Insights · AI pilot to production
How much does it cost to take an AI agent from pilot to production at a bank?
Claude Certified Services Partner(Anthropic)An AI pilot or AI MVP at a bank has no single price, because the price follows the stage. At S-PRO a design thinking pilot (workshops, an AI prototype and user testing, 2–4 weeks) comes first. A full solution pilot takes up to 2–3 months; its cost depends on scope and complexity. Production is estimated after discovery, because budgets depend on integrations, data, risk class and approvals, not on the model itself. We build on our own agentic platform, S-PRO Agents, so the pilot is built as the first step of the production system and is not thrown away.
01
Why bank AI pilots stall, and what moves them to production
A joint study by Wavestone and the University of St. Gallen, based on 30 semi-structured interviews with senior leaders at banks, insurers and technology providers in the DACH region (October 2025 to May 2026), found that most organizations remain in the early stages of adoption and names regulation as the primary adoption barrier. Data quality, governance and legacy technology are among the other blockers it lists. In practice, a pilot stalls for five concrete reasons, and each has a counter-measure that has to be built into the pilot, not added afterwards.
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| Why the pilot stalls | What moves it to production |
|---|---|
| It ran on sample data, not governed production data. | Build the pilot on the production data layer from the start, so it can be reused instead of rebuilt. |
| No evaluation set, so nobody can prove accuracy to risk and compliance. | Ship an evaluation set with the pilot, so risk and compliance can review measured accuracy. |
| No audit trail or human-in-the-loop design, so the model-risk review stalls. | Include a human-in-the-loop interface and an audit log in the pilot itself. |
| Security, outsourcing and data-residency approvals start too late. | Plan them from the start of the pilot, and finish it with the documentation for the production decision. |
| Each use case is built separately, with no shared data foundation. | Use one governed data foundation, so each new agent reuses it. |
Source: Wavestone, Agentic AI study: From GenAI to Agentic AI in Financial Services, published 9 July 2026. The five reasons and counter-measures are S-PRO's own delivery approach, not findings of the study.
02
What drives the budget
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| Driver | Lower cost | Higher cost |
|---|---|---|
| Integrations | 1–2 read-only systems | Core banking, CRM, KYC and documents, with write access |
| Data | Clean, structured | Unstructured documents, several languages, entity resolution |
| Risk class (EU AI Act) | Internal productivity | Client-facing or credit-related |
| Approvals | Existing AI policy | First agent in the bank, new outsourcing assessment |
| Hosting | Vendor-hosted API | Dedicated region or on-premise model |
03
What a production budget for a bank AI agent consists of
A production budget is more than the pilot plus a hosting bill. This is S-PRO's own delivery view of the line items, and which of them are paid once and which keep recurring after go-live. The rows are general budget components a bank should expect to see in any quote; they are not a list of S-PRO services. It shows no amounts: they depend on scope and are fixed in a quote, not in a list.
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| Line item | One-off or recurring | What drives it |
|---|---|---|
| Integration with core banking, CRM, KYC and document systems | One-off to build; recurring to maintain when the connected systems change | Number of systems, read-only or write access, quality of the available interfaces |
| Data readiness (access model, cleaning, entity resolution, multiple languages) | Mostly one-off; recurring where new data sources are added | Share of unstructured documents, number of languages, data ownership and access rights |
| Evaluation set and model-risk documentation | One-off to create; recurring to refresh when the agent, model or process changes | Use-case risk class (EU AI Act), depth of the bank's model-risk process, regulator expectations in the bank's jurisdiction |
| Security and third-party (outsourcing, ICT) review | One-off for the first agent; smaller for later agents that reuse the approved route | Hosting route, first AI agent in the bank or not, data-residency requirements |
| Human-in-the-loop interface and audit log | One-off to build; recurring to operate and store logs | Which actions need approval, number of reviewers, log retention rules |
| Hosting and model usage | Recurring | Volume, model and hosting route (vendor API, dedicated region, on-premise model); usually a small share compared with integration, data and approval work |
| Monitoring, support and incident handling | Recurring | Service level the business needs, number of integrations, who operates the system (bank team or partner) |
| Human oversight in operation | Recurring | Share of cases a person must approve, staffing of the review team |
| Change management and training | One-off at launch; recurring with each new user group or process change | Number of teams and locations, changes to the existing process |
