Choose
Define the provider type that fits the business problem.
Practical buyer guides
Resources for understanding budgets, evidence, risks and the questions worth asking before choosing an AI service provider.
Practical resources
Choose, compare and prepare an engagement
Compare
Use one framework to assess several proposals consistently.
Budget
Account for setup, tools, maintenance and internal effort.
Request evidence
Relate each piece of evidence to the use case it is meant to support.
Prepare the engagement
Clarify scope, owners, data and success criteria.
Provider type
Read the guide: AI agency, consultant or integratorAI agency, consultant or integrator
Choose the right role based on the decision, delivery work and level of integration required.
Prepare the brief
Read the guide: AI project briefAI project brief
Structure the need, data, constraints, deliverables and success criteria before contacting providers.
Engagement format
Read the guide: AI audit, POC, MVP or productionAI audit, POC, MVP or production
Choose the right engagement format and define the decision expected at the end of each stage.
Buyer guide
Read the guide: How to choose an AI service provider: 10 criteria before signingHow to choose an AI service provider: 10 criteria before signing
Compare business need, scope, evidence, data, integrations, security, budget, acceptance criteria, maintenance, exit planning and handover using one consistent framework.
Evidence
Read the guide: What evidence to request from an AI service providerWhat evidence to request from an AI service provider
Case studies, demos, deliverables, references and private evidence: distinguish a marketing claim from evidence that is useful for a buying decision.
Budget
Read the guide: AI project budgets for SMBs: pricing, ranges and recurring costsAI project budgets for SMBs: pricing, ranges and recurring costs
Break down scoping, data, POCs, integrations, deployment, training, tools, maintenance and internal time, with public AIPartnerLens amounts where available.
AI agency
Read the guide: How to choose an AI agencyHow to choose an AI agency
Assess scoping, evidence, data, proposals, production delivery, maintenance and reversibility.
Budget
Read the guide: AI agency pricingAI agency pricing
Separate discovery and scoping, POC work, development, integrations, tools, maintenance and internal effort.
Use cases
Move from an AI topic to a problem you can actually compare
These decision guides connect the business need to data, risks and provider type before you request several proposals.
AI automation
Start with a stable business process, not with a tool. A useful automation brief makes the trigger, inputs, rules, exceptions, human approvals and expected output visible before an agency proposes Make, n8n, APIs or an AI agent.
View the use caseCustomer service
Customer-service AI can answer recurring questions, assist agents, classify inbound requests or automate bounded actions. The useful starting point depends on knowledge quality, channel mix, helpdesk integration and the cost of a wrong answer.
View the use caseHuman resources
HR teams can use AI for administrative support, document drafting, internal knowledge and employee-service workflows. Higher-risk decisions involving hiring, performance or employment status need much stronger legal, fairness and human-review controls.
View the use caseManufacturing
Manufacturing AI only creates value when it fits real plant constraints: machine data, ERP or MES integration, operator workflows, latency, safety and maintenance. Start from a measurable bottleneck rather than a generic AI roadmap.
View the use caseSales productivity
An AI sales assistant can help research accounts, summarize calls, draft responses, retrieve product knowledge or prepare CRM updates. The best first use case is usually one where the seller remains accountable for the final decision or message.
View the use caseFinance operations
Invoice automation can extract fields, match invoices against purchase orders or contracts, detect discrepancies and route exceptions. The value comes from controlled exception handling and ERP integration, not from OCR accuracy alone.
View the use caseOrder processing
Order-entry automation can extract customer, product, quantity and delivery information from PDFs, email attachments or scans, then prepare an ERP record. The difficult part is resolving ambiguous references and exceptions without silently creating wrong orders.
View the use caseRFPs & proposals
A strong proposal draws on information scattered across the RFP, pricing rules, previous bids, technical documents, delivery constraints, supplier inputs and approved contract language. AI is useful when it reduces research and drafting time. It should not invent an offer, calculate a price without controlled rules or make commitments on the company’s behalf.
