We use AI where it makes the work more precise. Not where it makes it more superficial.
We map a candidate field faster and wider, also across countries and industries where the profiles rarely share the same job title.
Whether a candidate can carry the role in two years is an assessment. It cannot be derived from a CV or a profile text.
Large volumes of public material can be collected and structured, so the research starts from a better foundation.
Why would someone with a good job say yes to yours? That answer only exists in a conversation, and it moves along the way.
Which companies have actually built that kind of platform before? Where do those profiles usually come from? The patterns surface earlier.
The best candidates pick up the phone because they know us. Trust cannot be generated.
We can prepare a far sharper technical conversation, because we know what to ask about.
Sometimes the right recommendation is to reshape the role, wait or promote internally. That is a position, not an output.
The candidate market is small, and few have genuinely built with AI.
AI-native companies need leaders who understand both the technology and the product. Established companies need leaders and specialists who can change architecture and organisation without stopping the business. And the consulting and implementation firms need people who can turn AI into something clients can actually use.
These searches demand a different combination of business understanding, technical judgement, data literacy and knowledge of a small candidate market. They also demand the ability to tell a strong engineer apart from someone who has genuinely built with AI.
In AI-native companies the model is the product. In established companies AI reshapes the architecture, the processes and the organisation. Either way, technical leadership has to make architecture decisions and understand what the business actually gets out of them.
Most AI ambitions stand or fall with the data foundation. An AI search is therefore rarely about a single AI hire. It is also about the data engineering, platform and governance around it.
AI only reaches production through the advisors and implementation people who turn the technology into working solutions at the client. That is a different candidate field from product development, and it needs its own approach.
Titles say very little in AI and data. A strong engineer has not necessarily built with AI in practice. We look at what the candidate has built themselves, at what scale, and what was somebody else's work.
AI supports the research. The judgement is ours.
Search for CTOs, Heads of AI and other technical leaders where AI capability is decisive both for the technology direction and for leading the team. We have completed several confidential CTO assignments where the ability to understand and build with AI was central. Out of respect for those clients they are not named.
Broad transformation programmes where AI and data reshape architecture and organisation. For Rockwool we built the Data Science function from the ground up around Industry 4.0: Head of AI, Data Scientists and Data Engineers, plus Enterprise Architects and Solution Architects, particularly across SAP and Microsoft. See the Rockwool case.
The advisors and implementation people who bring AI out to clients. For Devoteam Management Consulting we have recruited Principal Consultants and consultants advising clients on the possibilities and gains of AI. For MDC Nordic we have recruited Principal Consultants focused on applying AI to faster D365 Business Central implementations.
We have also recruited commercial profiles for an international AI technology company. Two profiles were recruited to drive commercial development, and one has since progressed to become Country Manager in Denmark.
From argument to documented work.
These four examples are among the assignments we can talk about openly. They represent only part of the AI, data and technology search work we have done. The full cases live on industries and cases.
A Danish AI-native company that built an AI chatbot before ChatGPT reached the market. GlobeSearch recruited a CTO. The hire was decisive in professionalising the product and strengthening technical leadership.
See the case on industries and casesA Danish AI company using advanced AI to forecast how much renewable energy specific renewable sources can be expected to deliver. GlobeSearch recruited a CTO to professionalise the technical platform and create an architecture able to bring the company's different solutions together into a more coherent product.
See the case on industries and casesA Danish AI company with unique technology in human pose estimation, later sold to a large Japanese group. GlobeSearch recruited several key profiles: CTO, Principal Software Engineer, Frontend Engineer and Backend Engineer.
See the case on industries and casesGlobeSearch built Rockwool's Data Science department from the ground up as part of their Industry 4.0 agenda. We recruited a Head of AI, Data Scientists and Data Engineers, along with a long series of Enterprise Architects and Solution Architects, particularly across SAP and Microsoft. This was a broad technology organisation built over time, not a few isolated AI hires.
See the case on industries and cases
An AI agenda rarely succeeds on a single hire.
We have experience finding both the leaders and the specialists around them across AI, data, engineering, enterprise architecture and solution architecture.
See the full picture of the roles we fill.
The technical leadership accountable for architecture, delivery and product.
Ownership of the AI agenda, from applications and model choices to how the work is organised.
The people who turn data and models into something the business can actually use.
Pipelines, platform and data quality: the foundation under any AI ambition.
Principal, backend and frontend engineers who build the product around the technology.
The architecture across systems, often in SAP and Microsoft landscapes.
The architecture for the specific solutions that have to be integrated and run in practice.
Leaders who connect the technology to roadmap, customers and commercial value.
We currently see rising demand for Head of AI and related AI leadership profiles.
This is an observation from the market, not a case and not a description of any particular client or assignment.
AI does not change the structure of our work. GlobeSearch Connected Methods is the same: Calibration with leadership, structured research, confidential candidate dialogue, a shortlist within six weeks and an evaluation after six months.
What AI changes is how much of the market we can cover before the conversations begin.
We are happy to talk through the role, the market and what is realistically achievable, long before you commit to a process.
