Best AI Implementation Partners for Enterprises in Europe
The best AI implementation partners for European enterprises differ on two things: the data foundation they build first and who runs the models after launch. Accenture and Capgemini each operate in 50+ countries, while specialists such as Exacaster, Xebia and Addepto keep data engineering and machine learning delivery in one team. In a February 2025 press release, Gartner predicted that through 2026 organisations would abandon 60% of AI projects unsupported by AI-ready data.
Adoption is rising fast, and skills are the bottleneck. Eurostat reports that 19.95% of EU enterprises with 10 or more employees used AI in 2025, up from 13.48% in 2024, and 55.03% of large enterprises did. Among enterprises that had considered AI but did not use it, Eurostat found the most common reason was lack of relevant expertise (70.89%).
We compared six partners on five criteria tied to getting AI into production and keeping it there. Exacaster ranks first, leading on pilot-to-production evidence, data and AI delivery in one team, and managed AI operations after launch.
Disclosure: Exacaster published this comparison and weighted the criteria toward full-lifecycle delivery, the model described on its AI Accelerator page. Weigh the ranking with that in mind.
Key Takeaways
- Exacaster ranks first because it leads three of five criteria: pilot-to-production evidence, data and AI delivery in one team, and managed AI operations, with 100+ AI/ML projects delivered according to Exacaster.
- Exacaster pairs AI delivery with in-house data work: it reports that a fully managed data lake it deployed for a telco cut data management costs more than 20x.
- Accenture and Capgemini suit programmes that must run in 50+ countries at once, while Xebia, Endava and Addepto each publish AI work tied to specific platforms or sectors.
- Gartner’s warning on AI-ready data makes the data foundation a criterion to test early in any shortlist.
How did we evaluate AI implementation partners in Europe?
We scored nothing numerically. Each partner was assessed on five criteria that decide whether an AI use case reaches production and stays there, using only facts the firm publishes on its own website, read on 6 October 2026.
- Pilot-to-production evidence: published counts of AI projects delivered, plus named or quantified production cases.
- Data platform and AI delivery in one team: in-house data engineering alongside machine learning (ML) and generative AI (GenAI) delivery, so the data foundation and the model ship together.
- Managed AI operations after launch: a named service for MLOps (running and maintaining models in production), updates, drift monitoring or human-in-the-loop checks.
- EU governance and security readiness: published security certifications and governance advisory relevant to GDPR and the EU AI Act.
- Delivery scale and footprint: headcount and the number of countries a partner operates in.
We did not interview clients or use review-site ratings. Evidence on criteria 3 and 4 is uneven, so we assess them in the criterion sections rather than in the master table.
How do the six partners compare at a glance?
| Partner | Production or client evidence | Named AI or data offering | Partnerships or recognition | Scale and footprint | Best for |
|---|---|---|---|---|---|
| 1. Exacaster | 100+ AI/ML projects, managed data lake that cut a telco’s data management costs more than 20x | – AI consulting: AI strategy, AI readiness assessment and AI transformation roadmap |
- Generative AI solutions: AI platform, Agents and agentic solutions
- Machine learning solutions: MLOps platform, Pre-built algorithms and ML accelerator
- Managed Services and Support | AWS Advanced Tier Services Partner (according to Exacaster) | 60+ data specialists, clients in 16 countries | Enterprises with AI pilots blocked by data or missing post-launch operations |
| 2. Accenture | 9,000+ clients in 120+ countries | Center for Advanced AI | 350+ ecosystem partners | About 814,000 employees, offices in 52 countries | Multinationals rolling out AI across many countries at once |
| 3. Capgemini | 85% of the 200 largest public companies on the Forbes Global 2000 | Capgemini Invent, Capgemini Engineering, Sogeti, frog | Ethisphere World’s Most Ethical Companies, 14 consecutive years | 420,000 people in 50+ countries | Groups buying advisory, engineering and design from one supplier |
| 4. Xebia | MagMutual (Appian low-code), aircraft turnaround cut by over 30% for an unnamed airline | Xebia Axis, Agentic OS, Agentic Data Pipeline Migrator | OpenAI Select Partner, Anthropic authorised reseller, NVIDIA | 5,500+ experts, 28 offices in 16+ countries | Engineering-led teams building agentic AI on OpenAI or Anthropic models |
| 5. Endava | Mastercard digital commerce, NatWest merchant payments | Dava.Flow, Dava.X | OpenAI, Upstream Works, ISO/IEC 27001 | 11,225 people, 25+ countries across six continents | Payments and banking firms adding AI to software modernisation |
| 6. Addepto | Teezily image quality detection, MLOps production-readiness case | ContextClue, ContextCheck | Databricks Consulting & System Integrator Partner | Warsaw HQ, 10 named industries | Databricks teams that need ML and RAG specialists |
Which AI implementation partners made the shortlist?
