AI, analytics and uncertainty: How HR is rebuilding its foundations

Artificial intelligence is colliding with economic and geopolitical volatility to create the most uncertain period HR leaders have faced since the pandemic.

For many employees, it feels like a double blow: jobs at risk from technology on one side and from a shaky economy on the other. The old assumption that roles will “come back” after a downturn no longer feels guaranteed.

That anxiety is showing up in every senior HR forum. SiteMinder chief people officer Dionne Woo said that at a recent gathering of HR leaders she attended, planning for an unknown future dominated the conversation.

Economic cycles are familiar, she said, but this time the overlay of AI is different. People are now asking whether the roles lost to a downturn will ever re‑emerge in the same form.

Yet beneath this uncertainty, there is also a clear sense of direction. The way organisations use data, analytics and AI is rapidly becoming the new foundation of HR – and the choices leaders make now will determine whether they are overwhelmed by change or able to shape it.

 “You can’t leap into AI without getting your data right”

Chris Wood, director of people and culture at Nova Systems, argued that the profession has reached a turning point it has been talking about for two decades. In his view, the headline story is not AI itself, but the overdue realisation that HR must finally become truly data‑driven.

He hears constant enthusiasm about potential applications of AI but pointed out that the first wave of real value in HR is being seen in analytics and decision‑making. When leaders ask for a view of retention hotspots, risk areas or engagement patterns, they increasingly expect that view to be grounded in data science rather than intuition.

AI can help surface those patterns and even detect early warning signs, but only if the underlying data is reliable and accessible.

Wood is adamant that organisations “can’t leap into AI” if they have not already done the hard work on data and analytics. The models and agents HR teams are experimenting with will be powered by whatever sits in their HRIS, surveys and dashboards. If those foundations are shaky, the outputs will not be trusted, and HR’s credibility will suffer rather than improve.

He also drew an important distinction between insight‑oriented AI and robotic process automation. Using AI to interrogate data, spot trends and generate forward‑looking insight is one conversation. Automating repetitive tasks such as standard reporting or basic applicant sorting is another.

Nova Systems is interested in using automation to free HR administrators from spending days each month generating leave and liability reports out of large systems, but Wood is clear that this is not where the real transformation lies. The strategic shift comes when AI can look across multiple years of data, predict likely attrition patterns, and give HR enough lead time to act.

In that world, the role of people analytics changes. Rather than spending their time manually building dashboards and spreadsheets, analytics professionals focus on interpreting AI‑generated outputs, adding context and crafting stories that influence executives and boards. For Wood, that evolution – from producing data to generating insight and influence – is what the profession has been working towards for years, only now accelerated by AI.

AI as a business multiplier

At SiteMinder, Woo is approaching the AI challenge from the vantage point of a fast‑growing tech company that cannot afford to stand still. She described the business as growth‑oriented by design, which means planning even when the future is unclear.

Like many organisations, SiteMinder has been experimenting with AI for several years, but Woo says the last 12 months – and especially the last three – are when the impact has really started to materialise.

Every major system used by HR now comes with AI functionality built in. In learning, the team can upload policies or briefing documents and receive a draft set of training materials, including suggested quizzes and scoring models. The content still needs an expert hand to refine it, but HR is no longer starting from a blank page each time.

People systems are beginning to offer built‑in analytical summaries and trend identification, replacing at least some of the manual exporting and Excel work that used to be routine. Early forms of predictive analytics are appearing inside the platforms HR already uses rather than in separate, specialist tools.

The impact is not confined to HR. In sales, SiteMinder is using AI‑powered coaching tools that record calls, provide role‑play environments through trained bots, and generate consistent scoring. Managers still coach, but they now have a richer and more comparable data set to work with, and can easily see differences across teams, regions and individuals.

In product, AI is being embedded directly into customer‑facing offerings to provide better analytics and insights, while engineering teams are adopting AI to support coding and testing. Customer‑facing teams are also finding ways to integrate AI into their day‑to‑day work.

Woo’s assessment is that every function in the business is now using AI in some capacity. To push this further, SiteMinder recently devoted a senior leadership team session entirely to AI. Instead of encouraging cross‑functional mixing, leaders were grouped by function and challenged to identify concrete AI initiatives they could implement within three to six months, with modest cost and resource implications.

Each functional group then presented back its ideas, and those proposals are now being taken into detailed discussions with the executive team. Within HR, one of the next frontiers under active consideration is building a dedicated HR AI agent.

