AI adoption among enterprises has reached an all-time high. According to Deloitte’s 2026 AI report, worker access to AI increased by 50% in 2025.
Even heavily regulated industries like finance are actively adopting AI to process claims, detect fraud, and automate compliance reporting.
However, as the famous Spiderman dialogue goes, "With great power comes great responsibility."
As more enterprises use agentic AI to automate workflows and make autonomous decisions, the focus on governance has also increased. For example, since AI agents explicitly handle data, financial companies are expected to comply strictly with regulations such as the General Data Protection Regulation (GDPR), the Sarbanes-Oxley Act (SOX), the EU AI Act, and others.
However, only one in five enterprises has a mature governance model for AI agents.
There is an urgent need to establish safe and ethical use of AI.
According to an ADP survey, 8 out of 10 Canadian business leaders emphasized keeping humans in the loop for compliance when using AI.
That’s why many enterprises have started turning to Global Capability Centers (GCCs) to comply with regulations.
GCCs were typically cost centers that supported parent enterprises through backend processes. However, with the advent of AI, parent enterprises are now expecting GCCs to take more strategic roles, such as establishing AI governance and compliance.
Let’s find out how GCCs can help parent enterprises improve compliance in an AI-driven world.
How Regulated Enterprises Are Improving Compliance In An AI-Driven World?
While AI can automate processes and transform businesses, there is always a risk of non-compliance and bias. For example, some large language models (LLMs) may encounter model drift, in which an AI model’s performance, accuracy, and predictability change over time.
To avoid such situations, enterprises must urgently prioritize AI governance. Without governance, they could face reputational damage and penalties due to bias, discrimination, and data security risks.
In recent years, AI-driven, regulated enterprises have started focusing on AI traceability, auditability, and training on model behavior.
Let’s learn more about it.
- AI traceability
Enterprises use AI traceability to track and document how an AI model was trained, how it processes information, and how it makes decisions. The need for AI traceability stems from the fact that sometimes AI hallucinates and generates responses that even experts cannot fathom. They need a clear explanation of how the AI generates output to gauge its authenticity and accuracy. By tracking how the model is trained and how it processes information, enterprises can ensure regulatory compliance, identify bias, and build user trust. They can scrutinize every step of an AI operation and improve the AI system’s accountability.
- AI auditability
Like AI traceability, AI auditability also involves tracking and analyzing how AI systems operate.
To audit AI systems, enterprises need to maintain logs, documentation, and audit trails.
This is crucial because it helps enterprises manage system failures, detect unfavorable outcomes, and comply with regulations, such as the EU AI Act.
Typically, enterprises audit three aspects to determine the AI model’s accuracy and effectiveness:
- Data auditing: Enterprises audit the data fed into the AI models to ensure it is accurate and reliable.
- Algorithm auditing: The algorithm is audited to ensure it functions as intended.
- Outcome auditing: Enterprises audit the outcome generated by AI systems and compare them with baseline AI results. This helps them identify AI results with deviations and anomalies and eliminate bias and errors.
A regular AI audit helps enterprises reduce risks such as privacy breaches and biases, maintain trust among stakeholders, regulators, and customers through responsible AI use, and improve the AI’s performance through a continuous feedback loop.
- AI model behavior
Enterprises train AI models with labelled data sets to help them make decisions autonomously. As more users increase, the AI interacts with new datasets and continues to make predictions based on the training data. While this might work well initially, there is a chance of model bias and degradation over time.
To avoid such a situation, enterprises must monitor the AI model’s behavior. They must identify issues such as data drift, negative feedback loops, and model inaccuracies and correct them proactively.
Timely intervention will help enterprises prevent revenue loss, control regulatory risks, and maintain the model’s reliability.
- Data handling
Data handling is the process of collecting, cleaning, organizing, storing, and processing data needed to train and run machine learning models.
The process involves applying strict policies, controls, and technologies to manage throughout the AI lifecycle. As AI systems become more complex and autonomous, enterprises will need to automate end-to-end processes to maintain model accuracy and security.
Using automated workflows, integrating data privacy directly into AI pipelines, implementing continuous monitoring, and including humans in the loop to validate AI outputs are a few ways enterprises can manage data handling.
How GCCs Can Improve Compliance For Regulated Enterprises?
Managing AI traceability, auditability, or continuously monitoring AI model behavior can be time-consuming and resource-intensive. Take AI auditability, for instance. Enterprises need AI specialists to manage complex AI systems and audit all aspects, including algorithms, data inputs, and generated outputs.
However, human-in-the-loop is also essential, as failure in AI governance could cost enterprises billions in penalties, in addition to reputational damage, lost sales, and erosion of customer trust.
To address these challenges, regulated enterprises are turning to GCCs for help.
GCCs help parent enterprises with:
- Embedding guardrails directly into the AI systems to avoid compliance issues at a later stage
- Auditing AI models for bias and ensuring they meet the fairness standards
- Monitoring the AI models continuously to identify model or data drifts and address them before they escalate
- Maintaining detailed records of data lineage and training processes for future audits
- Designing cross-border governance frameworks that apply to different regulations
- Helping enterprises adopt regulations such as the EU AI Act by operationalizing governance
- Reducing AI governance failure cost, helping enterprises proactively manage risk rather than reactively respond to violations
By helping enterprises with governance, GCCs are quickly moving from cost centers to strategic and compliance partners that build scalable, operational compliance frameworks.
At Wissen, we help companies kickstart their GCC journey. From setting up an AI and engineering team from scratch to onboarding teams in weeks, designing workflows, and upskilling teams, we help companies become AI pros that help regulated parent enterprises improve compliance.
Don’t miss the golden opportunity of playing a significant role in the AI-driven world.
For more details on setting up GCCs, contact us.
FAQs
Q. Why are regulated enterprises increasing their focus on AI governance?
As AI systems become more autonomous, regulated industries must take precautions to comply with regulations such as GDPR, SOX, and the EU AI Act to avoid regulatory risks, penalties, and reputational damage. This has led regulated enterprises to prioritize AI governance.
Q. What role does continuous monitoring play in managing AI model behavior?
AI models run the risk of degrading over time due to data drift and generating results that could impact enterprises. With continuous AI model monitoring, enterprises can detect bias, vulnerabilities, or performance deviations early and take proactive measures to minimize their impact on the business.
Q. How are Global Capability Centers (GCCs) evolving in an AI-driven compliance landscape?
GCCs have moved from just supporting backend operations to becoming strategic compliance partners. They help parent enterprises embed compliance guardrails into AI systems, monitor AI models for deviation and vulnerabilities, and operationalize regulatory frameworks across different countries.