View the use caseProcurement operations
Supplier onboarding combines document collection, data entry, policy checks, approvals and ERP or procurement-system updates. AI can reduce repetitive work, but qualification rules and accountability must remain explicit.
View the use caseEnterprise knowledge
RAG connects a language model to controlled company knowledge so users can ask questions with source context. The real work is not the chatbot interface: it is source quality, permissions, retrieval evaluation, freshness and operational ownership.
View the use caseSupply chain
Inventory optimization combines demand patterns, lead times, service levels, constraints and business rules. A useful project should beat a clear baseline and help planners understand exceptions rather than replacing every decision with an opaque forecast.
View the use caseProduction planning
Production planning is a constraint problem before it is an AI problem. Machines, labor, changeovers, materials, priorities and maintenance windows all shape a useful schedule. The provider should make those constraints explicit and prove improvement against the current planning process.
View the use caseQuality control
Computer vision can support visual inspection when defects are observable and imaging conditions can be controlled. A credible pilot must include rare defects, normal variation and the real cost of false rejects and missed defects.
View the use caseMaintenance
Predictive maintenance is useful only when a signal gives technicians enough time and context to act. The project needs reliable equipment history, maintenance records and a definition of which failure modes are worth predicting.
View the use caseProduct content & e-commerce
The useful project is not asking a model to make up a product description. It is bringing together approved technical data, images, compatibility information and source documents, then generating a structured product record or product detail page that a catalog owner reviews before it reaches the PIM, CMS or e-commerce platform.
View the use caseEmail operations
The use case is easy to understand and easy to oversimplify: identify what an email is about, extract the useful fields, prioritize it and send it to the right team or system. AI adds value when messages are too varied for deterministic rules alone, but uncertain or sensitive cases still need a clear fallback.
View the use caseLegal & document operations
AI can accelerate clause extraction, compare agreements with an internal playbook and search a controlled contract corpus. The value comes from a better review workflow, not from pretending that a model can own the legal decision.
View the use caseSales operations
AI can research a company, enrich CRM records, detect signals and propose a priority. The project is only valuable if those outputs improve speed or conversion in the sales process rather than producing a score that nobody trusts.
View the use caseFinance operations
Most reconciliation logic should begin with identifiers, amounts, dates and explicit tolerances. AI becomes useful when documents or remittance text are inconsistent, but it should not hide the exceptions that still require accounting judgment.
View the use caseAccounts receivable
A useful reminder depends on the amount due, days overdue, payment history, open disputes and the commercial relationship. Automation can prepare and orchestrate follow-up, but sensitive accounts should be identified before anything is sent.
View the use caseMeetings & productivity
Transcribing a meeting is easy; producing a useful record is harder. The workflow needs to separate decisions, action items, owners, deadlines and uncertainty, then let participants correct what matters before anything is published or turned into a task.
View the use caseData & management reporting
Useful automated reporting starts with trusted data and stable metric definitions. AI can summarize movement, highlight anomalies and prepare questions, but it should not manufacture a causal explanation that the data does not support.
View the use caseSupport operations
Before building an autonomous chatbot, many support teams can create value by classifying requests better, detecting urgency, retrieving the right context and routing the ticket to the right queue.
View the use caseB2B AI provider comparison
Understand how to compare AI service providers by use case, budget, evidence, risks, tools and fit.
Read the comparison pageB2B AI service providers
Identify suitable profiles for automation, customer support, training, internal documents or AI strategy projects.
Compare profilesPreparation
Prepare the first discussion with a provider
A clear need, an initial scope and a few expected pieces of evidence make proposals easier to compare consistently.
Checklist before contact
Write down the business problem, users involved, existing tools, indicative budget and the evidence you want to review.
Guides complement the comparison. They do not replace legal, security or technical scoping when a project requires it.