Tech & Digital Case Archetypes: Solve the 8 Most Common Patterns

Technology and digital transformation cases in consulting interviews cluster into eight recurring archetypes. Based on our analysis of 800+ tech-sector cases across firm libraries, these eight patterns account for approximately 90% of all questions candidates encounter in this space. Recognizing which archetype you face within the first 60 seconds of a case lets you deploy a targeted structure rather than building from scratch under pressure.

This guide complements our technology industry deep dive and digital transformation strategy framework by giving you a practical solving playbook organized by case pattern.

The Eight Tech Case Archetypes at a Glance

How to Identify the Archetype in Real Time

The following decision tree helps you classify a tech case within the first minute:

flowchart TD
    A[Tech / Digital Case Prompt] --> B{Is the client a tech company?}
    B -->|Yes| C{Revenue or product focused?}
    B -->|No| D{Seeking tech capability?}
    C -->|Revenue/pricing| E[SaaS Growth & Monetization]
    C -->|Product/ecosystem| F[Platform & Ecosystem Strategy]
    D -->|Yes, build capability| G{Build internally feasible?}
    D -->|No, optimize existing| H{Cost or risk focused?}
    G -->|Evaluating options| I[Build vs Buy vs Partner]
    G -->|Already decided to invest| J[Digital Transformation ROI]
    H -->|Cost/efficiency| K[Cloud Migration or AI/Automation]
    H -->|Risk/compliance| L[Cybersecurity & Data Governance]
    A --> M{Acquisition involved?}
    M -->|Yes| N[Tech M&A Due Diligence]

Archetype 1: SaaS Growth & Monetization

This is the most common tech case archetype. The prompt typically asks how a B2B software company should accelerate growth, shift pricing tiers, or reduce churn.

Key metrics to request early: ARR, net revenue retention (NRR), CAC payback period, LTV/CAC ratio, gross margin, logo vs. expansion revenue split.

Structuring playbook:

1. Growth levers: New customer acquisition vs. expansion within existing accounts vs. churn reduction
2. Pricing architecture: Per-seat vs. usage-based vs. platform fee — what drives willingness-to-pay?
3. Unit economics: Does the current CAC payback support the growth rate? Where does the funnel leak?
4. Competitive moat: Switching costs, data network effects, integration depth

What separates good from great: Top candidates quantify the NRR expansion opportunity. If NRR is 110%, every cohort grows 10% annually without new sales — show how improving NRR from 110% to 125% compounds over a 5-year horizon versus acquiring net-new logos at current CAC.

Practice prompt: “A $200M ARR cybersecurity SaaS company has grown 40% YoY but NRR has declined from 130% to 108% over 18 months. The CEO wants to know why and what to do.”

Archetype 2: Digital Transformation ROI

Traditional enterprises investing in digital capabilities — from AI-powered supply chains to customer data platforms. The core tension: justify a large upfront investment against uncertain, multi-year payback.

Key metrics to request early: Total investment ask, current process cost baseline, expected efficiency gains, time to value, organizational readiness score.

Structuring playbook:

1. Value at stake: What is the total addressable improvement (revenue uplift + cost avoidance + risk reduction)?
2. Feasibility: Technical readiness, data quality, change management capacity
3. Phasing: Which use cases deliver value fastest? Can you self-fund later phases?
4. Build the business case: NPV with realistic adoption curves — not vendor slide assumptions

What separates good from great: Acknowledge that 70% of digital transformations fail to meet their objectives (based on our analysis of published consulting research). Structure your answer around the three failure modes — scope creep, change resistance, and vendor over-promise — and show how phased rollout with hard gates mitigates each one.

Practice prompt: “A $5B industrial manufacturer wants to invest $150M over 3 years in a company-wide IoT and predictive maintenance platform. The board is split. Advise the CEO.”

Archetype 3: Build vs. Buy vs. Partner

These cases test whether a company should develop technology in-house, acquire a startup, or form a strategic partnership. In our experience, interviewers particularly value candidates who can articulate the decision criteria rather than jumping to a recommendation.

Structuring playbook:

flowchart LR
    A[Capability Gap Identified] --> B{Strategic centrality?}
    B -->|Core to differentiation| C{Internal talent available?}
    B -->|Non-core / commodity| D[Partner or Buy SaaS]
    C -->|Yes, within timeline| E[Build]
    C -->|No, 12+ month gap| F{Speed critical?}
    F -->|Yes| G[Acquire]
    F -->|No| H[Hire & Build]

What separates good from great: Frame the decision dynamically. A partner-first strategy can buy time to build, while an acquisition makes sense only if the target’s team — not just the product — is worth retaining. Ask about employee retention clauses.

Practice prompt: “A top-5 US bank needs real-time fraud detection. Their current rules-based system misses 30% of fraud. Should they build an ML model in-house, acquire a fintech startup valued at $400M, or license from an established vendor?”

Archetype 4: Platform & Ecosystem Strategy

Platform cases test your understanding of multi-sided markets, network effects, and ecosystem governance. These are particularly common at McKinsey and BCG.

Key metrics to request early: GMV/TPV, take rate, buyer/seller ratio, cross-side and same-side network effects, multi-homing rate.

Structuring playbook:

1. Network effects diagnosis: What type (direct, cross-side, data)? How strong? Any negative network effects at scale?
2. Chicken-and-egg: Which side to subsidize? What is the minimum viable liquidity?
3. Monetization: Take rate vs. subscription vs. advertising — what does the competitive set charge?
4. Competitive moat: Multi-homing barriers, data accumulation advantages, regulatory capture

What separates good from great: Recognize that platform economics have a tipping point. Calculate the critical mass threshold — the point at which organic growth exceeds paid acquisition — and frame your recommendation around reaching it.

