AI is often described as "magic," but in a business context, magic is terrifying. Magic is unpredictable. Business leaders want predictability, reliability, and control.

The Black Box Problem

When an AI agent makes a decision (e.g. approving a loan, flagging a risk, or sending an email) users need to know why. A simple "Done" message isn't enough anymore.

We design our interfaces to show the "Thought Process." By exposing the intermediate steps of an AI chain (e.g., "Reading document...", "Extracting key terms...", "Drafting response..."), we build trust. It shows the user that the system is working logically and methodically, not randomly.

Human-in-the-Loop

The most critical pattern we implement is the "Draft & Approve" workflow. Never let an AI auto-send high-stakes communications without a human check in place.

Here are some other factors we consider to be important…

Confidence scores Visually indicating how sure the AI is about its output is essential for risk mitigation. If an AI categorises a support ticket as 'Urgent' but only has a confidence score of 65 per cent, the UI should reflect that ambiguity. We might use a yellow warning icon or a gentle prompt asking the human to verify. This prevents the user from blindly trusting a hallucination. It turns the AI from a black box into a collaborative partner.

One-click edits Making it trivial for a human to correct the AI before final execution is the difference between a helpful tool and a frustrating one. Text boxes are versatile but tiring. When we build interfaces at Paladin, we prefer structured inputs for corrections. If an LLM drafts a calendar invite, the date and time should be editable via a picker, not a chat command. This allows the human to fix errors instantly without having to argue with the bot.

The feedback loop The best AI integrations learn from their mistakes. When a human corrects the AI using the edit tools mentioned above, that action should be captured. We design systems where this correction data is fed back into the model or the prompt architecture.

If a user consistently changes the tone of an automated email from 'Formal' to 'Casual', the system should eventually learn to default to 'Casual'. This creates a sense of personalisation and ensures that the tool becomes more valuable the longer it is used.

Managing the wait Large Language Models (LLMs) can be slow. In traditional software development, a three-second delay is an eternity. In the world of generative AI, it is standard.

To mitigate this, we lean heavily on streaming responses. Rather than making the user stare at a spinning loader for ten seconds before the entire text appears at once, we stream the text token by token. This 'ghostwriter' effect keeps the user engaged. It provides immediate feedback that the request is being processed. It effectively buys the system time while maintaining the user's attention.

From magic to engineering The shift from treating AI as magic to treating it as software is where real business value lies. Magic is fun for a tech demo. Engineering is required for a production environment.

At Paladin, we believe that the success of an AI project is rarely about using the smartest model available. It is about how that model is wrapped in a thoughtful, human-centric interface. By exposing the thought process, keeping humans in the loop and designing for error, we build systems that businesses can actually trust.