Move AI from pilots to work that runs every day.
Decide which AI initiatives to scale, fix or stop, then build the agents worth connecting to your systems, with controls that security and audit teams can accept.
For CEOs, CIOs, CFOs and AI leaders.
Most AI doesn't fail in the model. It fails in the operation.
Value
Is it tied to a cost, a cycle time or a risk?
Data
Is the data complete and allowed for this use?
Control
Who approves, what is logged, how is it reversed?
Ownership
Who runs it and measures it after launch?
Every AI initiative needs an owner, an outcome and a production gate.
The output is not a list of ideas. It is a defensible decision for every initiative.
Illustrative portfolio · fictitious examples · final decisions depend on evidence and scope.
Where AI agents earn their place.
Pick an area to see typical candidates and the controls that come with them.
Finance & accounting
Agents that prepare reconciliations, explain variances and route exceptions before the close.
- Invoice and payment exceptions
- Variance explanations for controllers
- Bank-statement matching
- Approval limits and segregation of duties
- Every posting traceable to its evidence
Sales & service
Agents that classify requests, draft answers from approved knowledge and hand off to a person when it matters.
- Ticket and email triage
- Order status and credit-block follow-up
- Quote and proposal drafts
- Answers only from approved sources
- Clear hand-off rules to people
Supply chain & operations
Agents that watch exceptions in purchasing, planning and logistics and propose the next action.
- Purchase requisitions and supplier follow-up
- Planning and MRP exceptions
- Shipment delays and customer notices
- Recommend first, act later
- Limits per supplier, amount and plant
HR, IT and knowledge
Assistants that answer policy and IT questions from your own documents, with sources.
- Policy and benefits questions
- IT service desk first response
- Search across internal knowledge
- Access respects each user's permissions
- Personal data handled by policy
Five reasons AI stays stuck in pilot.
Each team runs its own experiment and no one decides what scales.
The use case is right but the data it needs is incomplete or scattered.
Nobody designed identity, permissions or the audit trail before the demo.
The assistant answers questions but isn't connected to the systems where work happens.
No owner, no quality measures, no plan to improve or switch it off.
Usually three to five weeks to know what to scale, fix or stop.
- Week 1
Inventory
Every initiative, pilot and request, with its owner.
- Weeks 2–3
Score
Value, data, control readiness and cost to run.
- Weeks 4–5
Decide
Scale, fix, pilot or stop, with a 12-month roadmap.
- Typical timing; the final plan depends on scope.
- Scored AI portfolio with a decision per initiative
- Control and governance model
- Roadmap and reference architecture for the first agents
Turn an AI idea into a decision record.
Use our scorecard, control matrix and evaluation worksheet before committing to a pilot.
Everything around the model that makes it work.
Knowledge & retrieval
Answers from your documents, with sources and permissions.
Tools & integration
Agents connected to ERP, CRM and service systems through APIs.
Orchestration
Multi-step work with hand-offs to people.
Evaluation
Test sets and quality checks before and after launch.
Observability
Logs, cost and quality visible per agent.
Governance
Identity, least privilege, approvals and reversal.
One portfolio decision. Four different kinds of work.
The next page depends on what the evidence says—not on which technology is fashionable.
AI agent engineering
Use when a bounded agent needs knowledge, tools, evaluation and an operating model.
See the engineering method →02 · SAPAI for SAP
Use when Joule, BTP or a custom agent must act inside controlled SAP processes.
See SAP use cases and controls →03 · ENABLEEngineering & integration
Use when the blocker is an API, workflow, portal or data connection around the core.
See the integration capability →04 · GOVERNEnterprise Agent Architecture™
Use to align business context, knowledge, actions, systems, governance and human oversight.
Read the framework →Questions to resolve before starting
Is this only for SAP customers?+
No. We assess and build AI across your business systems. When SAP is involved, we also cover Joule and SAP's own AI.
Which AI models do you use?+
The one that fits the use case, data rules and cost. We are not tied to one vendor.
Do we need a data platform first?+
Not always. The assessment shows which use cases can start with the data you have and which need work first.
How long does the portfolio assessment take?+
Usually three to five weeks, depending on how many initiatives and teams are involved.
Bring your list of AI ideas.
We'll tell you which ones are worth building first, and which to stop.