Enterprise AI buyers have effectively stopped evaluating model quality — the battle has shifted entirely to operational reliability and compliance infrastructure, where all three major providers are perceived as equally immature.
⚠ Synthetic pre-research — AI-generated directional signal. Not a substitute for real primary research. Validate findings with real respondents at Gather →
Every buyer interviewed independently converged on the same assessment: model performance is table stakes, and the real evaluation criteria are enterprise compliance readiness, API stability, and operational overhead — areas where OpenAI, Anthropic, and Google all fall short. Four of four respondents cited specific compliance failures (SOC 2 gaps, audit trail deficiencies, data residency ambiguity) as their primary evaluation blocker, not model capability. The unexpected finding: Google's enterprise credibility — their assumed advantage — is actively undermined by product discontinuation anxiety, with the CTO explicitly stating 'their track record of killing products makes me nervous about long-term investment.' For any provider, the highest-leverage move is not model improvement but delivering SOC 2 Type II compliance with transparent data handling out of the box — the CMO stated she would 'pay double' for genuine enterprise-grade compliance. The window is narrow: buyers are consolidating from 3+ providers to 1-2 within the next 12-18 months, and the provider who solves operational overhead first captures the enterprise relationship.
Four interviews provide strong directional signal on operational pain points, with unusual convergence (all four cited 60% progress toward goals, all four prioritized compliance over model performance). However, sample skews toward mid-market tech/fintech; manufacturing and regulated industries represented by only one respondent each. Pricing sensitivity and switching cost data are thin. Would need 8-12 additional interviews across verticals to validate whether compliance-first positioning resonates equally in healthcare, government, and traditional enterprise.
⚠ Only 4 interviews — treat as very early signal only.
Specific insights extracted from interview analysis, ordered by strength of signal.
CTO: 'I need to see real SOC2 reports and understand their data residency story.' CMO: 'SOC 2 reports that are six months old... I'd pay double what we're paying now just to stop having those conversations with the board.' CFO: 'What's my audit trail when regulators come asking? I've got Sarbanes-Oxley compliance to worry about.'
Retire model benchmark messaging in enterprise contexts. Lead with compliance certifications, data residency specifics, and audit trail capabilities. The phrase 'enterprise-grade compliance' resonates — but only when backed by current SOC 2 Type II reports and transparent data handling documentation.
CTO: 'Google feels like the safe enterprise choice on paper, but their track record of killing products makes me nervous about long-term investment.' Senior PM: 'Google's models are solid but feel academic.' CMO: 'Google has the infrastructure but their AI models feel like an afterthought bolted onto GCP.'
Anthropic and OpenAI can directly attack Google's enterprise positioning by emphasizing long-term product commitment and backwards compatibility guarantees. For Google: product continuity messaging must precede any capability claims — the 'Google Graveyard' narrative is actively shaping enterprise AI decisions.
CTO: 'I'm juggling OpenAI for code generation, Anthropic for document analysis, and Google for some legacy stuff... the operational overhead is killing us.' Senior PM: 'We've got different teams using ChatGPT, Claude, some folks spinning up their own Google AI stuff, and it's becoming a compliance nightmare.'
Position as the consolidation play. Messaging should emphasize breadth of use cases covered by a single integration, unified billing, and consistent security posture across all AI workloads. The provider who wins the consolidation decision captures 2-3 competitor displacements simultaneously.
CTO: 'OpenAI's enterprise offering still feels like they're figuring it out as they go — I had to build custom logging just to meet our audit requirements.' CMO: 'OpenAI feels like they're still figuring out enterprise needs as they go - their API terms changed like three times last year.' Senior PM: 'OpenAI has the brand recognition with leadership, but their enterprise controls feel bolted-on.'
For Anthropic/Google: exploit OpenAI's enterprise immaturity perception with specific claims about API stability, terms consistency, and native enterprise controls. For OpenAI: urgently address the 'beta tester' narrative by publishing API stability commitments and terms change notification policies.
CFO: 'I need hard numbers, not Silicon Valley promises about transforming workflows. Show me the before-and-after headcount analysis.' Also: 'Show me pilot results from similar manufacturers where they cut 2-3 FTEs worth of manual work, not just theoretical efficiency gains.'
