How Soft Skills Can Power Growth in the Age of AI
Artificial intelligence has moved from theory into practice at remarkable speed. Across boardrooms, government offices, and investment committees, conversations now revolve around how AI can reshape processes, reduce costs, and deliver efficiencies. Generative AI in particular has captured attention for its ability to perform data-heavy and repetitive tasks, from document drafting to market analysis, in a fraction of the time it would take a human.
Yet this focus on efficiency, while understandable, hides a strategic blind spot. Efficiency is often interpreted narrowly as doing the same work with fewer people. In this view, AI is seen primarily as a tool for reducing headcount and compressing costs. But that mindset risks undermining the very capabilities that make organisations competitive and resilient in the long term.
The real opportunity lies not in replacing human workers, but in augmenting human potential. According to PwC, AI could add as much as 14% to global GDP by 2030, the equivalent of US$15.7 trillion. Much of that value will be realised not through cost savings, but through productivity gains, enabling people to work on higher-value activities, innovate faster, and build stronger relationships with stakeholders.
The leaders who will succeed in the AI economy will be those who recognise that human skills, creativity, empathy, trust-building, and ethical judgement, are not made obsolete by AI. On the contrary, they will become more valuable as the world becomes more automated.
Technology as a Catalyst, Not a Replacement
We have been here before. The printing press replaced manual scribes, but it also democratised knowledge and fuelled entire industries in publishing, education, and commerce.
The industrial revolution displaced agricultural labourers, but it created urban manufacturing economies and spawned new professions in engineering, logistics, and management.
The computer age automated calculations and data entry, eliminating certain clerical roles, but it also gave rise to software engineering, digital marketing, cybersecurity, and a whole ecosystem of internet-based businesses.
The consistent lesson is this: societies and organisations that invest in complementary human skills during technological transitions don’t just survive, they lead. Those that fixate on cost savings without reinvesting in their people often fall behind.
Artificial intelligence is different in scope and scale, but the principle remains. AI can process and analyse information at speeds we cannot match. But the translation of that analysis into strategy, innovation, and trust-based relationships still depends on human critical thinking and insight.
The Current Landscape: Opportunity and Risk
The speed of AI adoption across sectors is breathtaking. According to McKinsey’s 2025 State of AI survey (a client), seventy‑eight percent of organisations reported using AI in at least one business function in early 2025, up from just 55% a year earlier. Use of generative AI alone rose from 33% to 71% of organisations during 2024.
As of December 2024, in the United States, a National Bureau of Economic Research (NBER) digest reported that 28% of employed respondents used generative AI at work, with 24.2% using it in the previous week and 10.6% using it every workday. That level of uptake came just two years after the release of ChatGPT, eclipsing the adoption trajectories of personal computers or the early internet.
In the United States, between August and November 2024, generative AI was estimated to assist between 6.0% and 24.9% of total work hours among users, and between 1.3% and 5.4% of all hours across the full workforce. On average, workers reported saving approximately 5.4% of their work hours per week thanks to generative AI use. Those everyday users who engaged AI regularly reported time savings of four hours or more, with each hour spent using AI yielding roughly 33% greater productivity compared to hours without the tool.
These numbers show that AI is rapidly embedding itself into daily work routines. In consulting, for instance, firms like McKinsey have deployed tools akin to “Lilli” to streamline access to knowledge and accelerate analyses across global teams. In legal services, Dentons and other firms use generative models to support case research and contract drafting, enabling lawyers to focus on advocacy and negotiation. In finance, AI systems analyse large datasets to surface investment trends that would previously have taken analysts weeks to uncover.
The gains are real and measurable. Yet this dynamism also carries a risk, with many organisations interpreting these efficiency gains as justification for reducing headcount, especially amongst entry‑level staff.
In 2025 investment banking and consulting, firms such as Goldman Sachs and JPMorgan confirmed that their AI projects were intended to automate the workflows traditionally performed by juniors. While saving cost, the consequence is fewer junior hires and fewer opportunities to build on‑the‑job experience and institutional knowledge, creating a risk in the medium to long-term.
This is not a benign shift. Without early-career roles, organisations may face talent shortages later in the future. Opportunities for employees to develop judgement, negotiation, client relationship skills, and reputational stewardship through practice begin to vanish, confirming that the risk extends beyond talent pipelines and that while AI is powerful, it may only unlock value in certain domains and used by people with contextual knowledg and experience. Where generative models overstep, they generate risk and degrade performance.
All together, the uptake rates, the time-savings, and the performance pitfalls, these findings outline a double-edged landscape. On one side lies opportunity: more productive output, faster decision-making, interesting new efficiencies. On the other lies risk: erosion of capability development, degraded outcomes in edge scenarios, and organisational brittleness without human judgement.
Deployed without human guardrails AI can create risk that damage trust and reputations, critical intangible assets for businesses, investors and governments. AI only adds value when it is integrated with respect for human learning, oversight, relationship-building, and soft-power dynamics. Efficiency alone is not enough.
The Human Factor: Why We Are Not Irreplaceable
Humans are social beings. Our societies, economies, and organisations are built on networks of trust, perception, and reputation. These are not abstract concepts, they are measurable assets that influence investment decisions, political stability, and market performance.
AI can simulate conversation and predict sentiment, but it does not experience emotion, understand cultural nuance, or navigate complex ethical dilemmas. It cannot build credibility over decades with clients, voters, or citizens from different cultures.
In business, this ‘human factor’ underpins customer loyalty and brand equity. In investment, whether in venture capital, corporate venture capital or family offices, the relationship between parties will influence and shape co-investor relationships, portfolio company confidence, and the ability to attract the right partners. In government, it determines public legitimacy and international influence.
These are precisely the domains where AI should be used as a support tool, not a substitute.
