The State of AI in B2B Software Evaluation: A Mid-2025 Perspective

Eighteen months ago, the idea that B2B buyers would use AI models to evaluate software was still largely theoretical. By mid-2025, it's become operational reality — not everywhere, not for every purchase, but in enough places to reshape competitive dynamics across multiple software categories.

The question is no longer will AI change how software is bought. It's how far it's come, where the gaps remain, and what comes next.

What's Changed: AI in the Research Phase

The most significant shift is in the research and shortlisting phase — the earliest stage of software evaluation, where buyers define their requirements and identify potential vendors.

Traditionally, this phase involved analyst reports (Gartner, Forrester), review sites (G2, Capterra), peer recommendations, and Google searches. Increasingly, buyers are adding — or substituting — AI queries: "What are the best project management tools for remote teams?" or "Compare Salesforce vs. HubSpot for mid-market companies."

This isn't replacing the other channels entirely. But it's becoming the first step for a growing share of buyers, which means it disproportionately influences which vendors make the initial consideration set.

Who's Using AI Most?

AI adoption in software evaluation isn't uniform. The buyer segments driving the most change share specific characteristics:

• Tech-forward mid-market companies (200–2,000 employees) — large enough to have real software needs, small enough to lack dedicated analyst subscriptions. These buyers were already using multiple online sources for research, and AI is a natural addition to their workflow.
• Individual contributors and team leads — people evaluating tools for their own teams, without formal procurement processes. They ask AI the same way they'd ask a knowledgeable colleague.
• Startup and scale-up buyers — fast-moving organizations that prioritize speed over process. AI-generated shortlists let them skip weeks of traditional research.
• Technical buyers (developers, IT, data teams) — already embedded in AI-native workflows, using tools like GitHub Copilot and ChatGPT daily. Asking AI for software recommendations is a natural extension.

Who's Not Using AI (Yet)?

Several buyer segments remain largely unaffected by AI-assisted evaluation:

• Large enterprise procurement teams — formal RFP processes, established vendor relationships, and compliance requirements mean AI plays a minimal role. The buying cycle is too structured and relationship-driven for AI shortlists to influence outcomes significantly.
• Regulated industries (healthcare, financial services, government) — compliance requirements, security reviews, and vendor qualification processes add layers that AI can't navigate. Buyers in these sectors may use AI for initial awareness, but trust in procurement decisions relies on established frameworks.
• Traditional industries with low AI familiarity — manufacturing, construction, and other sectors where AI adoption across all functions is still nascent. These buyers continue to rely on industry-specific channels, trade shows, and peer networks.

Categories Where AI Influence Is Highest

AI's influence on software evaluation isn't distributed equally across categories. The characteristics that make a category susceptible to AI-driven evaluation include well-defined feature sets, broad vendor landscapes, and abundant online content for AI to train on.

High AI Influence

• CRM and sales tools — well-documented category with clear feature comparisons. AI models have extensive training data from reviews, comparisons, and thought leadership content.
• Project management and collaboration — highly competitive with many viable options. Buyers frequently ask AI to help differentiate between similar solutions.
• Expense management — a category with clear functional requirements where AI recommendations carry significant weight because the evaluation criteria are relatively objective.
• Cybersecurity tools — technical buyers already using AI extensively, evaluating categories where feature comparisons are complex and AI can synthesize vendor capabilities effectively.

Lower AI Influence (for Now)

• ERP systems — implementation complexity, customization requirements, and multi-year commitments make AI shortlists less relevant. The evaluation is about fit, not features.
• Vertical-specific software — niche industries with specialized requirements that AI models lack deep knowledge about. Training data is thinner, and recommendations tend to default to better-known (often less appropriate) solutions.
• Platform and infrastructure — decisions driven by existing technology stacks, team expertise, and long-term architecture considerations that require context AI doesn't have.

AI is most influential where the buying decision is complex enough to need help, but modular enough that AI can meaningfully evaluate options. The sweet spot is categories with 10–30 viable vendors and evaluable feature sets.

The Gap Between AI Awareness and AI Reliance

One of the most important distinctions in mid-2025 is the gap between using AI in the evaluation process and relying on it. Most buyers who consult AI treat it as one input among many — a useful starting point rather than a definitive answer.

This matters for vendors because it means AI influence is primarily about consideration set formation. AI determines who gets investigated, not who gets purchased. The downstream evaluation — demos, trials, reference calls, pricing negotiations — still happens through traditional channels.

But the consideration set is enormously powerful. Behavioral research consistently shows that buyers' final choices are disproportionately drawn from their initial shortlists. Vendors excluded from the consideration set rarely overcome that disadvantage, regardless of their actual capabilities.

This creates a two-stage competitive dynamic: AI discoverability determines who competes, and traditional sales determines who wins. Both stages matter, but the first stage is a prerequisite for the second.

What Hasn't Changed

Despite the shifts described above, several fundamentals of B2B software buying remain unchanged in mid-2025:

• Complex deals still require human relationships. Enterprise software purchases involving six- and seven-figure contracts, multi-department stakeholders, and custom implementations aren't being disrupted by AI. The stakes and complexity exceed what AI-generated recommendations can address.
• Proof-of-concept and trials remain decisive. For most meaningful software purchases, hands-on experience outweighs any recommendation — whether from an analyst, a peer, or an AI model.
• Procurement processes add friction by design. Security questionnaires, legal reviews, compliance checks, and budget approvals create a gauntlet that AI doesn't accelerate. These steps exist precisely because the decision is consequential.
• Reference checks and peer validation persist. Buyers still trust people who've used the software more than they trust any external source. AI hasn't changed the fundamental role of social proof in high-stakes decisions.

Where This Is Heading: 2026 and Beyond

Several trends suggest how AI's role in software evaluation will evolve over the next 12–18 months:

Agentic AI in Procurement

The transition from AI as a research assistant to AI as a procurement agent is underway. Early experiments involve AI not just recommending vendors but automating parts of the evaluation process — scheduling demos, analyzing pricing structures, even drafting RFP responses. This shifts AI from influencing the consideration set to actively managing parts of the evaluation pipeline.

AI-Native Review Platforms

Review platforms are beginning to integrate AI-generated analysis alongside human reviews. This creates a feedback loop: AI trains on reviews, generates recommendations, and those recommendations influence which vendors attract more reviews. Vendors with strong AI presence benefit from a compounding advantage.

Real-Time Competitive Intelligence

As AI models update more frequently and gain access to real-time information, the lag between a vendor's market activity and its AI representation will shrink. This means that competitive positioning in AI becomes more dynamic — and more responsive to marketing, product releases, and market events.

Category-Specific AI Advisors

General-purpose AI models will be supplemented by category-specific AI advisors that have deeper knowledge of particular software markets. These specialized tools will produce more nuanced recommendations, further increasing AI's influence in categories where they're available.

What This Means for Vendors

For software vendors, the mid-2025 landscape presents a clear strategic imperative: understand how AI currently represents your brand, and take action where it doesn't.

This starts with basic discovery — assessing your AI discoverability across multiple models — and extends to content strategy, technical SEO, and the emerging discipline of Generative Engine Optimization. The vendors who invest in AI discoverability now will compound their advantage as AI's role in software buying continues to expand.

The state of AI in B2B software evaluation in mid-2025 is transitional. The direction is clear, even if the pace is uneven. Vendors who wait for AI-driven evaluation to become universal before responding will find that the competitive positions they need are already occupied by brands that moved earlier.