Why Generative AI Alone Isn't Enough: The Case for Explainable and Causal AI

Generative AI, particularly large language models like GPT-3 through GPT-5, has rapidly advanced in producing human-like content, handling multiple modalities, and improving accuracy with techniques like Retrieval-Augmented Generation and fine-tuning. GPT-5, for instance, integrates deeper reasoning and reduces factual errors, marking a major leap forward. However, LLMs remain black-box statistical learners that excel at pattern recognition but lack transparency and true causal understanding. In high-stakes domains, explainability and cause-and-effect reasoning are critical, which LLMs alone cannot provide. The future of AI will therefore be hybrid, combining the creativity of Generative AI with the transparency of Explainable AI and the rigor of Causal AI to build more trustworthy systems.

1. The Generative AI Revolution: Power and Progress

Generative AI, driven by large language models, has transformed what AI can do in a very short time. These models are trained on vast amounts of text (and increasingly multimodal data), allowing them to generate content that is often impressively coherent and contextually relevant. Some key strengths of modern LLM-based GenAI include:

Versatile Knowledge and Skills

Today's top LLMs can converse on almost any topic, write essays or code, translate and summarize text, and even interpret images or audio. They act as general problem-solvers across domains, thanks to having digested the broad knowledge of the internet. For instance, GPT-5 is a multimodal LLM that can analyze text, images, and audio together, enabling more complex interactions and outputs^[1].

Improved Reasoning and Memory

Through advances in model architecture and training, newer models have significantly better reasoning capabilities than their predecessors. OpenAI's GPT-5 notably builds in the advanced reasoning that earlier required specialized models (the "O-series"), enabling it to solve multi-step problems with greater accuracy. It can dynamically route queries to "fast" or "deep" reasoning paths as needed, providing a flexible balance between speed and complex reasoning. Models also come with expanded context windows (e.g. hundreds of thousands of tokens in GPT-5), meaning they can consider very large amounts of information or lengthy conversations and "remember" details within a session^[1].

Rapidly Evolving Capabilities

The field is moving fast – from GPT-3's release in 2020 to GPT-4 in 2023 to GPT-5 by 2025 – with each generation leaping in performance. Researchers report staggering improvements; for example, GPT-5 nearly doubled the accuracy on certain benchmarks compared to a prior OpenAI model. Competitors are also pushing forward (Anthropic's Claude, Google's Gemini, Meta's open models, and others), fueling a fast-paced innovation race. This means generative models are quickly addressing some of their own limitations: reducing obvious mistakes, adding safety layers, and enhancing factual correctness.

Customization and Knowledge Integration

Techniques like fine-tuning (training an LLM further on specific data) and Retrieval-Augmented Generation (RAG) have made GenAI more practical for real-world use. Fine-tuning or specialized variants (e.g., domain-specific chatbots) can align a model's output with industry jargon or tasks. Meanwhile, RAG allows an LLM to fetch relevant information from a database or the web during its response, keeping answers up-to-date and grounded in verifiable sources. This hybrid approach mitigates the problem of models "making up" facts by ensuring the model cites or uses a factual reference. It's a clear example of how integrating external knowledge can improve a pure LLM's performance.

Productivity and Accessibility

Generative AI can automate tedious tasks (drafting emails, summarizing documents), provide inspiration and creative ideas, and make knowledge more accessible. By conversing in natural language, these models level the playing field – for instance, helping non-experts get explanations of complex topics, or assisting those who aren't fluent in a language by translating or simplifying text. Tools like Microsoft's Copilot or Grammarly's AI features embed LLMs to boost daily productivity^[3].

In short, GenAI and LLMs represent a genuine revolution in AI's capabilities. They're incredibly powerful at generating and interpreting content, and they're getting better at reasoning and factual accuracy at a breathtaking pace. It's no surprise that virtually every industry is exploring how to leverage these models. However, amidst this well-deserved enthusiasm, it's critical to recognize that LLMs are not a panacea. There remain fundamental limitations in today's generative AI that can't be ignored, especially when we move from fun demos to mission-critical applications.

