Business analysis is about structuring ambiguity. You take messy, incomplete information and turn it into something a team can evaluate and act on. AI is surprisingly good at this — if you give it enough context about your situation instead of asking abstract questions.
The difference between "do a SWOT analysis" and a well-structured prompt is the difference between a generic textbook exercise and an analysis your leadership team would actually use in a meeting.
These five patterns cover core business analysis tasks: SWOT analysis, market sizing, strategic option evaluation, decision frameworks, and stakeholder communication.
Pattern 1: The Contextual SWOT Analysis
A SWOT analysis is only useful when it reflects your specific situation, not generic industry observations. This pattern produces a SWOT that reads like it came from someone who understands your business.
The Template
You are a business strategist conducting a SWOT analysis.
Company/product context:
- What we do: [describe your product or service]
- Target market: [who your customers are]
- Current stage: [startup, growth, mature — and approximate revenue range if comfortable sharing]
- Key competitors: [name 2-3 direct competitors]
- Recent developments: [anything that has changed in the last 6 months — new product, market shift, team change, funding]
Conduct a SWOT analysis with the following structure:
For each quadrant (Strengths, Weaknesses, Opportunities, Threats):
- List 4-5 items
- For each item, write one sentence explaining the specific evidence or reasoning
- Rank items within each quadrant by impact (highest first)
After the SWOT grid, provide:
- 2 strategic implications that emerge from combining quadrants (e.g., "Strength X could address Threat Y")
- 1 critical vulnerability where a Weakness intersects with a Threat
Constraints:
- Be specific to my situation — no generic items like "strong brand" without explaining why
- If you do not have enough information to assess something, say so rather than guessing
- Flag assumptions you are making so I can correct them
- Tone: direct and analytical, as if presenting to a boardWhy It Works
Standard SWOT prompts produce generic quadrants like "strong team" and "economic uncertainty." The "one sentence explaining the evidence" instruction forces specificity. Ranking by impact and adding cross-quadrant strategic implications transform a static grid into an actionable analysis. Asking the AI to flag assumptions keeps the output honest.
Example Output
For a mid-stage B2B SaaS company in the HR tech space:
Strengths (ranked by impact):
1. Deep integration with existing HRIS platforms (Workday, BambooHR) — reduces switching friction and shortens sales cycles because buyers do not need to replace existing tools
2. High NPS among current customers — indicates product-market fit and potential for expansion revenue through upsells
3. Proprietary compliance engine that auto-updates with regulatory changes — a meaningful differentiator since manual compliance tracking is a top pain point for HR teams
4. Founding team includes former HR executives — credibility in sales conversations and a sharper understanding of buyer needs than engineering-led competitors
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Weaknesses (ranked by impact):
1. Limited brand awareness outside the mid-market segment — enterprise buyers default to established vendors they already know
2. Sales cycle averages 4+ months — cash flow pressure and high CAC limit growth rate
3. No mobile app — increasingly expected by HR managers who work across multiple locations
4. Customer success team is stretched thin — 1:80 CSM-to-account ratio risks churn as account volume grows
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[Opportunities, Threats, and strategic implications would follow in the same format]
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Critical vulnerability: The combination of limited brand awareness (Weakness 1) and an incoming well-funded competitor expanding into your segment (Threat 2) means you could lose deals to a less capable product simply because they are spending more on market presence. This is the highest-priority item to address in the next two quarters.
Pattern 2: The Market Sizing Framework
Market sizing (TAM/SAM/SOM) is essential for strategic planning, fundraising, and go-to-market decisions. This pattern produces a structured estimate with transparent assumptions.
The Template
You are a strategy consultant helping with market sizing.
I need a market size estimate for [product/service] in [geographic market].
Context:
- What the product does: [describe the product]
- Target customer profile: [industry, company size, role of the buyer]
- Pricing model: [how you charge — subscription, per-seat, transaction fee, etc.]
