Having spent decades as a backroom topic for tech experts and sci-fi fans, artificial intelligence is now a pocket-and-purse companion for the everyday consumer and is seen as both an opportunity and a challenge at every corporate board meeting.
AI has the power to improve efficiency, reduce costs, and help enterprises gain a competitive edge. But a strange phenomenon referred to as ‘AI obsolescence before AI maturity’ is now rearing its head, along with the concept of ‘Zombie AI’, in which out-of-date AI systems still lurk within the IT stack as Shadow AI or ageing production.
In the rush to adopt large language models (LLMs), agentic AI, domain-specific AI tools, and cloud-native machine learning, organisations have tried to wring value out of emerging technologies before their competitors do. But this frantic pace means that if a more advanced or cheaper version of the technology is released, an enterprise’s research and implementation efforts can be wasted. Worse, organisations may fail to decommission existing AI deployments even before they have fully matured within the environment.
With the arrival of agentic AI, organisations must now confront the risk of stagnant systems lingering as zombie AI — models and services that are no longer relevant or accurate. Arising from abandoned pilot projects integrated into production systems, from legacy dependencies on AI tools, or even from a lack of governance, zombie AI poses a real risk to businesses. If not addressed, it will consume resources and budget that could be put to better use. Because it is not current, it could lead to flawed decisions or, worse, a cybersecurity breach.
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The Hidden Risks of Zombie AI
The longer it goes unaddressed, the greater the risk zombie AI poses. It can disrupt the very heart of the business by undermining operations, compromising compliance, and even weakening security. This makes it much more than just end-of-life technology; its impact goes far beyond wasted investment. If training data is out of date, every recommendation presented to decision-makers will be potentially invalid.
Governments are constantly working on updating regulatory frameworks to reflect dynamic markets. Accountability in AI is likely to be a key area of focus for authorities, which means zombie AI may pose a risk to compliance and security. And because they are older, zombie systems may not implement cybersecurity best practices, such as the principle of least privilege.
In the longer term, zombie AI can cause end-users and customers to lose trust in even current AI tools because they have long endured inaccurate, biased, or outdated results. As zombie AI continues to consume compute cycles and cloud resources, often without human oversight, the overall efficiency of the business will be under threat.
Building the Right Defence: A Governance Framework
Only strong, proactive governance can address both obsolescence before maturity and zombie AI.
AI must be treated like any other physical or digital asset. Its lifecycle should be explicitly defined, including details of the onboarding, monitoring, and retirement processes. Every AI system should be subject to ongoing monitoring, and this process should be guided by a decommission plan that is activated when appropriate to prevent zombie AI from arising.
In addition, benchmarks and KPIs will help organisations assess AI model performance against established business goals and target costs. If outputs are found to be below certain thresholds, project leaders can decide to retrain the model, upgrade the tool, or take the system out of service. Making these decisions requires a cross-functional team to oversee AI usage, often using a Human-in-the-Loop (HitL) model.
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These governance committees are vital in ensuring that AI systems are compliant, ethical, and efficient. To support these efforts, AI tools can be used to monitor other AI systems, alerting governance committees to model drift, bias, identity security issues, and resource inefficiencies.
No Dead Weight
If organisations can create the right governance, they will overcome their tendency to implement AI as a matter of urgency and replace it with a focus on the service life, relevance, and accountability of their systems. Some models will age more quickly than others but, with the right governance, the prompt retirement of an unsuitable model will be seen as an advantage rather than a chore.
Following hype is not always the wisest business decision. Operational maturity and business impact should be the main influences when it comes to the implementation of AI. AI should provide rich insights that enhance decision-making. Very little business value is derived from simply being the first to deploy the latest AI tool only to then neglect its existence.
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AI obsolescence before AI maturity and zombie AI are the hidden costs of the AI arms race. The risk of inefficiency and wasted investments is too great to leave the AI journey to chance. AI is not the finish line, nor is it the race. It is a new way of approaching the contest — through a disciplined lifecycle of monitoring, assessing, changing, and growing. Do not let AI haunt the enterprise. Do not let it be a rent-free tenant. Define its responsibilities and govern it to ensure it pulls its weight, and do not be afraid to depreciate it when its useful life has been exceeded.