| Ongoing change (model upgrades, new use cases, regulatory updates) | Recurring | How often the model, the process or the rules change; whether new agents reuse the same data foundation |
Why does the run cost never reach zero? The agent lifecycle
Most of the recurring lines in the table above come from the agent lifecycle, not from the model. S-PRO's AI agent concept describes the lifecycle of an agent in five stages:
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| Stage | What happens | What recurs and is therefore budgeted (S-PRO's view) |
|---|---|---|
| 1. Experimentation and testing | Agent platform set up, agent shell created, prompts engineered and versioned, LLM selected, knowledge base connected, responses tested in a sandbox, integrations developed | Mostly one-off; prompts and test sets stay versioned |
| 2. Formalisation | Problem statement, business case, risk profile review, prioritisation in the central backlog | Governance time of the business owner and risk |
| 3. Approval and deployment | Formal review, governance council, deployment to production by IT, access rights, user training and release | Approval cycles, repeated when scope changes |
| 4. Operations and monitoring | Usage and performance monitoring, cost tracked per agent, quality review, issues flagged | Recurring: monitoring, support, hosting and model usage |
| 5. Versioning and decommissioning | Model and knowledge-base updates, roll-back to a stable version, archiving of agent data, retirement of unused agents | Recurring: re-testing after every model or knowledge-base change |
Source: S-PRO AI agent concept (governance: the agent lifecycle). In S-PRO's experience a pilot quote often covers only stage 1 (estimated by the team); ask where stages 3 to 5 are in the quote and which of the six gates before production it covers.
A pilot quote may cover only part of the first five rows. Ask every vendor which rows are inside the price, which are left to the bank and which are excluded. For how to compare quotes line by line, see From AI proof of concept to production at a bank.
04
What regulation adds to the cost of taking an agent live
Regulation does not put a price on an AI agent. It adds recurring work: documentation, logging, vendor contracts and monitoring that have to be kept current after go-live. Which duties apply depends on the use case and the jurisdiction. This is a general reading of public texts for budgeting, not legal advice, and it shows no amounts.
Governance is a build item: at AMINA we set up roles, access rights and usage processes for the agent library (AMINA agent governance).
EU: AI Act, and only for some use cases
Annex III point 5(b) of Regulation (EU) 2024/1689 lists as high-risk the AI systems intended to evaluate the creditworthiness of natural persons or establish their credit score, with the exception of AI systems used for the purpose of detecting financial fraud (European Commission AI Act Service Desk, Annex III; official text on EUR-Lex). This does not make banking AI high-risk in general: a fraud-detection system is carved out, and an AML/KYC or customer-support agent is not listed in that point. For systems classified as high-risk under Annex III, the high-risk obligations apply from 2 December 2027, following the Digital Omnibus on AI, Regulation (EU) 2026/1744 of 8 July 2026, which amended Article 113 of the AI Act. The cost implication is qualitative: for a credit-scoring use case, the risk-management, documentation, logging and human-oversight duties of the high-risk regime become part of the build and of the recurring run.
EU: DORA, through the contract with the AI vendor
For EU financial entities, the Digital Operational Resilience Act (Regulation (EU) 2022/2554, applicable since 17 January 2025) puts its duties on the financial entity, not on the vendor. They reach an AI vendor through the contract: the bank maintains a register of information on all contractual arrangements for ICT services provided by third parties (Art. 28(3)), must put exit strategies in place for ICT services supporting critical or important functions (Art. 28(8)), and the contract must include at least a clear and complete description of the services and the regions or countries where services are provided and data is processed, including the storage location (Art. 30(2)(a) and (b)). Source: EUR-Lex. The practical effect on cost is the contract and exit-plan work, the register entry and the data-location commitments, which a bank should expect to see asked of any AI vendor.
US: model-risk guidance now stands apart from agentic AI
On 17 April 2026 the Federal Reserve, the OCC and the FDIC issued revised model risk management guidance (SR 26-2; OCC Bulletin 2026-13), which supersedes and replaces SR 11-7 of 2011 and SR 21-8 on model risk management for BSA/AML systems. The guidance states that generative AI and agentic AI models are novel and rapidly evolving and, as such, are not within its scope; it adds that a banking organization's own risk management and governance practices should guide the controls for tools it does not cover. The Federal Reserve letter expects it to be most relevant to banking organizations with over $30 billion in total assets. For a US bank this means an agent cannot simply be run through the model-validation routine the guidance describes, and the bank has to set and document its own governance and controls for it; the budget should allow for that documentation. Sources: Federal Reserve SR 26-2, OCC Bulletin 2026-13.