Develop industry-specific ROI calculators that translate AI capabilities into loaded-cost savings per FTE hour. Sales enablement materials for CFO conversations must lead with payback period math, not capability demonstrations.
The compliance-first positioning gap is immediately exploitable. Four of four enterprise buyers stated compliance infrastructure is their primary evaluation criterion, yet perceive all three major providers as deficient. The CMO explicitly stated willingness to 'pay double' for genuine enterprise-grade compliance with transparent data handling. A provider who launches with current SOC 2 Type II certification, documented data residency options, and native audit logging could capture consolidation decisions currently stalled on compliance concerns — potentially displacing 2-3 incumbent providers per enterprise account.
Enterprise consolidation decisions are happening within the next 12-18 months. Buyers managing 3+ provider relationships describe the situation as 'unsustainable' and 'a compliance nightmare.' The provider who fails to address operational overhead and compliance gaps will be consolidated out — not because of model inferiority, but because enterprises cannot justify the management burden. Second-mover disadvantage is severe: once a buyer consolidates to 1-2 providers, switching costs create 3-5 year lock-in.
CTO prioritizes API stability and backwards compatibility; CFO prioritizes headcount ROI math — same organization may weight criteria differently depending on who owns the decision
Buyers want domain-specific AI that 'understands fintech' or 'understands manufacturing' but also want to consolidate to one provider — tension between specialization and consolidation goals
All respondents want to reduce to 1-2 providers but none expressed confidence that any single provider could handle 90%+ of use cases, suggesting consolidation may stall without capability expansion
Themes that appeared consistently across multiple personas, with supporting evidence.
All four respondents independently identified enterprise compliance (SOC 2, audit trails, data residency) as their primary evaluation criterion and primary disappointment with all three providers.
"If one of them actually delivered on SOC 2 Type II compliance out of the box instead of making me jump through hoops for six months... I'm tired of being someone's learning experience when it comes to compliance."
Multi-provider environments are creating unsustainable management burden. Buyers consistently describe managing 3+ API integrations, authentication schemes, and rate limit policies as a primary pain point.
"Where's the conversation about API versioning hell? I'm dealing with three different authentication schemes, inconsistent rate limiting that changes without notice, and don't get me started on their monitoring dashboards."
Technical benchmarks and model capabilities are no longer differentiating — buyers assume rough parity and evaluate almost exclusively on operational factors.
"The technical benchmarks are table stakes - I want to know about SLAs, enterprise support that actually picks up the phone, and whether they understand what happens when my Series C startup suddenly can't access the models."
Buyers are actively planning to reduce provider count from 3+ to 1-2 within 12-18 months, creating a near-term competitive window for the provider who can demonstrate broadest use case coverage.
"I want to consolidate down to maybe two providers max, with proper enterprise SLAs and consistent pricing models. We're probably 60% there — the technology works, but the operational overhead is killing us."
Ranked criteria that determine how buyers evaluate, choose, and commit.
Current SOC 2 Type II certification, documented data residency options per region, native audit logging that meets Sarbanes-Oxley and GDPR requirements without custom development
All three providers perceived as deficient. SOC 2 reports described as 'six months old,' data residency described as unclear, audit trails require custom logging development.
Published deprecation policies with 12+ month notice, consistent authentication schemes, rate limiting transparency, versioning that doesn't break production integrations
OpenAI API terms changed 3x in one year; rate limiting described as 'changes without notice'; no provider offers meaningful backwards compatibility guarantees.
Single API covering code generation, document analysis, content creation, and customer service — eliminating need for 3+ provider integrations
Buyers currently using different providers for different tasks (OpenAI for code, Anthropic for documents, Google for legacy). No provider perceived as covering 90%+ of use cases.
Industry-specific case studies with FTE-equivalent savings, before-and-after headcount analysis, payback period calculators that translate to CFO budget language
Vendors provide 'fluffy case studies about increased productivity' rather than concrete FTE reduction or cost-per-task breakdowns against loaded labor costs.
Competitors and alternatives mentioned across interviews, and what buyers said about them.
First-mover with strongest brand recognition at executive/board level, but perceived as still maturing enterprise capabilities. 'Figuring it out as they go' is the dominant characterization.
Brand recognition with leadership and board members who read about ChatGPT; widest ecosystem of integrations and third-party tools; most familiar to end users.
