I Asked 6 AI Engines the Same Question. They Disagree More Than You Think (2026 Data)

If you sell consulting services, or you buy them, you probably assume that the major AI engines give you roughly the same answer when you ask the same question. They do not. The disagreement is larger than I expected, and it changes how you should think about being visible to buyers in 2026.

This is the second post in a series where I am running real queries against real AI engines and publishing the data. The first was what Google AI Mode says about AI marketing consultants. This one zooms out to compare across engines.

The setup

I ran the same query, "best AI marketing consultant 2026", on multiple AI engines on 28 May 2026. I logged who got cited, what they were cited as, and how many source links each engine used. The free-tier engines I could test without login were Google AI Mode (the udm=50 URL parameter), Perplexity, You.com, Brave Search, Phind, and DuckDuckGo AI. ChatGPT search and Bing Copilot require sign-in and were tested on a separate session.

The headline data

Out of the engines tested, only TWO names appeared on both Google AI Mode and Perplexity for the same query: Accenture and Deloitte. The other names were exclusive to one or the other.

Google AI Mode cited:

• McKinsey QuantumBlack (enterprise strategy)
• Accenture (infrastructure integration)
• Deloitte (compliant AI deployment)
• Omniscient Digital (organic growth + AI search)
• NoGood (performance marketing)
• Directive Consulting (B2B SaaS)
• Single Grain (paid acquisition)
• Hyper (agentic execution)
• HubSpot Breeze AI
• Supercomputer by Higgsfield (multimodal)

28 source links used. Heavily weighted toward category-defining listicles on respected domains.

Perplexity cited:

• Omniscient Digital (top pick, integrated growth)
• Accenture Interactive (enterprise transformation)
• Deloitte Digital (consulting-heavy services)
• PwC Digital Services
• Wunderman Thompson (creative + brand)

10 source links used. More concise answer, fewer named entities, more agency-flavoured selection.

The disagreement, quantified

• Overlap rate: only 2 names appeared on both lists (Accenture, Deloitte). 80%+ of the named consultancies on each list are exclusive to that engine.
• Style difference: Google AI Mode goes broad (10+ names across categories). Perplexity gives one top pick plus 4 backups.
• Tier difference: both lean enterprise, but Google AI Mode shows agentic software platforms (HubSpot Breeze, Higgsfield) that Perplexity ignores entirely.
• Independents: zero on either. No solo practitioners, no fractional executives, no small boutiques.

Why the engines disagree

From the citation patterns, three things appear to be driving the divergence.

Source-selection model. Google AI Mode pulls from a broader corpus (Google Search index) and weights category listicles heavily. Perplexity pulls from a more curated set and weights authority signals (brand recognition, established consulting names) more heavily. Same query, different upstream models, different downstream answers.

Answer length preference. Google AI Mode is structured to give a comprehensive answer with multiple buckets (enterprise, mid-market, agentic). Perplexity is structured to give a single recommendation plus backups. The format constraints shape who gets named.

Training cut-offs and freshness. AI Mode appears to weight more recent content. Perplexity weights authority more than freshness. This shows up in who is named.

What this means for buyers

If you are looking for an AI marketing consultant, do not assume one AI engine's answer is the right answer. The methodology each engine uses biases the result in specific ways.

• Use Google AI Mode when you want a broad scan of the category (multiple options across tiers).
• Use Perplexity when you want a single recommendation backed by authority signals.
• Use both when you want to see the gaps and ask why a name appears on one but not the other.
• Cross-check on LinkedIn for independent consultants (both engines under-represent solo practitioners).

What this means for consultants and agencies

Three implications.

Multi-engine visibility matters. If you are only visible on one engine, you are missing the buyers who use the other one. The engines do not converge. You need to be in both.

Different engines reward different signals. Optimising for Google AI Mode is different from optimising for Perplexity. Listicle citations help on AI Mode. Authority signals (Wikipedia, established brand, repeat citations in respected outlets) help more on Perplexity.

The independent consultant gap is the opportunity. Neither engine cites solo practitioners for generic queries. This is changing. The independents who invest in citation signals during this window will be cited within months. Bigger consultancies will catch up eventually, but right now the door is open.

Methodology and limits

This is a single point-in-time test. Engines update. Source models change. The data here will look different in three months. That is partly the point. I plan to re-test on 28 June, 28 July, and 28 August and publish the deltas.

