The urge to grab the latest AI fix for chronic government sluggishness is huge. But AI adoption fails a lot.
The problem is rarely the tech itself; it's the agency's lack of procurement maturity - the basic readiness to actually deploy and sustain Artificial Intelligence once the contract is signed.
Buying complex AI solutions when the state of foundational readiness is weak, is a near-guaranteed way to waste taxpayer money. In the currently tight economy of almost every western nation, the public is becoming increasingly sensitive to public funds wastage.
Project failure stands to severely damage public and stakeholder confidence in an agency, or even the government as a whole, if the application and the issue is big enough.
The Four Pillars of AI Maturity
An agency's AI maturity is defined by its readiness across four key pillars.
It's incumbent upon procurement professionals and other relevant members of an evaluation committee to assess these pillars as non-negotiable operational pre-requisite "readiness factors" for any vendor partnership.
1. Strategic Alignment: Solving the Right Problem
Many organisations fall into the trap of
purchasing AI for AI's sake. But
AI is a tool, not a strategy.
- The Solution Trap
Success begins with defining a clear, measurable public service outcome.
Procurement teams must challenge vendors to define the exact
public benefit that the AI solution will deliver, as well as to demonstrate a thorough risk analysis and mitigation strategy.
Both the vendor and the agency must ensure the projected expenditure targets a systemic problem, not just a superficial upgrade.
- The Operational Mandate
Alignment must combine top-down policy goals with bottom-up operational insights to ensure the solution actually works "on the ground" for the taxpayer.
2. Technical and Human Literacy: Managing the Skills Gap
AI tools don't
replace human experts; they
demand them.
- The Talent Crisis
According to the 2024 Salesforce Public Sector Survey of global government AI professionals, 60 percent report that a lack of relevant skills hampers their ability to apply AI solutions.
Maturity means staff must have the
literacy to operate the AI and, even more importantly, the
critical thinking
capacity to interpret its output and spot flawed decisions.
- The Operational Mandate
Agencies must budget for continuous upskilling.
Investment in AI must be immediately matched with a dedicated and fully funded human capital strategy to train staff on the new tools and the data science principles that underpin them.
3. Data Governance: Beyond the One-Time Clean-Up
The fundamental principle is simple: "Garbage In, Garbage Out" (GIGO).
A 2024 global survey of data professionals by Precisely and Drexel University reported that an estimated 70 percent of organisations struggle to trust the data they rely on.
Maturity means setting up
continuous data governance policies, not just a single, initial cleaning event.
- The Operational Mandate
Agencies must track
data lineage (where data comes from and its integrity) and auditability.
Without clean, reliable data, the AI simply automates GIGO faster, making the agency less efficient and compounding the cost of failure.
4. Legacy Infrastructure: The Integration Tax
Legacy systems are the silent killer of AI projects.
- The Hidden Cost
The U.S. Government Accountability Office (GAO} has repeatedly highlighted in audits of major Federal departments that up to 80 percent of IT budgets go towards maintaining aging legacy systems.
Every new AI tool must connect to this aging core, incurring a high
"integration tax".
- The Operational Mandate
Maturity requires
a rigorous audit of
existing legacy IT systems
to predict the integration cost before contracting.
If the new AI solution adds complexity or creates new data silos, the "efficiency" promised by the vendor will be eaten alive by the integration cost.
A Phased, Operational Approach
For
vendors chasing high-value government contracts, the pitch must move past simple technological capability.
The modern strategic vendor
partners with the agency to diagnose its maturity level before proposing a solution.
Agencies must adopt a
phased, operational approach; initial spending should focus on preparing the foundation (data, skills, systems).
Only when this foundational readiness is confirmed should the full technological solution be deployed.
This maturity assessment must become a mandatory pre-condition in the Artificial Intelligence procurement process, shifting the focus from buying tech to
building sustainable, intelligent operational capacity that truly delivers for the taxpayer.
