---
title: "Private Bank Copilot Journey | Swiss Private Bank | S-PRO"
url: https://s-pro.io/casestudy/swiss-private-bank-copilot-journey
lang: en
description: "How a Swiss private bank went from no AI to department-wide Copilot and agents on its own core data in six months. An S-PRO case study."
updated: 2026-11-04T18:57:37+00:00
source: S-PRO (https://s-pro.io)
---

Case studies

# Private Bank Copilot Journey: how a Swiss private bank moved beyond the Copilot experience

A Swiss private bank went from no AI in daily work to department-wide Microsoft 365 Copilot and agents on its own core data in six months.

[Book a first workshop](https://s-pro.io/get-in-touch)ISO 27001 and ISO 27701 certified

- Client a Swiss private bank, part of an international banking group
- Timeline May 2026 to today, 2027 roadmap agreed

Client name withheld at the client's request. Screens shown are from training material and prototypes with invented data.

- Geography Switzerland
- IndustryFinance
- ServicesAI enablement with the Copilot Booster method

## Intro

In six months a Swiss private bank went from no AI in daily work to department-wide Microsoft 365 Copilot adoption, configured team solutions and AI that retrieves data from its own core systems. S-PRO guided the bank up a six-level AI maturity curve, one level at a time, with the Copilot Booster method: training and adoption first, engineering and development second.

The engagement covered seven teams: front office, Credit, Compliance, Finance, Investments, HR and Operations. The bank is part of an international banking group; its name is withheld at the client’s request.

### Hours saved per person, per week (estimated by the team)

Credit 8h, front office 7h, Compliance 5h, Finance 4h, Investments 3h, HR 2h. Estimated by the team, after training and the first configured solutions.

30h

hands-on training built on each team's own working day

12

quick wins covered through the training

2

sophisticated agents built on the bank's own data

34

use cases collected and classified

---

## Goals and Challenges

### Starting from zero AI in the working day

In May 2026 AI was not part of anyone’s working day. Copilot had not been rolled out, and there were no shared prompts, agents or use cases. Some people expected Copilot to do everything once it arrived; others assumed it could do nothing useful with bank data. Neither view helped.

The pressure to move came from the work, not the technology. Teams were small and stretched. Copilot went live across the bank at the end of July 2026, and a new operational data layer on the core banking side was due in August.

### Where the time went

- **Credit: reporting eats the week.** Flat core banking exports, Access databases and VBA macros. One analyst spent most of his time producing reports.
- **Finance: numbers retyped by hand.** Figures pulled from several Excel files and typed into a recurring presentation; invoices coded manually against a supplier sheet.
- **Front office: meeting prep from five places.** CRM, core banking, inbox, market news and the last meeting’s notes, assembled by hand before every client call.

Credit: *what we heard from you*

**Credit application**Top priority · one file per client

1. 1.1Data collection per client
2. 1.2Real estate market analysis
3. 1.3Client financial analysis
4. 1.4Lombard collateral analysis
5. 1.5Pricing and policy exception check
6. 1.6Credit memo drafting
7. 1.7KYC client summary (front office)

**Market newsletter**Quarterly, UK and Switzerland

1. 2.1Macro and central bank research
2. 2.2UK and Swiss property market update
3. 2.3Newsletter drafting, shorter format

**Policy and regulation**Know the gaps first

1. 3.1Policy gap analysis
2. 3.2Regulatory change alerts
3. 3.3Procedure harmonisation

**Reporting and MIS**Less building, more reading

1. 4.1Automated MIS report runs
2. 4.2Covenant monitoring and RM chasers
3. 4.3Board-ready dashboards

---

## Solution

### How do you move a private bank up the AI maturity curve?

S-PRO measures AI adoption on a six-level scale. Levels 1 to 3 are about people using Microsoft 365 Copilot well, out of the box. Levels 4 and 5 need engineering: governed data, retrieval and purpose-built workflows. Level 6, fully agentic processes, is mostly market noise for a regulated bank today, because it is still hard to clear with governance and risk. The bank moved on two tracks: training and adoption first (Level 1 to 3), then engineering and development (Level 4 to 5).