API terms instability (changed 3x in one year per CMO), enterprise controls perceived as 'bolted-on,' buyers feel like beta testers for enterprise features.
Strong infrastructure credibility but AI products feel disconnected from enterprise DNA. Product discontinuation history creates significant trust deficit.
Existing GCP relationships and Workspace integration; perceived infrastructure reliability; enterprise sales motion familiarity.
Product discontinuation anxiety ('Google Graveyard' narrative), AI offerings feel 'academic' and 'bolted onto GCP,' lack of cohesive AI product vision.
Safety positioning resonates with compliance-conscious buyers, but perceived as newer/less proven at enterprise scale. Seen as the 'wild card' option.
Perceived technical depth on safety/alignment; differentiated positioning in regulated industries; Claude's reasoning quality for complex documents.
Newer player creates enterprise risk perception, less proven at scale, smaller ecosystem of integrations and third-party tooling.
Copy directions grounded in how respondents actually think and talk about this topic.
Retire model benchmark and 'best-in-class reasoning' headlines in enterprise contexts — buyers assume capability parity and filter out performance claims as noise
Lead with compliance specifics: 'SOC 2 Type II certified as of [date]' and 'Data residency in [specific regions]' — vague 'enterprise-grade security' language triggers skepticism
Replace 'AI transformation' narrative with operational burden reduction: 'One integration instead of three' and 'API stability you can build production systems on'
For CFO-targeted materials: translate all capability claims into loaded-cost-per-hour savings and FTE-equivalent reduction — 'saves 40 analyst hours per month at your $85 loaded cost'
The phrase 'implementation timeline' resonates; 'deployment process' does not — buyers want certainty on time-to-value, not process descriptions
Projected from interview analyses using Bayesian scaling. Treat as directional estimates, not census measurements.
Side-by-side comparison of sentiment, intent, buying stage, and decision role across all personas.
Complete question-by-question responses with per-persona analysis. Click any respondent to expand.
CTO expressing significant frustration with AI provider ecosystem maturity, struggling with operational overhead of managing multiple APIs while seeking enterprise-grade reliability and compliance that current providers haven't delivered.
Tell me what's top of mind for you on this topic right now — what are you wrestling with?
Look, we're knee-deep in evaluating these providers and honestly, I'm getting vendor fatigue from all the sales pitches. Everyone's claiming AGI is around the corner while I'm just trying to figure out which API won't randomly break my production systems at 2 AM. The real wrestling match is between OpenAI's ecosystem maturity versus Anthropic's safety positioning — because I've got a board that's simultaneously excited about AI potential and terrified about compliance risks. Google feels like the safe enterprise choice on paper, but their track record of killing products makes me nervous about long-term investment. What's keeping me up is that we're probably going to need multiple providers anyway, but I can't justify the operational overhead of managing three different API integrations. So I'm trying to pick the one that won't screw us over in 18 months when our usage scales or when they inevitably change their pricing model.
What's the most important thing you need to understand or solve here?
Look, I need to understand which of these providers actually gets enterprise security and compliance, not just talks about it. OpenAI feels like they're still figuring out enterprise needs as they go - their API terms changed like three times last year. Google has the infrastructure chops but their AI products feel disconnected from their enterprise DNA. Anthropic talks a good game on safety but I need to see real SOC2 reports and understand their data residency story. The bigger issue is I'm tired of being someone's beta tester. We got burned on a couple AI vendors already who overpromised on accuracy and left us cleaning up hallucinated outputs in production.
What does 'good' look like to you — and how far are you from that today?
Good looks like having one API that handles 90% of our AI needs without me having to think about it. Right now I'm juggling OpenAI for code generation, Anthropic for document analysis, and Google for some legacy stuff we can't easily migrate. That's three different rate limits, three security reviews, three vendor relationships to manage. I want to consolidate down to maybe two providers max, with proper enterprise SLAs and consistent pricing models. We're probably 60% there — the technology works, but the operational overhead is killing us. Every time we want to try a new use case, it's another vendor evaluation instead of just flipping a switch.
What would change your perspective on this entirely?
