Case Study: AI Chatbot - Ria | Magnus Consulting

AI Innovation

INDUSTRY: Organizational Consultancy

Global organizational consultancy

RIA: The Workforce Intelligence Chatbot

10,000

Global Respondents

Sector: Organizational Consultancy

400

Automated Test Questions

Size: Global enterprise, $2B revenue

Multi-Market

Deployment

Project: Public-facing AI chatbot for workforce data

Context
In 2024 our client, a global organisational consulting firm, conducted a global workforce survey across 10,000 respondents, generating a significant volume of proprietary data. The immediate focus was how to extract insight from that data in a timely and meaningful way.

At the same time, there was a broader ambition to demonstrate credible AI capability to the market as part of a board-level strategic priority.

Reframe
Instead of publishing a static report, the opportunity was to make the data interactive. This created two parallel outcomes:

How to turn insight into an interactive way to engage with prospects at a highly personal level and
How to showcase AI capability externally to a target audience.

Solution
We built RIA: a public-facing AI assistant that allows clients and prospects to query workforce data directly. The tool serves both as an audience engagement asset and a sales enablement resource.

Innovation
A structured evaluation framework, including 400 automated test questions, ensured accuracy, tone alignment, and brand safety before launch. Governance involved marketing, technology, AI leadership, and executive oversight.

The Impact

RIA became a commercial asset deployed across markets, accelerating access to insight and reinforcing our client’s credibility in applied AI. One investment delivered both operational efficiency and market-facing credibility.

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Professional services firms are increasingly expected to demonstrate practical AI capability, not just advisory expertise. At the same time, proprietary research represents a significant investment that must translate into visible market value.

The opportunity was to activate first-party data more efficiently while strengthening brand authority in applied workforce intelligence.

A large-scale global workforce survey covering 10,000 respondents across multiple markets. While the data was valuable, unlocking its full potential required both operational acceleration and strategic positioning.

We developed RIA: a public-facing AI assistant that enables users to query the dataset directly. The result was a live digital product that accelerated internal insight generation while reinforcing the client’s AI credibility externally.

The initial challenge focused on processing survey data more quickly.

The broader opportunity was to make the data itself the demonstration – transforming research into an interactive tool that clients could engage with directly.

This positioned the research as a live demonstration of capability, not just an internal efficiency gain.

1. Internal Proof of Concept

We began with an internal version of RIA, enabling research and content teams to query the dataset directly. Questions that previously required manual analysis could be answered instantly.

This phase validated usability and demonstrated practical value to internal stakeholders.

2. Public-Facing Product

Following validation, RIA was launched as the first public AI assistant. Prospects and clients could explore workforce trends directly, turning research into an interactive brand asset.

RIA functioned both as thought leadership delivery and sales enablement support.

Given the reputational risk of inaccurate AI outputs, a rigorous testing framework was implemented.

We created 400 structured test questions covering edge cases and typical user queries. Automated evaluation processes assessed accuracy, tone, and alignment with brand standards.

Sign-off involved marketing, technology, AI leadership, and executive stakeholders. The system was refined iteratively until it met enterprise-level standards.

AI handled scale and response generation – human oversight defined boundaries and evaluation criteria.

The initial sprint surfaced workforce insights within days, demonstrating the practical application of AI against a large and complex global dataset. This early result built internal confidence and momentum, leading to the expansion of the initiative from an internal tool to the organisation’s first public AI offering.

As the project progressed, collaboration extended across marketing, technology, AI leadership, and executive stakeholders to ensure brand safety and enterprise readiness. AI became embedded in both research interrogation and client engagement workflows, reinforcing AI as an enterprise-grade capability within the organisation.

The initiative demonstrated how proprietary research can be converted into a scalable product – creating both operational leverage and market-facing credibility.

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