AI Spend
Standalone AI investment modeling across hardware, software, and services
What is it
AI Spend is a standalone model (independent from IT Spend) that estimates company investment in artificial intelligence across three pillars:
Coverage: 500K+ enterprise companies. Generative AI is tracked as a cross-cutting overlay across all three pillars.
Pricing: 1.5 credits per enterprise AI Spend record, 0.5 credits per SMB.
What problem it solves
AI is the #1 technology priority for most enterprises, but identifying which companies are actually INVESTING (not just talking) is hard. AI Spend distinguishes between companies experimenting with ChatGPT and companies deploying GPU clusters. It answers: "Who has real AI budget, and where are they spending it?"
What you can do with it
- AI-ready targeting: Find companies with significant AI infrastructure investment
- Hardware vs. software positioning: Know if they're building or buying AI
- GPU/chip vendor analysis: Track NVIDIA, AMD, Intel AI hardware adoption
- AI services targeting: Identify companies using AI consulting/services
- GenAI opportunity sizing: Understand generative AI investment levels
Real-world example
AI Infrastructure Sales
An AI infrastructure vendor wants to find companies ready to scale ML operations. Using AI Spend, they identify 200 companies with >$5M in AI Hardware spend and growing AI Software investment. These companies have proven they're past the experimentation phase. Outreach references their AI commitment and offers infrastructure optimization. Result: 4x pipeline vs. generic AI messaging.
What the data looks like
Enterprise AI Spend Profile
Key fields you get
- Total AI spend (absolute dollars + % of revenue)
- Category breakdowns: AI Hardware, AI Software, AI Services (with subcategories)
- Generative AI spend overlay (subset of total)
- Detected AI tools/platforms: Which products are driving the spend
- Relative benchmarks: vs. industry average, vs. revenue peers
- Build vs. buy indicators: Hardware+software = building; services = buying
- AI maturity signal: Breadth of investment across categories
“Acme Financial is spending $24.5M/year on AI (above industry average), split across infrastructure ($8.4M), platforms ($10.3M), and services ($5.8M). The balanced investment across all three pillars indicates a mature AI organization — they're building (hardware), operating (software), and augmenting (services). 25% of their AI spend goes to GenAI. If you sell MLOps, AI governance, or model monitoring tools, they're ready.”
Business impact
Increases: Higher engagement when AI vendors target AI spend leaders
Decreases: Less time wasted on “AI curious” vs. “AI committed” accounts
Decreases: Shorter sales cycles with companies that have proven AI budget
Increases: Higher conversion on campaigns aimed at AI spenders
Directional outcomes HG customers commonly report. Actual results vary by program and data application.
Under the hood
How it's unique to the market
AI Spend is a DEDICATED model — not just a filter on IT Spend. It specifically tracks AI-category investments with granular breakdown (hardware vs. software vs. services). Combined with technographic detection of AI tools, HG provides both the "what they're using" and "how much they're spending" view that no one else offers.
How it's derived
AI Spend models use technographic signals (AI tool detections), firmographic inputs (industry, size), and disclosed AI investments where available. The model separates hardware, software, and services spend based on detected product categories and industry benchmarks. GenAI is tagged as an overlay based on specific generative AI tool detections.