1. **Level 1, chat user:** rewording, translating, summarising; one-shot prompts.
2. **Level 2, works inside the apps:** Copilot in Outlook, Teams, Word and Excel on the real mail, meeting or file.
3. **Level 3, configured for the team:** shared prompts, agents and curated team knowledge; knows where out-of-the-box AI stops.
4. **Level 4, AI data retrieval:** AI knows the source systems and pulls the right data into context on demand.
5. **Level 5, AI workflows:** purpose-built use cases on a governed data layer, evaluated, tested and confirmed by a human.
6. **Level 6, agentic:** agents own a process end to end. Market noise for now.

### Step 1. Talk to the teams: assess needs and classify use cases

We started with workshops and interviews in every department. The questions were simple: what does your day look like, where does time go, and what would you stop doing if you could? The interviews produced 34 use cases before any filtering, from accumulator pricing, adverse media, KYC and periodic review to meeting minutes, regulatory updates and a translation assistant.

Use case scope overview: *34 use cases, four categories*

**Sophisticated build**6 confirmed · 8 potential with frontier models

##### Confirmed: FI data

- Preparing credit applicationsCredit
- KYCFront / Compliance
- Compliance RAFs (periodic review)Front / Compliance

##### Confirmed: no FI data

- Call report and cross-border processFront
- Adverse mediaFront / Compliance
- Investment search assistantInvestment

##### Potentially in scope with frontier models · needs further validation

- Policies and proceduresAll
- Gap analysis on policiesAll
- Regulatory updateAll
- Quarterly market newsletterCredit
- Client proposal assistantInvestment
- Follow-up on CRM notesInvestment (core banking data)
- Third-party FI formsMiddle office
- Fund subscription formsMiddle office

**Quick wins · Copilot best practices**12 use cases

- Email assistantAll
- Translation assistantAll
- Document analysisAll
- RCSAAll
- Summarising regulationsAll
- Meeting minutesAll
- Report preparation (dashboard)All
- Data analytics and comparisonAll
- HR self-service chatbotHR
- CV scanning into ExcelHR
- Commentary assistantInvestment
- Stock-price drop researchInvestment

**Out of scope**8 use cases · better served by ETL, a dedicated tool or a separate project

- LTV analysisSeparate LTV project
- Credit simulationSeparate project
- Fraud detectionDedicated tool
- Survey creationForms / SharePoint
- Enhanced transaction monitoringDedicated tool
- Retrocession processingETL / desktop
- Accumulator pricingETL / desktop
- FX and OTC reconciliationETL / desktop

Every use case then went into one of four categories, signed off with the bank in a scope workshop in June 2026. Some requests were reporting or data problems, and the honest answer was ETL, a dedicated tool or Power BI, not AI. Saying so early kept AI credible for the cases where it fits.

### Microsoft Copilot or custom agents? Ten criteria for quick win versus build

A quick win is Copilot out of the box: one person, one prompt, a draft they check before it goes anywhere. A sophisticated build is engineered together with IT, tested, and maintained like any other system the bank runs. Every use case was assessed against the same ten criteria, five technical and five governance. If any governance criterion applies, or several technical ones do, the use case is classified as a build.

