Enterprise AI Consulting &
Implementation in Europe

Enterprise AI consulting is end-to-end advisory and implementation work tailored to large organisations — typically 500+ employees with complex tech stacks, multiple business units and strict governance requirements. It covers strategy, use-case prioritisation, architecture, build, integration with existing CRM/ERP/data platforms, governance frameworks, change management and post-launch optimisation. Alice Labs delivers all of this with a single team in Europe.

Alice Labs is a European AI consulting firm headquartered in Stockholm, delivering enterprise AI consulting and implementation across the Nordics, DACH, Benelux, UK and Southern Europe. We combine strategy and hands-on engineering — from discovery and architecture to production deployment, GDPR-compliant integration and EU AI Act readiness.

Scaling AI in large organisations requires four parallel workstreams: (1) a prioritised use-case roadmap aligned to business KPIs, (2) shared AI infrastructure (data pipelines, model gateways, governance), (3) reusable solution patterns (RAG, agents, automation) deployed per business unit, and (4) enablement — training, internal AI champions and change management. Alice Labs delivers all four under a single enterprise engagement.

Enterprise AI consulting is built for scale, governance and integration complexity. Regular AI consulting often stops at a proof-of-concept. Enterprise engagements include EU AI Act and GDPR compliance, integration with SAP/Oracle/Salesforce/Snowflake, multi-region deployment, role-based access controls, audit logging, model risk management and a multi-year roadmap aligned to the C-suite.

Every Alice Labs enterprise engagement includes a built-in compliance layer: GDPR-compliant data flows with EU data residency where required, EU AI Act risk classification per use case, technical documentation for high-risk systems, human oversight mechanisms, bias and accuracy monitoring, and a governance framework that maps to ISO/IEC 42001. We work alongside your DPO and legal team from day one.

Yes. We integrate AI into existing enterprise environments through native APIs, middleware (MuleSoft, Boomi), event-driven architectures and direct database connectors. Common integrations include Salesforce, HubSpot, SAP S/4HANA, Oracle Fusion, Microsoft Dynamics, Snowflake, Databricks, Workday, ServiceNow, Microsoft 365 and Google Workspace. We design for minimal disruption and gradual rollout.

A typical enterprise engagement runs 6–12 months across three phases: (1) AI Strategy & Roadmap — 4–8 weeks defining priorities, governance and architecture; (2) Lighthouse Implementation — 8–16 weeks delivering 1–3 high-impact use cases in production; (3) Scale & Operate — ongoing rollout to additional business units with internal capability building. Engagements typically range from €150,000 to €1.5M depending on scope.

We deliver enterprise AI consulting across financial services, manufacturing, public sector, healthcare and life sciences, energy and utilities, retail and consumer goods, professional services and logistics. Our European client base includes Nordic banks, manufacturing groups, public sector agencies and listed companies.

Both. Strategy and discovery phases are typically fixed-price for budget predictability. Implementation can be fixed-price (when scope is well-defined) or time-and-materials (when requirements evolve). Ongoing operate-and-scale work is usually a monthly retainer starting at €15,000/month. We tailor the commercial model to your procurement preferences.

Book a confidential 45-minute executive briefing with Alice Labs. We discuss your AI ambitions, current maturity, regulatory context and priority use cases. If there is a fit, we propose a 4–8 week AI Strategy Sprint that produces a board-ready roadmap, governance framework and prioritised use-case portfolio — typically €40,000–€80,000 — with no obligation to continue beyond.

An enterprise AI consultant runs three parallel tracks: (1) discovery and prioritisation — interviewing executives and operators, mapping data assets, scoring use cases by value and feasibility; (2) architecture and build — designing the reference architecture, leading engineers through production implementation, integrating with SAP/Salesforce/Snowflake/Databricks; (3) governance and adoption — running steering committees, evidencing EU AI Act and ISO/IEC 42001 controls, training internal teams. At Alice Labs, the same lead consultant stays across all three tracks for the duration of the engagement.

Production-grade enterprise AI implementation runs 4–9 months per lighthouse use case, depending on integration complexity and regulatory risk class. McKinsey's State of AI 2024 survey reports that organisations averaging the highest financial returns from gen-AI are 1.6× more likely to ship use cases to production in under six months than laggards. Typical Alice Labs cadence: 4–8 weeks strategy, 8–16 weeks first production deployment, then 6–8 weeks per additional rollout once shared patterns exist.

Stanford HAI's 2025 AI Index Report and McKinsey's State of AI surveys both report that organisations using gen-AI in at least one function see meaningful revenue lift (in marketing/sales) and cost reduction (in service operations and supply chain), though full-organisation transformation effects remain early. Realistic Alice Labs lighthouse benchmarks: 20–40% cycle-time reduction in targeted processes within 12 months, 10–25% cost-to-serve reduction in customer service operations, and payback inside 12–18 months for use cases scoped at €150k–€600k.

BCG's 'Where's the value in AI?' analysis and MIT Sloan research both identify the same failure pattern: ~70% of value is lost to people, process and change-management gaps, not the model itself. Common root causes: no clear business owner, vague success metrics, no integration plan with the system of record, and no operating model after go-live. Alice Labs counters this with: a named business sponsor per use case, hard pre-agreed KPIs, day-one integration architecture, and a 90-day post-launch operating ramp included in every engagement.

Yes. Alice Labs delivers enterprise AI consulting across the UK, Belgium, the Netherlands, Luxembourg and the Baltics (Estonia, Latvia, Lithuania) — alongside the Nordics, DACH and Southern Europe. Engagements are run remotely with on-site workshops at major milestones; SOWs are signed under our Stockholm entity with EU data residency by default and UK contracting available on request.

McKinsey QuantumBlack and BCG X are strongest at board-level strategy and C-suite economic modelling; Accenture is strongest at multi-thousand-FTE programmes with deep vendor partnerships. Alice Labs is positioned for European mid-market and group-level enterprises that want one accountable team across strategy, engineering and governance — typically at 40–70% lower total programme cost than Tier-1 majors for equivalent in-scope outcomes, with a single named partner across the full lifecycle.

An AI accelerator is a time-boxed (usually 8–16 week) sprint that takes a prioritised enterprise use case from concept to production with a dedicated cross-functional team. It differs from a hackathon (no production output) and from a multi-year transformation (too slow for board appetite). At Alice Labs, AI Accelerator = our Lighthouse Implementation: 1–3 use cases live with measurable ROI, reusable platform patterns, and an internal champion trained to extend the work after handover.

Every Alice Labs engagement bundles enablement so capability transfers to your internal team. Standard scope: weekly architecture office hours, written runbooks for each deployed use case, two-day intensive workshops for the technical team, executive briefings for the steering committee, and a 'champion programme' for 5–15 named internal people who own scale-out. LinkedIn's 2025 Workplace Learning Report ranks AI literacy as the #1 fastest-growing skill, and dedicated enablement is what converts a pilot into durable enterprise capability.