Transparency enables faster adoption of AI tools

In a McKinsey survey of the state of AI in 2024, 40 percent of the respondents identified explainability as a risk in adopting generative AI. Such doubt is understandable as AI tools are powerful but often operates as a “black box”.  

AI often supports or replaces an existing human process, which fosters even more awareness of the AI solution offering transparency on how it works and how it compares in performance to the current situation it is replacing or improving.  

– While transparency is essential to building trust in AI systems, it’s equally important to recognise the tangible value AI can deliver to businesses. When implemented responsibly and transparently, AI can streamline operations, reduce manual workloads, and uncover insights that drive better decision-making, says Gökhan Kolcak, Global Technology Director, Data & AI, at Twoday. 

Building trust to AI tools 

One of the cornerstones of responsible AI is transparency. It’s important that AI systems are understandable for people so that we can understand how decisions are made and make sure it’s done in a fair, unbiased and ethical way.  

– When people understand how and why an AI system makes its decisions, they’re more likely to trust its outcomes and feel confident that it operates without hidden biases, making transparency not just a feature, but a responsibility, says Kolcak. 

Improving internal understanding makes it more likely that your internal resources will use the AI solution and trusts the outcomes.  

Transparency also allows experts to audit and improve systems, reducing the risk of harm and unintended consequences. Ultimately, transparent AI supports responsible innovation and safeguards democratic values in a rapidly evolving digital world. 

In short, transparency demystifies AI. It turns a “black box” into a tool people can engage with, question, and improve. 

Explainable AI  

Transparency into how the models work and how the models' outputs are composed is the field of Explainable AI. It refers to a set of techniques and methods that make the predictions or behaviours of AI systems understandable to humans. Instead of simply delivering an output, explainable models provide insight into the why: Which features were most important, how confident the model was, and what patterns it used to reach its conclusion. 

For example, in a customer churn prediction model, explainable AI might highlight that recent drop-offs in engagement and changes in purchasing behaviour were the top contributing factors - offering both transparency and actionable insight. 

This is a needed capability in some sectors like finance and the public sector and provides a clearer picture of any biases that can arise, often due to the training data sampled for the project. 

Use Case: Transparency with AI in a Customer Service Centre 

twoday implemented an AI-driven solution for a large customer service centre that handles over two million calls and 500,000+ chat messages annually. In such a high-volume, high-stakes environment, ensuring the AI system operates ethically, reliably, and transparently was crucial. 

The project followed the EU’s seven principles for Trustworthy AI, putting Responsible AI into action at every stage:

1. Human Agency and Oversight

AI was designed as a support tool, not a replacement. Customer service professionals were trained to supervise, correct, and guide AI outputs, ensuring humans retained control. This human-in-the-loop approach helped avoid errors from unchecked AI suggestions. 

2. Technical Robustness and Safety 

The AI system was continuously evaluated using performance metrics. By collecting data from real interactions, the team retrained models and improved response quality. This feedback loop enabled early error detection and reinforced technical safety. 

3. Privacy and Data Governance 

Sensitive data from customer emails was handled in line with GDPR. Personal information was carefully protected during model training, and AI usage was made transparent to internal users, enhancing both data security and trust. 

4. Transparency and Explainability 

AI decisions were made understandable. Staff could see why the system flagged certain messages as urgent or important, based on vocabulary cues or customer history. This helped avoid “black box” behaviour and promoted user confidence in AI output. 

5. Diversity, Non-Discrimination, and Fairness 

The AI was continuously evaluated to ensure it responded fairly to all customer segments. It was retrained to avoid bias in how it prioritized or handled inquiries, reinforcing inclusive and equal service for all users. 

6. Environmental and Societal Well-Being 

Though not a direct focus in this case, the responsible design inherently contributed to societal well-being by increasing access to accurate information and reducing wasted human effort—factors that align with sustainable AI practices. 

7.  Accountability 

The team embedded clear governance throughout the AI lifecycle—from data usage policies to testing protocols. Staff were involved early in the project, giving them full visibility into the AI’s role, limitations, and behaviour, which enabled true accountability in deployment. 

Outcome 

The AI system enhanced efficiency without compromising on ethics, safety, or transparency. By embedding responsible AI principles, the solution became trusted, controllable, and aligned with EU ethical guidelines—a blueprint for scalable, ethical AI in any customer-facing environment. 

Summary 

– Transparency isn’t just about showing how AI works. It’s about proving that it works responsibly. When ethical principles are embedded from the start, AI becomes not only more efficient, but also more trustworthy, scalable, and aligned with the values that matter most to our customers, says Kolcak. 

This article explores why transparency is a critical element in the successful adoption of AI tools. It highlights how explainable AI builds trust, supports ethical decision-making, and ensures responsible use – especially in high-stakes environments like customer service. A real-world example shows how AI transparency can boost both user confidence and operational efficiency, making it a key driver for responsible innovation in business.