AI in Human Resources: the real operational impact

Human Resources is an area in many organisations where AI can have significant operational impact. The technology is now being embedded into day-to-day operations, in activities like answering employees’ questions and supporting training. The clearest impact appears where organisations can measure the tech’s outcomes, typically in time saved and the numbers of queries successfully resolved.

Fewer tickets, more first-time answers

IBM’s internal virtual agent, AskHR, was built to handle employee queries and automate routine HR actions. IBM says AskHR automates more than 80 internal HR tasks and has engaged in over two million conversations with employees every year. It uses a two-tier approach, where AI resolves routine issues, and human advisers handle more complex cases.

The company reports some operational benefits: a 94% success rate in answering commonly-asked questions, a 75% reduction in the number of lodged support tickets since 2016, and – the headline figure – a 40% reduction in HR operational costs over four years.

But it’s important to note that AI is not used by IBM to route queries to existing materials. The automation is capable of completing the transaction, thus reducing the need to hand-off queries to human staff.

Recruitment and onboarding efficiencies

Vodafone’s 2024 annual report describes an internal platform it calls ‘Grow with Vodafone‘. The company says it’s reduced its time-to-hire periods from 50 days to 48 days, made the job application process simpler, and added personalised skills-based job recommendations for applicants. That’s led to a 78% reduction in questions posed by potential applicants and those onboarding into new roles.

The company also has a global headcount planning tool that reduces the manual work needed to assemble necessary data, plus there’s an AI-powered global HR ‘data lake’ that standardises dashboards and reduces the need for manual reporting – stakeholders can dive into the data themselves and surface the insights they need.

Training and internal support

Big employers have challengers getting new staff up to speed quickly; so-called time-to-competence. Bank of Americas’ newsroom describes how its onboarding and professional development organisation, ‘The Academy’ uses AI for interactive coaching, with employees completing over a million simulations in a year.

The organisation operates ‘Erica for Employees‘, an internal assistant that handles topics like health benefits and payroll or tax forms for employees. It’s used by over 90% of employees – for the IT service desk, having Erica triage situations is impactful, with a reduction of more than 50% in incoming calls.

Such tools reduce hidden work (searching, repeating questions, waiting for answers) and its associated costs. Plus, a shorter time-to-competence is especially valuable in regulated and customer-facing environments.

Frontline work at big employers

Walmart’s June 2025 corporate update describes rolling out AI tools via its associates’ app, which include a workflow tool that prioritises and recommends work tasks. At the time of publication, it was early days, but based on early results, Walmart says team leads and store managers are beginning to see shift planning times down from 90 to 30 minutes.

As an employer of a diverse workforce, its app’s real-time translation ability (44 languages) is invaluable. The company is currently upgrading its associates’ software with AI to turn its internal process guides into multi-lingual instructions. It has more than 900,000 employees using the system every week users, with more than three million queries per day going through the associates’ conversational AI platform.

Workforce efficiencies at Walmart scale is impressive, but for every size of business, there are clear advantages to be gained from giving employees faster guidance and better support across multilingual teams. In addition to the immediate cost savings, simple-to-use and effective software of this type affects retention, safety standards, and service quality – all for the better.

Governance and human safety nets

Multinational bank, HSBC’s publication, “Transforming HSBC with AI” describes over 600 AI use cases in operation at the company, and says colleagues have access to an LLM-based productivity tool for tasks like translation and document analysis. In an environment where governance and data security are of paramount importance, it’s ensured that all automated systems abide by existing codes, something that’s enforced by dedicated AI Review Councils and AI lifecycle management frameworks.

In HR this matters, regardless of vertical. Governance decisions should shape what can be automated, how people data is handled, and how accountability is maintained into the long term. HR data is often personally-identifiable, so the highest standards – and their maintenance – are critically important.

Operational trade-offs

Operational impact is about trust as well as speed and efficiency. A self-service agent answering confidently but incorrectly creates rework, escalations, and causes problems. A pragmatic pattern for reducing risk is to keep humans in the loop, especially for complex decisions.

IBM’s two-tier model, Vodafone’s tailored job recommendations, and Walmart an HSBC’s data governance and security bring oversight. Hybrid service models plus data discipline and oversight are what enable AI to scale without undermining employee confidence or fairness.

Where this is heading

The pattern of successful operational deployment has been consistent in the cases of the HR function in these large enterprises. They each started with high-volume questions and repetitive transactions, expanded into hiring and training, and then pushed AI to the frontline where it can save time. The biggest gains come when AI turns HR from a service queue into a faster, more consistently-operating function.

(Image source: “Business Meetings” by thinkpanama is licensed under CC BY-NC 2.0.)

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