People analytics at the heart of AI’s successful workplace adoption

AI is disrupting nations, sectors, companies, individuals, and the very essence of how we work.

In this changing landscape, we are all figuring it out and experimenting with how to implement and adopt AI successfully in our context (1).

People Analytics teams are in a very privileged position to be at the heart of this technological and behavioural transformation. Companies that enable their People Analytics teams to contribute to AI’s business-wide and workplace adoption are going to have a competitive edge.

The question is: will people analytics rise to the occasion and seize the opportunity?

In this article I explore 5 reasons why People Analytics must be at the heart of AI’s successful adoption and implementation in the workplace, providing concrete examples and recommendations to navigate #AI’s revolution.

We have a historic opportunity to redefine how work is done.

As #PeopleAnalytics practitioners we will be able to look back to our careers and see we helped people in our respective organisations to navigate the crisis of the pandemic, the mass-scale experiment of hybrid working – including bringing evidence to the debate of return-to-office mandates - and how we adapted to the radical transformation of work and the workforce brought forward by AI.

My heart behind writing this article is to inspire and energise People Analytics teams to contribute, influence and lead with their expertise in this AI transformation. Through our input, we can augment how work is done through AI across the workforce, and to do so responsibly and sustainably.

Let’s jump to the 5 reasons why People Analytics must be at the heart of this AI workplace transformation!

1.    People Analytics is at the intersection of AI and the workforce

Food for thought

Let’s start with the basics: what is AI?

If you are a visual person like me, I’m sure you’ll appreciate this slide presented in the webinar co-hosted by Jasdeep Kareer, PhD (née Bhambra) and David Green 🇺🇦 (from the Insight222 team), on ‘Transforming HR With AI’ (2).

Quoting Jonathan Ferrar and David Green 🇺🇦 in the book Excellence in People Analytics (3), AI is:

“The programming of machines to perform specific tasks requiring human intelligence, such as visual and speech recognition, decision making and language translation.”

AI is ChatGPT, Copilot and GenAI, but is much more. AI’s umbrella also encompasses machine learning and natural language processing (NLP).

People Analytics teams have been leading in machine learning and NLP applications on people matters for years. For example, building and implementing predictive flight risk models, developing newer and more nuanced ways to explore survey comments for employee listening, and more recently inferring skills using AI algorithms (4).

If we step back, we can see People Analytics has in many ways already been championing AI in HR and across the workforce.

What is required now is an evolution of this championing role of AI, which must include a focus on GenAI alongside the opportunities and challenges it brings to the workplace.

An important gap can be closed through this, as otherwise the HR function may lag other areas in the business in AI’s thinking and adoption.

The opportunity for People Analytics teams to be relevant and add value to the business and employees alike couldn’t be riper.

Practical example

If your organisation is planning a town hall is likely you will ask employees to send their questions for senior leaders to address them during the event. However, only a small selection can be asked due to limited time.

That set of questions is an employee listening goldmine waiting to be analysed so that you can understand what matters most to employees about the topics discussed in that town hall. The challenge used to be that doing this type of text analysis was time-consuming and cumbersome, but here comes the AI opportunity.

Use the GenAI tool enabled in your organisation and enter a prompt to summarise those questions by theme. Invest a few minutes fine-tuning the results with your human judgment and context awareness. In less than 30min you can prepare an accurate and timeline one-pager summary of the key themes of that town hall’s questions. This is an insightful asset to be shared with the senior leaders to plan future comms and the most relevant post-town hall actions.

Recommendation

People Analytics teams to proactively seek to contribute and champion AI’s adoption and implementation in the workplace in their organisation.

Position this as what it is, a natural evolution of the scope of your team and articulate how the insights and expertise you can provide through a deep understanding of employee sentiment, behaviours, skills, and ways of working can help accelerate a successful AI embedding across the workforce.

2.    Experience building responsible guardrails

Food for thought

People Analytics teams had to build and strengthen their partnerships with legal and data governance teams as a core part of how they work.

This collaboration is a crucial cornerstone to ensure an ethical, safe, and responsible use of people data. When done well, this collaboration drives trust in leaders and employees in all People Analytics efforts.

People Analytics teams can capitalise on existing partnerships in this AI era.

These partnerships should be leveraged to build and co-shape healthy and sustainable guardrails for AI’s use with people data and across the workforce.

If these relationships are not formed in your organisation yet, see AI’s momentum as an opportunity to create this type of partnership.

The sustainable guardrails developed through this collaboration should include ethics and data governance but go beyond by considering how AI can enable a better inclusion and well-being experience at work (5).

The goal should be a responsible and ethical use of AI that results in higher workforce productivity and an enhanced employee experience.

Once you have built these guardrails make sure they are a live document that evolves and adapts to the changing AI legal landscape (6).

Practical example

Many People Analytics teams collaborated with legal and governance teams to build an ethics charter when developing their first predictive models, such as a flight risk model to enable proactive employee retention.