- Current pricing: [your price point or planned price point]
Provide a top-down and bottom-up market sizing:
Top-down:
- Start with the broadest relevant market category
- Narrow it step by step to your addressable segment
- Show each filtering step with the logic behind it
Bottom-up:
- Start with the number of potential customers that match your target profile
- Apply realistic adoption and conversion rate assumptions
- Calculate potential revenue
For both approaches:
- State every assumption explicitly
- Use ranges rather than single-point estimates where uncertainty is high
- Note which assumptions would have the largest impact if they are wrong
After both estimates, provide:
- A reconciled estimate (what you think the realistic market size is, considering both approaches)
- 2-3 questions I should research to refine the estimate
Constraints:
- Do not fabricate market research data or cite specific reports you have not verified
- Where you are estimating based on reasoning rather than data, label it as an estimate
- Use conservative assumptions as the default — I can always adjust upward
- Tone: rigorous and transparentWhy It Works
The "do not fabricate market research data" instruction is essential. AI models commonly generate plausible-sounding market size figures that are entirely made up. This prompt forces transparent reasoning instead of false precision. The dual top-down and bottom-up approach also highlights discrepancies that reveal where your assumptions need work.
Example Output
For an AI-powered contract review tool targeting mid-market legal departments:
Top-down approach:
- Global legal tech market: estimated broadly at several billion dollars (I am not citing a specific figure since I cannot verify a source — recommend checking recent Gartner or IDC reports for the exact number)
- Contract management is a subset: roughly 15-20% of legal tech spend based on the breadth of categories in the space
- Your target segment (mid-market companies, 200-2000 employees, US-based): further reduces by geography and company size
- AI-powered contract review within that segment: a portion of contract management budgets that is growing as the category matures
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Assumption with highest impact: the share of legal tech budgets allocated to AI contract review specifically. This category is new enough that the allocation is uncertain.
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Bottom-up approach:
- Estimated US companies with 200-2000 employees that have in-house legal functions: approximately 25,000-40,000 (estimate based on SBA size category data and typical thresholds for in-house counsel)
- Realistic addressable segment after industry and readiness filters: 8,000-15,000 companies
- At your price point of $500/month per seat, 3 seats average: $18,000 annual contract value
- Serviceable obtainable market at 2-5% penetration in years 1-3: $2.9M - $13.5M ARR
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Questions to refine: (1) What percentage of mid-market companies have in-house legal versus using outside counsel exclusively? (2) What is the current adoption rate of AI tools in legal departments at this company size?
Pattern 3: The Strategic Options Evaluator
When facing a major business decision — enter a new market, launch a product, change pricing — you need to evaluate options systematically, not just go with your gut. This pattern structures the evaluation.
The Template
You are a strategy advisor helping evaluate a business decision.
The decision: [describe the decision you are facing]
Context:
- Current situation: [where you are now — relevant metrics, market position, resources]
- Options under consideration: [list 2-4 options you are weighing]
- Key constraints: [budget, timeline, team capacity, risk tolerance]
- What success looks like: [how you will measure whether the decision was right]
For each option, analyze:
1. Expected upside: What does this option look like if it goes well?
2. Key risks: What could go wrong, and how likely is each risk?
3. Resource requirements: What does this take in terms of money, people, and time?
4. Reversibility: How easy is it to reverse this decision if it does not work?
5. Second-order effects: What does this decision enable or prevent in the future?
After analyzing each option:
- Provide a recommendation with your reasoning
- Identify the key assumption that, if wrong, would change your recommendation
- Suggest a low-cost way to test that assumption before committing
Constraints:
- Present tradeoffs honestly — do not make one option look like the obvious winner unless it genuinely is
- Flag where you are reasoning from limited information
- Be specific about risks — "it might not work" is not useful, "customer acquisition cost could exceed LTV if conversion rates are below X%" is useful
- Tone: candid and rigorousWhy It Works
The "reversibility" criterion is often overlooked in business analysis but is critical for decision-making. Easily reversible decisions should be made quickly; hard-to-reverse decisions deserve more analysis. The "key assumption" identification gives you a concrete next step: test that assumption before committing.