2. The Limitations of LLMs: Black Boxes, Hallucinations, and More

For all their power, LLMs have serious shortcomings that make them insufficient as stand-alone solutions in many contexts. Some of the most important limitations include:

Lack of True Understanding (Correlation vs. Causation)

An LLM doesn't actually understand the world the way humans do – it excels by learning statistical patterns in data. As a result, it often picks up on correlations rather than real cause-and-effect relationships. Experts note that traditional AI models – notably LLMs – rely heavily on statistical correlations rather than causal reasoning. In practice, this means a generative model might know that certain words or concepts often appear together without grasping the underlying logic or mechanism linking them. It can mimic reasoning by pattern matching, but it has no built-in model of reality's causal structure. This is one reason LLMs can stumble on problems requiring understanding of physical causality or temporal cause-effect – unless such patterns were obvious in their training data, they have no guaranteed way to deduce them.

Opacity and "Black-Box" Decision Making

Modern LLMs are massive neural networks with billions of parameters. How they arrive at a given answer is usually impossible for a person to trace. To a user, an LLM is essentially a black box that magically produces an output. Even AI developers struggle to interpret these models' inner workings. As one CTO recounts, after months of prying into these black-box systems, "it quickly became clear that we were never going to get full transparency" from a large language model's decisions. This opacity is more than a technical inconvenience – it erodes trust. Business leaders are naturally wary of any AI-driven decision that can't be explained; indeed, lack of trust in AI outcomes is slowing adoption in ~60% of enterprises, according to a McKinsey study^[2]. If an LLM suggests a critical business move or a medical diagnosis, stakeholders will rightly ask "why?" – and a black-box model can't answer that.

Hallucinations and Misinformation

LLMs sometimes generate outputs that are entirely incorrect or fabricated – a phenomenon famously known as AI hallucination. Because the model's goal is to produce plausible-sounding text, it may assert false "facts" with confidence. For example, ChatGPT-style models have been known to invent fake research papers or legal citations that look real but aren't. As the University of Leeds cautions, generative AI models do not actually understand content – they merely predict likely outputs, so they often sound convincing even when there is no factual basis^[3]. This poses obvious risks if one naively trusts everything the AI says. Every output needs verification, which slows down usage and can negate the speed advantage. Despite continuous improvements (GPT-4 and GPT-5 have steadily lowered hallucination rates^[1]), no LLM today is 100% reliable on factual correctness.

Bias and Ethical Issues

Because LLMs learn from vast datasets of human-generated text, they inevitably absorb biases present in that data. They might produce outputs that are prejudiced or toxic, unless carefully filtered. Moreover, they can generate harmful content (intentionally or not) – hence the need for safety mechanisms and human feedback alignment (e.g. OpenAI's use of reinforcement learning from human feedback, RLHF). While these safety layers help, they are not foolproof^[4]. Tuning a model to avoid one kind of bad output can have side effects (it might become too hesitant or introduce other biases). Complete value alignment with human ethics remains an open challenge.

Non-Deterministic and Unpredictable Outputs

LLM outputs can vary from one run to another, even for the same input prompt (unless a fixed random seed and temperature 0 is used, which is rare in deployment). This stochastic nature comes from the probabilistic sampling process. In casual use, a bit of randomness makes the model more interesting. But in production workflows – e.g., an AI agent handling a legal form – unpredictability is a problem. A typical LLM-based agent might "yield different answers on each run", making it hard to guarantee consistent behavior^[5]. This unpredictability is one aspect of why current LLM-based agents are considered "flaky" for strict environments like finance or healthcare. Consistency is also linked to explainability: if an AI gives different answers each time, how do we trace which one is correct or why it changed?

High Compute and Energy Costs

Large generative models are computationally hungry. They require specialized hardware (GPUs/TPUs) and lots of memory to run, and even more resources to train. For example, training GPT-4 was estimated to consume tens of thousands of megawatt-hours of electricity^[3] – contributing to a sizable carbon footprint. Using such models at scale can be expensive, and their latency (response time) is non-trivial compared to simpler programs. In contrast, a well-designed rule-based system or a causal model might run on a standard CPU with minimal delay. Thus, there's an efficiency trade-off: the general intelligence of an LLM comes at the cost of heavy computation, whereas specialized AI systems can be much lighter. This becomes important when an AI needs to handle millions of decisions quickly (e.g. fraud checks in banking) – a massive LLM might be overkill in both cost and speed, whereas a deterministic algorithm could handle the throughput more efficiently.

