Vertice has launched Ana, an AI negotiation agent designed to handle software purchasing and renewal negotiations at enterprise scale. The agent combines procurement benchmarks, vendor-specific tactics and outcome forecasting to negotiate with software providers, either autonomously or alongside human procurement teams.
Enterprise software procurement has a familiar problem: companies may have sophisticated procurement teams for their largest technology contracts, while smaller renewals quietly pass through with little scrutiny.
Vertice is trying to close that gap with Ana, an AI-powered negotiation agent built specifically for software purchases and renewals.
The company says Ana can run large numbers of negotiations simultaneously, using pricing benchmarks, previous vendor interactions and scenario modelling to determine how aggressively to negotiate and which commercial levers to use. Procurement teams can allow the agent to operate autonomously or use it as a copilot, reviewing individual negotiation rounds before anything is sent.
The distinction is important as agentic AI moves into enterprise procurement. Rather than simply summarizing contracts or suggesting an email, procurement agents are beginning to take responsibility for multi-step commercial workflows, including supplier communications, sourcing and negotiations.
Gartner published research in June 2026 specifically examining autonomous sourcing and automated negotiations, reflecting the growing importance of AI-driven negotiation technology for chief procurement officers. Gartner has also identified agentic AI readiness in sourcing and procurement as an emerging priority for organizations looking to improve speed and resilience.
Vertice says Ana is designed to address what it describes as a capacity problem. According to the company’s own data, one in seven software contracts automatically renew without oversight, while three in four contracts in the long-tail segment renew without review. These are company-reported figures, rather than independently verified market statistics.
The agent is built around Vertice’s proprietary procurement dataset. The company says the dataset covers more than 250,000 negotiated contracts, $75 billion in indirect spend, more than two million price points and 32,000 software vendors. Its public product materials currently report more than 4,000 negotiations and $500 million in negotiated spend, with average savings of 18% and renewal cycles shortened by 15 days.
Those figures are also Vertice’s own reported performance metrics, so they should not be interpreted as an independent benchmark for AI procurement agents.
Ana uses that historical information to establish a baseline before a negotiation begins. The system can compare a vendor’s proposed price against available benchmarks, identify potential alternative suppliers and model possible negotiation outcomes. Vertice says it uses Monte Carlo simulations to evaluate thousands of potential scenarios and updates the forecast as the vendor responds.
The practical target is the software “tail spend” that often falls below the threshold for intensive procurement attention.
For a high-value enterprise software contract, a procurement professional may spend considerable time reviewing pricing, terms, usage, renewal provisions and supplier alternatives. Applying the same process to hundreds of smaller SaaS contracts can be difficult to justify. An AI agent changes that equation by making the marginal cost of another negotiation much lower.
McKinsey’s research supports the broader business case. Its 2025 research found that 40% of procurement functions had implemented or piloted generative AI, while its analysis suggests agentic AI could make procurement organizations 25% to 40% more efficient.
Other McKinsey research has found that AI-powered procurement systems are already being used for contract analysis, sourcing and supplier negotiations. In one example involving a telecommunications company, AI-supported negotiations across long-tail software spend generated reported savings of 10% to 15%, while reducing the time procurement teams spent on analysis and emails by as much as 90%.
The emerging competitive landscape includes procurement platforms, enterprise software providers and specialized AI startups. The underlying technology also connects to the broader development of AI agents and autonomous enterprise systems, where LLMs are increasingly being given access to structured company data, business rules and external communication channels.
Ana’s approach combines those capabilities with a relatively narrow objective: negotiating software contracts.
The agent can also incorporate customer-specific preferences. Procurement teams can define priorities such as price, payment terms or contract length, provide historical information about a vendor and specify a preferred communication style. Vertice says Ana learns from previous customer email exchanges so its messages can be drafted in the organization’s voice.
That introduces an important governance question. An AI agent negotiating directly with suppliers has more commercial autonomy than an assistant that merely recommends a counteroffer. Vertice says customers retain control over approvals and signatures, even when Ana conducts negotiations autonomously.
The company also provides a searchable audit trail containing the negotiation strategy, rationale and communications. That record could become particularly important as procurement departments use AI to make decisions involving contract commitments, vendor risk and regulatory requirements.
The technology’s longer-term significance may extend beyond software renewals. Procurement is becoming an increasingly data-driven function, and AI agents can potentially bring continuous benchmarking and negotiation to categories that previously received limited attention.
For enterprises, that means the first generation of AI procurement tools may not replace procurement professionals. Instead, they could change what those professionals spend their time on. Routine negotiations and renewals can be handled by agents, while humans focus on strategic suppliers, complex commercial relationships and decisions that require organizational judgment.
Ana is an early example of that model: an AI procurement agent designed not simply to tell buyers what a contract says, but to participate in the negotiation itself.
Market Landscape
The AI procurement market is moving from analytics and contract summarization toward agentic systems capable of taking action. Gartner is now researching autonomous sourcing and automated negotiations as distinct procurement capabilities, while its 2026 guidance urges organizations to establish readiness for AI agents in sourcing and purchasing.
McKinsey reports that 40% of procurement functions have implemented or piloted generative AI and estimates that agentic AI could make procurement organizations 25% to 40% more efficient.
Vertice is targeting a particularly measurable use case: software procurement and SaaS renewals. Its competitive advantage, according to the company, is the volume of real-world procurement data used to inform negotiations.
The bigger shift is from AI-assisted procurement to AI-executed procurement, with governance, auditability and human approval becoming important controls as agents gain the ability to communicate with suppliers and influence purchasing decisions.
Top Insights
- Vertice’s Ana AI agent automates software negotiations using pricing benchmarks, vendor intelligence, scenario modelling and customer-specific negotiation preferences.
- The agent can operate autonomously or as a procurement copilot, allowing teams to maintain approval and oversight for strategic contracts.
- Vertice says Ana has conducted more than 4,000 negotiations, generating average savings of 18% and shortening purchasing cycles by 15 days.
- McKinsey estimates agentic AI could make procurement organizations 25% to 40% more efficient as repetitive purchasing work becomes automated.
- AI negotiation agents could bring commercial oversight to long-tail SaaS renewals that traditional procurement teams lack capacity to review manually.
Power Tomorrow’s Intelligence — Build It with TechEdgeAI