AI can monitor brand sentiment in real time, flagging shifts in public opinion. But it takes human judgement to decide how to respond in a way that protects or enhances reputation. AI can identify potential risks in a supply chain, but it takes human negotiation to resolve them in a manner that strengthens relationships rather than damaging them.
And I haven’t talked about the challenges AI faces surrounding cultural sensitivities, given that many US-trained models implicitly encode Western norms and reasoning frameworks.
A position paper by Amr Keleg from the University of Edinburgh rightly argues in my view that LLM development efforts often wrongly assume cultural homogeneity within the Arab world, even though social norms, values, and worldviews can differ dramatically across countries and communities. As a result, such models can inadvertently produce responses that feel tone-deaf, inaccurate, or contextually inappropriate in Arabic-speaking markets.
Responding to this gap, organisations in the Gulf have begun developing native Arabic models that better reflect local linguistics and cultural nuance. For instance, the UAE’s ATRC/TII released Falcon Arabic in May 2025. Trained on 600 billion tokens of native Arabic text spanning regional dialects, it matches the performance of much larger, non-Arabic models while requiring significantly fewer resources. Similarly, the open-source model Jais, developed through a UAE‑based collaboration, combines English and Arabic in its training and achieves state‑of‑the‑art performance in Arabic contexts.
Productivity Over Efficiency: A Strategic Reframing
Efficiency is about doing the same things with fewer resources. Productivity is about achieving more valuable outcomes with the same or fewer inputs. In other words, efficiency cuts; productivity expands.
Consider Disney’s ‘tiger teams’, cross-functional groups bringing together experts from animation, marketing, and data science. AI handles scheduling, data retrieval, and background research. The humans focus on creative problem-solving, audience engagement, and brand storytelling. The result is not just faster work, but richer, more innovative outcomes.
This is the model that leaders in all sectors should aim for: AI taking care of the mechanical and repetitive, humans driving the creative and strategic.
Business: Building Customer-Centric AI
In the corporate world, AI offers clear opportunities to improve customer service, optimise supply chains, and develop products faster. But the real differentiator will be how companies maintain and strengthen their human touch.
Customers may accept automated interactions for basic queries, but loyalty is built through personalised, empathetic engagement. If AI is deployed purely to cut costs by removing people from the process, the risk is that customer trust erodes. Once lost, trust is expensive, and sometimes impossible, to regain. And where trust is lost, so is reputation and the perception of leaders and organisations that deploy AI without an understanding of human nuances.
The companies that will thrive will use AI to empower their people, giving them better insights, freeing their time for higher-value conversations, and supporting them in delivering consistently excellent customer experiences.
Investment: AI as an Enabler of Trust-Based Deals
For investors, from corporate ventures with their specific cultures to single-family offices, AI can transform due diligence, market analysis, and portfolio monitoring. It can surface risks earlier and identify opportunities faster. But closing a deal still depends on human relationships.
In private equity or venture capital, deals are often won or lost on the basis of trust between parties. AI can help identify potential partners; it cannot replace the judgment required to assess whether those partners share your values, strategic vision, and appetite for risk.
And for portfolio companies, well, investors who bring both capital and reputation management and development expertise will be increasingly valuable. In highly regulated sectors, the ability and agility to manage public and government perception will be as critical to growth as financial performance.
As I said before, giving AI tools to a CEO or their Chief Investment Officer to issue communications that is outside of their individual area of expertise creates risk.
Government: Balancing Automation and Legitimacy
In government, AI can streamline administrative processes, analyse policy impacts, and support rapid decision-making. But governance is not just about efficiency; it is about legitimacy. Citizens want to know that human beings are making decisions that affect their lives, guided by empathy, ethics, and accountability.
If governments use AI solely as a cost-cutting tool, they risk creating perceptions of detachment and technocracy. If, instead, they use it to give civil servants more time to engage with communities, explain policy decisions, and negotiate internationally, they strengthen both their effectiveness and their credibility.
Within the UK Government, the focus has been to deploy AI to deliver solutions so that taxpayers money is better deployed to the delivery of front-line services. As an example, the UK Government’s Incubator for Artificial Intelligence delivered the Health Deterioration and Fall Prediction Tool to analyse real‑time patient vital signs, such as heart rate, temperature, and blood pressure, to predict early signs of health deterioration and fall risk in home care settings. Developed in partnership with health tech provider Cera, the tool, now deployed across two‑thirds of NHS integrated care systems as of March 2025, is used in over two million home care visits monthly. It generates high‑risk alerts with 97% accuracy and prevents around 2,000 falls and hospital admissions per day, reducing hospitalisation by up to 70% and saving the NHS over £1 million per day.
Trust, Perception, and Reputation as Growth Catalysts
In the AI economy, trust, perception, and reputation will be the decisive competitive factors. They will act as intangible commodities that can open or close the doors to growth.
An organisation with a reputation for ethical AI use will attract customers, investors, and partners who share its values. One that is perceived as using AI to hollow out jobs and cut corners will face resistance, regulation, and critical reputational damage.
Trust, as Warren Buffett said, takes years to build and seconds to lose. Perception shapes reality in markets, elections, and negotiations.
Reputation determines the quality of opportunities that come your way. These are human-led assets. AI can help measure and monitor them, but it cannot create or sustain them on its own.
The Human-Centred AI Future
Artificial intelligence is already reshaping businesses, economies and our world, but it will not replace the fundamental truth that human relationships, creativity, and judgement are the ultimate drivers of sustainable growth.
Leaders who treat AI as a partner in productivity, rather than a blunt instrument of efficiency, will unlock the greatest value. That means reinvesting productivity gains into their people, maintaining pathways for talent development, and embedding trust, perception, and reputation into their organisations and AI strategies.
Technology is changing the game, but human skills will help you win it!
