What is explainable AI, and why do you need it?

Your AI system just denied a loan application, but when the customer asks why, you can't give them a straight answer. The algorithm made the call based on hundreds of data points powered by big data AI, but the reasoning is locked in a black box that even you can't explain.

This isn't just a customer service headache. With new regulations demanding transparency and customers expecting accountability, explainable AI has become the difference between trusted systems that people actually use and expensive AI projects that collect dust. 

But when you can show the "why" behind every AI decision, you turn skeptics into champions and compliance risks into competitive advantages.

In this guide, we’ll unpack what explainable AI is, why it matters, and how you can start using it to build more trusted, transparent systems.

What is explainable AI?

Explainable AI (XAI) refers to artificial intelligence systems that can describe their decision-making process in terms you can understand. Think of it like getting a detailed receipt that shows exactly how your total was calculated, but for AI-powered recommendations or decisions.

This stands in stark contrast to "black box" AI models, where the logic remains hidden, which is a challenge AI analytics seeks to overcome. You get an answer, but no reasoning behind it.

The need for AI explainability is growing as AI becomes more common in your decisions and new regulations demand transparency:

• Transparency: You can see which factors influenced the AI's decision

• Interpretability: The explanations make sense even if you're not a technical expert

• Accountability: You can trace decisions back to specific inputs and logic

Why explainable AI matters to you

AI-driven decisions directly impact your revenue, compliance, and customer relationships. When you can explain the "why" behind those decisions, you build trust and avoid costly mistakes.

1. Avoid regulatory penalties and compliance risks

The world of AI regulation is catching up to AI fast. Laws like the EU AI Act now include strict transparency requirements for high-risk systems. In the US, regulations in finance and lending demand clear explanations for credit decisions.

Over 1,000 companies faced fines in 2024 for failing to meet AI transparency standards. Explainable AI models help you avoid these penalties by showing your work.

2. Build trust that drives adoption

Trust is the foundation of adoption for any new technology. When your employees, customers, and stakeholders understand how AI reaches its conclusions, they're more likely to accept and act on them.

"Companies that establish digital trust among consumers through practices such as making AI explainable are more likely to see their annual revenue and EBIT grow at rates of 10 percent or more."

— Tom Davenport, AI industry trends

3. Speed up AI adoption across your organization

Getting rid of the "black box" fear removes the biggest barrier to internal AI adoption. With trustworthy AI, your teams have been shown to adopt new platforms three times faster because people are more willing to use systems they understand and trust.

Just ask Verivox. Their teams were stuck with slow time-to-insight and limited options for exploring data. But once they embedded ThoughtSpot directly into their B2B platform, the shift was immediate: adoption soared to 70%, and the team was able to decommission two entire legacy dashboard tools. 

💡 Related read: See how WEX built trustworthy AI with self-service analytics

How explainable AI works

You don't need a data science degree to understand how XAI operates. Most explainable systems follow a simple, three-step process to turn complex calculations into clear reasoning.

Here's how AI explainability works in practice:

1. Input analysis: The AI identifies which data points were most influential in its calculation

2. Decision mapping: The system traces the path from those key inputs to the final output

3. Translation layer: Technical reasoning gets converted into human-readable explanations

ThoughtSpot's Spotter demonstrates this perfectly. When you ask a question, it doesn't just give you an answer. It shows you the data sources and logic it used, complete with natural language explanations that make sense whether you're a CEO or a data analyst. You can see exactly which metrics influenced the insight and drill down into the underlying data with follow-up questions.

📺 See how industry experts at Snowflake are building trust with explainable AI—watch on demand here. 

3 common methods for explainable AI

The different problems you face require different types of explanations. Here are three explainable AI techniques that solve specific challenges you face every day.

1. SHAP for understanding overall model behavior

SHAP (SHapley Additive exPlanations) gives you the "big picture" view of your AI model's behavior. It shows which features have the most impact on outcomes across all decisions, not just one.

For example, it might reveal that for your predictive analytics loan-approval model, income is responsible for 40% of the decision, while credit score accounts for 30%. This helps you audit your models for systemic bias and confidently explain your overall logic to regulators.

2. LIME for individual decision explanations

LIME (Local Interpretable Model-agnostic Explanations) explains one specific decision at a time. If you need to know why a particular customer's churn risk is suddenly high, LIME can pinpoint the exact factors.

This becomes incredibly valuable if your teams are customer-facing and need to provide specific, actionable reasons to customers about why they received a particular outcome.

3. Feature importance for instant insights

These techniques show you what the AI model is "looking at" when it makes a decision. In a fraud detection system, for example, an attention mechanism can highlight the specific transaction patterns that triggered an alert.

Your analysts can quickly validate the AI's findings and take immediate action based on clear, understandable reasoning.

Benefits of implementing explainable AI

Adopting XAI delivers tangible results that affect your bottom line, your team's efficiency, and your overall agility.

1. Maximize ROI from your AI investments

According to a Microsoft study, businesses that prioritize explainability can achieve up to a 3.5x return on their AI investments. Trusted AI gets used more, fails less, and delivers value faster.

