AI Operations Consulting -
Done-For-You AI for Operations

AI operations consulting (AIOps consulting) helps enterprises embed AI into core operational processes — supply chain, manufacturing, logistics, finance ops, customer ops — to reduce cost, cycle time and error rates. Unlike strategy consulting, we deliver working systems: process redesign, AI model integration into existing workflows, change management, and ongoing performance monitoring. Alice Labs has shipped 100+ AI operations engagements with measurable ROI inside 12 months.

Done-for-you means we own the full delivery: process discovery, use-case prioritisation, technical architecture, model selection, integration with your ERP/MES/WMS/CRM, testing, deployment, training and 90-day post-launch support. Your team focuses on the business; we deliver the AI. Typical scope is 8–14 weeks per use case with fixed-price commitments.

Traditional process optimisation maps and improves processes manually. AI operations consulting uses AI to do the work — automating decisions, classifications, predictions and routing inside the process itself. We combine both disciplines: lean process redesign first, then AI automation on the highest-leverage steps. This typically produces 30–60% cycle time reduction vs. 5–15% from process work alone.

The fastest-payback AI operations use cases are: (1) document automation in finance/procurement (3–6 month payback), (2) demand forecasting in supply chain (4–8 months), (3) predictive maintenance in manufacturing (6–12 months), (4) intelligent routing in customer ops (3–6 months), and (5) quality inspection in production (6–9 months). We start with a use case scoring matrix to find your highest-ROI starting point.

Every AI system we ship includes release management discipline: versioned model registry, automated CI/CD pipelines, drift monitoring, A/B testing infrastructure, rollback mechanisms, and on-call runbooks. We follow MLOps best practices and EU AI Act requirements for high-risk systems. Post-launch, we offer managed AI operations from €10,000/month covering monitoring, retraining, incident response and continuous optimisation.

Yes. We have shipped AI integrations with SAP S/4HANA, Oracle NetSuite, Microsoft Dynamics 365, Infor, IFS and dozens of custom ERPs. We use REST/SOAP APIs, event streaming (Kafka, Azure Event Hubs), RPA bridges where APIs are missing, and direct database connectors when sanctioned. Integration design is part of week 1–2 technical discovery — we never assume; we verify against your actual systems.

We define operational KPIs before development: cycle time reduction, cost per transaction, error rate, throughput, capacity unlocked, FTE hours freed, and revenue impact. Every implementation includes an automated dashboard with real-time KPIs and a 90-day post-launch review against the original business case. 85% of Alice Labs clients see measurable ROI within 12 months.

A focused single-process AI operations engagement runs €50,000–€120,000 fixed price for an 8–14 week delivery. Multi-process programmes run €150,000–€400,000. Managed AI operations (post-launch monitoring and optimisation) start at €10,000/month. We offer a 1–2 week paid discovery sprint (€5,000–€10,000) that produces a detailed plan, architecture, and budget — so you can decide on full delivery with confidence.

Yes. We deliver AI operations consulting across the Nordics, DACH, Benelux, UK, France and Southern Europe with EU data residency by default. Our team works in English with global enterprise clients, including remote-first delivery models for distributed operations teams. For US-based engagements, we partner with a US delivery network.

Operations teams are the hardest to change because their work is measured. We embed change management from week 1: shadow workflow observations, frontline interviews, hands-on pilot training, internal AI champions, gradual rollout with willing early adopters, and KPI dashboards that prove value to sceptics. Implementations with our change management approach achieve 3x higher adoption within 90 days vs. tech-only rollouts.