International: FSB sound practices
On 15 October 2026 the Financial Stability Board published the final report "Sound Practices for Responsible Adoption of Artificial Intelligence (AI)": 12 sound practices that financial institutions are encouraged to consider: four on organisation-wide governance and eight on managing risk across the AI lifecycle, including human oversight, cyber and ICT risk and third-party AI dependencies. The report says the practices are non-prescriptive and "do not constitute a binding international standard", and that it includes additional considerations for agentic AI and multi-agent systems, which may need more comprehensive treatment in future. In budget terms they point to governance, lifecycle controls and third-party risk work that is not a one-off. Source: FSB final report page. S-PRO's summary for banks: FSB sound practices for AI agents at a bank.
05
How S-PRO prices a bank AI agent
We build on Claude, Azure OpenAI or on-premise models. S-PRO is a Certified partner in the Claude Partner Network, and our team includes Claude-certified engineers and architects.
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| Stage | What you get | Duration | Price |
|---|---|---|---|
| Design thinking pilot | Workshops, AI prototype, testing with users, agreed scope and success criteria | 2–4 weeks | scoped per project |
| Solution pilot | The full agent on a real process, connected to your systems, with evaluation set, human-in-the-loop interface, audit log and documentation for the production decision | up to 2–3 months | scoped per project, depending on complexity |
| Production | Hardening, integrations, monitoring, model-risk and regulatory documentation | depends on scope | agreed per project, estimated after discovery |
Production phase
Monitoring and observability, role-based access, model-risk documentation, EU AI Act and FINMA documentation, and a run and support model. Production scope and price are agreed per project. In the first conversation we cover scope, data, controls and the exit criteria of the pilot; we prepare the estimate after discovery.
Why the pilot is fast, and why it carries into production. S-PRO has its own agentic platform, S-PRO Agents, the core of our products and client solutions. We do not start from a blank page: around the platform we build your solution, take it from a design thinking pilot through a solution pilot to a governed production agent, and run it for you as a service. Products already built on it include RegRadar (regulatory change monitoring) and Microsoft Copilot Booster (an agents hub that connects to Microsoft Copilot). S-PRO has delivered 100+ AI projects, 40+ of them agentic AI projects. See also AI agent development for banks.
06
What do public cost benchmarks say?
Public estimates of what a bank AI agent costs vary widely, because they price different things. Two vendor sources that state the basis of their figures agree less on amounts than on structure: the pilot is the smaller part of the total spend, integration is a cost line of its own, evaluation and monitoring are budgeted separately, and the model is a small share. We show only these ratios, not the vendors' amounts, because their scopes are not comparable with a regulated bank programme.
Teamvoy's figures are for mid-market fintech, not banks; ableneo's are its own estimates.
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| Source | What it says, as published | Date | Basis stated by the source |
|---|---|---|---|
| ableneo | Production "usually multiplies the figure 5 to 10 times" (the proof-of-concept cost); the model licence or token spend is "often less than 20 percent of the two-year total" | Undated; accessed 21 October 2026 | ableneo's own estimates for AI systems in banks; the page says ableneo shipped 34 production AI projects in 2025 |
| Teamvoy | Integration "consistently runs 25–40% of total build cost"; the model layer is 8–18% of build cost; the evaluation and observability scaffold is a separate cost line | Posted 18 May 2026, updated 5 October 2026; accessed 21 October 2026 | Mid-market fintech (Series B–D), not a bank; ranges across Teamvoy's own fintech engagements in the past 24 months, anonymized and rounded |
Third-party estimates, not S-PRO pricing. S-PRO scopes and quotes each project individually. Both sources are vendors' own pages, not independent studies; accessed 21 October 2026.
What this means for a bank
Integration, controls and audit, and monitoring appear as their own lines in these sources, which matches the drivers above, and the model is a minor share in both. Data quality is not priced separately in either, so we keep it in our own driver table. For how a pilot is built to be reused in production, see From AI proof of concept to production at a bank. For the audit trail and explainability work that sits behind the "controls" line, see How to make KYC AI agents audit-ready in a bank.
