How AI Search Is Recommending ERP Software: Monthly Trends

Executive Summary

NetSuite remains the category leader in October 2026 at 62.4% valid recommendation coverage, up 17.4 points from 45.0% in July 2026 and up 20.9 points from 41.5% in September. The lead over second place widened to 12.2 points as Acumatica rose to 50.2%, with Infor (50.1%) and Epicor (47.1%) close behind. Five brands moved outside normal month-to-month variation against the July baseline this period, all in the same direction: Acumatica, Epicor, Infor, NetSuite, and Oracle ERP Cloud each posted significant coverage gains. No tracked brand posted a significant decline.

NetSuite recorded the largest single-month movement in the category, up 20.9 points from September to October, followed by Infor (up 19.2 points), Acumatica (up 17.5 points), and Epicor (up 17.0 points). The pattern is a broad upward shift across the discovery and consideration tier rather than a single challenger breaking away, and it reverses the compression observed in September.

Oracle ERP Cloud was the fifth significant riser, up 11.2 points from July to October and up 13.2 points from September. SAP Ariba's coverage fell for a third consecutive month, down 1.8 points from July to October but still within normal variation; the benchmark flags it as a watch item rather than a significant decliner.

Each monthly run begins with 800 prompt-surface observations across the benchmark's defined AI/search surface universe. July carried 490 unique questions, August 546, September 499, and October 507. In each month, all 800 observations mentioned a tracked brand or competitor. Relevance held near-constant across the series: 758 relevant and 42 irrelevant in July, 743 and 57 in August, 760 and 40 in September, and 763 and 37 in October. The public metrics use the 596 qualified observations in July, 615 in August, 651 in September, and 643 in October that survive both qualification stages.

Key Findings

AI Response Inconsistency Alerts

One high-severity factual inconsistency was detected this period, spanning two AI platforms.

Acumatica

High severity, pricing model (per-user vs consumption-based). When asked "How much does ERP cost per month?", Google AI Overviews stated that Acumatica does not charge per user, describing a flat monthly fee based on computing power and data storage used. Gemini, answering the same question, listed Acumatica among systems averaging $50 to $150 per user per month. These two claims cannot both be accurate: a system cannot simultaneously avoid per-user pricing and be priced at a per-user monthly rate. Both responses drew on third-party cost and pricing pages rather than Acumatica's own published pricing, and the conflicting source pages carry the same divergence as the answers themselves: one referenced source states explicitly that "Acumatica does not charge per user at all, instead using a resource-based consumption model," while another lists "Software subscription $50–$200 per user/month." The conflict surfaced across Google AI Overviews and Gemini.

Benchmark Context

Questions This Section Answers

• What is the difference between the raw collection set and the qualified benchmark observations?
• How much did recommendation-shaped answer share and shortlist share rise between July and October 2026?

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

The qualified observation count is the denominator for every benchmark-level and brand-level metric that follows.

Benchmark-Level Metrics

Between July and October, the share of qualified observations carrying a recommendation or shortlist rose sharply, from 46.5% to 67.7%, and recommendation-shaped answers rose from 25.5% to 42.3%. August and September sat between the two endpoints at 48.6% and 44.7% shortlist share respectively, so October marks a step change from a relatively flat mid-series plateau rather than a continuation of an existing gradient.

AI Recommendation Trend

Questions This Section Answers

• Who leads ERP recommendation coverage in October 2026, and by how wide a margin?
• Which brands posted coverage gains that cleared the significance threshold against the July baseline?

A Broad-Based Coverage Expansion Lifts Five Brands Above Normal Variation

NetSuite extended its hold on first place in October 2026, with its 62.4% coverage sitting 12.2 points clear of second-place Acumatica, a wider margin than in any prior month of the series. Five brands — Acumatica, Epicor, Infor, NetSuite, and Oracle ERP Cloud — exceeded normal month-to-month variation against the July baseline, and every one of those movements was upward; the category-level gain reflects five separately significant increases layered on top of near-flat movement among the lower-coverage brands.

