OPINION: Opportunities, risks, and legal considerations when integrating AI in business

Artificial Intelligence (AI) has quickly moved from being a futuristic concept to an everyday business tool.

Across sectors either finance, healthcare, hospitality, retail, logistics, and even legal services, organisations are increasingly using AI to automate tasks, analyse data, enhance customer service, and improve decision-making.

But while AI offers undeniable value, it also introduces a new layer of legal, ethical, and operational questions that every business leader must confront. Integrating AI responsibly is no longer optional; it’s a strategic necessity.

This article explores how businesses are adopting AI, the key legal implications, and the safeguards organisation’s should put in place to use AI safely and effectively.

Why AI Has Become a Business Essential

Businesses are embracing AI because it significantly improves efficiency, speed, and competitiveness. Common applications include:

Customer Service: Chatbots, automated service lines, and predictive customer behaviour tools.

Human Resources: AI-powered recruitment tools, performance analytics, and employee monitoring systems.

Finance: Fraud detection, automated credit scoring, and risk assessment models.

Operations: Inventory forecasting, supply-chain optimisation, and workflow automation.

Marketing: Targeted advertising, personalised recommendations, and sentiment analysis.

AI enables organisation’s to work smarter, reduce costs, and access insights that were previously unreachable. However, for all these benefits, AI is not without risks.

Legal and Regulatory Implications of AI in Business

As AI becomes integrated into core business functions, it interacts with personal data, influences decision-making, and affects how organisations manage risk. This brings several legal obligations that businesses must understand.

a. Data Protection and Privacy Compliance

AI systems often rely on large amounts of personal data. In Kenya, the Data Protection Act, 2019, and its accompanying regulations provide strict rules on how organisations can collect, use, store, and share data.

Businesses must ensure that:

Personal data is processed lawfully, transparently, and for a legitimate purpose, Individuals are informed when AI tools are collecting or analysing their data and  that there Adequate safeguards which exist to prevent data breaches or misuse.

Data shared with third-party AI vendors is protected through proper contracts.

Failure to comply exposes organisations to regulatory sanctions and civil claims.

b. Bias, Fairness, and Non-Discrimination

AI models can unintentionally reproduce or amplify biases present in their training data. This can lead to unfair or discriminatory outcomes, especially in:Recruitment , Credit scoring , Insurance assessments, Lending and financial services anb Customer profiling

In such cases, businesses may face liability under anti-discrimination laws and employment regulations. Regular audits and human oversight are essential to ensure AI systems remain fair, objective, and transparent.

c. Intellectual Property (IP) and Ownership Challenges

AI raises new IP questions that traditional law was not designed to answer, such as:

Who owns content created by AI systems? Can AI-generated outputs be copyrighted? Are businesses allowed to use AI models trained on copyrighted material?

Organizations must clarify ownership through contracts and understand the licensing terms of AI tools they adopt. They should also protect any proprietary data or models they develop.

d. Liability and Accountability Issues

When an AI system makes a decision that causes harm, such as wrongful denial of a loan, a discriminatory hiring outcome, or financial loss, determining liability can be complex.

Key questions  that arise are :

Is the business responsible because it deployed the AI?Is the developer or vendor liable for the AI’s design flaws? Does the responsibility lie with the data provider?

Clear agreements and well-defined internal policies help allocate responsibility and limit exposure to legal disputes.

e. Transparency, Explainability, and Ethical Use

Regulators and customers increasingly expect transparency in automated decisions. Businesses must ensure their AI systems can be explained  especially in sectors like healthcare, HR, and finance, where decisions significantly impact individuals.A lack of transparency reduces trust and can lead to regulatory non-compliance.

Best Practices for Responsible AI Adoption

Businesses can integrate AI safely by implementing strong governance and compliance structures. Key steps include:

1. Conducting a Data Protection Impact Assessment (DPIA)

This helps identify risks related to data use and ensures compliance with data protection laws.

2. Establishing Clear AI Governance Policies

These guide procurement, deployment, monitoring, and review of AI systems.

3. Training Staff on Ethical and Legal Use of AI

Employees should understand how AI works and the legal obligations that come with it.

4. Strengthening Vendor and Third-Party Contracts

Contracts should cover data protection, liability, IP rights, and audit rights.

5. Regular Auditing and Monitoring of AI Tools

This ensures systems remain fair, effective, and legally compliant.

6. Maintaining Human Oversight

Human decision-makers must remain accountable, especially in sensitive or high-risk areas.

Innovation With Responsibility

AI offers businesses incredible opportunities through the  increased efficiency, improved customer experiences, and powerful data-driven insights. But AI also comes with responsibilities. Organisations must adopt it thoughtfully, ensuring compliance with data protection laws, addressing ethical risks, and maintaining strong internal governance.

The businesses that thrive in this new digital era will be those that embrace innovation while upholding transparency, fairness, and accountability.

The writer, Susan Mute, is an Advocate of the High court of Kenya