07
How many AI pilots reach production: what the evidence says
There is no single reliable number. Published figures come from different samples, different questions and different definitions of "pilot" and "production", so they cannot be added up or compared as one series. What the source below can support is narrower: it shows that many projects in financial services are still unfinished. One figure is quoted here, with its limits.
Riverbed, a vendor survey. In the financial-services findings of its global survey (1,200 business decision-makers, IT leaders and technical specialists across seven countries and multiple industries; fieldwork July 2025 by Coleman Parkes Research), Riverbed reports that only 12% of financial-services organizations have AI projects fully deployed enterprise-wide and 62% still remain in pilot or development stages. Riverbed sells IT-operations software, the figures are respondents' own reports and not a measurement of production rates, the release does not state how many respondents were in the financial-services subset, and "financial services" is wider than banks. Source: Riverbed press release, 4 February 2026.
The qualitative point matters more than any one figure: public "stall rates" vary widely with method, and the same project can count as failed, paused or still running depending on the definition. The Wavestone and University of St. Gallen study cited above is based on 30 interviews and is not used here for a percentage. For a bank the useful reading is therefore not "most pilots fail" but "which of the causes in the table above apply to our pilot", and that is a question about scope, data, controls and ownership, not about a headline percentage.
08
Why a pilot quote does not predict the production price
A pilot is built to answer one question: does the agent work on this process? A production system has to answer a different one: can the bank run it, audit it and defend it for years? Most of the work behind the second question is not in the pilot, which is why a pilot price cannot be multiplied into a production price. In our delivery view there are five reasons.
- The pilot may touch one or two systems; production touches all of them. Integration with core banking, CRM, KYC and documents, often with write access, is priced per system and per access level.
- The pilot runs on a controlled sample; production runs on every case. Edge cases, several languages and unstructured documents show up at volume and add data and evaluation work.
- Approvals are mostly outside the pilot. Security, outsourcing and model-risk review, and the documentation behind them, depend on the bank's own process. The first agent in a bank often carries much of this effort; later agents reuse it.
- Running the system is a recurring cost. Monitoring, support, human oversight and change management continue after go-live and are not part of a one-off pilot price.
- Scope changes between pilot and production. A pilot proves one process on one data set; production may add users, locations or a second use case. The price follows the scope that is finally agreed, not the pilot's.
This is why S-PRO builds the pilot on the production data layer from the start, ships an evaluation set, a human-in-the-loop interface and an audit log inside the solution pilot, and prepares the production estimate after discovery, once the scope is known. Published benchmarks point the same way: ableneo says production "usually multiplies the figure 5 to 10 times" the proof-of-concept cost (see the benchmark table above; a vendor's own estimate, not S-PRO pricing).
Results, already in production
What production looks like
An AI agent in production at a leading German neobroker. The agent classifies incoming support tickets, retrieves data from backend systems and drafts replies that follow the neobroker's tone-of-voice guidelines, BaFin-relevant disclaimers and internal compliance rules; a support agent approves each reply. The case reports: in 85% of cases the agent's draft reply was not wrong, 90% correct ticket classification and more than 40% faster ticket handling. Read the case.
A Swiss-regulated bank, delivered step by step. For AMINA Bank, S-PRO proposed a step-by-step strategy and co-developed the target architecture with the bank's team: solution architecture, an API-driven middle layer organized into business domains, and business analysis. Key elements of AMINA's existing data models and infrastructure were reused instead of rebuilt from scratch, and the first internal release has gone live. This is a digital banking platform, not an AI agent; it shows how we work inside a regulated bank's existing systems. Read the case.
Swiss private bank: from no daily AI use to a production plan for 2027
A Swiss private bank, part of an international banking group, moved up a six-level AI adoption scale in six months, one level at a time. Levels 1 to 3 are about people using Microsoft 365 Copilot well out of the box (see Copilot Booster and process automation). 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.
- Levels 1 to 3 (training and adoption): Copilot went live across the bank at the end of July 2026. 30 hours of hands-on training between August and October 2026 covered 12 quick wins; interviews and training covered seven departments.