If one of them actually delivered on SOC 2 Type II compliance out of the box instead of making me jump through hoops for six months. OpenAI's enterprise offering still feels like they're figuring it out as they go — I had to build custom logging just to meet our audit requirements. Google has the infrastructure but their AI models feel like an afterthought bolted onto GCP. If Anthropic could prove they understand enterprise security from day one, not as a retrofit, that would completely flip my evaluation. I'm tired of being someone's learning experience when it comes to compliance.
What question are you not being asked that you wish someone would ask?
Look, everyone's asking me "which model is better for coding" or "which has the best reasoning" - but nobody's asking me about the operational nightmare these providers are creating. Where's the conversation about API versioning hell? I'm dealing with three different authentication schemes, inconsistent rate limiting that changes without notice, and don't get me started on their monitoring dashboards. What I really want someone to ask is: "How do you sleep at night knowing your core product depends on APIs that could change their terms, pricing, or availability tomorrow?" Because that's the real question keeping me up. We've got customers depending on features built on OpenAI's API, and I have zero visibility into their roadmap or commitment to backwards compatibility. The technical benchmarks are table stakes - I want to know about SLAs, enterprise support that actually picks up the phone, and whether they understand what happens when my Series C startup suddenly can't access the models we've architected our entire platform around.
"How do you sleep at night knowing your core product depends on APIs that could change their terms, pricing, or availability tomorrow? Because that's the real question keeping me up."
CMO expressing significant frustration with AI implementation gap between vendor promises and enterprise reality. Caught between board pressure for ROI and vendors who can't deliver concrete business metrics. Willing to pay premium for compliance solutions that eliminate board friction.
Tell me what's top of mind for you on this topic right now — what are you wrestling with?
The board keeps asking me about our AI strategy and honestly, I'm tired of giving them the same non-answer. We've got pilots running with OpenAI for content generation and some Claude integration for customer service, but nothing that moves the needle on real business metrics yet. What's killing me is that every vendor comes in pitching "transformational AI" but when I ask for concrete ROI data, they give me these fluffy case studies about "increased productivity." I need to know if this thing is going to save me three marketing analysts' worth of work or help us improve NPS by 10 points. The board doesn't care about cool demos — they want to see $2M in cost savings or revenue impact by Q4.
What's the most important thing you need to understand or solve here?
Look, I need to understand which of these AI providers can actually deliver at enterprise scale without making me look like an idiot to the board. We're talking about potentially millions in spend here, and I've got board members asking pointed questions about ROI every quarter. The real issue isn't the technology demos - they all look impressive in a controlled environment. What I need to know is: which one won't crash when we're processing Black Friday traffic, which one has enterprise-grade security that'll pass our compliance audit, and frankly, which one won't suddenly change their pricing model or terms of service six months after we've integrated everything. I've been burned before by "revolutionary" tech that worked great until it didn't. My NPS scores and my job depend on picking the provider that'll still be reliable and reasonably priced two years from now.
What does 'good' look like to you — and how far are you from that today?
Good looks like having one AI platform that my entire marketing team can use without me having to babysit every implementation. Right now I'm juggling three different AI tools — one for content creation, another for customer insights, and a third for campaign optimization — and none of them talk to each other properly. We're probably 60% there. The tech works, but the integration headaches are killing us. I spent two hours last week in a meeting about API rate limits when I should've been reviewing Q4 strategy. Good means I can onboard a new team member and they're productive in a day, not a week of training sessions.
What would change your perspective on this entirely?
If one of these providers actually solved the compliance nightmare. Right now, every AI model feels like a security team's worst nightmare - data residency questions, audit trails that don't exist, SOC 2 reports that are six months old. The board keeps asking about AI governance and I'm stuck explaining why we can't get clear answers about where our data goes. If Anthropic or Google came to me tomorrow with genuine enterprise-grade compliance - not just checkboxes but real transparency into data handling, proper audit logs, and compliance frameworks that our legal team actually recognizes - that would be game-changing. I'd pay double what we're paying now just to stop having those conversations with the board.
What question are you not being asked that you wish someone would ask?
You know what nobody asks? "How are you actually measuring AI ROI for the board?" Everyone wants to talk about features and capabilities, but I'm sitting in quarterly reviews getting grilled about whether our AI spend is driving measurable business outcomes. The board doesn't care that Claude has better reasoning or that GPT-4 is faster - they want to see how it's impacting customer satisfaction scores, conversion rates, or operational efficiency. I need vendors who can help me build that story, not just demo their latest model benchmarks.
