MAKINAI — Category
AI Strategy & Transformation
How to define AI provider governance and performance management before hiring
Set metrics, forums, decision rights, escalation, and continuous improvement before signing with an AI consulting or services firm.
Read article ↗ENHow to evaluate an AI consulting ROI business case before hiring
Test the baseline, attribution, adoption, lifecycle cost, scenarios, and measurement plan before accepting an AI provider's ROI case.
Read article ↗ENBoutique AI firm, global consultancy, or systems integrator: how to choose
Compare a specialist boutique, global consultancy, systems integrator, and hybrid model across depth, scale, integration, governance, and coordination cost.
Read article ↗ENBuy, configure, or build a custom AI solution: how to decide
Compare packaged AI, configurable platforms, custom development, and hybrid architecture across differentiation, data, risk, speed, and total cost.
Read article ↗ENHow to define scope and change control before hiring an AI services provider
Use eight gates, four change classes, and a comparable impact card to adapt AI delivery without turning every discovery into open-ended scope, cost, or schedule.
Read article ↗ENHow to assess the financial stability and continuity of an AI services provider
Use eight dimensions, proportionate evidence, and disruption tests to determine whether an AI provider can deliver, absorb change, and transfer the service safely.
Read article ↗ENHow to run a competitive selection process for an AI consulting firm
Use seven gates to move from a broad provider market to an evidence-based, comparable decision without disguising speculative work as competition.
Read article ↗ENWhat internal team do you need to hire and govern an AI services company?
Build a small, accountable client-side team for outcomes, data, technology, risk, operations, and the contract—without outsourcing decisions that belong to the enterprise.
Read article ↗ENAI services contracts: how to negotiate liability, warranties, and indemnities
Allocate AI risk by control, evidence, and remedy—without demanding blanket unlimited liability or leaving critical failures uncovered.
Read article ↗ENShould you pay for an AI discovery phase before implementation?
Use eight evidence gates and a 32-point scorecard to decide whether paid AI discovery reduces risk or merely advances an implementation sale.
Read article ↗ENHow to evaluate whether an AI project timeline is realistic before hiring
Test critical path, capacity, data, dependencies and contingency with a 32-point scorecard before accepting an AI services firm's schedule.
Read article ↗ENOne end-to-end AI partner or multiple specialists: how to choose
Compare a prime AI partner, multiple specialists and a hybrid model across integration, governance, risk, capability, cost and exit.
Read article ↗ENHow to define AI project acceptance criteria and payment milestones
Turn AI promises into eight verifiable business, quality, safety, operations and transfer gates before releasing milestone payments.
Read article ↗ENHow to verify AI consulting case studies and client references before hiring
Turn case studies and testimonials into a verifiable chain of scope, baseline, team, delivery, outcome, failures and independent client evidence.
Read article ↗ENHow to conduct security due diligence before hiring an AI services company
Evaluate data use, access, models, subcontractors, development, testing, incidents and exit before granting real information or system access.
Read article ↗ENWho owns the code, data, and prompts in an AI services engagement?
Negotiate ownership, licenses, access and reuse for each AI asset—not one generic IP clause for the entire engagement.
Read article ↗ENFixed price, T&M, or outcome-based pricing for AI projects?
Choose an AI project's commercial model by uncertainty, measurability and control of risk—not by the promise of a simpler budget.
Read article ↗ENHow to choose a company to build an enterprise AI platform
Evaluate demand, models, data, evaluation, developer experience, security, observability, unit economics and portability before funding an enterprise AI platform.
Read article ↗ENHow to evaluate an AI consulting team before hiring
Assess the people who will actually deliver—roles, allocation, evidence, subcontractors, continuity and knowledge transfer—before signing.
Read article ↗ENHow to assess AI vendor lock-in before hiring a partner
Evaluate portability, dependencies, rights, economics and exit readiness before hiring an AI services partner—and test transition before a larger commitment.
Read article ↗ENHow to choose an AI evaluation and testing company
Select an AI evaluation firm by its ability to reproduce failures, test the complete system and connect technical findings to launch and operating decisions.
Read article ↗ENHow to evaluate AI consulting proposals: a 100-point scorecard
Compare AI consulting proposals on delivery evidence, operating risk and total economics—not on presentation quality, brand recognition or headline price.
Read article ↗ENHow to choose an AI adoption consulting firm
Choose an AI adoption partner through seven proofs: priority, work design, trust, capability, management, measurement and transfer.
Read article ↗ENHow to run a paid AI pilot before hiring an implementation partner
An evidence gate for testing value, reliability, operations and transfer before expanding an AI partner engagement.
Read article ↗ENWhat to include in an AI services contract and SOW
A seven-evidence framework for turning AI vendor promises into acceptance, operating and exit conditions.
Read article ↗ENAI strategy firm or implementation partner: how to choose
Choose a strategy consultancy, implementer or integrated AI partner using six signals: mandate, portfolio, evidence, system, governance and transfer.
Read article ↗ENHow to choose a data and analytics company for AI
Choose an AI data partner through six proofs: decision, source, contract, quality, control and operations—not a generic architecture diagram.
Read article ↗ENHow to choose an AI governance consulting firm
Choose an AI governance consultancy by its ability to turn principles into inventory, decisions, controls, evidence and operations—not by the number of policies delivered.
Read article ↗ENHow much do AI consulting services cost? A buyer’s framework
AI consulting cost depends less on the chosen model than on data, integrations, evaluation, risk, operations and transfer. Compare proposals using total cost per accepted outcome.
Read article ↗ENIn-house AI team or AI consulting firm? How to decide
The choice between an in-house AI team and an AI consulting firm depends on required strategic control and the capability-and-speed gap. For many companies, co-building with transfer is the strongest starting model.
Read article ↗ENHow to write an RFP for AI services: requirements and scorecard
A strong AI-services RFP compares evidence, risk and operational readiness—not technology labels, polished demos or vendor promises.
Read article ↗ENA practical framework for prioritizing AI use cases
Score AI initiatives across value, speed to return, technical feasibility, risk and strategic differentiation, then convert the result into explicit investment, pilot and stop decisions.
Read article ↗ENHow to choose an AI implementation company in Brazil: a 30-point scorecard
A practical scorecard for comparing AI consultancies, product builders and integrators in Brazil before committing budget and risk.
Read article ↗ENFrom AI Prototype to Product: A Practical Roadmap for Scaling and Monetization
A practical roadmap for assessing AI prototype readiness, building a reliable production product, selecting a revenue model, and deciding what to build in-house or source from partners.
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