For AI to fundamentally disrupt executive search, it won’t be because of better note-taking, calendar booking, or interview scheduling. Those things are useful. But they’re incremental.
To create a step-change, AI must first solve the base-layer challenge: finding the right people.
Compressing what typically takes weeks of market mapping and research into hours or even minutes results in additional candidates, consequently, a competitive advantage. When a firm can do this reliably, it will create a meaningful and valuable point of differentiation.
So, the burning question for executive search firms is how to use AI to identify candidates faster and more accurately. To do this successfully means solving one specific, difficult problem: How do you extract consistent, reliable meaning from professional profiles so that the right individuals can be identified and grouped, at scale?
This is not a question of executive search professionals becoming extinct. In reality, the future of senior hiring is more, not less, human. When the heavy lifting of market mapping, title interpretation, and candidate population building is handled faster and more accurately than ever before, the real value becomes concentrated in the judgment, trust, and above all, relationships of the professionals.
Solving the challenge of finding the right people, consistently, at pace and at scale, is revolutionary for the executive search industry.
This article is about how Savannah approached and solved this problem.
Executive search feels like an art, but it’s actually a system
There’s a widely held belief that executive search is more art than science. Knowing who the “right” people are for a role is primarily about instinct, experience, and judgment.
Judgement is fundamental. But beneath that judgement sits something far more structured.
At its core, executive search is the repeated act of converting a wish list — a brief — into a sequence of inclusion and exclusion decisions. Out of hundreds of millions of professionals globally, who are the 50 or so people that plausibly fit this role, in this context, at this point in time?
That “billion to fifty” problem is what researchers, in-house teams, and search firms work on every day.
Whether consciously or not, they are applying a consistent set of filters and heuristics to do this. The question is whether that structure is explicit, scalable and programmable.
What makes it hard to find the right people?
The first question is: which parts of people’s public profiles can be considered accurate, and which can’t?
The ★ symbol indicates the most reliable signals to guide decision-making.
Some data points are binary and reliable: location (current and historic) and companies worked at. Some are contextual but still strong: seniority progression, tenure, industry exposure, company characteristics (size, regulation, B2B/B2C, etc.). Others are weaker: self-authored summaries, skills lists, free-text keywords.
Keywords are indicators, but they’re inconsistently applied, frequently incomplete, and often inflated. In many sectors (professional services, consulting, legal), profiles are not keyword-rich at all.
If you step back, three attributes dominate early-stage relevance decisions: Where someone works, which companies they’ve worked at previously, and what roles they’ve held.
Location can be standardised. Company data can be enriched.
Job titles are the hard part.
Why job titles are such a fundamental challenge
Job titles look simple, but in this context, they aren’t.
They vary wildly by company, industry, geography, and seniority. Two people with the same title may be doing entirely different jobs. And yet, job titles remain the most compact signal of functional experience, scope, responsibility and career trajectory.
If you can’t reliably understand and group job titles, everything downstream degrades. Search becomes keyword-heavy. AI models have to reason over vast amounts of noise and precision collapses. This is why most recruitment platforms lean so heavily on user-generated Boolean logic. It’s an implicit admission that system-level job title understanding hasn’t been solved.
What systems have been used in the past to build candidate lists?
Vector embeddings and knowledge graphs are often presented as the solution to standardise titles and determine candidate relevance. On paper, both make sense. They identify similarity across large, complex datasets.
We tested these approaches and found that, in practice, they share the same fundamental limitation: They fix the context of similarity too early.
This causes problems because in executive search, similarity is never absolute. Two candidates might be considered “similar” because they have worked in the same industry, operated in PE-backed environments, led the same function or delivered growth in regulated markets.
However, which of these matters most depends entirely on the individual search and often changes as more becomes known throughout the course of the search.
Vector-based approaches struggle here because the embedding space implicitly defines what “similar” means upfront. From that point, you are ignoring people who do not meet the original definition of similar. For example, if you weight skills more heavily, you dilute industry context. If you prioritise industry, you weaken other signals. Changing that balance dynamically is hard without retraining models, which makes real-time adaptation impractical.
