Choose an AI tool, allocate licences – and is that it for the transformation? – Unfortunately, it’s not quite that simple. After all, whether AI really adds value within a company isn’t determined by the technology alone. If data is scattered across different systems, skills aren’t transparent, and learning, talent and performance run in parallel, AI lacks one crucial thing above all else: context. This is precisely why
Maika Kölle and Sabrina Sommer from Cornerstone OnDemand view AI maturity not as a traditional IT project, but as an HR transformation. In this interview, we discuss why companies shouldn’t start with AI, why skills are becoming the crucial foundation, why it’s still not necessary to spend years first building the perfect database – and what role people play in all of this. Let’s get started: SAATKORN: Maika and Sabrina, please introduce yourselves briefly to SAATKORN readers. What do you do at Cornerstone? Maika:
I’m an Account Manager at Cornerstone and responsible for ensuring our clients receive the best possible support from us. Our discussions very often begin with an organisation’s challenges and goals.
When it comes to AI in particular, I’ve noticed that there’s still a lot of uncertainty within HR: Where do we actually start? Who’s responsible? And how do we manage to really integrate AI into the organisation?
Sabrina:
I’m Sabrina Sommer and I’m from the Solution Consulting team at Cornerstone. I mainly bring a practical and technological perspective to the table: what foundations are actually needed, and what can already be implemented today?
SAATKORN: You state quite clearly that AI maturity isn’t a traditional IT project. Why is that, exactly? Maika:
Because AI transformation isn’t determined by technology alone.
Many companies start straight away with a tool. The initial results may be interesting, but they remain isolated. Because whilst AI can process information, it must also be able to understand what that information means for the organisation in question and for the people within it.
We like to compare this to a racing car. Many companies jump straight in because they don’t want to miss out on the AI hype. Then they realise that the road isn’t actually built for it yet.
The crucial question is therefore no longer whether AI is changing our world of work. That is already happening. The question is: have we created the conditions necessary for this to yield meaningful and sustainable benefits?
SAATKORN: And how do you build this ‘road’? Where should companies start? Sabrina:
We look at four phases for this: digitisation, networking, understanding skills, and then using AI effectively in day-to-day work.
It’s important to note that not every company is starting from scratch. Many organisations have long since been using systems for learning, talent management, recruitment or performance management.
That’s why the first question should always be: Where do we stand today? – And then it’s not about achieving the maximum level of AI maturity straight away, but about identifying the next step that brings the greatest added value to the organisation.
SAATKORN: Surely many companies already have plenty of HR systems and data. Why isn’t that enough? Sabrina:
Because these systems often exist in silos. Learning, for example, knows what content a person has consumed. But it may not know what target role that person is aiming for, what development goal they are pursuing, or what skills will be needed in the future.
Talent, on the other hand, knows roles and potential, but may lack context regarding what a person is currently learning.
If this information isn’t linked together, the context is missing. And that’s precisely when AI recommendations tend to remain rather generic.
SAATKORN: You describe skills as a kind of ‘currency of the future’. What makes skills so important to you? Maika:
Skills create a common language.
Essentially, organisations need to be able to answer four questions: What skills do we have today? Which ones will we need in the future? Where are the bottlenecks? And what potential can we develop internally?
Only when I can make skills and gaps visible can I derive targeted decisions from them. This applies just as much to learning as it does to talent, performance or internal mobility. When these perspectives come together, a much more complete picture of the organisation emerges.
SAATKORN: Is it then enough to build a comprehensive skills taxonomy once and for all, and consider the matter settled? Sabrina:
No. That is precisely one of the challenges.
Skills change very quickly. A static skills taxonomy can therefore become outdated relatively quickly. We need a dynamic view of skills that evolves and also takes market changes into account. And it mustn’t be kept separate somewhere. Skills must be integrated into the processes in which employees work, learn and develop anyway.
SAATKORN: That sounds like quite a big project. Does my entire data and skills architecture have to be perfect before I can make a sensible start with AI? Sabrina:
No, not at all. Perfect data isn’t a prerequisite for getting started. Companies can also start with a smaller area or a pilot project and build on that.
What’s far more important is that there’s a structured process in place for managing data quality and skills. AI can also help when building a skills architecture and significantly speed up processes. But we still need people to check and categorise the data.
SAATKORN: How can I even tell how AI-ready my organisation is today? Maika:
Not by how many AI tools we’ve bought. Instead, we should look at: How digitised are our processes? What data infrastructure do we have? How well are our systems and information interconnected? Do we have transparency regarding the skills within the organisation? And is this information actually factored into decision-making?
It’s not about reaching some sort of maximum maturity level as quickly as possible. The more important question is: where are we right now, and what is the next sensible step for our specific use case?
SAATKORN: And who should assess this AI maturity level? HR? IT? Senior management? Maika:
From our point of view, it shouldn’t be a single person or function. AI transformation affects the entire organisation. That’s why it should be examined collectively.
HR naturally plays an important role, because it’s about people, skills, learning and development. But the other functions and stakeholders within the company are just as essential. When we talk about transformation, we must involve the people who are affected by this change and who are expected to work with the technology.
SAATKORN: When learning, talent, performance and skills are linked together – what does that actually mean in practice? Maika:
The previously separate perspectives come together. Learning supports development within the relevant context. Talent highlights potential and internal mobility. Performance links goals and development. Skills reveal capabilities and gaps. And intelligence helps to contextualise this information and derive actions from it.
An organisation can then identify much more quickly where it stands, where changes are taking place, and where targeted measures are needed.
And this brings us full circle to our four phases: digitalisation generates information. Interconnection provides context. Skills reveal capabilities, gaps and potential. Only on this basis can AI personalise, support and scale effectively.
SAATKORN: With all this data, skills, systems and AI models – where does the human element actually fit in? Sabrina:
At the very centre. The best data and systems do not automatically lead to change. After all, in the end, it is not just the technology that changes. The way people work, learn and collaborate also changes.
That is why culture is a crucial building block. Technology creates opportunities. But people decide what comes of them. If people are afraid of making mistakes, if knowledge is withheld, or if managers fail to provide guidance, even the best technology and the best data won’t help.
AI maturity therefore requires a culture that enables learning. People need time and safe spaces to try out new ways of working.
SAATKORN: If you could give HR managers three pieces of advice to take away – what would they be? Maika:
Firstly: AI maturity is about HR transformation, not simply the introduction of a new technology.
Secondly: skills form a key foundation for identifying capabilities, gaps and potential within an organisation.
And thirdly: learning, talent and performance should not be viewed in isolation from one another. Only when this information is linked together does the context emerge that we need to utilise AI in a truly meaningful way.
Ultimately, it’s not about humans versus AI. We need both. AI can provide support, identify patterns and scale processes. But experience, creativity, context and human judgement remain crucial.
SAATKORN: Maika and Sabrina, thank you very much for the interview!
Further links
- Maika and Sabrina’s full presentation is available in the 4INSIDER media library.
- Fancy joining the next SAATKORN Online Conference live? You can register here.