Example Output
For deciding whether to launch a freemium tier for a paid SaaS product:
Option A: Launch freemium tier
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Upside: Significant increase in top-of-funnel volume. Free users become a marketing channel through word-of-mouth and organic sharing. Reduces reliance on paid acquisition. Potential to 3-5x your user base within 12 months.
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Key risks: Free users consume support resources without generating revenue. Existing paid users may downgrade if the free tier covers their core needs. Support costs could increase disproportionately. Estimated risk: medium-high if free tier feature boundaries are not clearly drawn.
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Resources: Engineering time to build usage limits and upgrade flows (estimate: 4-6 weeks). Ongoing infrastructure costs for free users. Likely need to add one support hire within six months.
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Reversibility: Low. Once you have free users, removing the free tier creates backlash and churn. You are committed once you launch.
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Second-order effects: Enables product-led growth motion. Changes your sales narrative from "try our demo" to "use the free version." But it also shifts competitive dynamics — competitors may match with their own free tier.
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Recommendation: Launch a usage-limited trial (14-30 days of full access) rather than a permanent free tier. This captures the top-of-funnel benefit with higher reversibility. If trial-to-paid conversion exceeds 8%, then consider a permanent free tier.
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Key assumption: That free users will convert to paid at a rate that covers the cost of supporting them. Test this by running a 90-day free trial pilot with 500 users and measuring conversion, support ticket volume, and infrastructure costs.
Pattern 4: The Decision Matrix Builder
When you need to compare multiple options across multiple criteria — vendor selection, hiring decisions, market entry priorities — a weighted decision matrix brings objectivity to the process.
The Template
You are a business analyst building a decision matrix.
Decision: [what you are deciding]
Options: [list the options being compared]
Evaluation criteria:
[List 4-7 criteria, and for each one specify:]
- Criterion name
- Why it matters
- Weight (how important it is relative to other criteria — use a 1-5 scale)
For each option, score it against each criterion on a 1-5 scale and provide a one-sentence justification for the score.
Present the results as:
1. A summary table showing options vs. criteria with scores and weighted totals
2. A narrative summary highlighting the top-scoring option and key tradeoffs
3. A sensitivity check: if you change the most subjective score by 1 point, does the winner change?
Constraints:
- Score based on the information I have provided — do not research the options independently
- If you do not have enough information to score a criterion, mark it as "insufficient data" rather than guessing
- Explain any close calls where two options score similarly
- Tone: objective and structuredWhy It Works
The sensitivity check is what elevates this from a basic scoring exercise to a useful decision tool. If changing one score by a single point flips the winner, you know the decision hinges on that specific judgment call — and you can focus your due diligence there.
Example Output
For selecting a CRM platform — comparing three options across five criteria:
| Criterion (Weight) | Option A | Option B | Option C |
|---|---|---|---|
| Ease of use (5) | 4 — clean interface, minimal training needed | 3 — powerful but steep learning curve | 4 — intuitive for basic tasks |
| Integration with existing tools (4) | 3 — supports main integrations but missing Slack connector | 5 — native integrations with our full stack | 3 — limited to standard integrations |
| Reporting capabilities (3) | 4 — strong built-in reports | 4 — excellent custom reporting | 2 — basic reporting, requires third-party add-on |
| Cost (4) | 5 — lowest per-seat cost at our team size | 2 — most expensive, enterprise pricing | 4 — mid-range, transparent pricing |
| Scalability (3) | 3 — may need to migrate above 50 seats | 5 — built for enterprise scale | 3 — unclear performance at scale |
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Weighted totals: Option A: 73, Option B: 71, Option C: 62
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Narrative: Option A leads narrowly, driven by cost and ease of use. Option B is close behind — its integration strength is a major asset. The gap is small enough that the decision likely comes down to how heavily you weigh integration completeness versus cost.
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Sensitivity check: If Option A's integration score increases from 3 to 4 (e.g., the Slack connector ships soon), Option A wins clearly. If Option B's cost score increases from 2 to 3 (e.g., you negotiate a discount), Option B overtakes. The decision is sensitive to both factors.
