AI in HR | A Roundtable Discussion

‍We recently brought a group of People & HR leaders together to share their experiences, approaches and insights about AI strategy and adoption in organisations and specifically HR. It was clear that even across diverse industries, businesses are facing shared challenges: how to integrate AI strategically, effectively and responsibly.

‍Here we share six key themes that emerged from the session, and what HR and business leaders should keep in mind when integrating AI into their strategies and practices.

1. The AI adoption landscape and organisational challenges

AI adoption looks very different depending on where you stand. Some companies have truly integrated AI into their daily business activities, reshaping (on average) 20–30% of roles. Others are held back by data/IT security fears, overwhelmed on where to start and resistance within their industry to embrace the opportunity and recognise the world of work is changing.

Successful and meaningful AI capability integration requires Executive drive and vision to define focus areas and clarity on the strategy direction. In large enterprises, security hurdles often limit teams to simple chatbots or restriction of AI tool use to just personal productivity, and even advanced users feel overwhelmed by the rapid change of pace and information overload. 

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2. Implementation, training and governance strategies

Before jumping into using tools, employees need to understand how AI works, its logic and its limitations. Common agreement in the discussion was that a structured technical training program is a good place to start - too many organisations are skipping the crucial step of even explaining what AI is, its history and how it works. If we don’t understand the technology, how can we genuinely (and safely) know how to leverage it?.

Effective AI adoption relies on:

• A strong HR–IT partnership for policy and security that’s backed by Executive sponsorship. This backing unlocks more ambitious testing and implementation targets
• AI coaches/super users embedded within teams
• Educating teams on comprehensive prompting skills
• Avoiding ad hoc adoption across an organisation or approving the use of too many tools. Intention and a strategy is critical to focus efforts into areas that make sense (without putting value creation, accuracy, compliance or customer experience at risk)

💡Tip: Start small. Map processes, measure time spent and identify manual, repeatable tasks.

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“Successful large-scale rollout requires Executive drive and vision.”

‍3. Measurement framework and ROI tracking

In the world of People & Culture, AI is already streamlining several People processes:

• CV analysis and interview question generation
• Performance review management
• Engagement survey analysis and sentiment insights
• Automation of repeatable processes 
• Routine tasks, such as new starter ergonomic setups for compliance checks 

AI can be expensive so rollouts should be treated as capital projects with clear ROI tracking.

💡Note: It’s important to measure success beyond time efficiency and money savings, including employee satisfaction and attributed commercial metrics such as revenue uplift.

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4. Talent and workforce transformation challenges

The discussion oriented towards hiring AI capability into organisations. Sourcing quality AI specialists for a business is tough. Traditional job boards just don’t cut it anymore - forward thinking organisations are going to where the idea makers around AI are (think X, Reddit) and connecting with talent there. Other companies require mandatory AI assessments for all new hires to test for attributes such as curiosity, continuous improvement and competency.

At the same time, there’s a risk of corporate knowledge loss as experienced workers retire and junior employees miss out on learning manual processes. 

To adapt, teams need to:

• Treat AI agents as team members with defined roles and oversight
• Create new performance review systems for AI work quality 
• Embed innovation into company culture and role expectations and make ChatGPT (or similar) adoption a baseline expectation

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“Make AI tooling adoption a baseline expectation.”

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5. Risk management and quality control

Across big and small companies, privacy is a highly topical issue. Some large firms block AI access entirely, due to security and compliance concerns. It’s important to strip all identifying information from employee data before using external AI platforms.

Brand and reputation risks are high, with the potential for customer-facing chatbots to cause poor experiences or “AI slop.” All automated work needs review to avoid errors and sharing out-of-date, or incorrect information.

Human oversight is non-negotiable for these reasons and to avoid the risk of over-reliance, which can lead to human critical thinking degradation.

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“Human oversight is non-negotiable.”

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6. Strategic framework development

Most businesses aren’t tackling AI strategically. Right from the beginning there needs to be clarity and strong business alignment. Start by asking three key questions:

1. What do we want AI to do?
2. What can it enable us to do more of?
3. What can’t it do?

From there: map processes, identify repetitive work and focus on value creation. 

The goal isn’t just efficiency. It’s smarter and more meaningful work supported by rapidly changing technology.

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We hope you enjoyed this discussion. Want to hear more from the WRC team? Head to our Articles page where the crew covers more of the most common and difficult People & HR challenges we see every month.

Need a people and culture partner who gets it? Reach out to start a conversation of your own around how WRC can support your team with an upcoming project or ongoing support. 

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