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As AI Evolves, So Must the Balance of Skills Behind It
In 2018, Amazon’s AI recruitment system made headlines for reproducing gender bias rooted in the historical data it was trained on. The model worked technically as intended, yet its outputs reflected structural imbalance.
The project was quickly scrapped, but the incident sent a clear signal to tech leaders and the world that success with AI would demand far more than code and engineering. It would require deliberate decisions about data governance, behaviour and design. That signal was just the beginning.
As AI moves from automating tasks to influencing decisions, and as quantum computing begins to scale AI into dimensions we cannot yet fully anticipate, the skills organisations rely on must evolve quickly alongside it.
For years, technology transformation revolved around one question: Can we build it?
Automation and Robotic Process Automation (RPA) focused on efficiency, predictability and process optimisation. If the code worked, then the job was done. AI is different. It generates outcomes that require interpretation and influences human behaviour, raising philosophical and psychological trade-offs that cannot be solved by engineering alone.
The question today is not just whether something can be built; it is whether it should be built. And if the answer is yes, there are equally important follow-ups: how it should be deployed, how it should be governed, and who is responsible. The consequences of poorly thought-out AI tools can range from short term to generational.
Of course, code remains foundational. Without it, nothing functions. But it is clear that code alone is no longer sufficient to generate desirable outcomes without additional guardrails.
What organisations need now is translation: in this context, the ability to connect engineering to outcomes, interpret model behaviour, anticipate human response, align stakeholders and embed AI systems safely into operations. It ensures technology is not built in isolation from the people it affects: a critical cornerstone of strong delivery teams in technology transformation.
To address this gap, companies often search for workforce “unicorns”, the magical hybrid of coder and translator, individuals who combine deep technical knowledge with strong translation skills. But they are increasingly rare, and organisations cannot rely on a small number of exceptional individuals to carry the shift alone.
To survive this rapid wave of AI adoption, companies must instead leverage the collective skills of their entire workforce and elevate what were once labelled “non-technical” or “support” into complementary capability alongside engineering. This includes strategy, governance, product thinking, behavioural insight and operational execution. All are decision-making functions that shape outcomes.
We are seeing encouraging growth in women and underrepresented groups building careers in technology. But language and labels matter because they signal who is trusted to lead, and too often, roles that coordinate, translate and operationalise technology are framed as secondary. In reality, they often determine whether systems succeed or fail in practice.
The complexity of AI is too high for one perspective alone. When organisations evolve how they define capability, value diverse contributions and enable collaboration across disciplines, they create systems that scale responsibly, ethically and inclusively. That shift may feel subtle, but it changes outcomes.
Code may have been king. Today, code and translation rule hand in hand.
About the campaign
techUK’s TechTogether campaign continues with a focus on ‘Evolving Online Safety'. Our insights this week focus on ensuring AI systems are designed, governed and deployed responsibly, with diverse perspectives shaping how technology impacts society, strengthening cyber defences and reducing vulnerabilities as organisations adopt new technologies and expand digital services, and addressing workplace culture, leadership and systemic barriers to ensure diverse voices shape the future of technology.
TechTogether week 4
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