The hidden role of human validation for AI in eCommerce

AI in eCommerce… why are so many strategies failing?  

Most eCommerce businesses have adopted AI strategies, aiming to enhance customer experience, improve efficiency, and reduce costs. 

But, as many industry analysts have well publicised over the years, a large proportion of AI initiatives fail, with most AI models either failing spectacularly or ending up in a no-progress loop, with more and more money going into them, but with no return. The reasons for failure? Poor data quality, missing feedback loops, and a lack of scalability. But here’s the thing: AI doesn’t fail because it’s bad, it fails because it’s left alone too early.   

So, how can you create an AI strategy for eCommerce that avoids failure, sustains momentum, and actually delivers the outstanding customer experience that drives repeat business? 

The answer? By directly addressing the challenges eCommerce businesses face in AI model development – and solving them with the right human support. 

AI in eCommerce - challenges that often lead to AI failure
 

Operational overload  

According to Gartner (2024), internal teams spend more than 41% of their time on data cleansing/model validation, instead of on innovation. This means significantly less time is being spent working on strategic initiatives such as improving customer experience, developing AI-driven personalisation, or the innovation that will fuel a company’s competitive advantage in eCommerce. Whilst data cleansing is essential, it’s also labour-intensive and repetitive. Time spent on back-end operational burdens slows the pace at which businesses can launch new products, optimise algorithms, or test new experiences.  

High compliance costs that divert resources

McKinsey (2025) states that EU AI Act implementation costs are averaging $3.7M per organisation, a significant financial burden, especially for mid-sized eCommerce players. These funds are often redirected from innovation budgets, AI development, or customer experience improvements, weakening AI strategy execution. For global eCommerce companies, complying with the EU AI Act alongside other regional regulations (such as those in the U.S. and APAC) leads to regulatory fragmentation. This not only demands significant time and money for legal reviews, documentation, audits, and risk assessments, but also forces companies to customise systems for each region, an approach that is both inefficient and costly. 

Talent scarcity

There is a critical shortage of professionals who understand how to manage AI responsibly. IDC (2024) reports that nearly 80% of companies lack experts in AI governance and oversight. Without these skills, AI systems are prone to errors, regulatory breaches, and unfair customer outcomes, undermining trust and slowing progress. Skilled professionals are required to guide ethical use, monitor model performance, and align AI with legal frameworks like the EU AI Act. But this can be a problem, particularly for smaller players, as they may lack the infrastructure to sustain this, giving larger incumbents an advantage. 

AI model drift

Model drift is a major issue for AI strategies in eCommerce because it means that AI models lose accuracy and effectiveness over time, essentially getting worse at their jobs. According to Gartner (2025), there’s an average 42% drop in model performance within just six months of deployment.  This can result in poor product recommendations, pricing errors, inaccurate demand forecasting, and ineffective personalisation – all of which directly impact sales, customer satisfaction, and trust. Without robust systems to detect and retrain drifting models, AI investments can quickly become liabilities, leading to missed opportunities and wasted resources. 

So, what are the solutions? 

Elevating operational overload  

One of the most effective ways to ease the burden on internal teams is to outsource routine data tasks like cleaning, labelling, and validation to a trusted service provider with AI expertise. These partners can also provide human-in-the-loop (HITL) support, where trained reviewers handle complex cases, such as unusual customer behaviour or regional nuances, that AI models often miss. At NashTech, we go further by managing the full AI lifecycle, from initial testing to ongoing performance monitoring. This reduces operational strain, ensures accuracy, and frees internal teams to focus on what truly drives value: innovation and customer experience. 

Some eCommerce companies may choose to handle this internally. Doing so requires investment in automation tools like AutoML, as well as building dedicated AI ops teams. Techniques like active learning and lightweight validation can help, but be warned, they still demand time, coordination, and upskilling.  

Navigating the complexities of compliance 

Outsourcing compliance tasks to business process management (BPM) partners with legal-tech and AI governance expertise can ease the load on internal teams. These partners handle documentation, audit trails, policy mapping, and localisation, ensuring alignment with regulations like the EU AI Act. With multilingual human reviewers, they also offer HITL checks for bias, transparency, and explainability, key compliance requirements. Many BPMs provide shared, certified frameworks that reduce duplication and speed up regulatory readiness. 

For companies choosing to manage compliance internally, this demands major investments in legal expertise, processes, and regional oversight. For businesses under pressure to innovate, outsourcing offers a more scalable, lower-risk path to stay compliant without slowing down growth. 

Ensuring access to the best AI talent  

For companies that prefer to keep everything in-house, investing in targeted training programs, hiring governance specialists, and building cross-functional AI oversight teams can work, though this route typically takes longer and requires greater upfront resources. 

One effective way to address the AI talent gap is to augment internal teams by partnering with BPM providers who offer access to experts in AI ethics, governance, and regulatory compliance. These partners can support HITL throughout the AI lifecycle, monitoring model decisions, flagging anomalies, and ensuring quality across languages and use cases. Some BPMs, like NashTech, also offer co-delivery models that include training and upskilling for internal teams, helping businesses build in-house capabilities over time. This approach allows smaller or mid-sized eCommerce companies to quickly close the skills gap without the high cost of hiring or building infrastructure from scratch. 

Keeping AI models functioning  

To maintain the accuracy and relevance of AI models over time, especially for critical customer-facing functions like personalisation and pricing, many eCommerce companies are turning to HITL solutions. By using external analysts to regularly test model outputs, identify drift, and validate predictions, businesses can ensure their AI systems continue to perform effectively. By outsourcing model retraining operations to BPM partners, tasks like data refreshes, labelling, and retraining pipelines will have human quality assurance built in. Continuous performance benchmarking, supported by HITL-driven A/B testing, adds another layer of confidence by directly comparing new and existing models in real production environments. Together, these practices keep AI models performing optimally without placing additional strain on internal teams, ultimately safeguarding the customer experience and business results.  

For companies that prefer to manage this internally, an alternative approach involves investing in in-house model monitoring tools, creating dedicated model ops teams, and training staff to identify and respond to drift and performance issues proactively. While this path offers greater control, it demands more time, infrastructure, and specialised talent to be sustainable at scale. 

NashTech has been helping brands like yours for over 17 years. When AI gets it wrong, we catch it. When AI gets it right, we’ve helped it learn.  

• 17 years of experience in human-in-the-Loop AI training. 

• Multi-lingual, high-quality teams. 

• Cost-effective, scalable support. 

• Trusted by some of the world’s largest technology giants 
• AI that works in the real world. 

Want your AI to actually work? 

Talk to us about how our human-in-the-loop validation can help your AI e-commerce strategy to succeed, quickly, accurately, and at scale.