Sophisticated solution, *or quick win?*

| Criterion | Sophisticated solution professional software · IT setup · engineered | Quick win out-of-the-box Copilot · self-service |
| --- | --- | --- |
| Technical |  |  |
| Documents | Too big for a context window: needs RAG over a managed corpus | Fits in a prompt or a few files |
| Data sources | Several systems via connectors (core banking, Bloomberg, email) | Already in M365 / SharePoint |
| Accuracy | Exact, reproducible numbers; low tolerance for error | Draft text a human reviews |
| Workflow | Multi-step, branching, scheduled or autonomous | A single on-demand prompt |
| Systems | Writes back into, or acts on, productive systems | Output a person pastes in |
| Governance |  |  |
| Ownership | Shared, team-owned agent that must be maintained | Personal, individual use |
| Risk | Regulated or high-risk process (FINMA, EU AI Act) | Low-risk internal productivity |
| Auditability | Traceability: logged sources, defensible rationale | No audit trail needed |
| Consistency | Same answer and style for everyone | One-off; per-user variation is fine |
| Data | Client or confidential: residency and access control | Public or non-sensitive |

#### Five technical criteria

- **Context volume and retrieval.** If the input fits into a prompt or a handful of files, Copilot can process it. A credit file with valuations, leases, statements and years of correspondence does not; it has to be indexed and retrieved from a managed document collection.
- **System integration.** Mail, Teams chats and SharePoint are already in Microsoft 365. Core banking, the KYC platform or a market data feed each need a connector with access rights, testing and an owner.
- **Deterministic output.** A loan-to-value or debt service coverage ratio must be correct and reproducible on every run: built and tested calculation logic, not generated by a language model.
- **Workflow orchestration.** One request, one response suits Copilot. Several steps, branches or scheduled runs are a workflow that has to be designed, with failure cases handled.
- **Write-back to systems of record.** If the output is written back into a credit application or client record, the integration must be built properly and every write traceable.

#### Five governance criteria

- **Ownership and lifecycle.** A tool the whole team relies on needs an owner who keeps instructions current and decides when it changes.
- **Regulatory risk classification.** Anything that feeds a credit decision, a KYC assessment or a regulatory report needs controls, review steps and a clear line on what the AI may decide.
- **Auditability and traceability.** Where a decision may have to be explained to an auditor or regulator, sources behind each answer must be logged. Copilot chat alone does not give you that.
- **Standardisation.** If every relationship manager must produce the same output in the same format, the logic is fixed in one shared build.
- **Data classification and residency.** Client data brings requirements on where data is processed, who may see which client and how access is controlled.

### Which Microsoft platform for which use case?

The classification also decides the platform. The more a use case needs integration, determinism and control, the further up the stack it goes.

- **Adopt (everyone, Levels 1-3): Microsoft 365 Copilot.** AI in Outlook, Teams, Word and Excel. Quick wins, covered in training.
- **Extend (makers and IT, Levels 3-4): Copilot Studio.** Low-code agents with connectors into business systems. Experiments and team agents.
- **Build (developers, Levels 5-6): Microsoft Foundry.** Custom AI apps, engineered workflows and multi-agent setups on the bank’s own data. Sophisticated builds.
- **Governance across all three:** Microsoft Entra (identity, least-privilege access for users and agents), Microsoft Purview (sensitivity labels, data loss prevention), Microsoft Defender (threat protection) and Agent 365 (inventory of all agents with admin, usage and cost).

Three examples, three verdicts: **credit applications** are a sophisticated build on Microsoft Foundry (large files, several systems, exact numbers, a multi-step workflow ending in the credit system, every governance question applies); the **quarterly market newsletter** is an experiment first on Copilot Studio (no client data, no regulated decision); **meeting minutes** are a quick win on Microsoft 365 Copilot (already in Teams, a person checks the draft, nothing goes back into a system).