Skills planning when the goalposts keep moving

All of this change is forcing HR to rethink how it approaches skills and workforce planning. Traditionally, leaders would look at their current state, project out five years, and ask what capabilities would be needed at a larger scale or in a more complex organisation. Under the AI lens, Woo argued, that exercise looks very different.

It is no longer only a question of scale or corporate maturity. It is a question of which skills will exist in the market at all, which will be in short supply, and which will remain relevant as tools evolve.

Only months ago, prompt engineering was being talked about as a pivotal job. Now, that focus has already shifted as models become more user‑friendly and as organisations recognise they need a broader mix of skills around data, product and change.

Some capabilities, such as data science, remain in acute shortage and come with significant cost and retention challenges. Woo is frank that companies cannot simply decide to hire large numbers of such specialists and expect the market to respond. Instead, she is advocating a blended approach that uses both internal and external pathways.

Internally, the priority is to spot employees who show a natural aptitude and curiosity for AI and data, and to give them opportunities to move into roles where those strengths can be developed. Passion and willingness to learn matter as much as formal qualifications at this stage. Externally, SiteMinder is bringing in highly specialised skills where necessary, sometimes through targeted permanent hires and sometimes through contractors for defined pieces of work.

Underlying all of this is a commitment to agility. Woo does not pretend that planning in this environment is straightforward; she describes it as something done “with difficulty.” The answer is to avoid locking in large bets on tightly defined skills, and instead build teams that include people able to move across projects and domains as technology and strategy evolve.

Levelling the playing field – and raising the bar

Wood offered a perspective on what AI is doing to individual capability. In his view, tools like generative AI have levelled the playing field on many transactional skills. Anyone can now create a convincing cover letter, a generic organisational development strategy or a high‑level workforce plan in minutes. Experience still matters, but access to polished output is no longer its exclusive domain.

That levelling effect increases the relative importance of qualities that cannot be automated. Relationship‑building, influence, judgment and the ability to make ideas land with real people become more central than ever.

The perfect job application produced with AI still has to stand up in an interview, where a candidate’s behaviour and impact matter more than their prose. The most intricately modelled HR strategy still requires HR leaders to persuade CEOs and boards to back it, and then to work through layers of scepticism and competing priorities in the line.

Wood pointed out that this is not a new argument. HR has spent 20 years talking about “getting a seat at the table,” speaking the language of the business, and using financial and workforce data to make the case for investment in engagement, capability and culture.

What AI has done is accelerate the timeline and raise expectations. If HR can produce sophisticated analysis and frameworks in hours instead of weeks, there is less patience for delay – and more scrutiny of whether those frameworks translate into outcomes.

At the same time, he sees a positive side for professional development. A graduate entering HR today could use AI to assemble a detailed organisational development strategy in half an hour, bring it to a senior leader, and use that as the starting point for a mentoring conversation.

Historically, the same level of exposure and learning might have taken years of experience to acquire. AI, in this reading, becomes an accelerant for capability – provided it is coupled with guidance, reflection and real‑world practice.

A new deal for HR: data, AI and the enduring human core

Taken together, Woo and Wood’s experiences suggest that HR is being pushed into a new kind of maturity.

On one side, there is a clear requirement to get serious about data. Without solid analytics, AI initiatives will stall or backfire, and HR will struggle to retain credibility in executive forums that are increasingly literate in technology and risk.

The long‑discussed ambition for HR to arrive at the board table with robust evidence as well as empathy is no longer aspirational; it is expected.

On another side, there is the opportunity to use AI to free HR from low‑value, repetitive work. Every reporting pack auto‑generated instead of manually compiled, every training program bootstrapped by a model rather than designed from scratch, buys back time that can be reinvested in strategic and relational work.

And then there is the human core of the profession, which neither Woo nor Wood see as dispensable. If anything, they argue, the ability to build trust, interpret context, read a room and influence decisions is becoming more important as tools make it easier to produce plans and content.

For HR leaders, the task now is to hold these threads together. That means investing in data foundations, taking practical steps to embed AI where it demonstrably helps, reshaping roles around insight and influence, and leading a candid conversation about what technology will and will not change.

The future may be uncertain, but passivity is not an option. Those who move first to rebuild their foundations – and who do so with both rigour and humanity – will be the ones shaping how work looks in 2026 and beyond, rather than simply reacting to it.