We’re hiring. Not incrementally, and certainly not slowly, but at a pace that forces uncomfortable questions about scale, capability, and judgment. Across the Maxwell Investments Group (MIG) ecosystem, we have expanded into multiple sectors simultaneously. Some sectors are adjacent, while others are known to us but can also qualify as entirely unfamiliar. This is not simply a strategy problem. It is, at its core, a people problem.
Because companies do not scale. People do.
And right now, we need over 100 executives, individuals capable of managing complexity, as well as navigating ambiguity, constructing new systems, and making high-stakes decisions in environments where the rules are still being written. This is Ghana, after all.
Yet in the process of hiring for this next phase, an unexpected constraint has emerged: the traditional markers of intelligence, the very signals we have relied on for decades, no longer reliably predict who will succeed at MIG.
The Signal Collapse of “Looking Smart”
At first glance, the modern talent pool appears extraordinarily strong. Candidates are more credentialed, more articulate, and more professionally curated than at any point in history. Degrees, certifications, side projects, and personal brands have become baseline expectations rather than differentiators.
In economic terms, what we are witnessing is a drastic signal inflation.
When everyone appears intelligent, the appearance itself loses informational value. The sociologist Randall Collins described this phenomenon in his work on credential inflation. He postulates that as more individuals acquire traditional markers of competence, those markers cease to function as meaningful filters.
Hiring, paradoxically, becomes harder, not easier, because the signals we rely on have become noisy.
When observation precedes theory
This realisation did not begin with academic literature. It began with an internal contradiction I have been forced to endure.
Within our own organisation, some of the most valuable individuals do not possess elite academic pedigrees. By conventional standards, they were not the “strongest” candidates on paper. Yet they demonstrate a pattern of behaviour that is difficult to ignore. They learn faster, adapt more fluidly, anticipate problems earlier, and navigate interpersonal dynamics with unusual precision.
Over time, they have become indispensable.
Conversely, candidates with near-perfect academic records have often struggled in environments characterised by ambiguity and rapid change, like at MIG. They are highly effective in structured contexts, but less so when confronted with incomplete information, shifting priorities, and real-world trade-offs.
This divergence aligns with decades of research in organisational psychology. While cognitive ability remains a strong predictor of performance in stable, well-defined roles, its predictive power declines significantly in complex, dynamic environments. Addressing this gap, psychologist Robert Sternberg’s theory of successful intelligence defines intelligence as the ability to achieve one’s personal goals in life, within one’s sociocultural context, rather than just achieving high scores on traditional IQ tests. He argues that traditional systems favour analytical skills, even though real-world success requires a broader mix of abilities.
What the data now confirms
What was once subjective is now empirically supported.
Google’s Project Oxygen and subsequent internal research found that traditional indicators such as GPA and test scores had limited predictive value for job performance, particularly in leadership roles. Instead, qualities such as learning ability, humility, and collaboration emerged as stronger predictors.
Similarly, the World Economic Forum’s Future of Jobs reports consistently rank skills like analytical thinking, creativity, resilience, and flexibility above purely technical competencies.
The implication is both subtle and profound, and it hits me right in the face. We have been selecting proxies for intelligence rather than intelligence itself. I’d like to change that.
Technology and the repricing of intelligence
If signal inflation was the first disruption, technology is the second, and far more consequential.
We all see how artificial intelligence has enhanced productivity but it has also fundamentally altered what it means to be “smart.” Tasks once considered hallmarks of intelligence, such as coding, data analysis and research synthesis, are increasingly automated or augmented by machines.
This raises an uncomfortable question: if a machine can outperform a human at these tasks, were they ever true indicators of intelligence, or simply manifestations of trained pattern recognition?
In economic terms, technical intelligence is being commoditised. And as with any commodity, its abundance reduces its marginal value. Diamonds wouldn’t be diamonds if everyone had a kilo in their cupboard.
This helps explain a phenomenon that might otherwise appear weird. Recently, there have been widespread layoffs of highly educated, highly skilled professionals. These individuals have not become less capable; rather, the relative value of their capabilities has shifted.
From intelligence to judgment
If traditional intelligence is no longer a sufficient differentiator, what replaces it?
The answer, increasingly, is judgment.
Judgment is the ability to operate effectively under conditions of uncertainty. It is not reducible to knowledge or technical skill. Rather, it emerges from the integration of multiple capacities: pattern recognition, contextual awareness, emotional intelligence, and the ability to make decisions without complete information.
This aligns with the work of Daniel Kahneman and Gary Klein who studied decision-making under uncertainty. While Kahneman emphasised cognitive biases, Klein’s research on firefighters and military leaders highlighted the role of recognition-primed decision-making, which is often colloquially described as intuition.
What we dismiss as “gut feeling” is frequently the product of compressed experience comprising of rapid, subconscious pattern matching built over time.
The Measurement Problem
So therein lies my operational challenge in all the above-stated. The traits that matter most, like judgment, adaptability, curiosity, resilience, are precisely the ones that are hardest to measure.