These limitations highlight why LLMs, on their own, are not enough for many real-world AI applications. In fact, the more powerful and complex the model, the harder it often becomes to explain or predict its behavior. As one governance report put it, we can approximate explanations of an LLM's outputs, but for many complex or creative tasks, complete explainability is beyond current capabilities. This is the classic accuracy-vs-explainability trade-off: simpler models are easier to interpret but may not perform as well, while complex models perform better but defy understanding. LLMs squarely fall on the "complex and accurate" side of that trade-off – which means we need new strategies to bring transparency, trust, and rigorous reasoning into the AI loop. Enter Explainable AI and Causal AI.

3. Explainable AI: Bringing Transparency and Trust to AI Decisions

Explainable AI (XAI) refers to a collection of techniques and principles aimed at making the results of AI models understandable to humans. In traditional software, if a program makes a decision, it's following a sequence of human-written rules – one can inspect the code to see why. But in machine learning and especially deep learning, the decision logic is encoded in numeric weights and nonlinear activations that don't map easily to human-readable reasons. XAI tries to bridge this gap.

There are generally two broad approaches in XAI:

Designing Interpretable Models

This means using models that are inherently transparent – for example, decision trees, which can be visualized as flowcharts of decisions, or linear models with a small number of features, where each feature's contribution is clear. When feasible, using a simpler model that humans can directly follow is ideal. This inherent explainability often comes at the cost of some accuracy, but it's valuable in high-stakes settings. For instance, a medical diagnosis system might use a constrained model that doctors can vet, rather than an inscrutable deep net.

Post-hoc Explanations for Complex Models

This is about extracting explanations from an already trained complex model like an LLM or deep neural network. Techniques like LIME and SHAP analyze how changes in input affect the output to infer which parts of the input were most influential on the decision. These can provide local explanations – e.g., highlighting which words in a text prompted a certain classification. Other methods include surrogate models (train a simpler model to mimic the big model's predictions, and then inspect the simpler model) and feature visualizations (for vision models, see what kind of input patterns maximally activate certain neurons).

XAI is crucial because it provides transparency, builds trust, and aids debugging. When an AI system can explain why it did something, stakeholders are more likely to trust it with important decisions. In regulated industries, explainability isn't just nice-to-have; it's often a legal requirement. The EU's proposed AI Act, for example, would mandate that high-risk AI systems offer meaningful explanations for their decisions. In finance, regulations already demand that if an algorithm denies someone credit, there must be a clear reason provided. XAI helps organizations comply with such rules by opening the AI's black box a little.

However, applying XAI to LLMs and other deep models remains very challenging. Many post-hoc explanation techniques struggle with the sheer complexity of LLMs that have billions of parameters interacting in non-linear ways. A tool like LIME might pinpoint parts of an input that affect the output, but it can miss complex interactions, and it doesn't truly open up the model's inner logic (which might be unfathomable in its detail). Likewise, using a surrogate model to approximate an LLM's behavior works only in limited scenarios – a simple model cannot capture the full nuance of, say, GPT-4's decision process.

Thus, while XAI can improve understanding, for the most advanced AI (like LLMs) we often get only partial explanations. We might know which words were key to the model's answer, or get a probability distribution over a few possible reasons, but we won't get a neat, human-like rationale from the model itself. In fact, some researchers suspect that fully explaining every aspect of an LLM's behavior may never be feasible, especially as models grow more complex.

That reality is prompting new research: some are working on "mechanistic interpretability," which aims to decipher the actual internal mechanisms of models (e.g., understanding specific neurons or circuits that correspond to concepts)^[4]. Others advocate for hybrid systems where parts of the system are explicitly programmed or constrained (and thus explainable by design). This is where we segue into Causal AI – a paradigm that puts cause-and-effect front and center, and often yields models that are more interpretable and reliable by nature.