Pattern 5: The Stakeholder Brief
Analysis is only valuable if the right people understand it and act on it. This pattern translates complex findings into a brief that a busy executive can read in five minutes.
The Template
You are a management consultant preparing a stakeholder brief.
I will provide my analysis or findings below. Rewrite them as an executive brief for [audience — CEO, board, department heads, investors].
My analysis:
[paste your data, notes, findings, or detailed analysis]
Brief structure:
- Executive summary: 3-4 sentences covering the key finding, its implication, and the recommended action
- Context: 2-3 sentences of background — just enough that someone who missed the last meeting can follow
- Key findings: 3-5 bullet points, each with a one-line data point or insight and one-line implication
- Recommendation: A clear, specific recommended next step with timeline and ownership
- Risks: 2-3 risks of the recommended action and a mitigation for each
- Decision needed: One sentence stating exactly what you need from this audience
Constraints:
- Total length: under 500 words
- No jargon that the target audience would not immediately understand
- Lead with conclusions, not process — they care about what you found, not how you found it
- Every number should have context ("$50K" alone means nothing — "$50K, which is 15% of our Q2 budget" means something)
- Tone: concise, confident, decisiveWhy It Works
The "lead with conclusions, not process" instruction solves the most common problem with analyst-to-executive communication. Analysts naturally want to show their work; executives want to know the answer first and the reasoning second. The "decision needed" section ensures the brief drives action rather than just informing.
Example Output
For presenting a pricing analysis to the leadership team:
Executive Summary
Our analysis of 2,400 customer accounts shows that customers on annual plans have 40% lower churn than monthly subscribers, but annual plans represent only 22% of our revenue. Shifting 15% of monthly customers to annual through incentives could reduce revenue churn by $180K annually. We recommend launching a migration campaign in Q3.
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Context
This analysis was prompted by last month's board discussion about improving net revenue retention. We examined all active accounts from the past 18 months, segmented by plan type and tenure.
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Key Findings
- Annual plan customers churn at 4% versus 11% for monthly — the strongest single predictor of retention in our data
- The average annual customer generates $2,800 in lifetime revenue versus $1,600 for monthly
- Only 8% of customers switch from monthly to annual without being prompted — active migration is necessary
- Customers who switch to annual within their first 90 days are 3x more likely to renew than those who switch later
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Recommendation
Launch a targeted migration campaign in Q3 offering 2 months free for customers who switch to annual within their first 90 days. Owner: Head of Growth. Timeline: Campaign live by August 1.
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Risks
1. Short-term revenue dip from discounted annual plans — mitigated by the 40% churn reduction over 12 months
2. Existing annual customers may request the same discount — mitigate by limiting the offer to monthly customers only
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Decision needed: Approval to run the Q3 migration campaign with a 2-month-free annual incentive.
Quick Tips for Business Analysis Prompts
- Include real numbers. The more specific data you give the AI — revenue, growth rates, team size — the more specific and useful its analysis will be.
- Name your competitors. Vague references to "the market" produce vague analysis. Naming specific competitors gives the AI concrete points of comparison.
- Specify your audience. An analysis for your co-founder reads very differently from one for your board. Always say who will read it.
- Ask for assumptions. Add "list all assumptions you are making" to any analytical prompt. This reveals where the AI is guessing and where you need to provide more data.
- Iterate on the analysis. Use follow-up prompts like "that risk assessment is too generic — be more specific about what could go wrong with option B" to sharpen the output.
When to Use Templates vs. Freeform Prompts
Use these templates for recurring analytical tasks — quarterly reviews, board prep, vendor evaluations, or strategic planning cycles. The structure ensures consistency and makes it easy to compare analyses over time.
Go freeform when you are exploring a novel question — "what would happen if we acquired company X?" or "is there a market for this idea?" — where the analysis needs to follow the question rather than a predetermined format. For those, use the CRAFT framework from our prompt writing guide to set up the context and let the AI reason through it.
For instant prompt generation without building templates manually, SurePrompts' AI Prompt Generator can structure your business analysis requests automatically.