Advances in AI and machine learning are transforming how organisations build software, enabling developers to write thousands of lines of code in a few moments. As AI moves from coding and experimentation to enterprise-scale deployment, success will depend on a more holistic approach – one that bolsters security, efficiency, and accuracy for everything after code. As such, here are five trends set to define how AI shapes operations in 2026.
Generative AI will finally deliver measurable ROI for organisations that use it with purpose
2026 will be the year generative AI shifts from hype to measurable impact, but only for organisations that adopt it purposefully. After a year of experimentation, the rush to apply AI everywhere – often to tackle problems that were already solved – delivered limited returns.
In the year ahead, the focus will move from flashy, front-end pilots to more purposeful back-office use cases, particularly in software development. Businesses currently expect around 16% ROI from their AI implementations, but projections suggest this could double in two years as approaches mature.
Early evidence shows businesses using purpose-built AI tools from specialised vendors outperform those using their own in-house builds by a factor of two. Those who consolidate tooling, prioritise real problems, and take a focused, disciplined approach will be the ones who unlock AI’s potential.
AI will send software supply chain security back by years
As AI accelerates software creation, it also expands the attack surface, especially across the supply chain. Many enterprises believe they have learnt lessons after 2023’s SolarWinds breach – but that doesn’t mean their AI has. With AI expanding software supply chain volume and complexity, similar incidents become more likely and severe, as a single compromised component could cascade across thousands of enterprises.
Most modern AI coding tools are trained on historical repositories. They lack real-time CVE awareness and will happily draw from vulnerable libraries. AI-generated code also typically lacks clear provenance; developers can’t trace where suggestions originated or whether they incorporate licensed code or vulnerable components. That makes it near-impossible to work backwards and identify if the company’s software is affected by issues like Log4Shell.
In 2026, scalable supply chain security will become non-negotiable. Software Composition Analysis must scan every dependency, SBOMs must be maintained continuously, and remediation must be automated. Policy-as-code will be essential for blocking insecure components at source, the only way to secure an AI-accelerated software supply chain.
Cloud costs will skyrocket without automated controls in place
With AI and ML workloads continuing to grow exponentially, many enterprises will see their cloud costs spike in 2026. Those lacking visibility into how AI workloads consume resources will be hit with overspends of up to 50%. Given cloud is now the second-largest line item after salaries for many enterprises, they simply cannot afford to leave spending to guesswork.
Real-time FinOps will become critical. AI-driven optimisation, anomaly detection and dynamic resource scaling will allow teams to control costs automatically, eliminate waste, and realise instant savings. This intelligent approach also removes the burden of managing resource scheduling for engineers, allowing them to focus on delivery while avoiding unnecessary consumption.
AI-driven code and new regulations will force enterprises to upgrade governance
In 2026, compliance and security will shift from background concerns to central pillars of AI adoption. Organisations will face a complex regulatory landscape, with the the EU AI Act, NIS2 and DORA creating a more unified European framework, for governance and compliance, which will demand more transparency, risk assessments, and algorithmic accountability.
At the same time, reliance on AI-generated or ‘vibe’ coding will continue to create high-stakes risks. Research shows up to 45% of AI-generated code contains vulnerabilities, with issues ranging from hallucinated dependencies to language-specific failures. Large organisations that lean heavily on AI without robust guardrails face inevitable breaches.
To stay ahead of compliance, forward-looking companies will adopt automated policy enforcement, continuous security scanning, and comprehensive audit capabilities. These trends will make 2026 the year that security and compliance drive the future of AI.
AI will shift from coding to quality control, clearing bottlenecks and taking humans out of the loop
Next year, organisations will move beyond using AI for coding alone, applying it to enhance testing and quality control. AI-powered coding is becoming more refined and is already helping teams move faster, but so far, productivity gains at the front end have been erased by downstream bottlenecks – more bugs, higher cloud costs, and greater security exposure. If left unchecked, this velocity problem will get out of hand fast – after all, no human can check thousands of lines of code and be expected to catch every issue.
2026 will bring a shift to continuous AI-driven quality control. This means crafting intelligent pipelines with multiple agents that can manage AI, optimise deployments, predict potential failures with high accuracy, and resolve incidents autonomously. These agents will use a variety of models depending on their specific function. Cutting-edge organisations will even implement AI-enhanced chaos engineering practices to automatically generate exploratory tests, check for potential breakages, and catch bugs in edge use cases.
This will be a real breakthrough for AI-assisted development in 2026, building trust in AI across the SDLC and ushering in a wave of automated processes that no longer require a human in the loop.
Securing the Future of AI-Driven Enterprise
As AI adoption accelerates, supply chains expand, cloud costs rise, and regulatory scrutiny intensifies, organisations will need to reassess how they build software from the ground up. Governance, security, and quality controls must be embedded across every AI-driven workflow, not added as an afterthought.
In 2026, resilience in software delivery will come from automation: policy enforcement, continuous monitoring, real-time cost optimisation, and intelligent quality control working together as a unified framework.
With full visibility and control across the development lifecycle, enterprises can unlock the measurable value of AI while protecting operational integrity and regulatory posture. Organizations that embed security as code, automated testing, and runtime verification into their pipelines will be the ones able to innovate both safely and at scale.
