Review workflows, data and constraints to define pilot tasks and an adoption sequence
Review workflows, data and permissions, then assess repetitive work and feasibility. Define priorities, proof-of-concept acceptance and conditions for implementation and operational rollout.
Before listing features, we define the inputs, decisions and next actions in a real working situation.
An illustrative scenario. Specific processing rules are agreed for the project.
There are many recurring tasks but no clear adoption order.
Choose a priority task and evaluate a focused pilot
When AI adoption is a priority but the tasks and sequence are not yet clear
Specific features, integrations and operating support are agreed in the project proposal
See the scope, role and supplied interfaces for each project
Original planning & project documents
Original planning & project documents
Details for technical reviewers. Architecture and acceptance criteria are agreed for the actual environment and project scope.
Compare operational value with integration effort. Expected benefit alone does not justify a pilot: a readiness gate checks data, access, evaluation cases, and accountable owners.
Concrete design decisions and acceptance scenarios to agree for the project.
ImplementationSamples, processing paths, errors, handoffs
ValidationCompare current work and the pilot on the same basis
ImplementationData, access, owner, acceptance and stop conditions
ValidationExercise high-risk exceptions and human intervention
ImplementationShared foundations, change approval, cost, training
ValidationReassess model changes and increased workload
Technology choices and acceptance criteria depend on discovery, integrations, and agreed scope. We define reproducible acceptance scenarios and handover evidence for the operating environment.
From visible features to failure conditions,
we work through the decisions before launch.
Review representative work samples and actual processing paths rather than relying only on workshop opinions. Locate bottlenecks in collection, extraction, judgment, system entry, or approval. Compare rules, search, conventional ML, and generative AI by task, documenting decisions that remain human-owned and actions the system must not perform.
Assess workload, handling time, and error cost alongside data access, quality, and API readiness. Separate reusable foundations from task-specific development and distinguish pilot cost from scaling cost. Put security, legal, and business decision points into the plan; defer candidates whose prerequisites are not available.
Build evaluation cases covering routine, rare, and high-risk work, with a comparable baseline. Measure correction needs, missing evidence, escalation rate, and cost per case alongside handling time. Classify whether failures arise from the model, missing data, or ambiguous process rules, and use agreed acceptance and stop conditions to choose the next step.
Assign ownership of exception queues and the criteria for approval or correction. Connect training, access provisioning, evaluation cadence, and change approval to operating procedures. Reassess changes to prompts, models, and source knowledge. Plan scaling around cost, security, and support demands, not just the number of users who can access the tool.
Understand your goals, workflows, and operating environment.
Define features, priorities, and integration requirements.
Build the agreed scope and review progress together.
Prepare testing, deployment, and operational handover.
Tell us what you want to build or improve
Our team will review your request and reply by email