What Changed This Month

Questions This Section Answers

• How did NetSuite's lead widen without expanding its raw mention presence?
• What separated NetSuite's placement gains from the coverage gains of Acumatica, Infor, and Epicor?
• Why is SAP Ariba flagged as a watch item rather than a significant decliner?

NetSuite: The Lead Widens on Stronger Top-Placement

NetSuite's valid recommendation coverage rose to 62.4% in October, up 17.4 points from 45.0% in July and up 20.9 points from 41.5% in September. Both movements clear the significance threshold, and the September-to-October jump is the largest single-month movement by any brand in the series.

The brand's top-three rate rose 10.3 points, from 27.7% in July to 38.0% in October, a significant gain, with top-three count rising from 165 to 244. Rank-one placements rose from 83 to 108, and the October top-three and rank-one gains came alongside a slight easing in raw mention presence, from 95.6% in July to 95.3% in October.

The distinction: NetSuite is not being surfaced in materially more conversations than it was in July, yet it is being converted into top-three and rank-one placements at a meaningfully higher rate. The coverage gain came from conversion quality rather than from expanded presence.

Highest-priority diagnostic: Which prompt categories and surfaces account for the conversion improvement, and is the gain concentrated in recommendation-shaped answers?

The Three-Brand Cluster: Acumatica, Infor, and Epicor

Acumatica, Infor, and Epicor each posted significant coverage gains across the full series and now occupy three consecutive positions. Infor's single-month move from September to October, up 19.2 points, was the largest of the three, followed by Acumatica at 17.5 points and Epicor at 17.0 points.

Acumatica rose to 50.2% coverage, up 14.8 points from July and up 17.5 points from September, with top-three rate up 4.6 points to 14.0% and rank-one placements easing slightly from 19 to 16. Infor rose to 50.1%, up 18.9 points from July and up 19.2 points from September, with raw mention presence up 12.0 points to 77.6% and top-three count rising from 41 to 55. Epicor rose to 47.1%, up 14.9 points from July and up 17.0 points from September, with top-three rate up 1.5 points to 14.6% and rank-one placements up from 14 to 21.

The distinction the reader should notice: all three brands gained coverage against baseline, but only NetSuite's top-three gain was significant. Infor's presence gain was the largest of the three, yet its top-three rate of 8.6% remains the lowest in the cluster, so the coverage gain did not arrive with a matching placement gain.

Highest-priority diagnostic: Which prompts and surfaces carried the coverage gains for these three brands, and are those gains concentrated in valid recommendation shortlists rather than top-three placements?

Oracle ERP Cloud: The Fifth Significant Riser

Oracle ERP Cloud's coverage rose to 30.3% in October, up 11.2 points from 19.1% in July and up 13.2 points from 17.1% in September. This is the largest series gain by any brand outside the top four, and it moved the brand clear of the lower-coverage group.

The brand's top-three rate rose 11.0 points, from 8.9% to 19.9%, a significant gain, with top-three count rising from 53 to 128. Rank-one placements rose from 7 to 23, a significant gain of 2.4 points. Raw mention presence rose 3.7 points to 50.5%, within normal variation for the brand.

The distinction: Oracle ERP Cloud's coverage gain was driven by top-three placement, not by expanded presence. The brand appears in roughly the same share of conversations as in July, but it is being placed higher within them.

Highest-priority diagnostic: Which surfaces and prompts account for the top-three placement gain, and did it come at the expense of a specific competitor?

SAP Ariba: A Third Consecutive Monthly Decline

SAP Ariba's coverage fell to 1.9% in October, down 1.8 points from 3.7% in July and down 0.7 points from 2.6% in September, the third consecutive monthly decline. The cumulative movement remains within normal variation for the brand, so the benchmark flags this as a watch item rather than a significant decliner.

Raw mention presence fell 6.1 points, from 9.2% in July to 3.1% in October, a significant decline, with present count dropping from 55 to 20. Valid recommendation count fell from 22 to 12. Top-three placements eased from 15 to 10, and rank-one placements fell from 10 to 6.

