Benchmarking Enterprise AI Assistants for Complex Voice-Driven Workflows

The Need for Advanced AI Assistant Evaluation

As AI assistants become integral to business operations, evaluating their performance in handling real-world, voice-driven tasks is crucial. Current assessments focus mainly on general conversational skills or narrow, task-specific capabilities, which do not adequately measure an AI's proficiency in managing complex enterprise workflows.

Salesforce’s Robust Evaluation Framework

To overcome these shortcomings, Salesforce AI Research & Engineering created a comprehensive evaluation system designed to assess AI agents across text and voice interfaces in enterprise settings. This framework supports products like Agentforce and evaluates performance in four critical business areas: healthcare appointment management, financial transactions, inbound sales processing, and e-commerce order fulfillment.

The benchmark employs human-verified test cases requiring AI agents to perform multi-step operations, utilize domain-specific tools, and comply with stringent security protocols. It aims to simulate real-world challenges that AI assistants encounter in specialized workflows.

Challenges in Enterprise AI Assistance

Unlike traditional benchmarks centered on general knowledge, enterprise AI assistants must integrate multiple tools and systems, adhere to strict compliance standards, and understand specialized terminology. Voice interactions introduce additional challenges, such as speech recognition errors, which affect multi-step task completion.

Framework Architecture and Features

Salesforce's benchmark uses a modular design encompassing:

Domain-specific environments
Predefined tasks with clear objectives
Simulated realistic interactions
Measurable performance metrics

It covers four domains: healthcare, finance, sales, and e-commerce. Tasks range from simple requests to complex conditional logic requiring multiple system calls. The framework evaluates AI agents on accuracy and efficiency, measuring conversational length and token usage. Voice-based assessments include noise injection to test resilience.

Implemented in Python, the benchmark supports multiple AI providers and configurable voice processing components such as speech-to-text and text-to-speech. An open-source release is planned to enable community extension for new use cases and communication formats.

Initial Findings and Future Directions

Tests with models like GPT-4 and Llama revealed that financial tasks pose the greatest challenge due to strict verification. Voice interactions resulted in a 5–8% performance drop compared to text. Multi-step tasks involving conditional logic further reduced accuracy. These results highlight ongoing difficulties in tool chaining, protocol adherence, and speech processing.