Return to guide ↑
- [4]U.S. Small Business Administration: Market research and competitive analysis (Undated institutional guidance)Return to guide ↑
- [5]U.S. Census Bureau: Census Business Builder (Undated resource; version displayed when checked: 5.12)Return to guide ↑
- [6]
Sources inform this guide; their organizations do not endorse STEMtiq. Project ideas and diagrams are original editorial explanations.