• Acceptance tests for non-deterministic AI outputPractical guide to setting up acceptance tests and quality control for non-deterministic output from language models in production.
• AI adoption in teams: change management that worksPractical step-by-step plan for successful AI adoption in your organization. Discover how to overcome resistance, achieve quick wins, and make adoption…
• Drafting an AI Policy for Your OrganizationDiscover why your organization needs an AI policy. Learn which components are crucial, view a sample table of contents, and get started right away.
• What to look for in an AI contract: SLA, Data, and LiabilityDiscover the crucial legal and technical points of attention in AI contracts: from data usage and model deprecation to SLAs for non-deterministic systems.
• AI Governance for SMEs: Using AI Responsibly WithoutDiscover how SMEs set up practical AI governance. No heavy legal bureaucracy, but a lightweight framework for secure AI integration.
• AI governance roles: who is responsible for what?Discover how to structure AI governance roles. From AI Solution Owner to CISO: a clear RACI matrix and division of tasks for responsible AI.
• Mapping the impact of AI on roles and tasksA practical method for analyzing the impact of AI on work by focusing on tasks rather than entire roles.
• Creating an AI Implementation Plan for SMEsRead how your SME can create a realistic, phased AI implementation plan. From defining goals to scaling and risk management.
• Procuring AI through a tender: requirements to set in advanceWhich requirements you, as a public or semi-public contracting authority, formulate in advance when procuring AI: risk class, data, management, and exit.
• Connecting AI assistants to ERP and CRM systemsArchitecture patterns, security, and semantic layers for the secure integration of AI assistants with enterprise ERP and CRM databases.
• AI Integration for SMEs: From Idea to ProductionPractical AI integration for SMEs. View our phased approach: from discovery and Proof of Concept to secure production and management.
• AI and intellectual property: rights to outputHow organizations arrange ownership, copyright, and licenses around AI output. Practical insights for B2B AI integration and risk management.
• Identifying AI opportunities with a workplace process auditLearn how a workplace process audit helps you identify promising AI use cases, analyze bottlenecks, and validate data requirements in advance.
• Choosing an AI Vendor: What to Look For?Discover what to look for when choosing a B2B AI vendor. Essential criteria such as GDPR compliance, reliability, roadmap, and exit strategy.
• Due diligence on AI vendors: a practical guideHow do you run thorough due diligence on an AI vendor? Learn to assess the financial, legal, technical, and operational risks.
• An AI Pilot in 30 Days: From Idea to Measurable ResultDiscover how organizations realize a targeted AI pilot within 30 days. Learn our week-by-week approach, scoping, and hard success criteria for a watertight…
• What to do when an AI project stalls: lessonsA practical recovery approach for stalled AI projects. Four-layer diagnosis, factual evaluation, and a triage checklist for project leaders.
• From Vague AI Request to Defined ScopeLearn how to turn a vague AI idea into a tightly scoped project. Discover our AI scoping canvas, acceptance criteria, and best practices for B2B integrations.
• AI project planning: realistic timelinesWhy do AI projects take longer than planned? Discover how to create a realistic schedule with time ranges and correctly estimate critical phases.
• Conducting an AI Risk Assessment and DPIALearn how to conduct an effective AI risk assessment and DPIA. Map out privacy, bias, errors, and vendor risks with this practical step-by-step plan.
• Calculating the ROI of AI: A Practical FrameworkDiscover a practical framework to accurately calculate the ROI of AI. Learn which direct and indirect benefits and costs to include in your business case.
• AI Security for Businesses: The Risks of LLM IntegrationsDiscover the key security risks of LLM integrations, such as data leaks and prompt injection, and protect your business data with our checklist.
• Setting up AI training for employeesDiscover how to set up an effective AI training program for employees. Includes learning objectives per role, practical assignments, and a sample curriculum.
• How to Effectively Prioritize AI Use Cases in YourAvoid wishlist inflation. Discover how to prioritize AI use cases based on value, feasibility, data readiness, and risk using a concrete scoring matrix.
• AI skills in job profiles and performance reviewsA practical guide to embedding AI competencies in job profiles, performance review cycles, and HR processes within organizations.
• AI Maturity Scan for OrganizationsPerform an AI maturity scan for your organization. Assess data, systems, people, processes, and governance across five levels and avoid pitfalls.
• Management after go-live: who owns an AI applicationDiscover how organizations organize ownership, costs, and quality monitoring for AI applications after go-live in production.
• Build or Buy: Develop Your Own AI Solution or Deploy anA strategic consideration for B2B organizations: compare costs, control, time-to-value, and vendor lock-in when choosing between building or buying AI.
• Setting up a realistic AI budget for businessesLearn how to set up a realistic AI budget for your business. Discover forgotten cost items, compare API versus hosting, and view useful calculation models.
• Change management for AI: from pilot to way of workingHow do you sustainably embed AI applications in teams? A practical guide to change management for the transition from a successful pilot to a fixed way of working.
• AI Chatbot for Customer ServiceDiscover the strategic approach, pitfalls, and ROI indicators for implementing an AI chatbot in your customer service. Read our B2B guide.
• Assessing data retention policies at AI vendors | LLMnetGuide to assessing data retention at AI vendors. Discover the crucial questions about retention periods, logging, and contracts.