As a result, we have had to (and even still) default to what is legible: CVs, degrees, years of experience, and structured interviews.
But legibility is not the same as accuracy.
Interviews in particular have become increasingly performative. I personally don’t even do them anymore. Candidates prepare extensively, mastering behavioural frameworks and rehearsing narratives designed to signal competence. The result is not necessarily a more accurate assessment of capability, but a more polished simulation of it.
In many cases, we select those who are best at “appearing” intelligent within controlled environments.
The structural lag of education
And this misalignment is not accidental. I think it’s systemic.
Modern education systems were designed for an industrial economy characterised by information scarcity, well-defined problems, and linear career paths. Success depended on correctness, consistency, and compliance.
Today’s economy, by contrast, is defined by information abundance, ill-structured problems, and non-linear trajectories. Success depends on exploration, adaptability, sense-making through distillation, conceptualisation, and the ability to take diverse, scattered data points and combine them into a cohesive, functional, or strategic whole.
In effect, we are training individuals for stability and then placing them into volatility.
Toward a new hiring paradigm
If traditional signals are insufficient, what replaces them?
The answer is not more data, but better data.
Rather than evaluating what candidates know, we must observe how they behave in complexity. This requires a shift from static assessment to dynamic evaluation, from interviews to what I might call “optimisation environments”.
I am doing that with my latest hire, on a 3-month probationary period, smart by all accounts on paper. But because of my current human capital dilemma as elaborated in this article, I have placed Smart (let’s call this person ‘Smart’) in ambiguous scenarios with incomplete information and competing priorities. Smart is required to make decisions, adapt in real time, and collaborate under pressure. I wish I could say I artificially created this environment specifically for the probationary period but no. This is what a regular day at MIG looks like.
My objective with Smart is not to test knowledge, but to reveal my priorities. How does Smart think. What does Smart optimise for? How does Smart respond to uncertainty? How does Smart recover from failure?
Because that is the work.
Redefining “Intelligence” for 2026
After confronting these shifts, I have provisionally arrived at a working definition of Intelligence. Intelligence is the capacity to navigate uncertainty by integrating technical understanding, human insight, and adaptive judgment.
It manifests not just in the accumulation of knowledge, but in the ability to deploy it effectively under changing conditions.
In practical terms, this means prioritising clarity in ambiguity, speed of learning, depth over surface-level fluency, emotional awareness, social awareness, creative problem framing, a healthy bias toward action, resilience under pressure, amongst others
These traits are not easily quantified, but they are consistently observable in practice.
The talent we are overlooking
There is however an uncomfortable implication.
Individuals who excel under this new definition of intelligence do not always perform well in traditional systems. They may appear average in standardised environments, struggle with rigid structures, or follow unconventional paths.
Yet in dynamic, real-world contexts, like at MIG, they often outperform.
The question then is not simply how to identify talent, but how much talent we have already excluded by relying on outdated definitions of intelligence.
The competitive advantage of seeing differently
We are attempting to hire 100 executives in a world where intelligence is abundant, signals are degraded, and technology is rapidly redefining value.
The tools that built the last generation of companies, tools like CVs, credentials, and conventional interviews, are no longer sufficient.
The organisations that will define the next decade will not be those with the most talent on paper. It will be those with the greatest ability to recognise real intelligence early, before it becomes obvious, before it is credentialed, and before it is widely legible.
Because in a world where machines can execute, the enduring advantage no longer lies in execution but in judgment, timing, perspective and the ability to see what others overlook.
So yeah, MIG is hiring. I just had to self-audit to check whether I understand what we are hiring for.
Thank you for reading. I welcome your reflections, questions, and suggestions for future topics. Subscribe to the Entrepreneur In You newsletter here: https://lnkd.in/d-hgCVPy, follow me on all social platforms at @thisisthemax, or get weekly updates via my official WhatsApp channel: www.bit.ly/whatsappthemax.
Wishing you a purposeful and successful week ahead!
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The author, Dr. Maxwell Ampong, serves as the CEO of Maxwell Investments Group. He is also an Honorary Curator at the Ghana National Museum and the Official Business Advisor with Ghana’s largest agricultural trade union under Ghana’s Trade Union Congress (TUC). Founder of WellMax Inclusive Insurance and WellMax Micro-Credit Enterprise, Dr. Ampong writes on relevant economic topics and provides general perspective pieces. ‘Entrepreneur In You’ operates under the auspices of the Africa School of Entrepreneurship, an initiative of Maxwell Investments Group.
Disclaimer: The views, thoughts, and opinions expressed in this article are solely those of the author, Dr. Maxwell Ampong, and do not necessarily reflect the official policy, position, or beliefs of Maxwell Investments Group or any of its affiliates. Any references to policy or regulation reflect the author’s interpretation and are not intended to represent the formal stance of Maxwell Investments Group. This content is provided for informational purposes only and does not constitute legal, financial, or investment advice. Readers should seek independent advice before making any decisions based on this material. Maxwell Investments Group assumes no responsibility or liability for any errors or omissions in the content or for any actions taken based on the information provided.
