Digital technologies are reshaping how governments operate. From automating mundane processes to analysing complex data sets, artificial intelligence (AI) holds great promise for improving efficiency and restoring public trust. Nowhere is this more evident than in procurement and governance. This article explores how AI is automating tender processes, the benefits and risks of deploying AI in public procurement, and the steps governments can take to ensure transparent, accountable and fair use of algorithms across the public sector.

Automating tenders: efficiency, fairness and auditability

Public procurement accounts for a significant portion of government spending. Traditional tender processes rely on manual review of thousands of pages of bids and require evaluators to check compliance with complex legal requirements. This manual approach is slow and vulnerable to errors, subjective judgements and even corruption. AI-based systems can streamline these steps:

Document analysis and compliance checks. Natural language processing and machine-learning algorithms can read and interpret tender documents, automatically flagging missing information, inconsistencies or non-compliance with rules. This reduces the time needed for initial screenings and ensures that all bids are evaluated against the same criteria.

Fair and consistent evaluation. By codifying evaluation rules into algorithms, governments can apply them consistently across all bidders, minimising the influence of personal bias. Automated scoring models can rank proposals based on objective criteria such as price, technical specifications, environmental impact and social value.

Audit trails and transparency. Every decision made by an AI system can be logged with a time stamp and supporting data, creating an immutable record. This audit trail enables oversight bodies and bidders to see why a certain bid was accepted or rejected, reducing disputes and increasing trust.

Classifying spending and benchmarking. Machine learning can automatically classify procurement spending into standard taxonomies (e.g. Common Procurement Vocabulary codes) to analyse trends, compare prices and identify opportunities for savings. This helps procurement teams make more informed decisions and fosters competition.

These capabilities have been successfully deployed in several countries. For example, Ukraine's ProZorro system applies analytics and AI to public procurement data, allowing stakeholders to monitor contracts, identify anomalies and streamline tender processes. Chile's ChileCompra platform centralises procurement data and uses AI to support framework agreements and real-time analytics. Other governments use AI to detect fraud and collusion by analysing patterns in supplier behaviour and financial transactions. Such initiatives have not only reduced costs but also increased competition and public confidence.

However, automating tenders is not without challenges. Models trained on historical procurement data may replicate past biases if certain suppliers were favoured over others. Poor-quality or incomplete data can lead to incorrect recommendations. And if algorithms are proprietary or opaque, bidders may question the fairness of the process. For these reasons, transparency, oversight and human review remain essential.

Streamlining public services with AI

Beyond procurement, AI is transforming other areas of government operations. Robotic process automation (RPA) and AI-driven workflows handle repetitive tasks like data entry, document verification and eligibility checks, freeing up staff to focus on complex cases. Chatbots and virtual assistants answer citizens' questions about benefits, taxes or permit applications 24/7, reducing wait times and improving service quality.

In infrastructure management, predictive analytics monitor data from sensors on roads, bridges and public buildings to predict failures before they occur, allowing timely maintenance. In law enforcement and social services, AI tools analyse large datasets to detect fraud, allocate resources and identify at-risk populations. Meanwhile, natural language generation can summarise lengthy reports and policy documents for decision-makers and the public.

When implemented thoughtfully, these technologies save time and money while improving accuracy and citizen experience. They also create opportunities to redesign services around user needs rather than bureaucratic structures. Crucially, AI should augment public servants, not replace them. Employees must be trained to supervise automated systems, interpret results and override decisions when necessary.

Transparent governance and algorithmic accountability

As governments adopt AI, public scrutiny of algorithmic decision-making is increasing. Citizens want to know how decisions are made, whether they are fair, and what recourse exists if the system makes a mistake. To build trust, governments must embrace transparent governance:

Risk-based classification of AI systems. Not all AI projects pose the same risk. A chatbot answering FAQs is less consequential than an algorithm deciding who receives housing benefits. Frameworks such as Canada's Directive on Automated Decision-Making classify AI systems into tiers based on their impact and prescribe corresponding levels of oversight and human involvement.

Algorithmic impact assessments. Before deployment, agencies should assess potential harms to privacy, fairness and human rights. This involves analysing datasets for bias, ensuring that sensitive attributes are excluded from models, and considering how different groups might be affected.

Transparency and explainability. Public entities should publish information about their algorithms, including purpose, data sources, decision logic and performance metrics. Where possible, governments can make code and datasets open for auditing, while protecting sensitive information. Explainable AI techniques can help officials and citizens understand why a particular decision was made.

Public participation and oversight. Mechanisms such as public consultations, citizen advisory boards and independent oversight bodies allow external stakeholders to provide input on AI systems. These mechanisms also enable grievance procedures if individuals believe they have been unfairly impacted by automated decisions.

Privacy and data protection. Governments must adhere to data-protection laws and ensure that personal data used by AI systems is collected with consent, stored securely and processed lawfully. De-identification, encryption and strict access controls reduce the risk of misuse.

Continuous monitoring and auditing. AI models should be tested regularly for accuracy, fairness and robustness. As the environment changes (e.g. shifts in market prices or demographics), models must be updated. Audit logs and independent evaluations help detect drift, unfair outcomes or security vulnerabilities.

Countries like Singapore have developed model AI governance frameworks that emphasise transparency, explainability and accountability across the public sector. The Open Government Partnership recommends reforms such as making underlying data publicly available, strengthening data-protection frameworks and creating independent oversight agencies. Implementing these principles helps prevent algorithmic discrimination, maintain democratic legitimacy and ensure that AI truly serves the public good.

Recommendations for implementing AI in public procurement and governance

For governments considering AI adoption, the following steps provide a roadmap for responsible deployment:

Start with clear objectives and use cases. Identify specific problems that AI can solve—such as reducing tender processing time or detecting fraudulent applications—and define metrics for success.

Invest in data quality and interoperability. Standardise procurement data, adopt open data formats and maintain accurate, up-to-date information. High-quality data is the foundation of reliable AI systems.

Choose transparent tools and demand explainability from vendors. When procuring AI solutions, prioritise vendors who can demonstrate how their models work, provide access to code or audit documentation, and comply with ethical guidelines.

Establish cross-functional governance. Create AI oversight boards with representatives from legal, ethics, data science, procurement and civil society. These boards should review proposed AI projects, approve risk assessments, and monitor outcomes.

Ensure human involvement and training. Maintain human review for high-impact decisions and train public servants to interpret AI outputs. Upskilling employees builds confidence and prevents over-reliance on automated systems.

Engage with stakeholders and the public. Explain how AI is used, solicit feedback and listen to concerns. Transparency and participation enhance legitimacy.

Update legal and regulatory frameworks. Adapt procurement laws, accountability requirements and data-protection rules to account for AI. Ensure that regulations encourage innovation while safeguarding rights.

By following these recommendations, governments can harness AI to improve efficiency and equity in procurement and governance while maintaining public trust.

Conclusion

AI is transforming the public sector, from automating tedious procurement processes to providing data-driven insights that enhance governance. Successful initiatives like ProZorro and ChileCompra show that AI can reduce costs, increase competition and deliver transparent, auditable outcomes. At the same time, the risks of bias, opacity and misuse require robust governance frameworks. By adopting risk-based oversight, ensuring transparency and explainability, engaging the public and committing to ethical principles, governments can leverage AI as a tool for fairer, more responsive and more transparent public services.