AI automation that fits real workflows, governance and data risk
Petatec finds AI use cases that can survive real users: one workflow, clear data boundaries, human review and a measurable operational result.
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Direct answer
Petatec does not start AI work with a model choice. We start with the workflow: where time is lost, where decisions repeat, what data is available, who reviews the result and how the output returns to existing tools.
Definition and business impact
AI automation is the controlled use of AI models, workflow logic, integrations and human review points to reduce repetitive work or improve structured decisions without removing accountability.
The value is strongest where people repeat the same information handling, screening, routing, summarisation or compliance tasks every week. The risk is strongest where AI is introduced without data boundaries, human review and measurable workflow outcomes.
AI workflow automation
Identify repeatable workflows and design AI-assisted steps for intake, classification, summarisation, routing and decision support.
AI recruiting
Implement structured AI interview and screening workflows with recruiter oversight, comparable answers and ATS handoff.
AI governance
Define ownership, acceptable use, data boundaries, audit logging, model review, escalation and human approval points.
EU AI Act readiness
Assess AI use cases against risk categories, documentation needs, transparency obligations and operational controls.
AI integration
Connect AI workflows with CRM, ATS, ticketing, document, email and internal systems through controlled APIs and data flows.
AI for SMEs
Prioritise low-risk, high-value AI use cases that can be governed by smaller teams without enterprise bureaucracy.
AI risk management
Review data leakage, bias, hallucination, access control, retention, auditability and supplier dependency.
AI interview systems
Design and support AI interview workflows where candidates get structure and recruiters receive consistent evidence.
How Petatec assesses it
- Start with a narrow workflow, not a broad AI transformation programme.
- Choose work that is frequent, measurable and easy for a human to review.
- Define what data can be used, where it is stored, who can access it and when it must be deleted.
- Place human review before AI affects candidates, employees, customers, compliance or financial outcomes.
- Connect the output back into ATS, CRM, Microsoft 365 or service desk tools so teams do not copy results manually.
Process
- 1Select one workflow with a clear owner, volume, data boundary and measurable target.
- 2Document data rules, review points, escalation paths and approved tools before pilot launch.
- 3Test the workflow on real examples while keeping the environment controlled and reviewable.
- 4Integrate approved steps with ATS, ticketing, documents, email or Microsoft 365 workflows.
- 5Measure adoption, exceptions, review quality and time saved before scaling to other departments.
Evidence used
- Workflow samples and current process documentation
- Data classification and access requirements
- Recruiting, ticketing or operations queue volumes
- Compliance and legal review constraints
- Baseline time-to-complete and error patterns
How Petatec turns this into a decision
The useful work is not the audit itself. It is the judgement that follows: what to change, what to leave alone and what to sequence first.
Situation
AI interest is high, but the use cases are vague.
Petatec view
Pick one workflow with volume, friction and a clear owner. Good first projects are usually narrow, repetitive and reviewable.
Risk if ignored
The project becomes an impressive demo that never changes how work gets done.
Situation
Recruiting teams lose time on first screening.
Petatec view
Use AI to collect structured evidence, keep recruiter oversight and return results to the ATS instead of creating a separate process.
Risk if ignored
Automation feels fast internally but weakens candidate trust and hiring evidence.
Situation
People are already using AI tools informally.
Petatec view
Set rules for approved tools, sensitive data, storage, review and escalation before the behaviour spreads.
Risk if ignored
Business data enters uncontrolled systems and no one can audit how outputs were produced.
Situation
The workflow may fall under AI regulation.
Petatec view
Classify the use case early and build transparency, documentation and human oversight into the design.
Risk if ignored
Governance added after rollout is slower, more expensive and less credible.
Common mistakes
- Starting with a model choice instead of a workflow problem.
- Using AI outputs as decisions rather than evidence for human review.
- Ignoring where source data is stored, retained or exposed.
- Automating a weak process before clarifying ownership and exceptions.
- Failing to measure the baseline before claiming AI productivity gains.
Practical recommendations
- Start with one workflow, one owner and one measurable target.
- Define governance before pilot launch, not after the first incident.
- Keep candidate, employee and customer-facing AI explainable and easy to escalate.
- Integrate AI output into existing tools so teams do not have to copy results manually.
- Scale only after users, exceptions and review quality have been tested.
FAQ
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