Enterprise leaders face significant pressure to demonstrate a return on investment from artificial intelligence. Yet, up to 87% of AI initiatives fail to transition from proof-of-concept to successful production implementations [1]. One significant factor that’s not discussed enough? Inadequate evaluation clarity and methodology. Many organisations still rely on informal, ad-hoc assessments – or “spot check and vibes” – that are simply not rigorous enough for enterprise. To effectively leverage the potential of AI, leaders need a systematic, data-driven framework for evaluating the technical performance of AI models and the commercial value of specific use cases. This article provides that framework, enabling you to maximise the value of your AI initiatives.

The cost of relying on gut feeling and superficial analysis is significant. Without a structured approach, investments in AI become speculative and biased, rather than strategic. To succeed, organisations must prioritise technical feasibility and tangible value, moving beyond the hype to focus on solutions that deliver measurable results. To limit exposure for each new iteration, AI evaluation needs to be repeatable and scalable.

Our framework emphasises a comprehensive evaluation across two critical dimensions: the performance of the AI model itself and the return on investment (ROI) of the proposed use case.

Successfully moving AI from concept to production demands robust technical evaluation—far more than just “spot checks.” Deploying poorly tested AI is risky; models might fail silently, exhibit bias, produce errors, or degrade over time, leading to poor business outcomes. Rigorous evaluation helps manage these risks by providing objective data on performance against clear benchmarks (like accuracy). Systematic testing is vital when selecting the initial model, confirming improvements during training, and assessing new models as they emerge.

To gain the necessary confidence, we recommend a multi-faceted evaluation strategy incorporating the following key methods:

What it is: This approach leverages skilled humans to create a high-quality “gold standard” dataset or manually review a representative sample of AI outputs for accuracy, relevance, and other quality criteria.

Why it Matters: It provides the most accurate assessment, especially in specialised domains where nuanced understanding is critical (e.g., regulatory compliance, financial advice suitability). This forms the ground truth against which models are measured.

Implementation: This requires focused effort to curate target labels across a diverse set of inputs, covering operational scope and difficulty. This demanding curation is the crucial transition from subjective to objective evaluation, often the first real challenge in quantifying AI value. It establishes the ground truth for scalable automated testing using metrics like exact match accuracy, string distance, or precision/recall. This process demands rigour. Based on Malted’s deep expertise in LLMs and domain areas like Financial Services, we know that achieving an effective benchmark typically requires iterative refinement.

Example: A bank ensuring regulatory compliance in emails and chats might first have experts manually review and tag communications against a detailed internal taxonomy, applying nuanced judgment. This human-annotated dataset then serves as a reusable ‘ground truth’ benchmark, enabling scalable, automated testing of AI monitoring tools or chatbot responses against established compliance standards.

What it is: This method uses another specialised AI model (often a language model) to evaluate the output of the AI system being tested, typically comparing it against a reference answer or assessing its quality based on predefined criteria.

Why it Matters: Beyond simple programmatic checks (like keyword matching), LLM-as-Judge provides nuanced quality assessment at scale. It can evaluate semantic meaning, relevance, and tone – aspects closer to human judgment – making it a more insightful automated metric for complex AI outputs, while retaining speed advantages over manual review.

Implementation: Calibration against human benchmarks is required to ensure the “judge” LLM’s evaluations align with desired quality standards. Crucially, the judge LLM itself needs evaluation, often using a smaller ‘gold standard’ set, to verify its reliability as a proxy for human assessment. Be mindful that judge LLMs can also have biases [2].

What it is: This involves humans randomly sampling and reviewing raw outputs from the AI system, akin to quality assurance (QA) in traditional software development. It goes beyond checking against a known benchmark.

Why it Matters: Years of working with AI systems, especially generative models like LLMs (even simpler ones like BERT [8]), show they can produce unexpected errors (e.g., topic drift, formatting issues, subtle inaccuracies, safety concerns) not easily caught by automated metrics alone.

Implementation: This shouldn’t be just a “vibe check.” The crucial step is to systematically document observed failure patterns, count their occurrences, and use these insights to create specific new automated test cases or refine existing benchmarks. This builds a tighter feedback loop.

What it is: This involves deploying two versions of a process – one potentially AI-assisted, the other the traditional method (or a different AI version) – to different segments of real users simultaneously.

Why it Matters: It measures how the AI performs in practice, directly assessing its impact on key business metrics and user satisfaction/preference within the actual workflow.

Implementation: A/B testing often combines quantitative measurements (e.g., task completion time, conversion rates) with qualitative feedback (user surveys, interviews), linking technical performance directly to business value and user acceptance.

Example: We recently A/B tested an AI-powered tool for streamlining RFP writing for a Fortune 100 company and found users preferred the AI workflow 71% of the time, demonstrating clear real-world value.