Use case: *credit applications*

Department:

Credit

Owner:

Credit team

Trigger:

RM request + annual review cycle

**DATA** Client financial data

##### Process (steps 1–7; marked steps get AI support)

1. Credit request from RM
2. External data collection
3. Internal data collection
4. Market analysis (RE)
5. Collateral analysis (Lombard)
6. Financial analysis (RE)
7. Obligor rating

##### AI ideas · executed as case management

- **Document analysis + memo population (RE)**
AI reads valuation, lease, insurance and registry data. Drafts the market narrative and fills memo sections.
- **AI fills in data for collateral analysis (Lombard)**
Portfolio anonymised from core banking; AI returns a liquidity, diversification and concentration view. Bloomberg for context.
- **AI fills in data for the financial analysis agent (DSCR / ICR)**
AI reads anonymised net worth statements and computes DSCR and ICR. Anonymisation is easy because the content is numbers.
- **KYC to credit application auto-population**
The KYC storyline written in the KYC platform auto-fills the client section of the credit memo. Replaces today’s retyping.

### Step 2. Analyse the IT landscape: map data needs to processes

A use case is only as good as the data it can reach. Together with the bank’s IT we mapped the landscape in five layers: interfaces (Microsoft 365 Copilot, AI inside the KYC and onboarding platform, MIS reporting); AI services (Copilot Studio over Microsoft Graph, Azure AI Foundry hosted in Switzerland); governed data (Microsoft 365 content, the operational data layer, document object storage); systems of record (core banking, client lifecycle and KYC, document and email archive); external systems (screening and adverse media, transaction screening, market and research data, product pricing platforms).

We then traced which processes (credit applications, KYC onboarding and refresh, periodic review, adverse media, call reports and cross-border, investment search) need which systems and how sensitive the data is. The client data points sit in the systems of record: ID documents, KYC profile and storyline, source of wealth and funds, net worth statement, beneficial owners, portfolio positions, transaction history, real estate valuations, call reports, email exchanges and adverse media hits. The risk rating is reached only through the systems of record.

Continuous *client file*

**Most high-value use cases consume the same client data.** Build the data foundation once, and every use case below it gets faster.

**Use cases that need client financial data**In scope for the sophisticated solution · deep dives follow

- KYCFront / Compliance
- Preparing credit applicationsCredit

All powered by the continuous client file

Client lifecycle

1. **Onboarding**KYC, account opening, source of wealth
2. **Account funding**Initial deposits, first trades
3. **Ongoing activity**Trading, transfers, portfolio events
4. **Lending events**Credit applications, drawdowns
5. **Periodic reviews**Annual RAF, 5-year KYC refresh
6. **Offboarding**Account closure (rare)

Client data points

- ID documents
- Portfolio positions
- KYC profile, storyline
- Transaction history
- Source of wealth
- Real-estate valuations
- Source of funds
- RM call / contact reports
- Net worth statement
- Email exchanges
- Beneficial owners (UBO)
- Adverse media hits
- Related companies
- RAF risk rating

Source systems

- KYC platform (KYC, credit memo, onboarding)
- Core banking (core banking, portfolio, transactions)
- Outlook (email exchanges, archived as PDF)
- Microsoft Teams (calls, meetings)
- SharePoint / local drive / OneDrive (documents, archives)
- WorldCheck / LexisNexis (screening)
- Bloomberg (market data)
- External sources, e.g. the commercial register

Client file *data model*

Six domains around one hub: every record hangs off the `business_partner`. Fields shown are the key ones only.

##### AParty and KYC

**business_partner**bp_id · bp_type · domicile · access_code · KYC review dates

**person (history)**person_id · record_start/end_ts · nationality · country_of_risk

**bp_person_rel**bp_id · person_id · rel_type

**person_rel**person → target · partic_perc

**identification**ident_type · valid_to

**client_document**doc_type · expir_date

**document_requirement**bp_type · doc_type · mandatory

**suitability_hist**calc · override · reason

##### BCoverage

**employee**login · department

**responsibility**bp_id · employee_id · role

##### DInteraction

**contact_report**contact_ts · subject · content · follow_up · due

##### FReal estate

**realty**property_type · city

**realty_valuation**provider · date · market_value

##### ECredit

**credit_application**affordability · ltv · status

**credit_limit**limit_type · amount · next_review

**collateral**collat_type · pledged_cont_id

**loan**loan_type · amount_chf

##### CWealth

**cash_transaction**direction · amount_chf · counterparty_country

**container**cont_id · cont_type

**asset**asset_class · maturity · lending_rate_pct

**position_value**ref_date · market_value_chf

Links: coverage, interaction, credit, wealth and real estate all reference the business partner. Collateral is pledged against containers and real estate; credit limits carry the collateral and loans; positions combine assets and containers.