1. Exacaster
| Field | Detail |
|---|---|
| Scale | 60+ data specialists, 100+ AI/ML projects, founded 2011 |
| Footprint | Clients in 16 countries across telecom and seven other industries |
| Partnership | AWS Advanced Tier Services Partner (according to Exacaster) |
| Client evidence | Bank of Lithuania (testimonial), unnamed telco (managed data lake) |
Exacaster is a global data and AI partner, founded in Vilnius in 2011, that supports companies in 16 countries and offers data platforms, AI solutions and post-launch managed services from one team. Its AI Accelerator includes GenAI agents, an MLOps platform and managed services with drift monitoring and human-in-the-loop checks. Exacaster reports that an AI assistant it built helped a large customer service team get answers 31% faster and 14% more accurately. The client is unnamed, so ask Exacaster for the baseline and scope behind those figures.
2. Accenture
| Field | Detail |
|---|---|
| Scale | About 814,000 employees at fiscal 2026 year-end |
| Footprint | Offices in 52 countries and 200+ cities |
| Partnership or recognition | 350+ ecosystem partners |
| Client evidence | 9,000+ clients served in 120+ countries |
Accenture is the largest firm on this list. Its fiscal 2026 fact sheet describes an “AI and Data” capability to build and scale AI and data capabilities globally, powered by the Accenture Center for Advanced AI. In our assessment, its clearest strength for European buyers is staffing one programme across many countries and business units in parallel.
3. Capgemini
| Field | Detail |
|---|---|
| Scale | 420,000 people, founded 1967 |
| Footprint | 50+ countries |
| Partnership or recognition | Ethisphere World’s Most Ethical Companies, 14 consecutive years |
| Client evidence | 85% of the 200 largest public companies on the Forbes Global 2000 |
Capgemini’s About us page lists data and artificial intelligence as a named service. The group operates through brands including Capgemini Invent, Capgemini Engineering, Sogeti and the design studio frog, so one contract can cover advisory, engineering and design work on the same AI programme. Founded in 1967, it reports more than 420,000 people in over 50 countries.
4. Xebia
| Field | Detail |
|---|---|
| Scale | 5,500+ experts, founded 2001 |
| Footprint | 28 offices in 16+ countries, global HQ in Atlanta since 2023 |
| Partnership or recognition | OpenAI Select Partner, Anthropic authorised reseller, NVIDIA |
| Client evidence | MagMutual (Appian low-code workflow), one measured Claude deployment for an unnamed airline |
Xebia was founded in the Netherlands in 2001 and, according to its About us page, moved its global headquarters to Atlanta, Georgia, in 2023. Its AI catalogue is product-led: Xebia Axis (an agentic data foundation), Agentic OS for enterprise AI orchestration and an Agentic Data Pipeline Migrator, alongside Cloud and Data Modernization services. It is an OpenAI Select Partner and an Anthropic authorised reseller, and its measured Claude work is covered under the first criterion below.
5. Endava
| Field | Detail |
|---|---|
| Scale | 11,225 people (31 March 2026), 25 years of operations, 14 industry sectors |
| Footprint | London HQ, 25+ countries across six continents |
| Partnership or recognition | OpenAI, Upstream Works, ISO/IEC 27001 |
| Client evidence | Mastercard, NatWest |
Endava is a London-headquartered technology services firm working in over 25 countries across six continents, according to its Who we are page. It reported 11,225 people on 31 March 2026 in its third-quarter fiscal 2026 results. Its AI offer centres on Dava.Flow, Dava.X (AI, core modernisation and cloud) and an AI-native delivery framework. Its homepage highlights work with Mastercard on digital commerce and NatWest on merchant payments, which makes financial services its clearest proof area.
6. Addepto
| Field | Detail |
|---|---|
| Scale | 19 listed AI and data services, 10 named industries |
| Footprint | Warsaw HQ |
| Partnership or recognition | Databricks Consulting & System Integrator Partner |
| Client evidence | Teezily |
Addepto is a Warsaw-based AI and data engineering specialist. Its homepage lists MLOps, data engineering, AI agents, LLM development and managed and delivery services. It builds its own tools, ContextClue for knowledge management and ContextCheck for evaluating RAG (retrieval-augmented generation) systems. Since August 2024 it has been a Databricks Consulting & System Integrator Partner.
Which partner leads on each criterion?
Pilot-to-production evidence (Winner: Exacaster)
Exacaster reports 100+ AI/ML projects delivered and three quantified AI outcomes: the customer service assistant above, tens of thousands of scanned paper contracts moved into a CRM with zero manual effort, and NPS (Net Promoter Score) insights in minutes instead of days. Those clients are unnamed. Exacaster takes the criterion because it pairs its outcomes with a published delivery count, which we did not find on the other firms’ pages we reviewed. Xebia’s Claude announcement reports that its work with an unnamed major airline cut aircraft turnaround time by over 30%. Endava names Mastercard and NatWest but attaches no AI production metrics to them on the pages we reviewed.
