Artificial intelligence is beginning to influence decisions across the global energy industry, from equipment maintenance and production forecasting to supply-chain management. For companies responsible for multibillion-dollar infrastructure projects, the attraction is understandable. Technology promises faster analysis, earlier identification of risks and greater control over increasingly complex operations.

But in procurement and contract management, the most important question is not how much artificial intelligence can automate. It is whether organisations can use these capabilities without weakening the professional judgement on which successful energy projects depend.

Energy procurement has already undergone considerable digital transformation. Electronic tendering, integrated purchasing platforms and automated approval systems have improved the way companies acquire equipment and services. The next phase, however, goes beyond digitising transactions. It involves using data to anticipate requirements, evaluate supplier risks and identify contractual problems before they affect operations.

The financial stakes are substantial. The International Energy Agency’s World Energy Investment 2026 projects global energy investment of approximately $3.3 trillion this year, with clean energy technologies attracting the larger share. Such investment involves extensive networks of manufacturers, engineering contractors, logistics providers and specialist service companies operating across multiple jurisdictions.

Managing these relationships requires more than efficient purchasing systems. It demands timely information, effective risk assessment and commercial decisions that remain sound when operating conditions change.

Consider a deepwater drilling programme involving equipment sourced from several countries. A conventional procurement system may record purchase orders, delivery schedules and contract milestones. More advanced analytical tools can examine historical supplier performance, identify potential delivery risks and flag inconsistencies between contractual commitments and operational requirements.

These capabilities can provide procurement professionals with valuable early warnings. A delayed component might be identified before it affects a drilling schedule, while unusual purchasing patterns could reveal opportunities to consolidate demand or renegotiate supply arrangements.

Artificial intelligence may also assist with reviewing lengthy contracts, identifying differences between contractual provisions and drawing attention to obligations requiring closer examination.

Yet identifying a contractual risk is not the same as understanding its consequences.

An automated system may flag a supplier’s delayed delivery history without recognising that the company possesses specialised technical capabilities unavailable elsewhere. It may identify an apparently unfavourable contract provision without understanding why the allocation of risk was necessary for a particular project.

In complex energy operations, these distinctions matter.

Having worked in procurement and contract management supporting major energy projects in Nigeria and Australia, I have seen how commercial decisions are shaped by circumstances that cannot always be reduced to numerical comparisons. Technical requirements, contractor relationships, regulatory obligations and operational priorities often interact in ways that demand careful professional assessment.

A procurement decision that appears financially attractive may create difficulties if it fails to account for equipment compatibility, maintenance requirements or the practical consequences of changing suppliers during an ongoing operation.

This is why artificial intelligence should be regarded as a decision-support capability rather than an independent commercial authority.

There is also the question of data reliability. Many organisations maintain procurement information across different systems, departments and historical records. Contract documents may contain inconsistent terminology, while supplier-performance information may be incomplete or difficult to compare.

An analytical system trained on unreliable information can produce conclusions that appear convincing but are commercially misleading. Faster analysis does not necessarily mean better analysis.

Before investing heavily in advanced procurement intelligence, energy companies must therefore strengthen the foundations of their information management. Accurate contract records, consistent supplier evaluation standards and clear responsibility for data quality are essential.

The implications are particularly relevant for Nigeria’s energy industry.

Operators must manage international sourcing arrangements while meeting local-content obligations, developing domestic supplier capacity and maintaining demanding operational standards. Artificial intelligence could help identify procurement trends, improve supplier assessments and strengthen oversight of contractual commitments.

However, automated evaluation must not become a barrier to emerging Nigerian suppliers whose capabilities may not yet be fully reflected in historical procurement data. A company with limited previous contract awards may still possess the technical competence and resources required for a new opportunity.

Professional evaluation remains necessary to distinguish genuine capacity limitations from gaps in available information.

The same caution applies to contract negotiations. Digital tools may compare prices, review standard provisions and highlight potential exposure, but they cannot independently determine the commercial priorities of a project or accept responsibility for the consequences of a negotiated agreement.

Those responsibilities remain with the people and institutions entrusted to make such decisions.

As energy companies expand their use of artificial intelligence, procurement professionals will need additional skills. Familiarity with data analysis, digital contract systems and automated risk assessment will become increasingly valuable. Equally important will be the ability to question technological recommendations, recognise their limitations and explain the reasoning behind major commercial decisions.

Organisations must also establish clear governance arrangements for the use of these tools, particularly where commercially sensitive information, confidential agreements and supplier data are involved.

The objective should not be to remove human involvement from procurement, but to direct professional attention towards decisions where experience, negotiation and accountability matter most.

This represents a significant shift in how procurement expertise should be understood. The professional of the future will not merely process transactions or administer contracts. Increasingly, the role will involve interpreting complex information, anticipating commercial exposure and connecting procurement decisions to wider operational objectives.

For Nigeria and other energy-producing economies, the opportunity is to adopt these technologies while strengthening the institutional capabilities required to use them responsibly.

Artificial intelligence will undoubtedly change how energy companies purchase equipment, assess suppliers and manage contracts. But the quality of those decisions will still depend on the knowledge, integrity and judgement of the professionals responsible for them.

In an industry where commercial errors can have lasting operational consequences, the smartest procurement system will not be the one that makes every decision automatically. It will be the one that helps people make better decisions.

Ngozi Ezeocha, a trained lawyer, is an energy infrastructure procurement and contract management professional with over 25 years of experience in the oil and gas industry, including major offshore developments and international energy operations.