Reporting: *a blueprint for MIS*

**Data layer**

**Core banking**

Flat exports

**Excel files**

Tools and trackers

**Bloomberg**

Market data

**ETL pipeline**

Extract, clean and load on a schedule. Client names stripped on the way in.

**Semantic model layer**

**Star schema**

Fact table (client ID, account ID, product ID, date, value) linked to Client (segment, advisor), Account (type, currency), Product (category, owner) and Date (month, quarter).

- Σ Volumes
- Σ Growth vs prior
- Σ Target vs actual
- Σ Key ratios

**Row-level security**

Each team sees only its own rows.

**Delivery layer**

**Board and group dashboards**

One source, refreshed on its own. No more copying numbers across.

**Workspace and apps**

Shared in one place with access control, not sent around as files.

**AI creation**

Copilot builds a report page from a plain-language request.

**AI drill-down**

Ask the numbers a question and get the answer with the chart.

Use case: *KYC onboarding and refresh*

Department:

Front + Compliance

Owner:

Compliance

Trigger:

Onboarding event + 5-year refresh

**DATA** Client financial data

##### Process (marked steps get AI support)

1. Client data capture
2. Independent research (all media)
3. Banker writes storyline
4. Banker fills KYC profile
5. Banker uploads supporting evidence
6. Compliance check and challenge
7. Profile history preserved
8. 5-year refresh cycle

##### AI ideas · continuous client file

- **AI deep research across sources**
Searches Google, WorldCheck, LexisNexis and Deep Research for linked companies and business activities the client did not disclose.
- **AI storyline draft from banker inputs**
Compiles a first-draft narrative from call reports, IDs, source-of-wealth corroboration and research. The banker edits and owns the final text; AI does not replace the thinking.
- **AI plausibility guardrail per KYC question**
Runs a checklist over each KYC field. Flags inconsistencies, weak answers and missing corroboration before Compliance review.
- **AI wealth corroboration gap finder**
Compares the wealth claimed in the storyline and fields against the uploaded evidence. Returns an explicit gap and the missing proofs.

Architectural outcome**Merged RAF, KYC update and 5-year refresh.** All three are auto-generated from the same continuous client file. Eliminates duplicate work.

Use case: *adverse media*

Department:

Front + Compliance

Owner:

Compliance

Trigger:

Continuous (nightly)

**DATA** Confidential, no client financial data

1. Nightly automated screening
2. Hits returned to Compliance
3. Manual hit review
4. Compliance flags Front, Front asks client
5. Deeper review for select clients
6. Quarterly close monitoring
7. Feeds into RAF

- **Hit triage with AI verdict and quote on true positives**
For each hit, AI returns a verdict and a supporting quote: true hit or false positive. The reviewer confirms or overrides; AI does not decide.
- No AI**Auto dispatch of hits to the relevant RM**
Maps each hit to the responsible RM and routes it directly, removing the Compliance forwarding step.
- **AI-assisted deeper search across sources**
For flagged hits, AI searches Google, WorldCheck, LexisNexis and Deep Research for corroborating or contradicting evidence.
- **Integrated dashboard with AI recommendations**
A dashboard where RMs see clients with hits and accept or override the AI recommendation on whether the hit changes the risk category under the bank’s policy. The email is kept for auditability.

