Are your developers AI ready (part 1)? Why strong foundations matter more than ever

The amplification effect

AI operates as an amplifier of what already exists in your organisation. Strong engineering practices become exponentially more powerful. Poor practices become exponentially more problematic.

For strong teams – those with excellent code quality, thorough testing practices and robust security protocols – adopting AI coding tools creates a multiplier effect. These teams use AI to handle routine tasks while focusing human creativity on higher-value problems.

Conversely, organisations with inconsistent practices adopting the same tools generate technical debt at unprecedented speeds. The very efficiency that makes AI powerful also makes it dangerous when misapplied.

The security blind spot

Many organisations integrating AI tools focus exclusively on productivity gains whilst overlooking the new attack surfaces these tools create.

The rush to implement AI without adequate security consideration is creating vulnerability gaps that weren’t present in traditional development workflows.

AI-generated code might look okay at first glance, but can introduce subtle issues with significant impacts:

• outdated dependencies bringing exploitable vulnerabilities;

• misconfigured systems that improperly protect access;

• subtle coding flaws causing performance bottlenecks or unpredictable system behaviours.

Strong security and engineering fundamentals catch these issues before deployment.

The integration challenge

Strategies for successful AI adoption shouldn't focus on the tools themselves. They should include integration with existing systems, processes and people.

Teams with well-documented architectures, effective communication channels and established engineering best practices can seamlessly incorporate AI into their workflows. Strong foundations support rapid innovation.

For others, AI implementation becomes yet another siloed initiative failing to deliver on its promises, creating friction rather than reducing it.

Building AI readiness

So what does it mean to be AI Ready? Our experience working with engineering teams leveraging AI tooling reveals three key pillars:

1. Engineering excellence - underpinned by rigour and discipline.

2. A culture of security - an organisation-wide security mindset.

3. Continuous learning - an evolving landscape requires continuous learning.

Related Insight

Course: Engineering best practices

Build better software by understanding the disciplines, practices and principles that underpin great code.

Engineering excellence

Teams that thrive with AI tools have mastered:

• Clean code principles and consistent patterns

• Robust testing methodologies that validate AI outputs

• Architectural clarity that guides AI tool usage

• Code review processes adapted for AI-generated code

Without these fundamentals, AI tools often generate plausible-looking, but subtly flawed, code that creates compounding problems over time.

A security-first culture

AI-ready organisations approach security proactively by:

• Understanding the unique risks of AI-generated code

• Implementing verification steps specific to AI outputs

• Applying threat modelling to new AI workflows

• Creating guardrails that prevent AI from introducing vulnerabilities

The most successful teams view AI not just as productivity tools, but as potential security vectors requiring dedicated attention.

Continuous learning

The AI landscape evolves almost daily. AI Ready organisations treat AI as more than a once-off implementation:

• Building knowledge-sharing mechanisms into company culture

• Providing safe spaces for experimentation

• Discovering, Defining and Documenting best practices

• Offering regular training and learning opportunities

Teams that turn AI into a sustainable, competitive advantage develop a culture of adaptability and learning.

The path forward

For organisations serious about leveraging AI effectively, the path forward is clear: invest in fundamentals as well as the tooling. This doesn’t mean delaying AI adoption. It means approaching AI implementation with a foundation-first mindset.

As AI tools become increasingly accessible, they’re rapidly transitioning from competitive advantage to competitive necessity. The real differentiator won't be which organisations use AI, but which ones have the engineering and security fundamentals to use it effectively.

In this new landscape, strong fundamentals aren’t just best practice: they’re business critical. The question is, are your teams AI ready?