The Ultimate Guide to Enterprise AI Procurement
By Zack Huhn, Enterprise Technology Association
Artificial Intelligence is no longer on the horizon, it’s here, embedded in everything from operations to customer experience. But for enterprise leaders, adopting AI isn’t just about saying yes to the future. It’s about making the right decisions now.
I’ve spent the past few years working with tech leaders, startups, and procurement teams across the country. And one thing is clear: AI procurement is broken—or at least dangerously outdated.
Procurement leaders are still using legacy tools and checklists to evaluate tools that think. That’s like using a ruler to measure the internet.
It’s time for a smarter, stronger framework.
At the Enterprise Technology Association, we’ve pulled together the latest insights, industry standards, and real-world lessons from early adopters to build what we believe is the ultimate guide to enterprise AI procurement—one that sets a new standard for how to do this right.
Here’s how to future-proof your AI procurement strategy—step by step.
1. Start with Readiness, Not Just Requirements
Before you launch an RFP, pause and ask: Is your organization even ready for AI?
That doesn’t just mean budget or leadership support. It means:
Do you have clean, structured data?
Do your teams understand how AI works?
Is your infrastructure cloud-native, or still clunky and siloed?
📌 Toolbox:
Deloitte’s AI Maturity Model
ETA’s AI Readiness Survey (launching soon)
2. Define the Right Use Case (And Avoid the Wrong Ones)
Some of the best early AI wins in enterprise came from narrow, high-impact use cases:
Automating document intake
Predictive maintenance in manufacturing
Intelligent ticket routing for support centers
Don’t start with moonshots. Start with real problems. Use design thinking. Map workflows. Talk to your front-line teams.
📌 Pro tip: Use an Impact vs. Feasibility Matrix to prioritize.
3. Build, Buy, or Partner? Choose Strategically
Enterprises often rush to “build” their own models—then get buried under cost, complexity, and compliance.
Here's a simplified decision matrix:
OptionProsConsBuildFull control, custom fitExpensive, slow, high riskBuyFast deployment, provenLess flexible, vendor lock-inPartnerShared innovation, hybrid valueRequires alignment and trust
Make this decision based on your internal talent, time horizon, and tolerance for experimentation.
4. Evaluate AI Vendors with a New Lens
Traditional vendor scorecards don’t cut it. You need a new set of filters:
Transparency: Do they explain how their models work?
Ethics: Do they audit for bias and fairness?
Security: How do they handle YOUR data?
Performance: Can they deliver on real-world use cases?
📌 Checklist:
Request white papers, case studies, and model documentation
Run a 30-60 day PoC in a sandbox environment
Evaluate MLOps and integration capabilities
📌 Resources:
World Economic Forum’s AI Procurement in a Box
ETA's upcoming AI Vendor Vetting Hub
5. Build Guardrails Around Governance and Ethics
AI introduces new risks: algorithmic bias, hallucinations, data leakage. You can’t just trust your vendors—you need to govern them.
Require:
Explainable AI documentation
Bias testing reports
Model update and retraining schedules
Alignment with global standards like OECD’s AI Principles
📌 Governance Tools:
NIST AI RMF
Microsoft’s Responsible AI Standard
Open Ethics AI Cards
6. Prioritize Security and Data Ownership
AI models are only as good as the data they’re fed—and only as secure as the systems that hold them.
Don’t sign anything until you’ve locked down:
Data processing policies (on-prem vs. cloud)
Encryption standards and SOC 2 compliance
Ownership and IP rights of trained models
An exit strategy with data portability
7. Write AI-Aware Contracts and SLAs
This is where most enterprise deals fall apart.
You must include:
AI-specific uptime SLAs
Retraining requirements for model drift
Bias and fairness benchmarks
Liability for AI failures
Compliance with privacy laws (GDPR, CCPA, HIPAA, etc.)
📌 Tool: Use ETA’s AI Contracting Clause Library (coming soon)
8. Plan for Change (People, Not Just Tools)
The real challenge isn’t deploying AI. It’s getting people to trust and use it.
Invest in:
Cross-functional implementation teams
Change management communication plans
AI fluency training for employees
Clear human-in-the-loop policies
9. Measure ROI with More than Just Dollars
Sure, track cost savings and revenue lift—but also measure:
Workflow speed gains
Accuracy improvements
Risk reduction
Employee satisfaction and trust
Scale AI like you’d scale any product: start small, prove value, and build an internal AI playbook as you go.
10. Your AI Procurement Toolkit (Free + Credible Resources)
ResourceDescriptionLinkNIST AI RMFU.S. AI risk frameworknist.gov/aiOECD AI PrinciplesGlobal AI ethics frameworkoecd.aiWorld Economic ForumAI Procurement Toolkitweforum.orgOpen Ethics AI CardsModel transparency templatesopenethics.aiEnterprise Technology AdvisorsAI readiness tools & solution matchmakingjoineta.org
Final Thought: Don't Just Procure AI—Build an AI Advantage
The organizations that win with AI will be the ones that treat procurement as a strategic capability—not just a checklist. That means rethinking your processes, educating your teams, and vetting vendors who align with your values and mission.
The future is moving fast. Let’s help each other get it right.
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Want support vetting AI solutions or building your enterprise AI strategy?
Let’s talk. Visit joineta.org or message me directly. We’ve got a network of advisors, vetted vendors, and the tools to help you move forward with confidence.
