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Popular starting points

Operations and digital products

Operational value emerges when AI is combined with clear rules, current process data and existing systems.

Three detailed examplesAll use cases

AI becomes productive through processes and systems.

A strong model alone is not enough in operational workflows. Reliable solutions connect current data, business rules, permissions and controlled system actions. Suggestions and exceptions remain visible, while critical constraints and approvals are explicitly safeguarded.

Typical starting signals

These situations indicate potential. They are not an automatic decision to use AI.

  • Case status and data are spread across several systems.

  • Planning and control depend on manually consolidated spreadsheets.

  • Exceptions and bottlenecks become visible too late.

  • Specialists handle many similar cases with recurring decisions.

  • Standard software does not fit a significant workflow.

Three detailed examples

The three examples explain the starting point, target state, systems, controls, measures and a sensible pilot scope.

Cross-system case handling

Orchestrate checks, status, approvals and system actions in one traceable workflow.

Starting point

A case moves between inboxes, CRM, ERP, ticketing and spreadsheets. Status and ownership are lost at handovers.

Possible target state

An orchestrated workflow performs defined checks, creates records, requests approvals, updates status and assigns exceptions to a person.

Data and systems

  • CRM, ERP and ticketing
  • APIs and RPA
  • Message buses
  • Internal specialist systems

Controls and boundaries

  • Bound actions and permissions explicitly
  • Design idempotent steps and retries
  • Provide logs, stop conditions and exception paths
  • Retain manual approval for critical actions

Useful measures

  • Normal cases completed end to end
  • Errors and retries by interface
  • Time and reasons in exception queues
  • Traceability of status and completed actions

AI-enabled specialist application or digital product

Combine domain logic, AI capabilities, roles and data sources in one focused application.

Starting point

An off-the-shelf tool does not adequately support a differentiating internal or customer-facing workflow.

Possible target state

A focused web or mobile application combines domain logic, AI capabilities, roles and existing data sources in one usable interface.

Data and systems

  • Product APIs and data platforms
  • Identity access management
  • Existing specialist systems
  • Model providers and operating platforms

Controls and boundaries

  • Evaluate representative cases before launch
  • Design understandable errors and fallback states
  • Account for model and provider changes
  • Monitor privacy, cost and latency

Useful measures

  • Success rate for the primary user task
  • Quality across representative test cases
  • Fallbacks, errors and abandoned flows
  • Usage, latency and operating cost of the core capability

Support planning, dispatch and capacity matching

Bring together orders, capacities, deadlines and dependencies, prepare feasible planning scenarios and surface conflicts before commitments are made.

Starting point

Orders, availability, required capabilities, assets and deadlines are maintained in different systems. Planners reconcile information manually, respond to short-notice changes and often discover bottlenecks or dependencies late.

Possible target state

An assistant connects approved planning data, applies defined constraints and prepares traceable scenarios for sequence, timing and capacity matching. Hard constraints are checked deterministically. Conflicts, missing information and the effects of changes remain visible; binding commitments and staffing decisions stay with accountable people.

Data and systems

  • ERP, order and production planning
  • Dispatch, scheduling and capacity planning
  • Approved availability and capability data
  • Material, asset and transport information

Controls and boundaries

  • Show hard constraints, assumptions, data freshness and optimisation criteria
  • Use team- or asset-level capacity where possible and minimise personal data
  • Do not assess individual performance, suitability or conduct hidden employee monitoring
  • Require accountable approval before priorities, customer commitments, staffing decisions or system changes take effect

Useful measures

  • Planning scenarios that satisfy all defined hard constraints
  • Bottlenecks and deadline conflicts found before confirmation
  • Unplanned changes after approval and their causes
  • Effort and time to an approval-ready planning proposal
Smallest useful pilot

One defined order type, a bounded planning horizon and one team or asset group; compare suggestions with historical and current plans before enabling any write-back to production systems.

Related services

Further opportunities

Further tasks that may benefit from support depending on the workflow, available data and accountability.

  • Demand and volume forecasting

    Prepare expected volumes as ranges with drivers and uncertainty.

  • Improve master data quality

    Suggest duplicates, missing values and conflicting mappings for review.

  • Detect process anomalies early

    Flag unusual patterns or status paths with transparent baselines.

  • Prepare maintenance needs

    Use operational and sensor data to prioritise potential inspection or maintenance needs.

  • Support visual quality inspection

    Inspect images for known defect patterns and escalate uncertain cases.

  • Structure shift and operational handovers

    Summarise events, open incidents and next actions from approved sources.

Data, systems and boundaries

Typical data and systems

  • ERP, production, order and workflow systems
  • APIs, integration platforms and controlled RPA
  • Operational, sensor, quality and master data
  • Roles, business rules and event logs

What requires particular care

  • Validate hard constraints and safety rules deterministically
  • Control write access, approvals, retries and rollback
  • Do not allow autonomous safety-critical or irreversible actions
  • Monitor data drift, model quality, cost and operation continuously

Relevant next steps

Services

Practical knowledge

Other areas

Which use case fits your current situation?

We structure the task, data, systems and risks and propose a testable first scope.

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