"I'd pay double what we're paying now just to stop having those conversations with the board"
A frustrated manufacturing CFO struggling to justify AI investments without concrete ROI data and comparable benchmarks. Despite CEO pressure and departmental requests, he demands hard numbers on headcount reduction rather than vague productivity claims, while also concerned about regulatory compliance risks that vendors inadequately address.
Tell me what's top of mind for you on this topic right now — what are you wrestling with?
Look, I'm getting hit from all sides on this AI stuff. My CEO read some article about ChatGPT boosting productivity by 40% and now wants to know why we're not using it. Meanwhile, I've got department heads asking for budget to experiment with these tools, but nobody can tell me what specific headcount we'll save or what measurable ROI we'll get. The real problem is I can't find decent benchmarks. When we evaluated our ERP system, I had ten comparable manufacturers to reference. With AI, everyone's making claims but the data is all over the map. How do I justify a $100k annual spend on OpenAI Enterprise when I can't point to another Detroit manufacturer saying "this saved us two FTEs in accounts payable"? I need hard numbers, not Silicon Valley promises about "transforming workflows." Show me the before-and-after headcount analysis.
What's the most important thing you need to understand or solve here?
Look, I need to know which of these AI providers can actually move the needle on headcount efficiency. I'm not interested in fancy demos or theoretical capabilities - I need concrete data on how many FTE hours each platform saves per month. OpenAI's been the headline grabber, but headlines don't justify budget allocations. Google's got enterprise credibility but their AI feels scattered across too many products. Anthropic's the wild card - newer player, but if they can deliver measurable productivity gains at a better cost-per-FTE-hour saved, I'm listening. Bottom line: show me the ROI math that makes sense against our current $85K fully-loaded cost per analyst.
What does 'good' look like to you — and how far are you from that today?
Good means I can justify every dollar spent to the board with hard ROI numbers. Right now we're piloting some AI tools but I'm still waiting to see real headcount impact - not just "productivity gains" but actual FTE reduction or delay in hiring. We're maybe 60% there because the tools work but the vendors keep talking about innovation and efficiency instead of giving me the cost-per-task breakdowns I need. I want to see: this replaces X hours of analyst time at $75/hour loaded cost, here's your payback period. Until I can walk into a board meeting with that math, we're not where we need to be.
What would change your perspective on this entirely?
If any of these providers could show me a clear path to reducing our accounting close cycle from 12 days to 8 days with measurable accuracy improvements, that would be game-changing. Right now everyone's talking about "AI transformation" but I need concrete use cases — like automating journal entry reviews or flagging anomalies that currently take my team 40+ hours each month. Show me pilot results from similar manufacturers where they cut 2-3 FTEs worth of manual work, not just theoretical efficiency gains. The ROI math has to be bulletproof because my board will grill me on every dollar spent on "experimental" tech.
What question are you not being asked that you wish someone would ask?
Nobody asks me about implementation risk and what happens when these AI models screw up. Everyone's pitching me on the upside — "Look how fast Claude can analyze contracts!" or "See how GPT can automate your reporting!" But what's my liability exposure when the AI hallucinates numbers in a board deck? What's my audit trail when regulators come asking? I've got Sarbanes-Oxley compliance to worry about. If I'm using AI to help prepare financial statements and it makes an error, that's on me personally. The vendors all punt on this — "Oh, you should always have human review." Great, so now I need the AI AND the human headcount. Where's my ROI? The real question is: which of these providers has actually thought through enterprise risk management, not just the fancy demo?
"Great, so now I need the AI AND the human headcount. Where's my ROI? The real question is: which of these providers has actually thought through enterprise risk management, not just the fancy demo?"
Senior PM at fintech company struggling with fragmented AI tool landscape across teams, seeking enterprise consolidation while navigating compliance concerns. Frustrated by vendor focus on benchmarks over operational reliability, wants domain-specific fintech understanding built-in rather than custom prompt engineering. Main barrier is inability to properly evaluate vendors without committing to full sales cycles.
Tell me what's top of mind for you on this topic right now — what are you wrestling with?