Don’t reinvent the wheel, but rather leverage existing documentation and governance to expand on it and use AI’s attention-grabbing momentum as an opportunity to reinforce some of the existing data governance and ethics principles across relevant stakeholder groups.

This will lead to a more responsible application of AI and data, mitigating risks and allowing the organisation to focus on its benefits.

Recommendation

Adopt a proactive and ‘orchestrator’ mindset and, if this has not happened yet, start bringing together the legal, ethics, governance, D&I and wellbeing experts in your organisation to co-shape responsible AI guardrails.

You can get started by brainstorming together questions such as:

• ‘How are we going to provide inclusive access to AI’s tools?’ Hearing “AI won’t replace your job, but someone who uses it will” can cause fears amongst employees of being left behind. We can mitigate this risk by ensuring an inclusive AI implementation across the workforce.
• ‘How should teams use the time saved by AI’s productivity gains?’ Hopefully, we will all come up with an answer that strikes the balance of decreasing the risk of burnout and making work more meaningful and value-adding.

AI cannot fully replicate human intelligence, which is why human supervision remains a must-have safeguard of AI models (7).

The aim should be transparent and collaborative guardrails that enable the organisation and employees to experiment safely and effectively, helping the organisation to realise the new opportunities promised by AI’s democratisation across the workforce.

3.    Product management mindset

Food for thought

Research tells us that most employees are keen to use AI in their jobs, and are already experimenting with it, whereas leaders are focused on testing and building guardrails for its mass-scale responsible usage (8).

AI’s democratisation will be key, and People Analytics teams are well equipped to product manage this transformation as they are experienced in democratising people data & insights across different groups of stakeholders, such as HRBPs, HR CoE leads, senior leaders and line managers.

Something key to understand is that AI’s democratisation is way more than a tech challenge or button that you ‘switch on’. A more holistic approach is needed.

AI is both a technology and behavioural transformation.

A product management mindset will also focus on championing the culture and behavioural shift that can make or break AI’s adoption in the workforce.

This entails giving attention to the technical aspects but also focusing on key enablers such as providing easy access inclusively, increasing awareness, providing education, prioritising use cases, and finding ways to track the impact of AI’s usage.

Making this culture and behaviour transition effortless and shifting habits at the organisational, team and personal will be key to success (9).

Practical example

I love this success story from Phillips 66 using Copilot in Viva Glint to summarize 14k employee comments in 15 seconds (10).

This used to take hours, and from team members with enough experience to identify patterns in the comments and the ability to translate them into insights.

This is a major tech feat but is way more than that. Phillip 66’s example shows the sheer power AI has with text data and is a great example of making a behavioural shift effortless by plugging AI into existing tools (listening tech), processes (generating insights from employee comments) and objectives (understanding the voice of employees to drive action).

Through AI, text summarisation of employee listening comments can be democratised into the hands of all line managers across the organisation.

The implications of people insights so easily democratised at such high speed and accuracy is going to have huge implications on organisations, transforming the behaviours People Analytics teams, managers and employees will have to display for success.

I am excited to see how People Analytics teams are going to enable and shape this transformation.

Recommendation

Wear with pride a product management hat that focuses of turning AI’s democratisation into both a technology and behavioural success (11).

Think beyond your People Analytics team and consider enabling this transformation for the rest of HR and the workforce.

4.    Partnerships to champion digital & behavioural transformation

Food for thought

Partner, partner, partner.

The scale of the digital and behavioural transformation required with AI’s democratisation across the workforce is massive.

A cohesive and joined-up approach is needed, not well intended but siloed efforts.

There are 3 partnership levels People Analytics should consider for AI’s transformation:

1. Partner strategically. Ensure alignment with the overall company's AI strategy. Make sure your efforts are not out of tune with the overall direction of travel for AI in your organisation, but rather add your melody to the company-wide AI symphony.
2. Partner cross-functionally. Particularly with functions like tech, data, legal and governance. This mix of skills and points of view should enable divergent thinking and innovation, required to come up with an approach to AI’s transformation that will be effective for the overall organisation and the different personas that will be adopting it across the workforce.
3. Partner within HR. Some of the teams to invite to the party are people development (AI skills growth), employee experience (behavioural change required with AI), data governance (responsible use of AI), D&I and well-being (inclusive and people-focused AI implementation) and SWP (AI’s impact on people planning). Keen to hear in the comments if you think other teams should be involved in this AI-HR partnership and why!

Practical example

Over the past few years, we’ve had to adapt and change to the reality of hybrid working. This entailed for most organisations a company-wide effort focused on people, technology, and workplace transformation, in which cross-functional collaboration was key for success.

Many organisations achieved this through a transformation team. At Legal & General, we called it the Future of Work Programme (12).

A similar cross-functional approach is needed for AI’s implementation in the workplace to ensure the AI digital and behavioural transformation has the right reach, depth, and connectedness across the organisation.

Recommendation

Don’t fly solo to drive AI’s digital & behavioural transformation.

If a cross-functional team has already been created, articulate why you can be a strong partner. If this cross-team has not been formed, start building bridges and consider starting it proactively.