The distinction: SAP Ariba's presence decline is significant even though its coverage decline is not, meaning the brand is being surfaced in fewer ERP conversations and converting those appearances into recommendations at a modestly lower rate. The small counts of 12 valid recommendations and 20 present observations in October mean each placement carries more variance.

Highest-priority diagnostic: Which surfaces and prompt categories account for the presence decline, and which brand is appearing where SAP Ariba previously surfaced?

Notable Divergence: The Infor-to-Lower-Tier Gap

The gap between Infor and three lower-coverage brands widened across the series. The Infor-to-SYSPRO gap widened by 20.8 points from July to October, the Infor-to-SAP Ariba gap by 20.7 points, and the Infor-to-Sage Construction Management gap by 19.3 points, none of these widening in every month.

This is a category observation about repositioning rather than a single brand's problem. The widening reflects the combination of Infor's significant coverage gain and smaller net declines or modest moves among SYSPRO, SAP Ariba, and Sage Construction Management over the same period.

Highest-priority diagnostic: Which prompts migrated from the lower-coverage brands into the middle tier, and which surfaces carry that migration?

Buyer-Intent Interpretation

Questions This Section Answers

• Which buyer-intent clusters did the qualified observations cover in October 2026?
• What can the benchmark not yet answer about pricing and head-to-head comparisons?

In October 2026, all 643 qualified observations fell into the Brand Recommendation cluster, meaning every qualified prompt in the benchmark sought a recommendation or surfaced brands in a recommendation context. No qualified observations fell into the Pricing & Value or Multi-Brand Comparison clusters.

The public benchmark can speak directly to which brands AI systems recommend for ERP software, but it cannot yet answer questions about how AI systems handle pricing discussions or head-to-head comparisons between named brands. The single pricing-related inconsistency surfaced this period, in which Google AI Overviews and Gemini gave conflicting accounts of Acumatica's pricing model, illustrates the commercial relevance of that gap even though those observations fall outside the qualified recommendation set.

Brand Opportunity Summary

Questions This Section Answers

• Which brands are significant risers in October 2026 and what diagnostic question does each one raise?
• Which low-coverage brands carry more variance per placement because of small recommendation counts?

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations (query, surface, recommendation outcome, rank, sentiment, and citations where exposed). Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of CiteWorks Studio's AI Visibility Market Discovery research program.

• AI Visibility Industry Market Methodology
• AI Visibility Industry Market Metrics
• AI Visibility Industry Market Standards

Report-Specific Interpretation Notes

• Small-count movement: brands with fewer than 50 valid recommendations in a month, such as SAP Ariba (12), Microsoft SharePoint (1), Sage Construction Management (4), and SYSPRO (34), carry more variance per placement; their percentage movements should be read with that context.
• Qualified denominator vs raw collection: percentages are calculated against the 643 qualified observations in October, not the 800 raw prompts collected; the 157 prompts that did not qualify are excluded from brand-level rates.
• Directional analysis: month-over-month movement identifies changes worth investigating; it does not by itself establish the cause of those changes.
• Multi-month series: the July-to-October comparison captures the full period, while the September-to-October movement identifies the most recent shift; movement that clears the significance threshold against baseline should be distinguished from single-month variation.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

The aggregate percentages hide the questions that matter for commercial action: which high-intent prompts are won and lost, which competitor takes the recommendation when a brand loses, what attributes AI systems associate with each ERP option, and which external sources shape those answers. For NetSuite, the benchmark shows a leader whose conversion quality improved without expanded presence. For Infor, the benchmark shows a large presence gain that has not translated into top-three placement. For SAP Ariba, the benchmark shows three consecutive monthly declines but not which prompts or competitors are behind them.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. This moves from knowing that coverage changed to understanding the mechanics behind the change, and from there to actions that improve recommendation placement where it matters most.

Request an AI visibility audit