We're hitting this wall where our current AI tooling is just... scattered. We've got different teams using ChatGPT, Claude, some folks spinning up their own Google AI stuff, and it's becoming a compliance nightmare. Legal is freaking out about data governance, and I'm stuck trying to figure out which provider actually makes sense for enterprise use. The thing that's really bugging me is that everyone talks about model performance, but nobody's addressing the operational headaches. Like, Claude might be great at reasoning, but can I actually audit what my team is feeding it? OpenAI has the brand recognition with leadership, but their enterprise controls feel bolted-on. I need to consolidate this mess before we scale further, but every vendor demo feels like they're solving different problems than the ones keeping me up at night.
What's the most important thing you need to understand or solve here?
Look, we're about to make some serious AI infrastructure decisions that could lock us in for years, and I honestly don't know if we're optimizing for the right things. Everyone's fixated on benchmarks and model performance, but what I really need to understand is operational reality — which provider actually delivers consistent uptime when we're processing thousands of transactions? Which one won't surprise us with API changes that break our integrations? We're building financial products here, so "fast and smart" means nothing if it's not also "boringly reliable." I need to cut through the marketing noise and understand how these models actually behave in production environments similar to ours.
What does 'good' look like to you — and how far are you from that today?
Good means I can ship features 30% faster without sacrificing quality, and our engineering team stops spending weekends debugging production issues that better tooling could have caught. Right now we're maybe 60% there — we've got decent CI/CD and our user research process is solid, but our AI tooling is still pretty fragmented. I want one provider that can handle code review, documentation generation, and user interview analysis without me having to manage three different integrations. The reality is I'm juggling OpenAI for some tasks, Claude for others, and still manually doing way too much synthesis work that should be automated by now.
What would change your perspective on this entirely?
If one of these providers actually built something that understood our domain context without me having to become a prompt engineer. Right now I'm spending hours crafting the perfect prompts to get decent code reviews or user story analysis. The winner will be whoever figures out how to make their model actually *know* fintech - understanding regulatory constraints, payment flows, fraud patterns. OpenAI keeps pushing ChatGPT Enterprise but it's still generic. Google's models are solid but feel academic. Anthropic at least admits they're focused on safety, which matters when you're dealing with financial data. But none of them get that I need something that works day one, not a science project I have to train.
What question are you not being asked that you wish someone would ask?
The real question nobody asks is "How do you actually test these models before committing?" Everyone's pitching demos with cherry-picked examples, but I need to know: can I run my actual use cases through a sandbox for two weeks without signing a contract? We're not buying productivity software here — we're potentially restructuring how our entire customer support and compliance teams operate. I want to see how GPT-4 handles our specific regulatory language versus Claude's approach to financial document analysis versus Bard's integration with our existing Google Workspace setup. But good luck getting that level of access without already being knee-deep in a sales cycle.
"We're not buying productivity software here — we're potentially restructuring how our entire customer support and compliance teams operate. I want to see how GPT-4 handles our specific regulatory language versus Claude's approach to financial document analysis versus Bard's integration with our existing Google Workspace setup. But good luck getting that level of access without already being knee-deep in a sales cycle."
Specific hypotheses this synthetic pre-research surfaced that should be tested with real respondents before acting on.
Does the compliance-first evaluation pattern hold in highly regulated industries (healthcare, government, financial services) or is it specific to mid-market tech?
If compliance is universally the top criterion, it validates major repositioning investment. If it's segment-specific, messaging should be tailored by vertical.
What is the actual switching cost and timeline for enterprises consolidating from 3+ AI providers to 1-2?
Understanding consolidation friction determines how aggressive displacement messaging can be and whether 'rip and replace' or 'land and expand' is the right motion.
Which specific compliance certifications and data handling documentation would unlock budget approval from enterprise legal/security teams?
Buyers say 'compliance' but the specific requirements vary by industry and company size. Precise requirements enable product prioritization.
Ready to validate these with real respondents?
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Synthetic pre-research uses AI personas grounded in real buyer archetypes and (where available) Gather's interview corpus. It produces directional signal — hypotheses worth testing — not statistically valid measurements.
Quantitative figures are projected from interview analyses using Bayesian scaling with a conservative ±49% margin of error. Treat as estimates, not census data.
Reflect internal response consistency, not statistical power. A 90% confidence score means high AI coherence across interviews — not that 90% of real buyers would agree.
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"OpenAI vs. Anthropic vs. Google: how do enterprise AI buyers actually perceive the model providers?"