- Levels 4 to 5 (engineering): a retrieval layer on the bank's operational data and two purpose-built agents on the bank's own data. The credit application agent is a prototype; a human reviews and owns every credit decision.
- Production, 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.
- What drove cost and timing: 34 use cases were classified against ten criteria, five technical and five governance. Some requests were reporting or data problems, and the honest answer was ETL or Power BI, not AI. Access rights, hosting location and environment ownership were settled before client data went near a model.
Read the full case study: Private Bank Copilot Journey
A leading German neobroker: a support agent with reported results
For a leading German neobroker, S-PRO built a Zendesk app for the support team. It classifies each incoming ticket and drafts a reply; the operator checks the draft and sends it. Built on Amazon Bedrock and Anthropic models. Read the case study
- In 85% of cases, the agent's draft reply was not wrong.
- 90% correct ticket classification.
- More than 40% faster ticket handling.
The operator checks every draft and sends it. For a bank, that is the pattern to copy: agree the numbers that count before the pilot, measure against them, keep a named person in the loop.
AI support agent for a leading German neobroker
The agent drafts each reply from account history, order status and policies, and a support agent approves it.
Policy answers in 30 seconds for a Swiss bank
Employees of the Swiss subsidiary of an international bank search internal policies and procedures through a role-based AI assistant that shows the source of every answer.
Earlybird
An AI platform for one of Europe's most established venture capital firms: data aggregation and analysis, automated due diligence and risk prediction for investment decisions.
View case10
Sources
- Wavestone, Agentic AI study, 9 July 2026: wavestone.com
- ableneo, how much does it cost to build an AI system in a bank: ableneo.com
- Teamvoy, cost of production AI in fintech: teamvoy.com
- Riverbed press release, 4 February 2026: riverbed.com
- European Commission AI Act Service Desk, Annex III: ai-act-service-desk.ec.europa.eu
- Regulation (EU) 2024/1689 (AI Act): eur-lex.europa.eu
- Regulation (EU) 2026/1744 (Digital Omnibus on AI), 8 July 2026: eur-lex.europa.eu
- Regulation (EU) 2022/2554 (DORA): eur-lex.europa.eu
- Federal Reserve, SR 26-2, 17 April 2026: federalreserve.gov
- OCC Bulletin 2026-13, 17 April 2026: occ.gov
- Financial Stability Board, Sound Practices for Responsible Adoption of AI, final report, 15 October 2026: fsb.org
Sources 1 to 3 were opened on 20 to 21 October 2026, sources 4 to 11 on 21 October 2026. The EU texts were read in the Official Journal version.
FAQ
Questions buyers ask us
How long does it take to move a generative AI pilot into production at a regulated bank?
It depends on scope, integrations and internal approvals, so S-PRO does not quote one number for every bank. What we can state from our own delivery: a design thinking pilot (workshops, AI prototype, user testing) takes 2–4 weeks, and the full solution pilot on a real process takes up to 2–3 months. For building a custom AI agent end to end, we typically see 3 to 6 months depending on complexity and scope, and simple task-specific agents can take as little as 8–12 weeks. Security, outsourcing and data-residency approvals and the documentation for the production decision are a common reason pilots stall, so they should be planned from the start of the pilot, not after it.
What does a fixed-scope AI agent pilot for a Swiss private bank typically include?
One agent on one real process, with agreed scope and success criteria. In the design thinking phase (2–4 weeks, from CHF 20k) that means workshops, an AI prototype, testing with your users and an agreed scope. The solution pilot (up to 2–3 months, cost depending on complexity) then adds the full agent connected to your systems, an evaluation set, a human-in-the-loop interface so a person approves what affects a client file, an audit log, and the documentation for the go/no-go decision on production. The model can run through the Anthropic API, Amazon Bedrock or Google Cloud Vertex AI, or an Azure OpenAI or on-premise model; EU and Swiss data residency options are agreed per project with your security and legal teams. You own the code and the prompts.
Is the model (Claude or GPT) a large part of the cost?
Usually not. Model run costs depend on volume and are small compared with integration, data and approval work.
Can we reuse the pilot in production?
Yes, if it was built on the production data layer from the start. That is how we build pilots.
Who owns the code and prompts?
The client owns the code and the prompts.
How do we get a price?
