Hiring for Adjacency.

Must A + B always lead to C? Or can A + D create S — a different, better solution?

Executive hiring has a default logic. Functional capability (A) plus industry experience (B) is expected to produce C: the candidate who looks like the obvious fit. A commercial leader from the same sector. A technology executive from a direct competitor. A country head who has run the same market before.

That logic holds — until the organisation isn’t trying to reproduce what it already has. This briefing sets out the case for hiring adjacency: keeping the capability that matters, sourcing the experience from somewhere unexpected, and looking for S instead of C.

From Industry Fit to Problem Fit

Take a business shifting from product-led to customer-centric. The conventional search starts with competitors. But what is the executive actually being hired to solve — deep knowledge of the product category, or the ability to build segmentation, shift sales behaviour, open new channels and move the organisation toward long-term customer relationships?

If it’s the latter, the real talent pool is bigger than the industry. An executive in an entirely different sector may have already solved the same problem — just with a different product. That is industry fit giving way to problem fit.

Mapping Adjacency: Four Places to Look

Adjacency isn’t about opening a search indiscriminately across every industry. It’s about finding meaningful similarity beneath obvious difference — and it reframes the talent-mapping question from “which companies look like us” to “where else does this problem exist.”

Crystallised Intelligence Meets Fluid Intelligence

Crystallised intelligence is knowledge built through experience — industry expertise, customer knowledge, commercial judgement, pattern recognition earned over a career. Executive search is naturally good at reading it; a CV is, in many ways, a record of exactly this.

But organisations don’t hire executives only to handle familiar problems. Fluid intelligence is the ability to reason through unfamiliar situations and solve problems where past experience offers no obvious answer.

The strongest adjacent candidate carries substantial crystallised intelligence from one environment and the fluid intelligence to translate it into another — recognising a problem’s underlying structure without assuming the old solution can simply be copied across.

AI Is Raising the Stakes for Adjacency

This distinction between crystallised and fluid intelligence used to be interesting. With AI adoption accelerating, it has become urgent.

Generative AI use in the enterprise nearly doubled in two years, and the large majority of organisations now use AI in at least one business function — adoption that was a minority position as recently as 2023. Whatever an executive knew cold three years ago about their industry’s data, reporting or customer patterns is now, in part, something a tool can surface in seconds. Crystallised knowledge that once took a career to accumulate is depreciating in real time.

That shift changes the value of the three things this framework has been separating all along:

• Crystallised knowledge is depreciating faster. Domain facts, historical patterns and standard analysis are increasingly retrievable rather than earned — which erodes the advantage of “they already know this industry cold.”
• Learnable context is expanding. AI-assisted onboarding means an adjacent hire can absorb product, customer and sector specifics far faster than before — shrinking the real cost of hiring outside the industry.
• Fluid intelligence is the scarce asset. The ability to redesign a workflow, judge what AI output to trust, and reason through a genuinely unfamiliar situation cannot be looked up — and it is now the harder thing to find.

AI doesn’t just create a new capability to hire for. It shortens the shelf life of every other one.

There is also a fifth adjacency emerging alongside the original four: AI-transformation adjacency — experience redesigning a workflow, retraining a team or rebuilding a process around AI-enabled tools, regardless of the industry it happened in. Because most sectors are still early and roughly comparable in AI maturity, this is one of the few adjacencies today where almost no industry has a durable head start. A leader who has done this in logistics or retail may be more valuable to a healthcare or financial-services organisation than one with two more decades in-industry but no experience leading through this kind of change.