Static knowledge graphs face a similar issue. While they capture rich relationships, they are typically queried in fixed ways. Changing which relationships matter most often requires new queries, manual re-engineering, or redesigning the graph itself.
In practice, while same-industry experience may be the primary signal a lot of the time, a meaningful minority of searches hinge on far more specific secondary signals, for example, scaling experience post-acquisition, turnaround leadership, or operating under particular regulatory pressures.
When context is fixed too early, tangentially relevant candidates – critical in executive search – are consistently missed.
That’s the problem Savannah set out to fix.
What the AI system Savannah built (MapX) does differently to find the right people
Savannah’s AI model and approach (MapX) do two things differently: first, translating disparate job titles into their underlying meaning. Turning inconsistent, subjective titles into structured, comparable role definitions that can be used reliably at scale.
Then, by adding dynamic context.
This means greater accuracy in candidate identification; relevance is calculated in real-time based on the specific question being asked. This system results in:
- paths through the graph that change depending on what you’re searching for
- relationships that can be weighted differently as context shifts
- representations of roles that adapt as new information is introduced
The result: 32% more accurate candidate lists than current methods.
In late 2025, we ran a comprehensive test to understand how effective Savannah’s MapX system was in accurately identifying relevant candidates against a brief compared to commonly used approaches.
We built a ground-truth dataset of 4,000+ job titles, spanning all functional areas, multiple seniority levels, the 500 most frequently occurring job titles globally, and a long tail of ambiguous roles. Each title was labelled in context, based on analysis of the individual’s entire professional profile, not the title string alone.
We then categorised these 4,000+ job titles using three different methods to compare accuracy:
We tested:
- LLM only (GPT-5)
- Our NLP model only – BERT + rules (MapX)
- The combined approach (GPT-5 and MapX)
The results of a comprehensive test on MapX versus other methods for identifying the right candidates
MapX and GPT-5 working together were 32% more accurate than GPT-5 only. Or to put another way, the combination of MapX AI’s translation layer with GPT-5 delivers 53% less classification errors compared to GPT-5 alone.
These results show the significant positive impact of embedding an accurate job title translation layer. Without this, even the most advanced graph or neural system is built on weak initial data.
What does this result mean for executive search?
Building a high-performing job title translation system which works alongside dynamic candidate profile analysis paves the way for significant positive changes in the executive search industry.
Savannah is already experimenting with Agentic AI on several potential use cases. For example:
- conversational talent mapping, where intent is clarified through dialogue
- adjacent and multi-path searching across non-obvious career routes
- dynamic weighting of attributes as priorities change
- identification of genuinely transferable candidates, not just title matches
This capability provides the opportunity to improve solutions for:
- assessment and psychometric grouping
- compensation benchmarking
- organisation design and workforce analytics
- job architecture and role comparison
As desk research cycles speed up, the increased speed in information transmission (from weeks to understand a market to hours and then minutes) creates enormous possibilities. Businesses can quickly investigate multiple possible leadership talent strategies. They can benchmark their leadership against the market and key competitors, quickly seeing what skills and experiences are common or rare, informing buy versus build succession plans. Search firms can more accurately estimate how easy or difficult it will be to deliver on an assignment and price accordingly. Businesses can test different markets, discuss potential profiles and align on what they want and, more importantly, what the market can deliver.
This technology provides new scope for elevating executive search relationships to become truly strategic, partnering on shaping talent strategies, developing deeper levels of trust and enabling more effective account development and expansion.
The breakthrough isn’t a single model. It’s building the interface that makes finding the right people a solvable problem at scale.
That’s what MapX does. Without the MapX translation layer, AI in executive search remains a helpful assistant. With it, AI becomes a system that can reason, adapt, and scale, with significant implications for the industry at large. Contact us to hear more.
