AI Tools for HR Consultants: What to Use and What to Skip

HR consultancies face a particular challenge with AI tools: the market is full of products built for in-house HR teams at large companies, not for independent consultancies or small advisory practices serving multiple clients simultaneously. The tools that work for a 500-person company’s internal HR department don’t map onto a 10-person consultancy managing 30 client relationships.

This article distinguishes between the types of AI tools available and which category is worth investing in for a UK HR consultancy or advisory firm.

The Three Categories of AI for HR Consultants

Not all AI tools serve the same purpose. For HR consultants specifically, it helps to think in three distinct categories.

For most HR consultancies, Category 1 tools (general AI assistants) are immediately accessible and genuinely useful for drafting. Category 3 (operational infrastructure) is where the compounding value lives, but requires a build investment.

What General AI Assistants Are Good For

A tool like ChatGPT or Claude is a genuine productivity aid for an HR consultant doing knowledge work: drafting employment contracts, writing disciplinary letters, summarising legislation, preparing for client meetings by synthesising background notes. These are well-suited to the generative capability of language models.

The limitation is that they have no memory of your clients, no access to your files, and no connection to your existing systems. Every session starts from scratch. That’s manageable for one-off tasks, but not for the structured, repeatable work that runs through a consultancy.

Where Operational AI Infrastructure Makes the Difference

The most impactful AI applications for HR consultancies are the ones that connect your existing data environment into a structured system with memory and context. Specifically:

• Client knowledge base — a system that indexes all your client correspondence, meeting notes, HR policies, and case history, and makes it queryable in plain English. Before a client call, ask: “What were the key issues in our last quarter of work with this client?” and get an accurate answer in seconds.
• Employment law knowledge retrieval — a base trained on UK employment law, ACAS guidance, and your own precedent library. Allows consultants to surface relevant guidance rapidly without full research each time.
• Client onboarding automation — handling the intake, organisation, and routing of new client information, so the administrative part of starting a new engagement is handled automatically.
• Client portal with AI assistant — at Cornerstone HR, a mid-sized HR advisory firm, we built a client portal where clients can query an AI assistant trained on their own employment policies and the firm’s guidance. Routine questions are answered instantly; complex ones are routed to a consultant. This reduced first-response time and freed consultants from repetitive query work.
• Document generation from templates — generating contextual first drafts of employment contracts, settlement agreements, grievance letters, and similar documents by drawing on client-specific variables from your CRM.

What to Be Cautious About

AI tools that generate employment law advice autonomously

Any AI tool that presents itself as able to give employment law advice to clients without consultant review carries real risk. Language models can confabulate — produce plausible but incorrect outputs, especially on specific statutory thresholds, recent case law, or jurisdiction-specific rules. AI should support the HR consultant’s judgement, not replace it.

Generic HR SaaS with “AI features”

Many HR platforms have added AI features to their marketing in the last 18 months. In most cases, these are narrow functions — an AI-assisted job description writer, or a sentiment analysis tool. Useful individually, but they don’t address the operational flow of a consultancy and can’t be connected to your actual client data and history.

GDPR and Data Considerations

HR consultancies handle sensitive personal data. The GDPR implications of using AI tools that process this data are significant. The key principle: data should not be sent to third-party AI services without appropriate data processing agreements and client consent.

Operational AI infrastructure built within your own systems boundary — where processing happens on your infrastructure, not on an external service — directly addresses this. It also means you can give clients confidence that their data is not being used to train external AI models.