Applied AI and intelligent workflows
Identify where intelligent assistance can create measurable value, with appropriate control over errors and consequential actions.
Evaluate machine learning and language models within defined business tasks. Combine generative capabilities, deterministic controls and human oversight with measurable acceptance criteria.
Applied AI within business workflows
Evaluate machine learning and large language models for language interaction, information processing and task assistance. Combine generative capabilities with deterministic rules, validation and controlled actions where the workflow requires predictable behavior.
Design around data access, human review, evaluation, operating cost and the consequences of an incorrect output. Model selection and deployment follow those requirements, with measured acceptance criteria before release.
Start with a task that can be defined and evaluated: helping a user find information, assisting with a structured document, supporting language interaction or recommending an action for review. Identify the information required, the permissible outputs and the consequences of uncertainty. This provides a basis for deciding whether machine learning, a language model, deterministic processing or a combination is appropriate.
Generative output needs to be considered within the surrounding system. The application determines what information can be supplied, how generated material is checked and which actions are permitted. Workflows involving sensitive information or consequential operations require explicit authority and an appropriate review path. A fluent response is not, by itself, evidence of a correct result.
Evaluation covers useful task completion, factual or structural errors, misuse, information exposure and behavior when the model or a supporting service is unavailable. Operating constraints include latency, model availability, device or server resources and the cost of repeated work. These results inform both the implementation and the scope of any product claim.
Deliverables can include a feasibility assessment, evaluation corpus, integration design and operational acceptance criteria. Governance records the intended use, the people responsible for oversight and how changes to data, models or workflows will be reviewed.