Where should an operations team start with AI?

At intake. It is high volume, text-based, measurable and fails safely because errors are caught downstream by an existing review step.

AI use case

AI in operations

AI in operations and back office: intake, scheduling support, exception handling, supplier communication and reporting, with complexity, risks, KPIs and ROI considerations.

Miguel Torres, Founder, Merjora · Updated 3 September 2026

In short

Operations teams get the most from AI at the intake and exception layers: turning unstructured requests into structured work items, explaining why an exception occurred, drafting supplier or customer communication about it, and compiling the recurring operational reports that consume analyst time. Physical-world optimisation — routing, capacity, scheduling — usually needs data infrastructure that most mid-market operations teams do not yet have.

The workflow today

Requests and updates arrive in mixed formats, are converted into work items, exceptions surface, someone investigates and communicates, and performance is reported weekly.

Where AI helps

Structuring unstructured intake into work items
Clustering and explaining exceptions rather than listing them
Drafting supplier and customer updates
Compiling recurring operational reports with commentary
Checking submissions against procedure before they enter the queue

Common use cases

Order and request intake normalisation
Exception triage and root-cause clustering
Supplier communication drafting and chasing
Shift handover and daily operations summaries
Procedure compliance checks on submitted work

Expected benefits

Less time converting messages into system records
Faster exception resolution
Consistent operational reporting
Fewer avoidable escalations

Implementation complexity

Moderate. Text-based intake and reporting are straightforward. Anything touching planning or scheduling depends on data quality in the operational systems and is a bigger programme.

Data requirements

Access to the operational system of record
A defined work-item schema
Historical exception records with resolutions

Risks and controls

Misstructured intake propagating errors deep into the process
Automated supplier communication creating contractual ambiguity
Reports that read fluently while resting on stale data

Example workflow

Inbound message is parsed into a structured work item with confidence flags
Low-confidence items go to a review queue
Exceptions are clustered by likely cause with supporting evidence
Draft communication is prepared for the owner to approve
Weekly summary reports throughput, exception rate and ageing

KPIs to track

Intake processing time per item
Exception rate and mean time to resolve
Rework rate
On-time completion
Report preparation hours

ROI considerations

Intake volume and average handling time give the primary value driver
Exception reduction is usually a process fix, not a model fix — attribute carefully
Reporting time saved is real but small; do not let it carry the business case

Real-world example

Publicly documented industry example — not a Merjora client.

BAM Ireland: digital and AI-supported construction operations →

Frequently asked questions

Where should an operations team start with AI?: At intake. It is high volume, text-based, measurable and fails safely because errors are caught downstream by an existing review step.

Could this apply to your business?

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