What's needed to take AI to the enterprise level

While there’s undeniably a lot of enterprise interest in AI, a question remains: what will it take for AI to level up into verticalized, enterprise-grade use cases?

It boils down to a very human concept: trust.

Trust— that the recommendations or outputs were generated with the right context, accuracy range, and guard rails. Trust —that the decision pathways were structured as desired. That it did so without unlawful bias or discrimination.

Enterprises are unlikely to deploy automated decisions, outputs, or recommendations, without a certain level of trust. Only with trust — from values like transparency, reliability, explainability —comes true growth and widespread adoption. Not because AI worked *automagically*.

Here are four practical ways design can help build trust in AI systems, and bridge the adoption curve. We are deeply looking into “picks-and-shovels” companies that are building tools that help with visualization and observability in AI— we would love to hear from you!

1. Transparency

AI systems can be complex and opaque, making it difficult for enterprises to understand how they work or why they make certain decisions. To address this, designers should aim to make AI systems transparent and explainable, by providing users with clarity on why it is making certain decisions. Potential design opportunities include:

Feature importance: Visualize the relative importance of different features, or provide explanations of how each feature is being used to make decisions.

Decision Pathways: Clear illustrations of the sequence of steps or rules that the system is following to make decisions, along with explanations of why it's necessary.

Data Sources: Provide information about the sources of the data that the AI system is using: types of data that are being used, where the data came from, and how the data was collected and processed.

Model Performance: Visualize metrics, usage, costs, latency, along with explanations of how the model is being evaluated and validated.

2. Context

Design can help add context to a recommendation or outputs, and help users make a more informed decision. For example: if an AI system is used to make recommendations for financial investments, the interface could add relevant info about market trends, historical performance, and risk factors.

3. Feedback

Allowing users to provide feedback on the system's outputs can help build trust. For instance, interfaces with mechanisms to correct errors or to indicate whether the system's outputs are useful or relevant.

E.g., in personalized shopping recommendations, the interface could allow users to provide feedback on the recommendations (useful or relevant?), and use this to improve the system's accuracy and trustworthiness over time.

4. Intuitive Design

Use familiar visual metaphors, providing clear labels and descriptions, and design for accessibility and inclusivity.

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