Written by: Karol Sobieraj, Founder & CEO, Digital Colliers
The curiosity budget was never infinite. It just felt that way in 2024. Everyone had a few tens of thousands to spend on pilots. The question was which model, which vendor, which use case. Nobody asked whether the pilot would survive a budget review six months later.
That window closed. Companies abandoning most of their AI initiatives jumped from 17% to 42% in a single year. The average organisation scrapped 46% of proofs-of-concept before production. This is not disillusionment with the technology. This is pilots meeting procurement.
The experiment window closed
In 2024 you could spin up an AI pilot with a credit card and a vendor trial. The conversation was about potential. In 2025 the conversation is about line items. Your CFO wants to know what the pilot costs to run at scale, who owns the liability, and whether it has a deadline or just keeps consuming budget.
Most pilots were never designed to answer those questions. They were built to test a capability. Testing is useful. But when 88% of AI proof-of-concepts never reach widescale deployment and only four out of every 33 POCs graduate to production, the gap between testing and shipping is not a capability problem. It is a planning problem.
What procurement actually wants
Procurement does not care about your prompt engineering workflow. Procurement cares about three things. First, does this project have a end date or does it drift. Second, can you put a number on the outcome. Third, who gets fired if it breaks something.
The reason 95% of enterprise GenAI pilots deliver zero measurable P&L impact is that most of them were never scoped to create P&L impact. They were scoped to explore. Exploration is legitimate. But exploration does not survive a budget cut. Production workloads with hard commitments do.
Projects that survive have hard edges
The pattern you see in teams that ship AI into production is sharp project boundaries. Not open-ended research. Not continuous improvement loops. Not innovation theatre. Hard deadlines, fixed scope, measurable outcomes.
If your project is "improve customer support with AI" it will not survive procurement. If your project is "reduce average handle time from 8 minutes to 6 minutes by September using an AI triage layer" it has a chance. The difference is not the technology. The difference is that one has a shape and the other does not.
The projects that make it through budget review are the ones where you can write down:
- What gets delivered by what date
- What the baseline metric is today
- What the target metric will be after deployment
- Who owns the system in production
- What the monthly run cost is
The new project shape
Operators shipping AI into production in 2026 are working backwards from the deadline. Not forwards from the capability. They pick a business process that already has a manual workaround, a clear success metric, and a forcing function. Regulatory filing deadline. Contract renewal cycle. Quarterly close process.
Then they scope the AI piece small enough to ship in 60 to 90 days. Not 12 months. Not 18 months. Two to three months from kickoff to production. That timeline forces you to cut scope. It forces you to pick one metric. It forces you to avoid custom model training and stick to APIs you can call today.
The forcing function is what protects the project. If the AI component misses the filing deadline, someone manually completes the filing. The system has a fallback. That fallback is what makes procurement comfortable. The project is not a bet on AI working perfectly. It is a bet on AI reducing manual work, with a human escape hatch if it does not.
This is not the future of AI deployment. This is the present. The teams treating AI pilots like R&D projects are the ones abandoning 46% of their proofs-of-concept. The teams treating AI pilots like production systems with deadlines are the ones shipping.