Do you have the right skills to scale AI?

The artificial intelligence revolution has moved beyond proof-of-concept. UK organisations have demonstrated AI’s potential through countless pilots, yet most struggle to move from experimentation to enterprise-wide impact. The bottleneck isn’t technology – it’s people. According to the Bank of England, only 14% of companies that invested in AI received immediate productivity gains. Meanwhile, research from Nash Squared reveals that 52% of UK technology leaders are now experiencing AI skills shortages – a 114% increase in just 18 months, making AI the scarcest tech skill in the UK.

 

This skills gap is seen in impactful ways:

– Technical teams build sophisticated models that business units don’t understand
– Business leaders request AI solutions for problems that don’t require Generative AI or even machine learning
– Data scientists spend time on data wrangling rather than model development
– Well-intentioned initiatives fail because surrounding teams aren’t equipped to make them succeed

 

The scaling paradox

 

The skills that launch AI projects differ from those that scale them. Early initiatives rely on specialists who can work within complex scenarios with limited data. But scaling demands something entirely different:

What scaling actually requires:

– Data engineers who build robust pipelines that won’t break under production loads
– Business analysts who identify where AI adds genuine value versus where simpler solutions suffice
– Change managers who help teams adapt processes around new capabilities
– Broad AI literacy across the organisation – not deep technical expertise, but sufficient understanding to use AI tools effectively

 

Mid-sized organisations feel this mismatch the most. Unlike large enterprises with dedicated AI centres of excellence, they lack the luxury of separate teams for research and production. The same individuals who prove AI’s potential must also scale it – a transition requiring skills many data scientists never developed during formal or on-the-job learning.

 

The building blocks of AI capability

 

Successful AI scaling requires capabilities across four interconnected areas:

Technical foundations which shift as AI matures. Production systems need data engineers building reliable pipelines processing anywhere from hundreds to millions of transactions daily, MLOps practitioners automating deployment and monitoring, and AI architects designing systems that integrate seamlessly whilst remaining adaptable.

Business translation bridges the gap between technical possibility and commercial reality. These analysts identify high-value use cases, translate business requirements into technical specifications, and explain technical constraints in business language. They’re often more valuable than additional data engineers of scientists because they ensure AI addresses genuine business needs – vital for AI success.

Operational excellence ensures AI systems deliver consistent value. Data quality skills ensure model inputs remain reliable, monitoring experts detect performance degradation before it affects outcomes, and governance ensures AI systems comply with evolving regulations whilst remaining explainable.

Organisational change determines whether AI systems actually get used. Without trainers and change managers who can build trust in AI-driven decisions, technically excellent systems gather dust whilst teams continue familiar manual processes.

 

The literacy imperative

 

Perhaps the most critical capability is widespread AI literacy. This doesn’t mean teaching everyone to code – it means building sufficient understanding that people can work effectively alongside AI systems.

Research by Forrester found a stark reality: business leaders expect 70% of employees to use data significantly in their roles, yet only 40% of employees report receiving adequate data skills training. This gap between expectation and preparation creates inevitable friction when AI systems get deployed.

 

AI literacy includes:

– Understanding what questions AI can reasonably answer versus those requiring human judgment
– Recognising when AI outputs seem plausible but may be unreliable
– Knowing when to seek expert review
– Using AI systems effectively without needing to build them personally

The most successful organisations treat AI literacy as a core business competency. They provide role-specific training that connects AI capabilities to actual job responsibilities, offer ongoing support as systems evolve, and create communities where users can share experiences.

 

Building versus buying capability

 

The best answer almost always involves both internal development and external expertise, but the balance depends on strategic priorities and resource constraints.

 

Internal capability delivers:

– Deep understanding of specific business contexts
– Knowledge that remains within the organisation
– Quick response to changing needs without procurement delays
– External capability provides:

– Accelerated progress and cross-industry expertise
– Scalable support that flexes with needs
– Specialist skills needed only intermittently

 

Research from SnapLogic found that 51% of US and UK organisations acknowledge they don’t have the right mix of skilled AI talent in-house to bring their strategies to life, despite 93% considering AI a business priority. The most effective approach combines both – external partners accelerate initial implementation whilst simultaneously upskilling internal teams through structured knowledge transfer.

 

The need for upskilling

Developing AI capabilities requires more than traditional training programmes. Adult learners need approaches that connect new skills to actual work requirements.

 

Effective AI upskilling:

– Starts with clarity about what different roles actually need to know
– Embeds learning into actual business initiatives rather than treating it as separate activity
– Provides ongoing support as AI systems evolve and new capabilities emerge
– Creates communities of practice where users share experiences and solutions

A Skillsoft survey of UK organisations found that only 10% are fully confident their employees possess the necessary skills to meet business goals within the next 12-24 months. The most successful programmes deliver both capability development and business outcomes simultaneously – teams learn data pipeline concepts whilst building the pipelines their business unit needs.

 

Governance as an enabler

 

As AI scales beyond isolated pilots, governance evolves from optional oversight to essential enabler. Effective governance provides guardrails that enable confident, rapid deployment whilst managing risks appropriately.

 

AI governance requires distinct skills:

– Understanding of algorithmic bias and fairness considerations
– Expertise in model explainability requirements across different contexts
– Judgment to distinguish between high-risk applications requiring extensive oversight and low-risk implementations that can – proceed with minimal bureaucracy

 

Many organisations struggle to find individuals with these specialised governance skills. Building this capability typically requires combining skills from multiple domains – data governance, legal understanding to interpret evolving AI regulations, and technical specialists who understand how models actually work.

 

The competitive reality

The AI skills gap creates different future paths. Organisations that solve it scale AI capabilities across operations, building competitive advantages. Those that don’t remain trapped in proof-of-concept, watching as the gap between AI investment and AI impact widens.

This divergence isn’t purely about financial resources. Research shows that 89% of UK tech leaders are either piloting or investing in AI projects – up dramatically from 46% previously. Yet success depends not on investment levels but on whether organisations are systematically building the capabilities to use AI effectively.

 

Moving forward strategically

 

Addressing AI skills gaps requires structured approaches:

– Assess current capabilities across all four domains – technical foundations, business translation, operational excellence, and organisational change
– Develop capability roadmaps that align with AI deployment plans, specifying which capabilities to build internally and which to access externally
– Connect skills development to business value delivery through programmes that build capabilities needed for specific initiatives whilst simultaneously delivering business outcomes

Create cultures of continuous learning where upskilling becomes part of normal operations rather than special programmes
The question isn’t whether your organisation has perfect AI capabilities today. It’s whether you’re building the skills required to scale AI tomorrow and whether you’re doing it fast enough to remain competitive as the technology continues to evolve.