MBA Knowledge Library · Technology & AI
AI for Business: Use Cases, Limits, and Verification
AI can help with drafts, explanations, classification, and analysis, but fluent output is not evidence of correctness. Start with a bounded task, suitable information, and a review method that catches meaningful errors.
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Which kind of AI are we discussing?
This guide focuses on generative assistants used for business knowledge work. They can produce text, code, summaries, and other outputs from instructions and context. Predictive models, rules-based automation, and tools that act in other systems have different capabilities and controls.
Do not assume an assistant can access current sources, private files, or connected systems. Capabilities depend on product, plan, permissions, and setup. Verify the actual configuration rather than relying on a generic description of AI.
Choose a task that can be checked
| Task | Potential use | Review needed |
|---|---|---|
| Drafting | Turn approved facts into a first draft. | Check meaning, claims, tone, and audience. |
| Summarization | Extract points from supplied documents. | Trace statements and check omissions. |
| Classification | Suggest labels for defined records. | Test known examples and review exceptions. |
| Analysis support | Suggest formulas or compare scenarios. | Recalculate, inspect assumptions, and reconcile inputs. |
| Process design | Draft a checklist from a described workflow. | Validate with the people doing the work. |
| External actions | Prepare changes for another system. | Define authority, review points, and recovery. |
A good pilot has clear inputs, bounded outputs, known evaluation criteria, and a capable reviewer. “Run finance” combines too many judgments, sources, and authority questions to evaluate usefully.
Understand the limitations
Generative systems can produce unsupported or false statements, often called hallucinations or confabulations. They can omit context, misread documents, invent citations, or generate code that fails on edge cases. NIST’s Generative AI Profile identifies confabulation, privacy, information integrity, and human-AI interaction among relevant risk areas.
Sources can improve grounding without guaranteeing faithful use. Asking for confidence does not establish a calibrated probability of correctness. Another model’s agreement is not independent source evidence. Identical prompts can produce different conclusions.
Write a usable task brief
Specify goal, audience, sources, scope, assumptions, output format, and review standard. Separate source facts from instructions and make uncertainty visible.
“Using only the approved procedure below, draft a one-page onboarding checklist for a reporting analyst. Preserve every required approval. Separate required steps from suggestions. For each required step, identify its source section. List missing or contradictory information as questions instead of inventing answers. Return a draft for human review.”
This prompt makes the task inspectable; it does not ensure every instruction is followed. The reviewer still compares the checklist with the approved procedure and tests whether a new analyst could use it.
Use an evidence-based verification loop
- Check completeness: required items, exceptions, and exclusions.
- Trace facts: inspect actual documents or authoritative sources, not citation appearance.
- Recalculate: verify units, dates, signs, denominators, and assumptions independently.
- Test: try known cases, boundary conditions, and failure cases in an appropriate environment.
- Review meaning: a valid calculation can answer the wrong question.
- Approve use: the authorized person decides whether the output is ready for its destination.
Match review to consequence. Brainstorming and a client financial report require different evidence. An educational explanation is not automatically ready for a production ledger.
Worked example: reporting-checklist pilot
A fictional team pilots AI-assisted drafts of 10 recurring checklists. The same qualified reviewer applies an agreed standard to manual and assisted work. Timing includes corrections. These are original illustrative numbers, not product-performance claims.
| Measure | Manual baseline | AI-assisted pilot |
|---|---|---|
| Items | 10 | 10 |
| Preparation or drafting | 10 × 25 = 250 minutes | 10 × 8 = 80 minutes |
| Review and correction | 10 × 10 = 100 minutes | 10 × 15 = 150 minutes |
| Total labor time | 350 minutes | 230 minutes |
| Difference | — | 120 minutes less total time |
Preparation falls by 170 minutes, but additional review uses 50 minutes. Net time reduction is 120 minutes, or 2 hours. At an assumed $40 hourly rate, that is $80 of capacity value before subscription, setup, training, and other costs. It is not automatically $80 of cash savings.
Suppose initial drafts omit 2 of 40 required approval steps, both corrected before release. That is a 5% omission rate among required steps, not a measure of all possible quality issues. Faster drafting still needs review. Ten items cannot establish a stable long-term performance estimate.
Handle information and permissions deliberately
Determine which information is permitted in the service and which account or contract is approved. Check current retention, training-use, access, and data-handling terms for that arrangement. Do not infer terms from another provider or subscription.
Use the least information needed. Fictional examples or redacted excerpts may suffice. Confirm redaction removes meaningful identifiers from attachments and metadata where relevant. Keep real sensitive records in approved workflows.
For connected tools, distinguish a recommendation from execution. A proposed email, record edit, or payment instruction needs appropriate authority before becoming an external action. Content in a retrieved document is information to assess, not permission to change another system.
Keep a pilot log
Record task version, inputs, tool and relevant settings, reviewer, time, errors, corrections, and approved result. Define conditions that would pause or narrow the pilot. Retest when task, model, sources, or integrations change.
Measure completed acceptable work rather than text-generation speed. Track review burden, material omissions, adoption, and actual use. Stop or redesign a pilot that cannot be checked reliably at reasonable cost.
Common mistakes
- Choosing a tool before defining the job.
- Treating polished output as verified fact.
- Checking citations only by title.
- Ignoring review time when claiming savings.
- Granting broad access for a narrow task.
- Allowing a draft to become an external action without appropriate review.
Frequently asked questions
Can AI perform calculations?
Some assistants use calculation or code tools; others may generate numbers as text. Verify the method, inputs, and result.
Does a supplied document prevent invented answers?
No. Output can still misstate, omit, or overextend the source. Trace important claims.
Should AI handle every repetitive task?
No. Rules, spreadsheets, or conventional automation may be more predictable. Compare them against actual requirements.
What is a sensible first pilot?
A bounded, reviewable task with appropriate data and modest consequences, such as an internal checklist draft from an approved source.
Start with one controlled experiment
Write the task, permitted inputs, acceptable output, review method, owner, baseline, and success criteria. Run a small pilot, record total effort and corrections, and decide whether evidence supports wider use.
Sources and example notes
- NIST: Generative AI Profile
- NIST AI 600-1: Full Profile
- Canadian Centre for Cyber Security: Careful Adoption of Agentic AI
Examples, calculations, checklists, and practical recommendations are original educational illustrations, not research findings or universal benchmarks. Adapt decisions to actual evidence, policies, and obligations.