I remember a site visit I did over 20 years ago. It was a pharmaceutical manufacturing facility, and I was there in my role as recruiter to understand the roles hired within the business. Like most recruiters who have spent time in operational environments, I asked a lot of questions. They hired at scale, across a wide range of skillsets, with a focus on hiring on time against product supply and attrition plans and expectations.
On this particular visit, walking alongside the site leader, we rounded a corner and I saw something I hadn’t encountered before: a huge robotic arm dominating part of the production floor. When I asked about it, the answer was simple: “That’s the bot.”
Naturally, my next question was: What roles will you need to hire because of the bot?
The response surprised me. “We don’t really know yet. There are one or two people who know how to use it. We’re not scaling it at this time.” And then we moved on – back to familiar conversations about existing automation, and the people required to run, clean, fix, feed and receive the outputs of the automated processes that turned R&D into marketable products.
At the time, that robot felt like an anomaly. Interesting, impressive – but not yet transformational.
Ten Years Later: Investing in What Works
Fast forward a decade and I was Global Head of Talent Sourcing at AMS, responsible for manufacturing talent and supporting around 80,000 hires a year for more than 100 enterprise clients worldwide. I still remember the day my Director handed me a £1m budget and asked that we invest it uncovering and using what works – complete with clear productivity targets for the years ahead.
So we invested.
We maximised the use of available data. We tested the things that might work. We introduced new processes and identified scalable, enterprise-grade tools that could genuinely benefit AMS and our clients. Alongside our consulting and advisory colleagues, we reviewed hundreds of tools and technologies being considered by AMS and by clients across the globe.
That exercise taught me something fundamental: identifying use cases that work across multiple sectors is incredibly hard. When your hiring spectrum ranges from Goat Counter (yes, really) to Director of Oncology R&D, the bar for usefulness is high.
Some tools needed to be isolated to small pilot groups so we didn’t waste time or money. Others needed to be tested at scale to generate meaningful feedback, spark new ideas and build confidence. All of it required discipline, curiosity and a clear definition of what “good” actually looked like – particularly when it came to capturing, interpreting and generating insight from talent data.
At that point in time, beyond data aggregation, much of what we saw was about mobile-enabled processes and platforms. The tools that survived were the pragmatic ones – the ones that delivered real, measurable value. Time-to-hire reductions of 20% weren’t unusual. In a competitive business, that translates directly into commercial advantage for our clients.
Those are the tools that succeeded. Many of them are still around today. They helped us lead – and continue to lead – in effective data use and talent insight production.
And here’s a quiet truth from that era: this is also the point at which identifying talent became relatively easy. LinkedIn, combined with X-ray search capability, fundamentally changed sourcing. The challenge began to shift from finding people to engaging, converting and retaining them.
Twenty Years On: What’s Actually Different Now?
Now, two decades on from standing eye-to-knee with that manufacturing robot, I find myself reflecting on what’s here now – and what’s coming next.
The difference today is not just the technology itself. It’s the level of corporate investment and intent behind it. Organisations are investing as they never have before, creating the conditions for widespread adoption of technology and, increasingly, for unlocking the value of AI.
So what do I see making a real difference in enterprise talent acquisition right now?
- Chatbots: Relatively easy to build and deploy, with clear ROI. Fewer people are needed to answer repetitive questions or guide candidates through processes – and experience often improves, not degrades.
- Process automation and enabling platforms: Including solutions like our own next-generation recruiter platform, AMS One. Automating the right processes and adopting the right applications in an agile way will continue to deliver significant customer and user satisfaction gains.
- AI as a coach, editor and encyclopedia: Most powerful when used as a thought partner – not as an oracle. It supports better communication and decision-making rather than replacing it.
- Advanced data processing: Enabling the future of marketing effectiveness, workforce planning, business intelligence and talent insight at a depth we simply couldn’t achieve before.
Notice what’s missing from that list: hype. The value is not in the novelty. It’s in application.
Technology Has Always Been About People
It would feel odd to talk about technology without talking about people. At AMS, one consistent theme over decades of evolution in talent acquisition has been the commitment and imagination of the people driving change – for us and for our clients.
That hasn’t changed.
We still need the committed and the curious, the people willing to try things, fail fast, and discover what actually works. We need people who care deeply about user experience – for recruiters, candidates and enterprise customers alike – and about the real-world impact of what we build.
Knowing what “good” looks like remains a critical anchor. In a rapidly evolving technology landscape, that clarity keeps us grounded while everything else shifts around us.
The Next Shift: Learning to Work With the Bot
I believe we are on the cusp of a significant movement in learning.
Not just learning about technology – but learning how to work with it. We are finally starting to build the skills, confidence and judgement required to use the bot well. To know when to trust it, when to challenge it, and when to ignore it altogether.
Twenty years ago, that robotic arm didn’t change hiring overnight. But it was a signal of what was coming.
Today, AI is no longer an anomaly hidden around the corner of the factory floor. It’s here, embedded in our processes and platforms. The organisations that will win are not the ones chasing every new tool – but the ones investing in people, learning, and thoughtful application.
In other words: we’re not just building better bots. We’re now building the talent needed to use them.
