Article: The Explainability Imperative: Why Black Box AI Is Dead in Enterprise

AI is no longer experimental; today it’s embedded in how businesses predict demand, approve loans, assess risk, and allocate resources. But as AI models become more powerful, their decision-making processes often become more obscure.

This growing opacity is not just a technical issue. It is becoming a strategic liability.

According to McKinsey’s 2024 survey, only 17% of organizations are proactively addressing explainability, despite 40% identifying it as one of the most significant risks in artificial intelligence. This gap between capability and comprehension is not a tooling oversight. It is a deepening crisis of trust.

What issues does the Black Box pose?

Opaque, unexplainable artificial intelligence systems, which are commonly called “Black Box” systems, present a serious challenge for enterprises, especially for those operating in regulated, high-stakes environments. From healthcare and insurance to logistics and finance, decision-making processes must be accurate, auditable, and interpretable.

When stakeholders cannot understand why an AI system made a certain recommendation or decision, adoption slows, scrutiny increases, and accountability breaks down. IBM’s 2023 Global AI Adoption Index reported that over 50% of enterprise IT leaders cite the lack of explainability as a critical barrier to scaling AI projects across the organization.

The academic research echoes this concern. The Alan Turing Institute (2023) notes that while post-hoc tools like LIME and SHAP offer partial insight into model behavior, they often lack the robustness required for compliance and enterprise-level governance. Ideally, explanations need to be integral to the model.

XAI Market Landscape

As mentioned, the global explainable AI (XAI) interest is growing, mainly driven by highly regulated industries such as healthcare, finance, and the public sector.

A reference of governmental leaders acting on this as regulatory bodies are the European Union’s AI Act, the U.S. Algorithmic Accountability Act, and sector-specific guidelines such as the FDA’s framework for AI in medical devices, which all emphasize transparency and traceability in AI-driven decisions.

The Way Forward

The future of enterprise AI will not be shaped by merely accurate models. It will be defined by more trustworthy systems that build on understandable reasoning and human-centric accessibility.

To achieve that, organizations must:

• Integrate interpretability into model development from the start
• Adopt native explainability frameworks, not just post-hoc tools
• Embed human oversight into AI decision-making loops

Establish governance structures that prioritize transparency Explainable AI has become, very quickly, a basic need to trust Artificial Intelligence for Enterprises.  

References:

• McKinsey & Company (2024). Building AI trust: The key role of explainability. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/building-ai-trust-the-key-role-of-explainability
• IBM (2023). Global AI Adoption Index. Available at: https://filecache.mediaroom.com/mr5mr_ibmspgi/179414/download/IBM%20Global%20AI%20Adoption%20Index%20Report%20Dec.%202023.pdf
• Mittelstadt, B. (2023). Principles alone cannot guarantee ethical AI. arXiv:2304.11218 Crook, J., Mittelstadt, B., and Wachter, S. (2023). Explanations for AI in the Enterprise. arXiv:2307.14239
• Alan Turing Institute (2023). Explaining decisions made with AI. Available at:  https://www.turing.ac.uk/research/publications/explaining-decisions-made-ai