GenAI Enterprise Application Integration Design: [Programme or application landscape]

Design document for bringing generative AI assistants and agents into enterprise applications: ERP, CRM, IT service management and workplace productivity suites. It records the use case portfolio per application, the choice between vendor-native copilots and agents (for example SAP Joule, Salesforce Agentforce, Microsoft 365 Copilot and Copilot Studio, and ServiceNow Otto, formerly Now Assist) and custom agents on the enterprise AI platform, grounding data and permissions, integration points across suites, human oversight, data residency, security, evaluation, licensing and cost, regulatory duties under Regulation (EU) 2024/1689 (the AI Act) as amended by Regulation (EU) 2026/1744, rollout and risks. Vendor product names change often: check each one at the vendor's current documentation. Replace every [bracketed] value and every row marked Example. The regulatory rows are a documentation format, not legal advice.

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Contents

1. Scope and context
2. Use case portfolio
3. Vendor-native or custom
4. Vendor capability baseline
5. Grounding data and permissions
6. Integration points
7. Human oversight and autonomy
8. Data residency and processing
9. Security
10. Evaluation and acceptance
11. Licensing and cost
12. Regulatory and governance duties
13. Rollout and adoption
14. Risks and traceability
15. Glossary and references

1. Scope and context

1.1 Outcomes

1.2 Landscape

2. Use case portfolio

Prioritise with three questions: does the use case change a measured outcome from section 1.1, is the data it needs clean and correctly permissioned today, and can a person check its output faster than doing the work. Defer use cases that fail the second question until the data work is done.

3. Vendor-native or custom

3.1 Decision criteria

3.2 Worked scoring example

Example for UC-002 (customer-facing service agent). Weights sum to 100; scores run from 1 (poor) to 5 (strong); the weighted total is the sum of weight times score, divided by 100. Agree the tie rule before scoring: totals within 0.10 of each other are treated as a tie and decided by the control and portability criterion.

Check: vendor-native is (25 x 4 + 20 x 5 + 15 x 5 + 15 x 3 + 15 x 2 + 10 x 3) / 100 = 380 / 100 = 3.80; custom is (75 + 60 + 30 + 60 + 75 + 40) / 100 = 3.40; hybrid is (100 + 80 + 60 + 45 + 60 + 30) / 100 = 3.75. Vendor-native and hybrid are 0.05 apart, inside the agreed 0.10 tie band, so the control and portability criterion decides (hybrid 4 against vendor-native 2): the Example decision is hybrid, which also lets the order status tool be reused by two other channels. Record the decision in the decision log (architecture/architecture-decision-log) and the wider sourcing case with the build vs buy analysis (architecture/build-vs-buy-analysis).

3.3 Decisions per use case

4. Vendor capability baseline

The table lists the main product families as named on vendor pages checked in October 2026. Names and packaging change often; confirm against the vendor documentation and your order form.

For each product in scope record: general availability or preview status in your region, whether it is on by default after an upgrade, the admin switch that disables it, the model providers and subprocessors the vendor lists, and the audit log it produces.

5. Grounding data and permissions

Embedded copilots answer from what the signed-in user can access. That makes existing oversharing visible: a file shared with "everyone" five years ago becomes an answer today. Treat permission hygiene as a launch prerequisite.

Rules for grounding:

• Exclude fields that the AI never needs (bank accounts, national identifiers, health data) using the suite's masking or field-level controls, even when the user may see them.
• Keep the vendor's grounding inside its own suite. Reach other suites through governed tools (APIs, MCP servers or agent hand-offs) that apply the target suite's permissions, not by copying data into the vendor's index.
• Review sensitivity labels and retention policies, so that labelled content is handled as designed in answers, summaries and generated files.
• Test with real personas: run the same questions as users in different roles and compare what each one sees.

6. Integration points

Use the integration strategy (architecture/integration-strategy) for the general interface rules, and the MCP server specification (ai-data/mcp-server-spec) for each tool the platform exposes.

7. Human oversight and autonomy

Design oversight in detail with the human oversight design (ai-data/human-oversight-design), and record agent behaviour in an AI agent specification (ai-data/ai-agent-spec) per custom agent.

8. Data residency and processing

Check each product's documentation for overflow routing (calls sent to other regions when capacity is short), the list of model subprocessors, and whether customer data is used to train models. For EU personal data, update the record of processing (compliance/gdpr-ropa) and run or update the DPIA (compliance/dpia). Assess sovereignty requirements with the data residency and sovereignty assessment (compliance/data-residency-sovereignty-assessment).

9. Security

Threat-model each acting agent with the agentic threat model (ai-data/agentic-threat-model) and assess each vendor's AI terms with the AI vendor risk questionnaire (compliance/ai-vendor-risk-questionnaire).

10. Evaluation and acceptance

Vendors update models and prompts without your release. Schedule regression runs in each suite's release calendar (preview sandboxes where the vendor offers them) and use the suite's own evaluation tools where they exist. Plan the method with the LLM evaluation plan (ai-data/llm-evaluation-plan) and the agent evaluation plan (ai-data/agent-evaluation-plan).

11. Licensing and cost

11.1 Licence models in scope

11.2 Worked cost example

Example, using illustrative unit prices that are not vendor list prices.

The sum is 18,000 + 16,000 + 240 + 80 = USD 34,320. Licence and consumption charges dominate; token cost on the platform is under 1 % of the total, so negotiate the vendor commercial terms first. Compare against the value: if UC-002 contains [6,000] contacts per month that previously cost [USD 4] each, it saves [USD 24,000] against USD 16,240 of cost. Add the cost of review time and of the evaluation runs. Model the full lifecycle in the TCO analysis (architecture/tco-analysis).

12. Regulatory and governance duties

Dates follow Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744; check the consolidated text on EUR-Lex.

Register each use case in the AI use case intake (ai-data/ai-use-case-intake) and the regulatory applicability register (compliance/ai-regulatory-applicability-register). A custom agent that the organisation builds and places in service under its own name may make the organisation a provider for that system; record that analysis.

13. Rollout and adoption

Support adoption with role-based training, prompt guidance in the users' language, champions per team, and a feedback channel whose items are triaged weekly. Track active users, outputs accepted without edit, hand-off rates and complaints per use case.

14. Risks and traceability

15. Glossary and references

Standard and licence note

Based on the public product documentation of the vendors named above (checked in October 2026) and on Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744 (EU legislative texts are free to reuse). Vendor features are described in original wording for mapping only, and no vendor text, checklist or diagram is reproduced. The structure, criteria and examples are original to this collection. Check the current vendor documentation, your contracts and the consolidated legal text before use. The regulatory rows are a documentation format, not legal advice. SAP, Joule, Salesforce, Agentforce, Microsoft, Microsoft 365 Copilot, Copilot Studio, Dynamics 365, Microsoft Agent 365, ServiceNow, Otto and Moveworks are trademarks of their owners; this template is not affiliated with or endorsed by them. Template licence: MIT.