6Cs Trend 6: Control and Oversight to Ensure Ethical and Effective AI Deployment

By Kevin Shepherdson, Founder & CEO, Straits Interactive

As AI systems increasingly take on agentic tasks—autonomously executing complex workflows like policy drafting and resource scheduling—ensuring robust control mechanisms is critical for aligning AI outputs with organisational values. "Control," the final trend in the "6Cs" framework for 2025, emphasises the need for oversight in deploying AI technologies. With AI playing a pivotal role in digital transformation, the necessity for AI governance has become paramount. AI governance ensures ethical, transparent, and compliant AI deployment, aligning technological innovation with organisational and regulatory standards.

This article explores trends in AI control, real-world applications, challenges SMEs face, actionable recommendations, and the critical role of AI governance in driving digital transformation responsibly.

What is AI Governance and Why is it Important for SMEs?

AI governance refers to the frameworks, policies, and processes that ensure AI systems operate ethically, transparently, and in alignment with organisational values and regulatory requirements.

For SMEs, AI governance is essential for:

1. Mitigating Risks: Preventing ethical breaches, biases, and non-compliance with regulations like the EU AI Act.

2. Building Trust: Fostering confidence among employees, customers, and stakeholders by ensuring accountability and transparency in AI operations.

3. Driving Digital Transformation: Aligning AI deployment with business goals to enhance efficiency, decision-making, and innovation while maintaining control over outcomes.

Key Developments in 2024

1. EU AI Act

The EU AI Act introduced a regulatory framework mandating transparency and accountability in AI operations. It set a global benchmark for ethical AI deployment by categorising AI systems based on risk levels and requiring organisations to demonstrate compliance.

Example: A logistics SME deploying AI for real-time route optimisation used the Act’s guidelines to ensure their system avoided discriminatory outputs, such as prioritising specific regions over others without justification.

2. FTC Oversight

The Federal Trade Commission (FTC) highlighted governance lapses by requiring companies to delete AI models trained on improperly sourced data, emphasising the importance of ethical data usage.

Example: A retail SME removed an AI tool trained on third-party customer data without consent, averting a potential data privacy violation and rebuilding customer trust.

2025 Trends in AI Control

1. Agentic AI Tasks

AI systems are increasingly handling high-stakes workflows autonomously, such as:

1. Drafting internal policies and compliance documents.

2. Scheduling resources and managing dynamic project timelines.

Scenario: A manufacturing SME uses an AI agent to allocate machinery and workforce resources based on real-time production needs. The system autonomously adjusts schedules to optimise efficiency.

2. Ethical Oversight

Organisations are implementing advanced monitoring systems to ensure AI compliance with ethical guidelines and regulations. These systems identify potential biases, misalignments, or unethical behaviors in real time.

Scenario: An HR software company integrates an ethical oversight tool into its AI-driven recruitment platform, ensuring unbiased hiring recommendations across gender, age, and ethnicity.

3. AI Accountability

Real-time tracking tools are becoming standard to monitor AI actions, providing traceability and enabling quick remediation of errors or malfunctions.

Scenario: A financial SME deploys a real-time tracking system for its AI-powered fraud detection tool, logging every decision to ensure transparency and accountability.

Challenges SMEs Face

1. Limited Resources

Implementing robust oversight mechanisms and complying with regulations like the EU AI Act can be resource-intensive for SMEs.

2. Expertise Gaps

Many SMEs lack in-house expertise to design and manage effective AI governance frameworks.

3. Balancing Innovation and Compliance

Striking a balance between leveraging cutting-edge AI technologies and meeting stringent regulatory requirements is a constant challenge.

4. Transparency Barriers

Ensuring explainability of AI decisions is particularly difficult with complex models, such as deep learning systems.

Actionable Recommendations for SMEs

1. Start with Governance Fundamentals

1. Establish a governance framework for AI deployment, including ethical guidelines, compliance policies, and monitoring protocols.

2. Appoint an AI governance officer or committee to oversee AI operations and ensure alignment with organisational values.

Tip: Use available templates from industry bodies or government initiatives to design cost-effective governance structures.

2. Leverage SaaS Solutions for Oversight

1. Adopt SaaS platforms (such as Capabara) that offer built-in compliance and monitoring tools tailored for SMEs.

2. Use automated systems to flag potential compliance violations or ethical risks in real-time.

Tip: Opt for cloud-based solutions with modular features to scale governance capabilities as your AI adoption grows.

3. Focus on Explainability

1. Choose AI tools with explainable outputs to help employees and stakeholders understand the system’s reasoning.

2. Train employees in AI literacy to interpret and trust AI-generated recommendations.

Tip: Partner with AI vendors offering explainable AI (XAI) solutions to ensure transparency.

4. Collaborate with Legal and Ethical Experts

1. Work with legal advisors to navigate regulatory requirements like the EU AI Act and data privacy laws.

2. Engage with AI ethics consultants to design unbiased and socially responsible AI systems.

Tip: Join industry associations or forums to stay informed about evolving regulations and best practices.

5. Build Employee Trust

1. Involve employees in the AI deployment process to foster trust and address concerns early.

2. Use feedback mechanisms to improve AI systems and demonstrate their value to the workforce.

Tip: Conduct regular training sessions to ensure employees understand how AI systems work and their limitations.

Real-World Example

Streamlining Compliance with AI Accountability

A regional financial advisory firm adopted an AI-powered compliance tool to manage regulatory filings. By integrating a real-time tracking system, the firm ensured all AI-driven actions were logged and auditable. This increased transparency not only reduced the risk of non-compliance but also improved client confidence in their services.

Conclusion: Ensuring Safe and Ethical AI in 2025

For SMEs, the rise of agentic AI tasks presents immense opportunities to improve efficiency and decision-making. However, these advancements require robust control mechanisms to align AI operations with ethical standards, regulatory requirements, and organisational values.

Key Takeaways for SMEs:

1. Prioritise Governance: Start with basic frameworks and scale oversight mechanisms as your AI systems grow.

2. Invest in Transparency: Choose AI tools that provide explainable outputs to build trust among employees and stakeholders.

3. Leverage Partnerships: Collaborate with SaaS providers, legal experts, and industry bodies to navigate AI compliance and ethics.

By adopting these strategies, SMEs can responsibly harness the power of agentic AI systems, ensuring safe and effective deployment while staying competitive in a rapidly evolving AI landscape.

This article is the final instalment of a 6-part series Generative AI in the Workplace: 6 Trends Every Organisation Should Take Note of in 2025.