How to protect skills pipelines in the age of AI

In our sixth Tech Flix documentary ‘The AI Paradox: AI is Scaling, Skills are Not’, we explored the growing tension inside organisations as AI adoption accelerates and raises important questions about what happens to the future of tech teams that digital transformation depends on.

It’s a question moving quickly up the agenda for digital leaders. Because while AI is scaling productivity across engineering, data and platform teams, it’s also reshaping how technologists learn, develop and progress.

To bring a frontline leadership lens to this shift, we spoke with Nash Squared’s CIO, Ankur Anand. His perspective reflects both the opportunity, and the structural workforce questions leaders must now address.

Are junior roles really at risk?

Much of the AI debate centres on entry-level disruption. In sectors where repeatable processes are being automated, from customer service to back-office functions, headlines suggest junior roles are first in line for impact.

In our latest Tech Flix film, Josh Nesbitt, CTO at Genio, recognises that concern. As AI becomes embedded into workflows, organisations inevitably question how many early-career roles they need.

But in technology teams, he sees a more nuanced picture. At Genio, junior hiring has not slowed. He argues that reducing entry pathways would weaken the long-term skills pipeline, creating future capability gaps rather than solving short-term efficiency pressures.

The disruption narrative may hold true in some sectors where AI replaces high-volume, repeatable tasks. But in technology teams specifically, the picture is more complex, and Ankur’s perspective on this suggests something different is happening.

The productivity surge reshaping tech delivery

AI is already reshaping how technology teams operate, from the way products are conceptualised to how they are built and deployed. Development copilots, automation tools and enhanced access to internal data are compressing delivery timelines and enabling smaller teams to achieve faster outcomes.

Ankur points to recent initiatives delivered by technologists with less than a year of experience, including platforms launched in just a few months. These projects were AI-enabled from day one and were never meant to follow a traditional learning curve.

As Ankur explains, AI is dramatically boosting early-career productivity. “The impact of AI on fresh talent is very high. Their productivity is now almost as good as people with three to five years of experience.”

Access to internal data, coding support and automation tools allows junior engineers to interpret tasks, build solutions and contribute meaningful outputs far earlier than before.

In effect, AI is compressing the gap between entry-level and mid-level capability and redefining what early-career talent can achieve.

The real pressure point: mid-level roles

While much of the external narrative focuses on AI threatening junior roles, Ankur sees the structural shift falling heaviest on the mid-experience layer.

As junior engineers accelerate upward and senior leaders augment decision-making with AI insight, work traditionally handled by those in the 2–5 year experience bracket is being redistributed. Smaller, AI-enabled teams can now deliver the same throughput that once required larger delivery layers.

As Ankur explains, “It’s creating more risk for the people with mid-level experience as compared to the more experienced people versus the juniors.”

This doesn’t signal a reduction in technology careers, but it does signal role evolution.

Mid-career technologists must increasingly upskill into higher-value domains such as architecture, security, enterprise platforms and AI governance. In practice, this is creating two distinct skill trajectories across technology teams.

Two future capability pathways

AI is not simply accelerating productivity. It is also reshaping the kinds of capability technology teams depend on.

Two distinct skill trajectories are emerging.

1. AI-enabled engineering environments

These are high-automation development settings where engineers work alongside AI to accelerate delivery.

Developers increasingly rely on AI for:

• Code generation
• Documentation
• Test creation
• Rapid prototyping

In these environments, output scales quickly and early-career engineers can contribute far sooner than traditional development models allowed.

2. Experience-led, high-judgement environments

Other domains continue to depend heavily on deep human expertise and accountability, particularly where risk, architecture and long-term system integrity are involved.

These include:

• Systems architecture
• Platform engineering
• Security and threat modelling
• Data governance and lineage
• Resilience and reliability engineering

Here, AI can accelerate analysis and assist decision-making, but it cannot replace professional judgement or operational responsibility.

AI-native talent vs experience-led delivery

Another dynamic emerging inside tech teams is generational adoption. Younger technologists, having trained in AI-enabled environments, are often more fluent and experimental in their usage.

More experienced engineers, by contrast, may rely more heavily on established delivery approaches. Ankur sees this gap narrowing quickly, not because experience loses value, but because AI becomes native across the workforce.

As AI adoption deepens, experienced technologists will be required to integrate it into decision-making, design and oversight.

How technical skills development is evolving

AI’s impact on skills development is not uniform across the technology estate. While some environments are now built with AI deeply embedded in development workflows, many organisations will continue operating complex legacy platforms for years to come.

As Ankur explains, this creates different patterns of AI adoption across teams.

In modern product and platform environments, AI is accelerating coding, testing and documentation, allowing teams to deliver new capabilities faster.

In legacy environments, where systems are tightly integrated and risk-sensitive, AI is more commonly used to support testing, documentation and code analysis rather than core engineering.

For digital leaders, this means capability must develop across two technology realities, one in AI-enabled product delivery and the other in long-term stewardship of legacy platforms.

Governance: the safeguard against capability risk

As AI becomes embedded across technology delivery, governance is expanding beyond traditional code oversight into a broader enterprise risk discipline.

AI can accelerate development and unlock significant value, but it also introduces new operational, regulatory and reputational risks if adoption moves faster than oversight.

For technology leaders, governance now spans multiple areas of responsibility, including:

• Model lifecycle oversight
• Data lineage, privacy and consent frameworks
• EU AI Act and UK regulatory compliance
• Vendor and model provider risk management
• Hallucination, Bias and Factual reliability controls

In this environment, governance is no longer a supporting function. It is a core capability that ensures AI-enabled delivery remains secure, compliant and resilient.

AI may increase the speed and value of technology delivery, but without strong governance frameworks it can also increase systemic risk.

Rethinking tech operating models

As AI embeds further, technology workforce structures are set to evolve. In our recent Tech Flix film , some digital leaders reflected on the possibility of an “inverted pyramid” workforce shape emerging as AI adoption scales across organisations.

For technology teams specifically, Ankur does not personally see this model fully materialising. While AI is increasing the output and impact of early-career talent, factors such as tooling investment, governance demands and architectural complexity still require experienced oversight.

He expects organisations to move toward more blended operating models that bring together:

• AI-enabled early talent
• Experienced engineers
• Governance and risk capability
• Data and platform expertise

Rather than flattening teams entirely, Ankur predicts AI is more likely to redistribute where value is created, shifting how different layers contribute rather than removing the need for structure and senior accountability.

Protecting the future skills pipeline

For digital leaders, the question is not whether AI changes how capability is built. It is how to ensure the skills pipeline keeps pace with the technology reshaping delivery.

Ankur doesn’t see AI weakening long-term technology capability. He sees it changing how it develops.

AI is accelerating early-career contribution. Junior engineers can now deliver meaningful outputs far sooner, supported by AI agents, automation tooling and access to organisational knowledge. The distance between entry-level and mid-level productivity is compressing.

But capability is not formed through output alone.

Senior technologists remain essential for architectural design, governance, risk accountability and platform resilience. Their role is also evolving to guide how AI-enabled delivery happens in practice, ensuring speed does not compromise quality or scalability.

The responsibility of leaders is pipeline design. Faster junior productivity must still translate into deep expertise over time. That means creating environments where engineers move beyond AI-assisted execution into ownership, problem-solving and decision-making.

As Ankur emphasises, AI is already embedded in how technology teams operate. The priority now is shaping workforce models that protect long-term capability while unlocking the productivity gains AI enables today.

Protecting your skills pipeline in the age of AI starts with the right talent strategy. Explore how we help organisations build AI and Machine Learning capability that lasts.