### Step 3. Training and adoption: teach the working day (Level 1 to 3)

People do not adopt Copilot in one jump, and feature tours do not move anyone up a level. A senior relationship manager told us non-technical staff need to see the advantage in their own day before they accept a new system. So we walked people through a normal working day: catching up on the inbox in the morning, letting Copilot take notes in a meeting, following up on what was agreed, and turning a raw export into a reusable tracker in the afternoon.

A day in the *office*

**One working day, five moments.** Not a tool tour: the same day you already have, with Copilot doing the parts nobody enjoys.

1. 08:00
**Start of the day**
What did I miss

Everything that arrived overnight, grouped, with what is still unanswered on top.
2. 10:00
**The meeting**
Copilot takes the notes

One click in Teams. It listens, then writes the minutes and the action points.
3. 10:45
**After the meeting**
Two ways to ask

What was agreed and what is still open. Point Copilot at it, or just name it.
4. 11:15
**The inbox**
Set it up once

Teach it how you write, let it find a slot, and let the folders sort themselves.
5. 14:00
**Training check**
From export to tracker

Raw training data into a sheet you reuse every month, overdue flagged.

The meeting: *it takes its own notes*

**Switch it on when you set the meeting up.** After that there is nothing to press: two things sit in the meeting bar, and the third starts on its own.

1. 08:00 Start of the day
2. 10:00 The meeting
3. 10:45 After the meeting
4. 11:15 The inbox
5. 14:00 Training check

What you are looking at

1

**Facilitator**

Writes the notes while you talk. Summary, decisions and action points build up as the meeting runs.

2

**Copilot**

Ask it during the meeting: what was agreed so far, or what you missed when you joined late.

3

**Transcription**

Starts on its own, by the organiser’s settings. Everyone in the meeting sees the notice.

**Before you start** None of this runs unless the organiser switched Copilot on. In your own meetings, turn it on in Meeting options before you send the invite.

Morning: *what did I miss?*

**Two prompts.** One you run when you sit down, one that runs on its own every week. Change the parts in brackets and they are yours.

1. 08:00 Start of the day
2. 10:00 The meeting
3. 10:45 After the meeting
4. 11:15 The inbox
5. 14:00 Training check

**What did I miss?**Copilot Chat · paste and run

I am an [external consultant to the bank]. Help me get on top of my [the bank] mailbox. Give me an overview of every mail I have not answered yet, grouped by topic, and inside each topic sorted by how long it has been waiting. For each one tell me who is asking, what they want from me, and how many days it has been sitting. If you cannot tell whether I already replied, say so instead of guessing.

**Every week, on its own**Scheduled prompt · compliance example

Check whether I received the [Market Weekly] newsletter in the last seven days. If I did, give me a table with every person or company named, a one-line summary of the story, the link to the article and the edition. If nothing arrived, say so in one line.

Key capabilities in this step · [ ] the details you fill in

**Scheduled prompts**

Write it once and set a time. It runs without you and the result is waiting.

**Work IQ**

Copilot already sees your mail, chats, meetings and calendar. You do not attach anything.

Compliance training: *a tracker you reuse*

**Raw data in, a working sheet out.** Built once with Copilot; next month you only paste in the new export.

1. 08:00 Start of the day
2. 10:00 The meeting
3. 10:45 After the meeting
4. 11:15 The inbox
5. 14:00 Training check

**The raw export**

Straight out of the training system. One row per person per course, codes instead of names, dates in mixed formats.

**Copilot builds the sheet**

Clean columns, a status per person and course, overdue flagged, and a summary by department.

**Next month, same sheet**

Paste the new export into the raw tab and everything recalculates. You do not ask again.

**Build the tracker**Copilot in Excel · on the export

This is the raw export from our training system. Build me a tracker I can reuse every month. Keep the export untouched on its own tab. On a second tab, give one row per employee with their department and the status of each mandatory course: completed, due within [30 days], or overdue. Use formulas, not typed values, so it updates when I paste next month’s export in. Add a summary by department with the completion rate, and list anything you could not match instead of guessing.

