Which AI Solution Does Your Business Need? A Non-Technical Guide
The right AI solution depends on the task. Business leaders should distinguish reporting, rules, predictive models, optimisation, generative AI, computer vision, AI workflows and agents before choosing a product or architecture.
Begin with the task, not the label AI
Vendors use AI to describe products with very different capabilities and operating requirements. Define the input, required output, action, acceptable error and human authority. These conditions narrow the solution before product comparison begins.
Use reporting when the need is visibility
A dashboard or business-intelligence system is suitable when the organisation needs a consistent view of current or historical performance. It does not need AI merely to combine measures, apply definitions and present evidence. Natural-language access may improve usability, but the underlying requirement remains reliable reporting.
Use rules or conventional automation when conditions are explicit
Rules are strong when inputs are structured and the correct action can be stated in advance. They are easier to test and explain than a probabilistic model. Robotic process automation can move information through stable interfaces, although it becomes brittle when screens or exceptions change.
Use predictive machine learning for repeated uncertain outcomes
Predictive models estimate an outcome from observed patterns. They can support demand forecasting, failure prediction, fraud detection, churn or risk classification. The business needs representative historical evidence, a later outcome for evaluation and an action that benefits from the prediction.
Use optimisation for constrained choices
Optimisation compares choices under explicit objectives and constraints. It is suited to scheduling, routing, allocation and planning. Prediction may provide an input, but the optimisation model determines the best feasible action according to the stated objective.
Use generative AI for variable language and content
Generative AI can draft, summarise, extract, translate, retrieve and support interaction across variable language. It should not be treated as an exact database or calculation engine. Important outputs need grounded sources, acceptance tests and human review appropriate to their consequence.
Use computer vision or speech models for sensory information
Computer vision can classify images, inspect defects, read documents or detect conditions in video. Speech models can transcribe and help analyse calls. Performance must be evaluated under the lighting, equipment, accents, noise and cases that occur in the intended environment.
Use an AI workflow when the route should remain explicit
An AI workflow places one or more AI tasks inside a designed sequence. It is appropriate when the trigger, steps, controls and exception categories are known. What is an AI workflow? explains how interpretation can vary while the operating route remains controlled.
Use an agent when context must determine the next action
An AI agent can choose among permitted tools and routes while pursuing a bounded goal. It is useful where the next step cannot be enumerated economically and contextual choice creates material value. Additional autonomy requires broader evaluation, monitoring and recovery. What is an AI agent? gives a practical definition.
Many useful solutions combine several capabilities
A service workflow might use retrieval for policy, a predictive model for risk, rules for mandatory checks and a person for material authority. The architecture should assign each task to the simplest dependable component. Business Systems Design and Architecture connects these components with people, information and controls.