4. Causal AI: Understanding Cause and Effect for Robust AI

While XAI is about explaining models, Causal AI is about building models that explain the world. It represents a shift from purely correlational machine learning to methods that explicitly capture cause-and-effect relationships in data. The concept is heavily influenced by the work of scientists like Judea Pearl, who emphasized that to truly understand and predict the world (and not just fit statistical patterns), an AI needs to grasp causality – knowing X causes Y rather than just "X is associated with Y".

In practice, Causal AI involves creating structured models (often graphs) that encode causal relationships. For example, a causal model for disease diagnosis might know that a certain virus causes certain symptoms, and not the other way around. These are often represented as causal graphs or Bayesian networks: diagrams where nodes are variables and arrows denote influence (causation) from one to another. Importantly, a causal model isn't just an observational tool – it supports interventions and counterfactuals. You can ask "What if…?" questions: What if we increase the price of our product – how would sales change, all else being equal?; or What if this patient hadn't been a smoker – would they still likely develop lung disease? Traditional AI, which learns correlations, struggles with such questions because it can't distinguish correlation from true causation. Causal models are built to handle them by separating the underlying generative process of the data from spurious correlations.

The benefits of Causal AI include:

Explainability and Transparency by Design

Because a causal model explicitly maps out relationships (often in human-understandable concepts), it is inherently more explainable. It's clear how inputs lead to outputs through the chain of causal links. Users can trace which factors contributed to a decision and how. In fact, causal AI was embraced by some because "explainable AI wasn't quite it – explaining something doesn't mean someone will understand it… then we discovered causal AI", where the explanations are more natural. For instance, instead of an opaque statistic, a causal model might output: "We approved the loan because the applicant's stable job caused a high credit score, which in turn predicts a low risk of default." This reads more like human reasoning, which makes sense – it's following cause-and-effect logic.

Determining Why, Not Just What

As one practitioner put it, unlike conventional AI that relies on correlation, "causal AI helps determine the underlying cause-and-effect relationships that drive decisions". It allows systems to truly answer the "why" questions. Why did the model recommend this action? Because X led to Y which led to Z – you can follow the chain. This is vital for domains where understanding the rationale is as important as the outcome (e.g., making policy decisions, scientific research, strategic business moves).

Ability to Evaluate Interventions (Counterfactual Reasoning)

Causal models let us simulate interventions: e.g., "If we do X, the model predicts Y will change". They support counterfactual analysis: "If X hadn't happened, would Y still have happened?" This is incredibly useful for decision-making. For example, in healthcare a causal model can help decide treatment: If we give this drug, will the patient improve? – something a correlational model can't directly tell because it only knows correlations from historical data, not the outcome of new interventions. Causal AI essentially embeds a knowledge of mechanisms, so you can do "what-if" experiments safely in silico. As Le Maitre explained, causal AI allows us to ask "what if" questions and evaluate counterfactuals to truly understand an AI-driven outcome's reasoning^[2].

Deterministic, Consistent Results and More Efficient

Causal AI models, unlike probabilistic systems such as LLMs, deliver deterministic and consistent results. It is ensuring identical inputs always produce identical outputs, which makes decisions transparent, auditable, and reliable for applications like loan approvals or tax calculations. They also tend to be less data-hungry and more computationally efficient, since their design leverages expert knowledge and causal structures rather than massive datasets, allowing them to generalize with fewer examples and operate through lightweight equations or graphs rather than billions of parameters^[5].

One way to illustrate the power of causal (and explainable) approaches is an example from advertising technology. A company found that using causal AI for ad targeting not only improved accuracy but also allowed them to explain to clients why a certain audience was chosen – a level of transparency traditional black-box AI couldn't provide. The result was a 10x return on investment for their campaigns, because they could both target better and build trust with customers about the targeting decisions^[8]. In general, across finance, healthcare, supply chain, and other sectors, businesses operate on cause-and-effect; leaders say that if companies don't understand the root causes of their challenges, they struggle to make meaningful improvements. Causal AI is seen as the key to injecting that understanding into AI systems. It's telling that experts predict Causal AI will be a major next leap in AI adoption, precisely to tackle the trust and transparency issues – it's poised to expand significantly as organizations recognize the limitations of purely correlation-based AI and the need for explainability^[2].