**Keep in mind** The sheet only knows what is in the export. Check a handful of rows against the system before the result goes to anyone.

Every department then worked through the quick wins from its own interviews, on its own documents: Credit on credit files, committee minutes and valuation summaries; Finance on Excel-to-presentation reporting and invoice coding; Compliance on mandatory training tracking and client news screening; the front office on client meetings and call reports. In total: 30 hours of training between August and October 2026, covering all 12 quick wins.

Every prompt followed the same five ingredients: the goal, the context, the source to work from, the format of the answer, and the boundaries. Habits: the first answer is a draft; give examples, not long descriptions; point Copilot to the exact file; start a new chat for a new task; the last 20% of polish stays with the human. To keep this going, each department has one AI champion who collects use cases, keeps shared prompts current and gets deeper sessions with S-PRO.

Take these with you: *lessons learned*

**Eight things that make open work with Copilot go well.** Pick one and try it tomorrow, on something real.

1. 01
**Don’t expect to one-shot it**
The first answer is a draft, not the deliverable. Plan for rounds.
2. 02
**Add context**
Internal: who it is for, what exists. External: examples, constraints.
3. 03
**Brainstorm in chat first**
Shape the idea in chat, then take it to Word or PowerPoint.
4. 04
**Go step by step**
Best results, and it still triggers your own creativity.
5. 05
**Examples beat descriptions**
One good example says more than a long instruction.
6. 06
**Point to the exact files**
If you know where it is, name it. Don’t make Copilot search.
7. 07
**Start new sessions**
AI gets worse as a session gets long. New task, new chat.
8. 08
**The last 20% is yours**
The final formatting and polish, and proofreading, are still up to the human.

### Example outcome: the credit application agent (Level 5 prototype)

The relationship manager asks for a credit application in plain words. The agent reads the client file, runs the collateral and financial analysis, checks policy and drafts the memo with a source on every figure. Anything outside the guidelines is flagged, not overridden. A human reviews and owns the decision. It is a prototype built with invented client data.

---

## Technology

Beyond Copilot: *what development adds*

Built with Copilot Studio, Microsoft Foundry and the Agent Framework. Users still work in Copilot and Teams.

**Connection to other systems**

Core banking, KYC platform and market data through APIs and MCP, not only Microsoft 365.

**Runs on its own**

Starts from a schedule, a new email or a new file. No one has to type a prompt.

**Multi-step workflows**

Several steps in one run: collect, check, calculate, draft and send.

**Human approval**

Stops for sign-off before anything leaves the bank or changes a record.

**Specialist agents**

A front agent hands tasks to agents for the credit memo, KYC or the newsletter.

**Custom code where needed**

Where no-code stops, we develop the rest: calculations, models, rules and file handling.

**Controlled environment**

Swiss region, private network, and its own identity and access rules per agent.

**Tested before go-live**

Test sets and quality checks before launch, then monitored in production.

- **Microsoft 365 Copilot** (adopt), **Copilot Studio** (extend), **Azure AI Foundry** (build, hosted in Switzerland)
- **Governance:** Microsoft Entra, Purview, Defender, Agent 365
- **Data:** Microsoft Graph, operational data layer on the core banking side, document object storage
- **Method:** S-PRO Copilot Booster

---

## Results

- From no AI in daily work to department-wide Copilot adoption within six months of starting in May 2026; Copilot live bank-wide since end of July 2026.
- 34 use cases collected, classified with the bank in June 2026; ETL, Power BI or dedicated tools named where AI was not the right answer.
- 30 hours of hands-on training (August to October 2026), 12 quick wins covered.
- 2 sophisticated agents built on the bank’s own data.
- Estimated time saved per person per week (estimated by the team): Credit 8h, front office 7h, Compliance 5h, Finance 4h, Investments 3h, HR 2h.
- One AI champion per department keeps shared prompts and agents current.

