To choose an AI consulting partner, first define the work you need help with: assessing a use case, developing the software, integrating it with existing systems or operating it after launch. Then compare providers against that scope. A workshop, a model evaluation and a production implementation require different deliverables and skills.
For Swiss companies, the question is increasingly practical. In AXA's 2025 survey of 300 SMEs, deliberate AI use rose from 22% to 34%, and 57% of AI users reported time savings. Those results show adoption, but they do not establish which kind of consulting support an individual company needs.
EY's 2026 Swiss survey, with 604 respondents, found 89% using AI at work and 31% reporting a pilot or proof-of-concept stage in their company. The AXA and EY samples differ, so their percentages should not be treated as directly comparable measures of company readiness.
Check experience beyond the prototype
An AI application that reads internal data or updates a CRM needs an access model, logging, tests and a recovery plan. Ask a prospective partner to explain those parts of a comparable deployment. The answer should cover what happens when a model, data source or integration changes.
EY's survey identified data quality and silos as the most frequently reported obstacle, followed by security and privacy concerns and a shortage of skilled people. Ask who on the proposed team will address those dependencies and what they need from your staff.
Evidence from Swiss financial institutions
In its 2025 survey of around 400 supervised institutions, FINMA found roughly half using AI or developing applications, with another 25% planning use within three years. Data quality, data protection and explainability were prominent concerns.
FINMA's survey findings provide useful context for financial firms. They also illustrate why sector experience matters when selecting the project team.
Nine questions worth asking
Use the same questions with each shortlisted provider. Ask for examples and name the person responsible for each part of the proposed engagement.
Do they start with the business case?
Ask which process should improve, who uses it and how the improvement will be measured. A recommendation should explain the expected value and the alternatives considered.
How will they assess your data?
Ask how sources will be connected, permissions enforced, quality checked and access logged. The assessment should identify missing data and preparation work before development is priced.
Can they build what they propose?
Identify who will write the pipelines, APIs, interface and deployment configuration. If another team will implement the proposal, check how responsibilities and acceptance criteria will be handed over.
Can they integrate with what you already run?
Describe the documents, APIs, CRM, ERP and support tools involved. Ask for relevant integration experience and how the provider will test against the access and network constraints of your environment.
Can they explain where your data goes?
Request a data-flow diagram covering processing locations, subprocessors, storage, logging and retention. Ask how access rights are enforced and which responsibilities remain with your organisation.
How is success measured?
Agree the metric before the work starts: handling time, automation rate, answer quality, error rate, cost per case, or a business KPI. Then agree what result would justify stopping.
How do they evaluate model quality?
Ask for representative evaluation cases, acceptance thresholds and results by failure category. Check how model and retrieval changes are tested alongside ordinary software tests before release.
What happens after launch?
Agree on monitoring, model versioning, rollback, incident handling and documentation. Name the operational owner and define support hours and escalation times in the contract.
What would they not solve with AI?
Ask for an example where the team recommended ordinary software, a process change or stopping a pilot. The answer should explain the evidence and the trade-off.
Assess Swiss data protection during discovery
The Federal Data Protection and Information Commissioner states that the Swiss Federal Act on Data Protection applies to AI-supported processing of personal data. Transparency and, for processing likely to present a high risk, a data protection impact assessment need to be considered when planning the system.
As of September 2026, Switzerland has no general AI act; a consultation draft is expected by the end of 2026. Swiss organisations should also assess whether their activities fall within the EU AI Act's scope. Article 50 transparency obligations apply from 2 August 2026, while parts of the high-risk regime have later application dates.
Document the data the system needs, where it is stored, who receives it and which users may access each record. Define the actions an agent may take and the approvals required. Resolve legal interpretation with the relevant privacy and legal specialists, and reflect the resulting requirements in the architecture.
Compare strategy, model and implementation specialists
Strategy only
Useful for assessing priorities and developing a roadmap when an implementation team is available.
- Workshops, use case maps, business cases
- Ask who builds it, and get a name
- Works when you have a strong internal engineering team
Models only
Useful for specialised model and data work. Confirm who owns the surrounding application and its integrations.
- Data science, fine-tuning, evaluation
- Ask about identity, permissions and deployment
- Works when integration is already solved
End to end
Covers assessment, engineering and operation. Check the named team, subcontractors and responsibilities at each stage.
- Use case selection through to running the system
- Software, cloud and DevOps alongside model work
- Ask for a case where they recommended stopping
How we approach it at Alpine Edge
Alpine Edge combines AI implementation, software engineering, cloud infrastructure and DevOps. That lets us work on the model alongside authentication, company data, internal APIs and deployment. We agree on scope and ownership during the assessment, including what your team will operate afterwards.
- 01Audit
Understand before proposing
- Use cases scored on value, feasibility and data availability
- Data sensitivity, storage locations and who may access what
- Existing infrastructure, identity and integration points
- Compliance requirements, including FADP obligations
- 02Build
Implement and integrate
- Self-hosted or private LLMs where data cannot leave
- Retrieval over your own documents, with permissions preserved
- MCP servers and agents connected to approved tools and systems
- PII detection and redaction, encryption, role-based access, audit logging
- 03Run
Operate and maintain
- Monitoring, logging and cost tracking
- Model versioning and rollback
- Iteration against the metric agreed at the start
- Knowledge transfer, so the capability stays with your team
For a private AI deployment, the handover includes the surrounding application as well as the model: data access, retrieval, integrations, permissions, monitoring and operating instructions. Those components need clear owners and maintenance arrangements.
Choose a partner whose proposal explains who will build the system, how you will judge it and who will keep it running.