We start with a first conversation about scope, data, controls and pilot exit criteria, followed by discovery. We prepare the estimate once those are clear. A design thinking pilot is from CHF 20k and takes 2–4 weeks; production pricing is agreed per project.
Why do most bank AI pilots never reach production?
Because they run on sample data, have no evaluation set, lack an audit trail and human-in-the-loop design, and start security and outsourcing approvals too late.
What does a production budget for a bank AI agent consist of?
In S-PRO's delivery view: integration with core banking, CRM, KYC and document systems; data readiness; an evaluation set and model-risk documentation; security and third-party review; a human-in-the-loop interface and audit log; hosting and model usage; monitoring and support; human oversight in operation; change management; and ongoing change. The first five are mostly one-off to build, though they recur when systems, the model or the process change. Hosting, monitoring, support, oversight and ongoing change recur after go-live. The table on this page lists them with what drives each; amounts are fixed in a quote for the specific scope.
What does regulation add to the cost of taking a bank AI agent into production?
It adds recurring documentation, logging, contract and monitoring work rather than a fixed fee, and which duties apply depends on the use case. In the EU, AI Act Annex III point 5(b) makes AI systems that evaluate the creditworthiness of natural persons or set their credit score high-risk, with fraud detection excluded, and the high-risk obligations apply from 2 December 2027. For EU financial entities, DORA duties reach an AI vendor through the contract (register of information, exit strategy, contract content, data locations). In the US, SR 26-2 of 17 April 2026 replaced SR 11-7 and states that generative and agentic AI models are not within its scope. The FSB published 12 non-binding sound practices on 15 October 2026. This is a general reading of public texts, not legal advice.
Why is a pilot price not a good predictor of the production price?
Because production adds work the pilot does not contain: integration with every system the process touches, volume and edge cases, the bank's security, outsourcing and model-risk approvals, and recurring costs for monitoring, support and human oversight. The price also follows the scope finally agreed, which often differs from the pilot's. S-PRO prepares the production estimate after discovery, against the agreed scope.
What do public benchmarks say about the cost of taking an AI agent to production at a bank?
Published estimates vary widely because they cover different scopes, so we only cite ratios from two vendor sources that state their basis. ableneo writes that production "usually multiplies the figure 5 to 10 times" the proof-of-concept cost and that the model licence or token spend is "often less than 20 percent of the two-year total". Teamvoy, writing about mid-market fintech rather than banks, puts integration at 25–40% of total build cost and treats evaluation and observability as a separate cost line. These are third-party estimates, not S-PRO pricing; S-PRO scopes and quotes each project individually.
What does an AI MVP for a bank cost and how is it different from a pilot?
An AI MVP is the smallest working version of an AI product that real users can use; a pilot proves one use case in a controlled setting. At S-PRO both start with a design thinking pilot, which starts from CHF 20k and takes 2–4 weeks. A full solution pilot takes up to 2–3 months and its cost depends on scope and complexity. Production is estimated after discovery, once integrations, data, risk class and approvals are known. Because we build on our own platform, S-PRO Agents, the pilot is the first step of the production system. See from AI proof of concept to production at a bank.
What does an AI pilot for a wealth manager cost and what is included?
S-PRO starts a wealth manager with a design thinking pilot from CHF 20k, 2–4 weeks: workshops, an AI prototype and user testing on one use case, for example a relationship-manager copilot. A full solution pilot is sized after that stage, and production is estimated after discovery. See AI copilots for wealth management teams and AI agent development for banks.
Why does an AI agent cost money after go-live?
Because a production agent has a lifecycle: monitoring, cost tracking per agent, quality reviews, re-testing after every model or knowledge-base change, roll-back and eventual decommissioning. These are recurring lines, separate from the build.
Do we need a fully agentic process (Level 6) to get value from AI at a bank?
No. In S-PRO's six-level scale, Level 6 is mostly market noise for a regulated bank today. A Swiss private bank moved from no AI in daily work to department-wide Copilot adoption in six months, and plans Level 5 in production in 2027, starting with credit application and client file.
How do you know a pilot is good enough to fund production?
Set a measurable target and test against it. In one support-agent project for a leading German neobroker, the reported results were: in 85% of cases the agent's draft reply was not wrong, 90% correct ticket classification and more than 40% faster ticket handling, with an operator approving every reply.
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