What This Means for Hiring Managers, Going Forward

If crystallised knowledge is depreciating and fluid intelligence is the scarce asset, the hiring process built to reward the former needs to change. Five shifts follow directly:

1. Re-weight the scorecard toward learning velocity. Years in the industry and familiarity with the current tech stack should carry less weight than evidence of how fast someone has adapted through a prior wave of change.
2. Screen for AI-transformation adjacency explicitly. Add it as its own lens in talent mapping, alongside customer, business-model, capability and transformation adjacency — and search across industries for it on purpose.
3. Interview past the buzzword. “AI transformation leadership” is now a common CV line and an unreliable signal on its own. Press for the specific workflow changed, the resistance encountered, and what was measurably different afterward.
4. Widen the search on purpose. Because most industries are at an early, comparable stage of AI maturity, the usual moat — “only our direct competitors have done this” — is weaker than usual. This is a genuine, time-limited opening to broaden the talent pool.
5. Use AI to de-risk the adjacency itself. The same tools reshaping the market can shorten an adjacent hire’s ramp-up on domain specifics — turning what used to be the strongest objection to hiring outside the industry into a manageable onboarding plan.

None of this argues for hiring adjacency as a reflex. It argues for treating AI-transformation experience as seriously as industry experience has traditionally been treated — and for recognising that, for the first time in a while, most organisations are searching for that capability from a genuinely level starting point.

The Transferability Question

Industry experience doesn’t become irrelevant. Some knowledge genuinely has to exist on day one — regulation, technical complexity, licensing, specialised relationships. The discipline is in separating three things, and not defaulting all three into the first category:

When organisations treat all three as non-transferable, the specification narrows until the “perfect candidate” can only come from a handful of direct competitors. One hiring risk goes down. Another — a talent pool defined entirely by familiarity — goes up.

The Familiarity Premium

Familiarity is comfortable. We know that company, understand that title, trust that they’ve done this before. The candidate is easier to benchmark, easier to explain internally, easier to defend if the appointment doesn’t work out.

Familiarity and future capability are not necessarily the same thing.

The contradiction shows up most clearly when organisations say they want transformation or reinvention, while requiring candidates whose careers look identical to the executives already in the room. If the business is trying to build something different, the talent pool may need to look different too.

From Experience to Learning Velocity

Adjacent hiring adds a layer beneath the standard interview. Alongside what someone has done, the sharper questions probe what they can do next:

• How quickly can this person read an unfamiliar environment?
• Which parts of their past experience transfer — and which should they leave behind?
• When has a previously winning playbook failed them, and how did they know?
• What have they had to unlearn?

Experience tells you what someone has encountered. Learning velocity tells you what they may be capable of encountering next — and it rarely shows up on a CV.

Hiring for What Comes Next

Every executive appointment is an exercise in prediction. We study what someone has done because we’re trying to judge what they might do next — but past similarity and future capability are not the same thing.

Hiring for adjacency doesn’t mean abandoning the conventional candidate. Sometimes A + B genuinely should lead to C. It simply means not assuming C is the only possible answer — because somewhere outside the obvious pool may be an executive who understands the customer but comes from a different category, who knows the model but not the industry, or who has never faced this exact situation but has repeatedly shown the ability to learn, adapt and solve unfamiliar problems.

The objective is not always to find C. Sometimes, it is to recognise S.

That is the broader task of executive search: not simply locating the candidate everyone expects to find, but recognising the possibilities the organisation didn’t know to look for.

Placements Track Record

Role	Organisation
VP, Digital and AI strategy	Singapore based Conglomerate
Director – IT Enterprise Applications and Digital	Global Medical Technology Company
Head of Digital Technology	Global Medical Device Company
Group Chief Information Security Officer (CISO)	Singapore Healthcare Cluster
Group Information Security officer (GISO)	Singapore based Technology Company
Head of Engineering	Geospatial Analytics Company
Chief Digital Officer	Global Asset Management Group
Chief Marketing Officer	A Leading Consulting firm
Manager, Group Governance & Sustainability	Leading Real Estate Group
Head of Communications	Singapore Headquartered Healthcare Cluster
General Manager, Retail	Integrated Omnichannel Commerce and Beauty Supply Chain Solutions
Head of Commercial	Global Decarbonisation Solutions and Carbon Management Platform
Head of Government Affairs	Global MNC in Consumer Goods
Head of Digital Marketing	Consumer Retail Group
Sales Director, APAC	Global Smartcard Component Manufacturer

Meet the author.

• Karen Yap Partner, Commerce & Industry