Beyond the technical performance of the model, it’s equally important to assess the value of each specific use case. ROI measurement must extend beyond simple cost reduction to encompass a broader range of value drivers:

• Cost Reduction: Automating repetitive tasks can lead to significant cost savings. For instance, automating invoice processing with AI can reduce processing time by 98%, freeing up accounts payable staff for higher-value activities [3]. Track metrics such as the reduction in full-time equivalent (FTE) hours and the cost per transaction to quantify these savings.
• Risk Mitigation: In highly regulated industries, AI can play a crucial role in mitigating risk. AI-powered fraud detection systems in banking can detect fraud by up to 87-94%, while simultaneously reducing false positives by 40-60%, resulting in millions saved in losses and penalties [4]. Monitor metrics such as the reduction in fraud losses and the number of compliance violation penalties avoided.
• Revenue Generation: AI can drive revenue growth through improved customer experiences and more effective marketing campaigns. For example, AI-powered personalisation engines have been shown to increase user conversion rates by 35% at large retailers and increase user retention rates by 80% [5]. Key metrics to track include increases in lead generation, conversion rates, and customer lifetime value.

When evaluating the ROI of an AI project, it’s crucial to consider the total cost of ownership, not just the initial investment. This includes:

• Deployment Cost: Account for compute requirements and integration expenses. Real-time AI applications can demand substantial infrastructure investments due to the significant differences between peak loads and sustained use, potentially doubling the initial project budget. Model size is a key driver of deployment cost, with Small Language Model solutions costing tens of thousands a year compared to millions of dollars for Large Language Models. Check out our previous blog posts [7] on SLMs for a more detailed comparison.
• Maintenance: Plan for ongoing model monitoring, retraining, and governance. According to Gartner [6], maintenance costs for AI systems can be substantial, with organisations that deployed GenAI reported spending an average of $2.3 million in fiscal year 2023 just for the proof-of-concept phase.
• Time-to-Value: Accelerate development cycles by building a Minimum Viable Product (MVP) with clear boundaries for AI functionality. Prioritise early integration of the AI solution into existing workflows to identify and address potential design flaws, ensuring a smoother and faster path to production and value realisation. This reduces the costly amount of time spent on design, build, test, and pilot phases.
• Risk Assessment: Identify and mitigate potential technical risks (hallucinations, inaccurate outputs), reputational exposure, and regulatory compliance issues (e.g., GDPR, CCPA). Failing to comply with data privacy regulations can result in fines and reputational damage.

Adopting this evaluation framework has significant strategic implications for enterprise leaders adopting AI. By focusing on data-driven insights, you can:

• Align AI investments with overall business goals: Ensure that AI projects directly support your strategic priorities and contribute to the bottom line.
• Prioritise high-impact opportunities: Focus resources on use cases with the most significant potential for ROI that are technically feasible, maximising the return on your AI investments.
• Mitigate risk: Proactively identify and address potential technical, reputational, and regulatory risks, safeguarding your organisation’s reputation and financial stability.
• Foster a culture of accountability: Establish clear metrics and track progress towards achieving desired outcomes, ensuring that AI initiatives deliver tangible results.

Don’t let your AI investments become another data point in the 87% failure rate. To ensure your AI initiatives deliver tangible results, conduct a thorough audit of your current AI evaluation processes. Implement a structured framework that incorporates both model performance and use case ROI assessment. Implementing this framework isn’t just about avoiding failure; it’s about strategically maximising the transformative potential of AI for tangible business outcomes. Successfully navigating AI adoption is critical for future success.

[1] Cooper, R.G. (2024). Why AI Projects Fail: Lessons From New Product Development. IEEE Engineering Management Review, pp.1–8. doi:https://doi.org/10.1109/emr.2024.3419268 

[2] Guo, Y., Guo, M., Su, J., Yang, Z., Zhu, M., Li, H., Qiu, M. and Liu, S.S., 2024. Bias in large language models: Origin, evaluation, and mitigation. arXiv preprint arXiv:2411.10915.

[3] Santoshkumar Anchoori (2024). AI-Driven Document Processing: A Novel Framework for Automated Invoice Data Extraction from PDF Documents. International Journal For Multidisciplinary Research, 6(6). doi:https://doi.org/10.36948/ijfmr.2024.v06i06.32247.

[4] Olawale Olowu, Adeleye, O., Okandeji, A., Ajayi, M., Adebayo, N., Omole, M. and Chianumba, E.C. (2024). AI-driven fraud detection in banking: A systematic review of data science approaches to enhancing cybersecurity. GSC Advanced Research and Reviews, 21(2), pp.227–237. doi:https://doi.org/10.30574/gscarr.2024.21.2.0418 

[5] Harshavardhan M, Jyoti Ainapur, Kalyan Rao. K, Kumar, A., Prajwal Prajwal, Saiteja Saiteja and Reddy, V. (2024). ‘Leveraging Artificial Intelligence in Marketing: Case Studies on Enhancing Personalization, Customer Engagement, and Business Performance’. [online] 13(9), pp.131–136. doi:https://doi.org/10.35629/8028-1309131136.

[6] Gartner. (2025). Here’s Why the ‘Value of AI’ Lies in Your Own Use Cases. [online] Available at: https://www.gartner.com/en/articles/ai-value 

[7] Large language models are not always the answer: the rise of small language models: https://malted.ai/large-language-models-are-not-always-the-answer-the-rise-of-small-language-models/

[8] Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR, arXiv:1810.04805.