Here’s how Jennifer Belissent, Principal Data Strategist at Snowflake, breaks down the approach that delivers trust: 

💡 Related read: Get more AI literacy takeaways from Jennifer Belissent 

2. Make confident decisions faster

Explainable AI lets you understand not just what is happening, but why. This allows you to catch potential errors before they impact your business and combine the AI's data-driven insights with your own expertise.

3. Reduce failed AI projects

When people understand and trust AI systems, they actually use them. This dramatically reduces the number of AI projects that get built but never adopted, saving you time and resources while improving your success rate.

Unlike traditional BI platforms that often leave you guessing about how insights were generated, platforms like ThoughtSpot Analytics make every AI-generated insight transparent by default. You can see the data sources, understand the reasoning, and explore further with natural language questions.

How to overcome explainable AI challenges

Implementing XAI isn't without hurdles, but they're manageable when you know what to expect and how to address them.

Balancing accuracy with interpretability

Sometimes, the most accurate AI models are the most complex and hardest to explain. You might face a tradeoff between a 97% accurate black-box model and a 95% accurate explainable one.

For your high-stakes decisions, a slightly less accurate model that you can trust and explain is almost always the better choice. The 2% difference in accuracy rarely outweighs the benefits of understanding and trust.

Managing technical complexity

You don't need a team of PhDs to implement XAI. Augmented analytics platforms handle the technical complexity, allowing you to focus on governance and change management instead of getting lost in algorithms. As Dr. Gary Marcus puts it in Can We Tame AI Before It's Too Late?

"Nobody can look you in the eye and say, 'I understand how human intelligence works'. If they say that, they're lying to you. It's still an unexplored domain."

The key is choosing platforms that make model explainability accessible without requiring you or your team to have deep technical expertise.

Where explainable AI delivers results in the real world

Explainable AI is already delivering value across industries. Here are specific examples of how it's being applied to solve real problems you're facing.

1. Financial services and lending

In banking, XAI provides clear, compliant reasons for automated loan application denials, helping satisfy Fair Lending regulations. One European bank reduced customer disputes by 30% by giving loan officers transparent reasons to share with applicants.

2. Healthcare diagnostics

In medical imaging, healthcare analytics models highlight specific areas in X-rays or MRIs that may indicate disease. Explainability in AI allows doctors to see exactly what the AI flagged, letting them validate suggestions with their expertise for faster, more accurate diagnoses.

3. Retail personalization

Instead of just showing customers recommended products, XAI can explain why items were recommended. This transparency builds trust in marketing analytics engines, reduces the "creepy" feeling from AI predictions, and can increase conversion rates.

How to implement explainable AI successfully: 4 best practices

Getting started with XAI is a practical process. Follow these steps to build a strategy that works for you.

1. Audit your current AI systems

Start by listing all the AI systems you currently use and identify which ones make high-stakes decisions. Prioritize based on risk and regulatory exposure.

A simple starting question: "Can you explain this AI's last 10 decisions?" If the answer is no, that system should be your first priority.

2. Choose the right explainability approach

Your use case determines the best method:

• Customer-facing decisions: Need instant, individual explanations (LIME)

• Regulatory reporting: Need global model explanations (SHAP)

• Internal analytics: Need conversational explanations that adapt to follow-up questions

3. Build governance frameworks

Document what you consider a "sufficient" explanation. Establish review processes for high-impact AI decisions and create clear escalation paths when outputs can't be explained adequately.

4. Invest in change management

Successful XAI adoption is as much about people as technology. You need to prepare your teams for this new way of working with data.

This is where embedded AI analytics becomes powerful. With ThoughtSpot Embedded, you can deliver explainable AI insights directly within the applications your teams use every day. Instead of forcing them to learn new platforms, they get transparent, conversational analytics right in their existing workflows. 

The platform's natural language interface means anyone can ask follow-up questions and understand the reasoning behind insights without technical training.

Ready to experience explainable AI? See how transparent, AI-powered analytics works in practice. Start your free trial today.

Put explainable AI to work for you

Explainable AI isn't just a compliance checkbox. It's a strategic advantage that builds trust, accelerates adoption, and improves your decision-making. The XAI market is projected to exceed $21 billion by 2030, and, as the AI trends 2025 report shows, companies that prioritize transparency today will lead tomorrow. 

While legacy BI platforms often keep AI insights locked in black boxes, modern BI platforms make every insight transparent and actionable. 

Start your free trial to experience how explainable AI can change decision-making across your organization.

FAQs about explainable AI

1. How much does implementing explainable AI typically cost?

Costs range from free open-source options to enterprise platforms, but the real investment is often in change management and training, which can match your technology spend.

2. Can explainable AI provide instant explanations for live analytics?

Yes, modern XAI methods like LIME can provide instant explanations for real-time predictions, making them suitable for operational use cases where immediate understanding is needed.

3. What's the difference between AI explainability and interpretability?

Interpretability means you can understand the model's logic directly, while explainability means the AI can describe its decision-making process after the fact. Most AI you'll encounter in a business context focuses on explainability.

4. Which explainable AI method works best for financial services compliance?

Financial services typically use SHAP for regulatory reporting because it provides global model explanations that satisfy audit requirements, though LIME is valuable for individual loan decisions.

5. Will adding explainability slow down my AI models significantly?

XAI adds five to 15% processing time on average, but the gains in trust, adoption, and risk mitigation usually outweigh this minimal performance impact.