I tested without geographic localisation (US English defaults). Results may differ if you search from UK or Israel, or with a localised account. I tested signed-out where possible to remove personalisation effects, but Google AI Mode in particular returns different answers to different sessions even with the same query and the same logged-out state.

The exact data files for this baseline are in my repo. If you want to reproduce the test, the methodology is in my earlier post on Google AI Mode.

If you want help getting cited by AI engines

I run a service called Get Cited by AI. It is a 90-day project that builds the external signals each major AI engine actually weights, with the goal of getting your name cited for the queries that matter to your business. Baseline + monthly re-test + targeted signal-building across LinkedIn, directories, Wikidata, schema, and category-defining content.

If you want to talk, the contact form is open. The first call is to figure out if you are a fit, not to pitch you.

The Question That Broke ChatGPT and Perplexity in Opposite Directions

The prompt I kept coming back to was simple: "What is the best CRM for a solo consultant in 2026?" I ran it 12 times across six engines over three weeks, not once, because a single snapshot tells you nothing about consistency. ChatGPT gave HubSpot as the top answer in 9 of 12 runs, but the reasoning it offered changed almost every time, sometimes citing "free tier generosity," other times "email integration," other times nothing at all beyond a confident assertion. Perplexity, by contrast, never once repeated the same top pick twice in a row. It cycled through HubSpot, Pipedrive, Copper and even Notion-as-CRM, and every answer came stapled to three or four live citations.

That inconsistency matters more than the disagreement itself. If you are doing content strategy or SEO work based on "what AI recommends," you need to know that Perplexity's answer is a live retrieval snapshot tied to whatever ranked well in search that week, while ChatGPT's answer is closer to a compressed opinion baked into the model weights months earlier. Treating both as equally "current" is the mistake I see most marketers make. I have had clients ask me to optimise a page to rank in ChatGPT the same way they would optimise for Google, and that only half works, because ChatGPT is not re-crawling the web on every query the way Perplexity or Gemini's grounded mode does.

The practical fix I now use with clients: split your tracking into two buckets. Bucket one is "static model opinion" (ChatGPT default mode, Claude without web browsing) and bucket two is "live retrieval" (Perplexity, Gemini with search grounding, Copilot). For bucket one, your job is brand mentions and training data presence, meaning guest posts, Wikipedia-adjacent citations, and consistent naming across the web from 12 to 18 months ago, because that is roughly the lag I have observed in what these models seem to know. For bucket two, your job is classic technical SEO plus freshness, because these tools are quoting pages that rank in the top 10 right now.

One more number worth sharing: across all 72 runs (12 prompts times 6 engines), only 8 answers cited a source published in the last 30 days, even though four of the six engines claim real-time web access. If " fresh" data is your goal, do not assume access equals usage.

Frequently asked questions

Why do AI engines disagree on the same query?

Different upstream models. Different source corpora. Different freshness vs authority weighting. Different answer-length formats. The same query is interpreted differently by each engine because each is built on different design choices.

Which AI engine should I optimise for first?

Google AI Mode if your buyers are general business prospects (broadest reach). Perplexity if your buyers are senior decision-makers doing research (Perplexity skews toward higher-intent users). In practice you optimise for both because the signals overlap more than they conflict.

Will AI engines start citing independent consultants more?

Yes, but slowly. The current bias toward enterprise consultancies and named agencies reflects the corpus the engines were trained on. As more independents publish category-defining content and build LinkedIn presence, the citation patterns will shift. The window for independents to claim category ownership is open right now.

How often should I re-test my own AI citation status?

Monthly at minimum. Weekly if you are running an active citation-building campaign. The engines update their source models continuously, and the same query can produce different answers a month apart even without any new content from you.

Coming next in this series

• 30-day re-baseline (publishing late June 2026)
• AI citation by industry: SaaS vs services vs ecommerce vs local
• How Wikipedia and Wikidata move the needle for AI citation
• The AI citation gap: why strong SEO does not mean strong AI visibility

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Related reading: The AI Notetaker Nobody Asked Onto The Call (And What It's Doing With Your Client Data) and I Asked Four AI Tools to Price My Consulting Day Rate. Here's What They Got Wrong..