Leverage learnings and existing relationships in previous efforts in democratising people data and driving a data-driven culture within the HR function, senior leaders, and line managers.

This intentional collaboration across teams can enable a much smoother, effective, and efficient implementation of AI across the workforce.

5.    Common purpose: enhanced employee productivity and experience

Food for thought

AI’s promises are plenty:  cost saving, revenue, profits, share price increase, employee experience, productivity, more meaningful work…and the list goes on (13).

Turning these promises into reality requires measurable evidence of AI’s adoption and its impact on employees.

In other words, it entails measuring AI's impact on employee productivity, efficiency, experience, perceptions, behaviours, culture, and skills. Understanding these can facilitate reaping the promised benefits of AI for employees and organisations.

People Analytics’ purpose is all about enabling better decision-making and actions through data-informed decisions on these matters.

This is why People Analytics teams even exist in the first place.

With AI’s democratisation in the workplace, we are living in a historical opportunity for People Analytics to be true to their purpose and help their organisations enhance employee productivity and experience. Let’s all rise to the occasion!

Practical example

Many organisations are currently trialling and experimenting with Copilot, Microsoft’s flagship AI product. Copilot is easily accessible through their web browser, Bing, and is being embedded in most of the major work-related Microsoft apps and products.

These trials are generating incredibly rich data on AI usage and adoption.

Combining this data with people data should enable the organisation to gain a deeper understanding of AI’s influence on work productivity and its impact on employee sentiment, behaviours, and skills. For example, by understanding how job effectiveness or well-being scores from AI early adopters vary from those not engaging with AI at work yet (14).

Use People Analytics to measure your first AI experiments and trials, to build the case for a responsible mass-scale roll-out across the workforce.

Recommendation

Find a way to measure impact by coming up with a transparent and clear set of principles to prioritise AI in workplace use cases and by tracking their success.

AI is an incredibly powerful force prone to disrupt and change how we work for good.

An intentional channelling of AI’s power can help organisations, teams, and employees to create a better work experience that allows them to go faster and further in a sustainable fashion.

Conclusion

AI’s effective adoption and implementation present big challenges and exciting opportunities, which are already in the DNA of People Analytics teams' experience, skills, mindset, partnerships, and purpose.

People Analytics teams are therefore in a pole position to add real value to this historical digital and behavioural transformation in which work is changing to never be the same, and drive positive outcomes for individuals, organisations, and society.

People Analytics friends, the moment to rise and shine is now. Will you?

Thanks for reading! Would love to hear your thoughts and ideas on this topic.

Would you agree (or disagree) People Analytics is at the heart of AI’s effective adoption and implementation? What other reasons would you like to add?

Finally, if you are championing AI in your organisation and would like to connect to brainstorm and share ideas and experiences, please reach out as this type of conversation is 100% my cup of tea.

We have pretty much a blank canvas in front of us, let’s paint it together!

#AI #ArtificalIntelligence #DataDrivenHR #HumanResources #EmployeeExperience #ChangeManagement #FutureOfWork #HRTransformation

References

A list of fantastic resources that helped me to grow and get inspired to write this article. You won't regret looking at them:

(1) A new future of work: The race to deploy AI and raise skills in Europe and beyond ( McKinsey Global Institute , 2024).

(2) Jasdeep Kareer, PhD (née Bhambra) and David Green 🇺🇦 , Transforming HR With AI ( Insight222 Webinar, 2024).

(3) Jonathan Ferrar and David Green 🇺🇦 , Excellence in People Analytics (Kogan Page, 2021).

(4) The State of Skill Inference White Paper: A teardown of 7 popular data sources used for AI-driven skill inference ( TechWolf ).

(5) Financial & professional services: The future of AI & the workforce ( City of London Corporation & KPMG , 2024).

(6) Targeted consultation on artificial intelligence in the financial sector (European Commission. Directorate-General for Financial Stability, Financial Services and Capital Markets, Union, 2024).

(7) SmartAssistant: Native AI Built to Power Data-Driven Hiring ( SmartRecruiters , 2024).

(8) Work Trend Index Annual Report: AI at Work is Here. Now comes the Hard Part ( Microsoft and LinkedIn , 2024).

(9) Matt Furness , A Manifesto for Effortless Culture Change (Click People Consulting Ltd, 2024).

(10) Mike Walsh, Watch how Phillips 66 used Copilot in Viva Glint to summarise 14k employee comments in 15 seconds (techcommunity.microsoft.com, 2024).

(11) The Product Manager's Guide to Building Generative AI ( Visier Inc. , 2024).

(12) PwC with Legal & General , MCA Awards 2023 Winner (The Management Consultancies Association, 2023). #LandGlife

(13) John MacDonald ( Google Workspace ), Return on employee, fueled by Generative AI (LinkedIn, 2024).

(14) Work Trend Index Special Report: What Can Copilot’s Earliest Users Teach Us About Generative AI at Work? A first look at the impact on productivity, creativity, and time ( Microsoft , 2023).