It's worth noting that Causal AI and Explainable AI are complementary: Causal models are usually more explainable, and conversely, insisting on explainability often leads you toward causal or rule-based methods. Both aim for transparent, understandable AI. We should also acknowledge that causal modeling isn't a magic wand either – discovering valid causal relationships can be challenging (it may require experiments or great data), and causal models, if wrong, can mislead just as badly as any model. But the big difference is that a causal model's mistakes are visible and debatable (we can examine its assumptions), whereas an LLM's mistakes are often hidden until a wrong output manifests.

So, where does all this leave us? We have on one side GenAI/LLMs with phenomenal capabilities but opaqueness and unpredictability; on the other side, XAI and Causal AI providing determinism, interpretability, and trust, but sometimes at the cost of flexibility or raw performance. The good news is, this isn't an either-or choice. In fact, the frontier of AI is about combining these approaches to get the best of both worlds.

5. Better Together: Combining Generative, Explainable, and Causal Approaches

To build AI systems that are both powerful and trustworthy, we can integrate generative AI with explainable and causal methods. Such hybrid systems leverage the strengths of each approach while mitigating their weaknesses. Here are some ways this combination is playing out and what it promises for the future:

LLMs as Natural Language Interfaces, with Deterministic Engines Behind the Scenes

One practical architecture is to use an LLM for what it's great at – understanding and producing human language – but not for making the core decisions. Instead, a deterministic, rule-based or causal inference engine handles the decision logic, and the LLM translates inputs (user queries) into structured queries or criteria for that engine, then turns the engine's output back into a user-friendly explanation. In other words, the LLM becomes a smart front-end, and a causal/XAI model is the back-end. Researchers at Rainbird (an AI company) describe this as using a graph-based deterministic system as the primary reasoning engine, with the LLM serving only as a natural language interface layer^[6].

For example, imagine a financial compliance system: the user asks in plain English, "Is this transaction suspicious under regulations?" The LLM interprets this and extracts key features (amount, country, parties involved), feeds them to a knowledge graph of fraud rules, and then explains the result ("Yes, because it violates X regulation on transfer limits between these countries") back to the user. This way, you get a conversational experience but with guaranteed consistent logic underneath. Such a system would have zero hallucinations (the LLM isn't inventing facts, it's constrained by the graph), perfect repeatability (the graph will give the same verdict every time for the same data), and complete traceability (every decision can be traced through the rules in the graph). In fact, this approach achieves "true causal reasoning that follows explicit logical pathways," and it is not subject to the whims of training data biases the way LLMs are.

Many organizations in regulated industries are gravitating toward this pattern – sacrificing some of the open-ended flexibility of an unconstrained LLM in exchange for reliability and clarity where it matters most. And as a bonus, because the deterministic engine is usually far simpler than an LLM, the system can be more compute-efficient for the decision part, using the heavy LLM only for language tasks.

Validation and Oversight of LLM Outputs

Another integration strategy is to let the LLM produce an answer (taking advantage of its knowledge and fluent reasoning), but then validate or correct that answer using an external knowledge base or rule system. Here, the deterministic component acts as a watchdog or editor. For instance, if an LLM in a medical app suggests a treatment, a medical knowledge graph could cross-check that suggestion against known contraindications and either flag a warning or veto it if it contradicts medical guidelines. This is a bit like how a human doctor might use an AI recommendation but double-check it against a protocol. Such human-in-the-loop or rather symbolic-in-the-loop setups aim to catch the mistakes of the generative model. Rainbird's experts note that using a knowledge graph as a validation layer to verify and correct LLM outputs can significantly improve safety^[6].

It doesn't fully eliminate the black-box (since the LLM still produces something opaquely), but it ensures that final decisions respect the known rules. One can think of it as LLM + rigorous filter. This approach is already seen in some question-answering systems where the LLM must cite sources for its answers: if it can't find a source, the answer might be withheld or marked as unverified.

