GroveAI
strategy

How do I prioritise which processes to automate with AI?

Quick Answer

Prioritise processes for AI automation using a framework that scores each opportunity on three dimensions: business impact (time savings, cost reduction, quality improvement), technical feasibility (data availability, complexity, integration requirements), and organisational readiness (stakeholder support, change management needs). Start with high-impact, high-feasibility opportunities that build confidence and demonstrate value.

Summary

Key takeaways

  • Use a structured scoring framework across impact, feasibility, and readiness
  • Start with processes that are high-volume, rules-based, and data-rich
  • Quick wins build organisational confidence for more ambitious projects
  • Avoid starting with your most complex or politically sensitive process

A Practical Scoring Framework

Score each candidate process on a 1-5 scale across three dimensions. Business impact considers: how much time the process consumes, the cost of errors, the volume of transactions, and the strategic importance to the organisation. Technical feasibility assesses: whether suitable data exists, the complexity of the decision-making involved, how well-defined the process rules are, and what system integrations are required. Organisational readiness evaluates: whether the process owner supports the initiative, how much change management is needed, whether the team is willing to adopt new tools, and whether the process is stable or frequently changing. Multiply the scores to create a composite ranking. Focus first on opportunities that score highly across all three dimensions.

Characteristics of Ideal AI Automation Candidates

The best processes for initial AI automation share common characteristics. They are high-volume, involving many repetitive transactions. They are rules-based, following relatively clear decision logic even if that logic is complex. They are data-rich, with historical records and structured inputs. They have measurable outputs, making it straightforward to demonstrate improvement. They are not politically sensitive, reducing resistance to change. Examples include invoice processing, email categorisation, data extraction from documents, standard report generation, routine customer enquiries, and compliance checking. Starting with these types of processes delivers visible wins quickly, building the evidence and organisational confidence needed for more ambitious AI initiatives.

FAQ

Frequently asked questions

Not necessarily. The most expensive process is often the most complex and politically sensitive. Start with a moderately impactful process that is technically straightforward to demonstrate value quickly, then tackle larger opportunities with proven capability and organisational support.

Start with one or two processes for your first AI project. Trying to automate too many processes simultaneously dilutes focus and increases risk. Scale up after demonstrating success with initial use cases.

Factor data remediation into the project plan and timeline. Sometimes it is more effective to choose a slightly lower-impact process with better data quality for the first project, while preparing data for the higher-impact opportunity in parallel.

Yes. Process owners provide essential insight into the practical realities, edge cases, and political considerations that quantitative scoring alone cannot capture. Their involvement also builds the stakeholder support needed for successful implementation.

Use the objective scoring framework to create a transparent, evidence-based ranking. Share the methodology and scores openly. Where scores are close, consider which project builds capability that benefits subsequent initiatives, creating a natural sequencing.

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