### What we learned: five things we would do the same way again

1. **Start from the working day.** People adopt what saves them time on Tuesday morning.
2. **Say what AI is not for.** Pointing reporting problems to automation or Power BI kept AI credible.
3. **Out of the box first, engineering second.** Level 3 showed exactly where Copilot stops; that made the case for the data layer obvious.
4. **Governance is part of the design.** Access rights, hosting location and environment ownership were settled before client data went near a model.
5. **Skip Level 6 for now.** Fully agentic processes still raise real governance and risk questions in private banking.

---

## Future outlook

### From proof of concept to production

- **Next, from retrieval to workflows:** with retrieval on the operational data layer in place, the credit application agent moves from prototype to build.
- **2027, Level 5 in production:** credit application and client file first, on the bank’s own Azure AI Foundry. Rule-based integration first, intelligence on top.
- **Ongoing:** department champions keep Level 3 alive while engineering runs.

We pick you up where you stand: a first workshop, interviews with your teams and a priority roadmap for the use cases that are worth building.

#### Related

- [Case studies](https://s-pro.io/works)
- [AI process automation and Copilot Booster](https://s-pro.io/ai-process-automation)
- [AI agent development for banks](https://s-pro.io/ai-agent-development)
- [AI copilot for relationship managers at a Swiss private bank](https://s-pro.io/ai-copilot-relationship-managers-swiss-private-bank)
- [From AI pilot to production in a bank](https://s-pro.io/ai-agent-pilot-to-production-cost-bank)

*Last updated: 4 November 2026.*

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## FAQ

### How can an AI copilot help relationship managers at a Swiss private bank?

In this case the front office spent time preparing client meetings from five places: CRM, core banking, inbox, market news and the previous meeting’s notes. The team estimated 7 hours saved per person per week after training and the first configured solutions. Where a use case touches client data or feeds a regulated decision, the bank classified it as an engineered build, with defined access rights and logged sources, not as a prompt.

### Is Microsoft Copilot enough for a bank, or do we need custom AI agents?

Both, for different use cases. Meeting minutes, email drafting and summaries are quick wins in Microsoft 365 Copilot. Use cases that need large document sets, several systems, exact calculations, multi-step workflows, write-back or traceable sources, such as credit applications, are built on Azure AI Foundry. The bank classified 34 use cases against ten criteria (five technical, five governance) to decide.

### How long did it take the bank to move from no AI to Copilot and custom agents?

The engagement started in May 2026. Copilot went live bank-wide at the end of July 2026, 30 hours of training ran from August to October 2026, and a 2027 roadmap to put the credit application agent into production has been agreed.

### Why does the roadmap skip fully agentic (Level 6) processes?

Agents that own a process end to end are still hard to clear with governance and risk in private banking. The bank focuses on Levels 4 and 5: retrieval on a governed data layer and purpose-built workflows with human confirmation.

### How does S-PRO start a Copilot and AI agent engagement with a bank?

With a first workshop, interviews with your teams and a priority roadmap for the use cases worth building. A design thinking pilot starts from CHF 20k and takes 2-4 weeks.

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Contact

You will talk to engineers who build for banks, not to a sales script. Share your ideas, findings and questions with us.

- **Security and privacy first**ISO 27001 and ISO 27701 certified.
- **Claude Partner Network**S-PRO is a Certified partner in the Claude Partner Network. Our team includes Claude-certified engineers and architects.
- **Proven in regulated finance**Banks, wealth managers and fintechs in Switzerland, the EU and the US.

Who you’ll talk to

**Michael Barskyi**Head of Banking & Fintech

**Dustin Schulze**Senior Business Analyst

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[hi@s-pro.ioEmail](mailto:hi@s-pro.io)[+41 41 539 15 83Phone](tel:+41415391583)

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- [EN](https://s-pro.io/casestudy/swiss-private-bank-copilot-journey)
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