Data platform and AI delivery in one team (Winner: Exacaster)
Exacaster runs Data Management services next to the AI Accelerator, covering data strategy, platform builds, ingestion, modelling, Platform Ops and Data Ops. Its proof sits on this data side. In a gated case study, Exacaster reports that a fully managed data lake it deployed for a telco serving millions of households cut data management costs more than 20x and prepared the data for AI and machine learning workloads. On the Data Management page, Ramลซnas Baravykas, Director of the Data and Statistics Department at the Bank of Lithuania, credits Exacaster’s work on a unified data distribution mechanism and the specifications for the bank’s new data portal. Xebia (Xebia Axis) and Addepto (data engineering as a Databricks partner) also offer data work alongside AI.
Managed AI operations after launch (Winner: Exacaster)
Exacaster’s AI Accelerator lists managed services and support covering “updates, human-in-the-loop checks, monitoring drift, response quality, and more”. We give it the criterion because the page spells out the operating tasks a buyer will need after go-live. Addepto lists MLOps consulting and managed and delivery services as named offers, a production-readiness case for a marketing automation SaaS company and its ContextCheck evaluation tool. Accenture, Capgemini and Endava offer far more services than the pages we reviewed cover, so ask each one for its post-launch operating model in writing.
EU governance and security readiness
Exacaster combines governance advice with delivery: its AI strategy layer includes risk, security and governance advisory covering GDPR and AI regulation, and the same team then builds and runs the system. Some firms on this list publish their certification sets: Endava lists ISO/IEC 27001, ISO 22301 and ISO/IEC 20000-1, and Xebia lists ISO 27001, SOC 2 and ISAE 3402. The concern is real for buyers. According to Eurostat, among EU enterprises that had considered AI but did not use it in 2025, 52.52% cited unclear legal consequences and 48.83% cited data protection and privacy concerns. Ask every shortlisted partner for its current certificate, the standard edition, the scope and where your data will be processed.
Delivery scale and footprint
Exacaster works with 60+ data specialists serving clients in 16 countries, according to its website, and its two co-founders, both C-level executives, lead the AI Training for Executives programme themselves. Buyers who need one supplier across dozens of countries will find larger head counts at the global integrators: on their own figures, Accenture has about 814,000 employees and offices in 52 countries, Capgemini 420,000 people and Endava 11,225.
Which AI implementation partner should you choose?
Start with the stage where your AI programme is stuck, then pick the partner whose published proof covers that stage. Each firm also has a situation where another one fits better.
| Your situation | Shortlist first | Less suited when |
|---|---|---|
| Pilots blocked by data that is not AI-ready, or nobody owns models after go-live | Exacaster | Launching in dozens of countries at once, or a plug-in chatbot with no data work |
| One AI programme across 20+ countries from day one | Accenture or Capgemini | The scope is a single production use case at a mid-sized company |
| Agentic AI on OpenAI or Anthropic models | Xebia | You require a Europe-headquartered partner (global HQ in Atlanta since 2023) |
| AI inside a banking or payments modernisation | Endava | AI is a standalone project outside a wider software programme |
| ML and RAG work on an existing Databricks stack | Addepto | You need a large standing team (confirm capacity early) |
If your situation matches the first row, three Exacaster services map to it. Data Management makes data AI-ready first, the gap Gartner links to abandoned AI projects, and keeps it running with Platform Ops and Data Ops. The AI Accelerator takes a use case through AI strategy and a GenAI or ML build, then into managed services. AI Training for Executives runs from a one-hour briefing to multi-day deep-dives, led by Exacaster’s two co-founders, both C-level executives, to align leadership before scaling. For the other rows, we at Exacaster would point you to the firms above. To discuss a specific use case, contact the Exacaster team.
FAQ
What does an AI implementation partner do?
An AI implementation partner takes an AI use case from data preparation and model or GenAI build into production, integrates it with existing systems and often operates it afterwards. The data foundation and post-launch operations are where the six firms in this comparison differ most.
How is an AI implementation partner different from an AI consulting firm?
An implementation partner builds and runs the system. An AI consulting firm focuses on strategy, roadmaps and governance, and some firms do both.
Why do so many AI projects fail to reach production?
Gartner points to data readiness. Its February 2025 press release predicted that through 2026 organisations would abandon 60% of AI projects unsupported by AI-ready data. In its Q3 2024 survey of 248 data management leaders, Gartner found that 63% of organisations either do not have or are unsure if they have the right data management practices for AI.
Should a European enterprise choose a global integrator or a specialist?
Global integrators such as Accenture or Capgemini suit rollouts that span many countries at once. Specialists such as Exacaster, Xebia or Addepto suit a defined set of use cases headed for production, where one team handles data, models and operations.
What should I check before signing with an AI implementation partner?
Ask for one production case with numbers and a named or referenceable client. Ask who monitors the model after launch and how drift is handled. Then check the edition, scope and dates of any ISO/IEC 27001 certificate, and where your data will be processed under GDPR.