LLMs Enhancing Causal Discovery and Knowledge Engineering

Interestingly, the synergy works in the opposite direction too – generative AI can help build better explainable/causal models faster. One bottleneck in deploying causal or rule-based systems is the knowledge engineering effort: figuring out the rules or causal graph structure often requires manual work by experts. Now, LLMs are being used to accelerate that. For example, specialized LLMs fine-tuned on reasoning tasks can read through documents (like regulatory texts or technical manuals) and automatically extract structured knowledge to form draft knowledge graphs or rule sets. This can compress what used to be months of manual work into days^[6].

CausaLens (a Causal AI platform) similarly notes that a generative model can suggest causal relationships and even provide explanations for them, giving data scientists a head start in building causal graphs^[7]. The human expert remains in the loop to vet and adjust these suggestions, ensuring the final model makes sense, but the combination dramatically boosts productivity. In short, GenAI can serve as an intelligent assistant for creating explainable models.

It's worth emphasizing how fast this synergy is developing. Not long ago, the idea of an AI that could both generate rich language and be perfectly explainable seemed far-fetched. But now we see prototypes – for instance, a legal AI assistant that uses an LLM to read a case file and draft an argument, while a knowledge graph of laws cross-checks every assertion for legal validity before it goes out. Or in healthcare, an AI clinician that chats with patients to gather symptoms (using GenAI), but then matches those against a causal model of diseases to reach a diagnosis and explain it. Such AI-native systems are on the horizon, and they will fundamentally change how organizations operate, enabling automation of complex tasks with confidence and clarity.

6. Conclusion: Toward an AI-Native Future with Both Power and Reason

The current wave of Generative AI, led by advanced LLMs, has unquestionably unlocked capabilities that were science fiction only some years ago. This "GenAI revolution" is real – models are writing code, poetry, and academic essays; they're assisting in research and education; they're even beginning to augment how we make decisions. And with each iteration (GPT-4, 5 and beyond), they are getting more powerful, more reliable, and more aligned with our needs.

However, we must not lose sight of the complementary revolution that needs to happen in parallel: making AI trustworthy, explainable, and causally sound. Generative prowess alone is not enough if we cannot trust the answers or understand the reasoning. The hype around LLMs should be tempered with a clear view of their blind spots – and a plan to address those. Explainable AI and Causal AI form the core of that plan. They remind us that accuracy without accountability can be dangerous, and that predictions without understanding can lead us astray.

The future of AI in truly impactful roles (think healthcare, finance, governance, autonomous systems) will likely belong to hybrid approaches. Integrated systems will use LLMs for what they do best – interpreting complex inputs, generating options and insights, interacting with humans – and use causal, explainable models to provide a stable decision-making backbone. Organizations that become AI-native will not rely on a single giant model for everything, but orchestrate multiple AI components, each with a clear role. Just as human organizations rely on both creative thinkers and meticulous analysts, AI-native organizations will deploy creative generative models alongside rigorous logical engines.

Such a combined strategy offers a powerful virtuous cycle: the generative AI brings speed, flexibility, and breadth of knowledge, while the explainable/causal AI ensures depth of understanding, consistency, and trust. When these work in concert, the result is AI that is both novel and reliable, both intuitive and accountable. We won't have to choose between performance and explainability – we'll have systems that deliver both, each reinforcing the other's strengths^[6].

In closing, it's clear that GenAI and LLMs will only get better fast. But it's also clear that bigger and better models won't alone solve the fundamental issues of trust and understanding. For that, we need Explainable AI to open the black boxes, and Causal AI to ensure our AI systems grasp the real-world consequences of their choices. By embracing all three – Generative, Explainable, and Causal AI – in a unified approach, we can build the next generation of AI systems that are not only intelligent but also transparent, ethical, and effective. In a world increasingly shaped by AI decisions, that combination will be key to making sure AI serves us in the ways we intend – delivering not just impressive outputs, but explainable and causally sound outcomes that we can trust and act upon with confidence.

Disclaimer: This blog post incorporates insights from multiple sources and industry research. All opinions and strategic interpretations are our own.