We Bridge the AI Execution Gap That Others Miss

Artificial Intelligence is often taught in rigid, theory-heavy ways, disconnected from how it works in practice. Students and even PhD graduates emerge from academia without the hands-on, problem-driven experience needed to succeed in fast-paced industry roles. Even the most talented learners struggle to make the leap from research to real-world AI development—leading to frustration, inefficiency, and lost potential.  We’ve seen this play out in several ways:

• A top-ranked PhD graduate in machine learning struggled to deliver value in an industry role because they had never worked with real business data—messy, incomplete, and governed by strict legal constraints. Their models were theoretically sound but failed to align with the business needs or timelines.

• A postgraduate researcher trained on clean, curated datasets found themselves overwhelmed when tasked with building an AI pipeline from scratch in a startup. The challenge wasn’t model accuracy—it was navigating real-world ambiguity, stakeholder demands, and data engineering issues.

• Even junior data scientists in established firms sometimes lack the skills to communicate model results to non-technical stakeholders or to justify trade-offs between accuracy and explainability—crucial in regulated sectors like healthcare or finance.

Our work with industry professionals and business leaders has revealed a different but equally pressing issue: AI is often misunderstood and misused in business contexts. Many companies make critical mistakes—overestimating AI’s capabilities or failing to allocate the right resources needed to support it. Others struggle with AI productionisation, MLOps, cloud integration, and scaling solutions effectively. Even within AI teams, professionals may lack the domain-specific knowledge required to move from experimentation to deployment. We’ve seen this across multiple sectors:

• In the retail sector, businesses often expect AI to deliver instant results, overlooking the fact that success depends on data readiness. Without clean, well-structured, and timely data, even the most advanced AI models cannot function effectively. Yet, many retail leaders underestimate the importance of data preparation, assuming that “AI magic” can happen without proper groundwork.

• In financial services, we’ve encountered firms that assign a single data scientist or engineer to “solve AI,” failing to understand that meaningful AI adoption is a cross-functional effort. Successful AI requires collaboration between technical experts, domain specialists, IT, compliance, and operations. Without building the right team and investing in cross-department coordination, initiatives often stall or underdeliver. Furthermore, they investing heavily in proof-of-concept models that never reach production—simply because the real-world systems, data infrastructure, or stakeholder alignment were never in place

• In the credit industry, AI is frequently applied to tasks like credit scoring, risk modeling, and fraud detection—but often without sufficient checks or understanding of the data context. Some organisations deploy models trained on biased or incomplete data, leading to compliance risks and poor decision outcomes. Others lack the in-house capability to validate model fairness or ensure explainability, assuming that off-the-shelf AI solutions can be trusted without adaptation or oversight.