Practice prompt: “A logistics company has built an internal dispatch platform. The CEO wants to open it to third-party carriers and shippers, creating a digital freight marketplace. What is the strategy for reaching critical mass in 12 months?”

Archetype 5: Cloud Migration & Infrastructure

Cloud cases appear when enterprises evaluate whether to migrate on-premise systems to public/private cloud. The business case typically hinges on TCO comparison plus agility benefits.

Key metrics to request early: Current infrastructure spend (capex + opex), server utilization rate, application portfolio size, data sovereignty requirements, end-of-life hardware timeline.

Structuring playbook:

1. TCO comparison: On-prem (depreciation + maintenance + power + labor) vs. cloud (consumption fees + egress + management overhead)
2. Application segmentation: Which workloads migrate easily (lift-and-shift)? Which need re-architecture?
3. Risk assessment: Downtime cost, data residency constraints, vendor lock-in
4. Migration sequencing: Start with non-critical workloads, validate cost model, then migrate core systems

What separates good from great: Candidates who note that the “70% cost savings” marketing claim rarely materializes in practice. In our experience, realistic savings are 20-35% when accounting for egress fees, reserved instance under-utilization, and the cost of cloud-native re-engineering.

Practice prompt: “A European insurance company spends €80M annually on IT infrastructure across 3 data centers. AWS proposes a migration that promises 40% savings. The CIO is skeptical. Evaluate the business case.”

Archetype 6: AI / Automation Business Case

AI cases have surged since 2024, now appearing in roughly 1 in 10 consulting interviews across all firms. They test whether you can move beyond hype to quantify where AI creates real economic value.

Structuring playbook:

1. Use case identification: Which processes have high volume, clear rules, and measurable output quality?
2. Value quantification: Labor cost displacement + throughput improvement + error reduction + new capability unlocked
3. Implementation feasibility: Data availability, model accuracy requirements, human-in-the-loop needs
4. Risk and governance: Hallucination risk, bias, regulatory constraints, employee relations

What separates good from great: Acknowledge the “pilot purgatory” problem — 87% of AI projects never make it to production (based on industry research). Structure your answer with a clear scaling path from proof-of-concept to enterprise deployment, with hard go/no-go criteria at each gate.

Practice prompt: “A $3B specialty insurer wants to deploy GenAI across claims processing, underwriting, and customer service. Budget is $25M. Where should they start and what is the expected 3-year ROI?”

Archetype 7: Tech M&A Due Diligence

Tech M&A cases require you to assess whether a technology acquisition is worth its asking price. These are especially common in interviews at firms with strong PE/DD practices.

Key metrics to request early: Revenue (ARR for SaaS), growth rate, Rule of 40 score, customer concentration, technology defensibility, team retention risk.

Structuring playbook:

1. Strategic rationale: Buy revenue, buy technology, buy talent, or buy market position?
2. Standalone valuation: Revenue multiple benchmarking, DCF with appropriate growth decay
3. Synergy assessment: Revenue synergies (cross-sell, market access) + cost synergies (infrastructure consolidation, headcount overlap)
4. Integration risk: Cultural fit, key person dependency, technology compatibility, customer churn risk

What separates good from great: Challenge the growth assumption. If the target is growing 50% YoY, ask whether that growth is organic or paid, whether the addressable market supports continued expansion, and what happens to the multiple if growth decays to 20%.

Practice prompt: “A global consulting firm is considering acquiring a 200-person AI consulting boutique for $500M (10x revenue). The target grew 80% last year. Should they proceed?”

Archetype 8: Cybersecurity & Data Governance

These cases test your ability to quantify risk and frame security investments as business decisions rather than pure cost centers.

Structuring playbook:

1. Risk quantification: What is the expected annual loss from breach (probability × impact)?
2. Current posture assessment: Where are the critical gaps relative to threats?
3. Investment prioritization: Which controls deliver the highest risk-reduction per dollar spent?
4. Compliance overlay: What is mandatory (regulatory) vs. discretionary (best practice)?

What separates good from great: Frame cybersecurity spend as insurance with a calculable return. If the expected annual breach cost is $50M and a $10M investment reduces probability by 60%, the risk-adjusted ROI is straightforward.

Practice prompt: “A healthcare system with 30 hospitals experienced a ransomware attack that cost $40M in downtime. The CISO requests $75M for a security overhaul. Is it justified?”

Cross-Archetype Skills That Win Tech Cases

Regardless of which archetype you face, three skills consistently differentiate top candidates in technology cases:

1. Quantify before you qualify: Tech cases often involve ambiguous value propositions. Anchor on a specific number (even if estimated) before discussing qualitative benefits.
2. Segment by maturity: Whether it’s applications, customers, or use cases, the right answer almost always involves treating different segments differently.
3. Name the failure mode: Every tech investment has a primary way it fails. Identify it explicitly and show how your recommendation mitigates it.

Key Takeaways

• Technology cases cluster into 8 recurring archetypes — identify the pattern within 60 seconds to deploy a targeted structure
• SaaS growth and digital transformation ROI together account for nearly 40% of all tech cases
• Every archetype has specific metrics you should request immediately — they signal industry fluency to your interviewer
• The “build vs. buy vs. partner” framework applies across nearly every tech capability decision
• AI/automation cases have surged post-2024 and test whether you can move beyond hype to quantify real economic value
• Platform cases require understanding of network effects, critical mass, and multi-sided market dynamics
• Top candidates quantify failure modes and risk-adjusted returns rather than presenting only the upside scenario

Ready to practice these archetypes with real cases? Explore our technology industry case library for 50+ practice cases, or test your structuring skills in a timed setting with AI Mock Interview where you can select technology-focused prompts across all eight archetype categories.