• Data Quality for AI: Prevent Your AI Pilot from Stalling onDiscover how to quickly assess and pragmatically improve data quality for your AI project. Prevent failed AI pilots with our practical audit approach and…
• Phasing out an AI application: here's howLearn how to properly phase out an AI application. From data retention and model deprecation to contracts and dependencies within your IT landscape.
• Incident protocol for language model errorsPractical guide to setting up an incident protocol for errors, hallucinations, data leaks, and drift caused by language models in production.
• Building and steering internal AI ambassadorsBuild an effective network of internal AI ambassadors. Learn how to select and train champions, steer them, and achieve measurable adoption.
• Evaluating a PoC: criteria for go/no-goDiscover the concrete criteria for an AI PoC go/no-go decision. Analyze accuracy, cost, adoption, latency, and governance before production rollout.
• Subsidies and funding for AI projectsOverview of innovation grants and financing options for AI initiatives in SMEs, including WBSO, the MIT scheme, tax frameworks, and calculation methods.
• Human-in-the-loop for AI: architecture and setupA practical guide to human-in-the-loop for AI. Discover routing logic, escalation triggers, review interfaces, and reliable quality control.
• Hiring AI Expertise: In-House Team or External Agency?Discover the costs, hybrid models, and decision criteria for SMEs when choosing between an in-house AI team or external AI consultancy.
• Setting up an internal AI hub that lasts | llmnet.nlHow do you set up an internal AI hub that prevents sprawl, develops reusable building blocks, and ultimately makes itself redundant?
• Internal communication when introducing AIRead how to communicate effectively about introducing AI in your organization, with a focus on uncertainty, facts, sequencing, and openness.
• Building a cost-benefit analysis for an AI project | consultancy.llmnet.nlGuide for SME decision-makers on building a cost-benefit analysis (CBA) for AI projects. Method for alternatives, cost structure and benefits.
• Costs and benefits of AI: substantiating the business caseDiscover how to realistically substantiate the business case for an AI project with concrete cost items, measurable benefits, and hard risks.
• Aligning LLM deployment with strategic multi-year goalsAlign LLM deployment with strategic multi-year goals using a clear framework for portfolio management, agile architecture, capabilities, and risk control.
• Monitoring LLM performance and latency in productionA practical guide to monitoring LLM latency, tail latency, token usage, error margins, and semantic drift in business-critical production environments.
• IT infrastructure for local LLMs: on-premise requirementsHow do you prepare your IT infrastructure for local LLMs? Read all about capacity, network segmentation, VRAM limits, and management overhead.
• Managing unexpected operational costs of LLMsPractical methods for structurally managing unexpected operational costs, token leakage, and hidden expenses of language models in production.
• From pilot to production: why AI projects fail and how toDiscover why many enterprise AI pilots fail in practice. Learn how to prevent this by focusing on change management, data quality, and ROI.
• Regression Testing for Model Updates | LLMnetLearn how to set up robust regression tests for AI systems to reliably catch quality drops and feature loss from weekly model updates.
• The ROI of AI: three worked examplesSee three fully worked examples of AI ROI in customer service, document processing, and reporting, including licenses and maintenance.
• Scenario analysis for market shifts driven by AIA methodical framework for scenario analysis of market shifts driven by language models. Anticipate structural changes in your sector.
• Tackling Shadow AI: From Invisible Risk to StrategicEmployees are using AI tools outside of company policy. Discover why banning them doesn't work and how to turn Shadow AI into safe, strategic innovation.
• Getting stakeholders on board with an AI projectRead how to build support for AI projects within SMEs. Explore stakeholder analyses, specific communication strategies and pitfalls.
• Predicting token consumption and API spend per use caseCalculate and forecast token consumption and LLM API costs per use case with realistic formulas, caching scenarios, and architecture choices.
• EU AI Act Category Checker | consultancy.llmnet.nlGet an indicative assessment of which risk category your AI application falls under the EU AI Act. Includes role determination (provider vs. deployer) and an overview of obligations.
• On-Premise vs Cloud LLM TCO CalculatorCompare the total cost of ownership of cloud LLM API usage versus your own hardware over three years, including a client-side calculator and checklist.
• From four roadmaps to one: which approach fits which organizationDiscover which AI roadmap best fits your organization. Compare pilots, integration paths, implementation plans, and scaling methods.
• Evaluating internal data storage for RAG architecturesCompare dedicated vector databases with relational extensions for RAG: seven criteria for storage choice, cost, and management overhead in enterprise architectures.
• Preventing Vendor Lock-In When Switching Between LLMsDiscover how organizations prevent vendor lock-in with LLM providers through API abstraction, prompt portability, embedding migrations, and strong contracts.
• Vendor Contracts for AI: What to CheckReviewing vendor contracts for AI: discover what to watch for regarding data rights, training, SLA guarantees, liability, model drift, and exit clauses.
• Change Management in AI: Dealing with Resistance andDiscover how effective change management helps with AI implementation. Learn to overcome resistance, leverage early adopters, and make adoption measurable.
• Accounting for AI Investments to ShareholdersHow do you account for AI investments to shareholders and regulators? A practical framework for KPIs, risks, and financial reporting.
• When to stop an AI initiative: criteriaDiscover when and how to stop an AI initiative in time, with clear technical, financial, and organizational criteria for project leaders.