Artificial Intelligence:
Risks and Key Considerations
in Procurement 2026
Between January 2025 and March 2026, UK public bodies alone published over 470 AI-related procurement notices, representing billions in potential and awarded spend. Yet, as implementation shifts from theoretical potential to systemic reality, modern buyers are realising that traditional market engagement approaches and software contracts are completely unequipped to handle the qualitative risks of Artificial Intelligence.
"Models can be world-class, but if the data is messy, the workflows unclear, the accountability ambiguous, or the people behind the system poorly equipped, the results will disappoint. Nowhere does this matter more than in public services, where the test of any AI system is not whether it makes a service cheaper or faster, but whether it produces better outcomes for the citizens who rely on those services."
— Antonio Weiss, Author of AI Demystified
Most technology procurement processes treat governance as a one-time, pre-award checklist. But AI models evolve, drift, and degrade. When an algorithm produces a biased result, a flawed prediction, or a legal complication, who takes responsibility? Without a proactive, multidisciplinary framework, public sector organisations are inadvertently exposing themselves to hidden financial traps, regulatory backlash, and severe and unknown liabilities. To navigate these complex risk areas successfully, organisations must transition from passive consumers to informed buyers.
Download the Insight Paper
Discover how to transition from reactive procurement risk management to future-ready AI procurement governance.
Download the full insights paper to uncover the 28 critical risks mapped across the comprehensive STEEPLED framework (Social, Technological, Environmental, Ethical, Political, Legal, Economic, and Demographic):
- Value Gap: Why the hidden operational costs of managing AI errors often dwarf the promised efficiency savings.
- Trap of Supplier Lock-In: How restrictive licencing, opaque usage tiers, and high exit barriers are quietly draining lifecycle budgets.
- Data Paradox: Why relying on fragmented external datasets is scaling historical bias and undermining system safety.
- Offshore Environmental Blind Spots: Navigating the obscured carbon footprints and heavy local resource demands of dominant hyperscalers.
- Liability Gap: How to protect your organisation when the legal boundaries between human override and algorithmic recommendation blur.
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Don't let your procurement phase become an algorithmic blind spot. Gain immediate access to international case evidence, emerging contract standards, and concrete response pathways designed to safeguard public trust and organisational equity, whilst also realising the benefits of AI.
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