How Buyers Evaluate AI Software

We’ve entered the era of “AI vaporware”. Big claims, fragile tech, and minimal insight into the data that powers it. If you're a B2B buyer, read this 👇 before you invest $50,000/yr on fancy new AI tech: We all know how quickly the tech landscape can shift. Just a few weeks ago, Xandr (a $1B DSP used by some martech platforms) suddenly shut down. Not because it wasn’t working. Microsoft simply sunset it to focus on its own advertising ecosystem and first-party data strategy. Now we’re seeing a new wave of risk: this time, dressed up as AI innovation. Fast launches. Flashy claims. Shaky foundations. But with AI, it's 10x faster. "AI-powered!" everyone screams. Sure. But powered by what? Trained on what? Is it built to last, or built to raise a Series F? If you're evaluating new AI vendors, here are the questions I'd ask before signing on the dotted line (shout out to Chad Holdorf): 1. Model & Intelligence - Can I trace how the model makes decisions? - What training data was used? Is it proprietary or public? - How is model performance tracked and improved? - Can models be tuned or retrained for our use cases? 2. Infrastructure & Ownership - Who owns the infrastructure and hosting? - What happens if the provider changes cloud vendors or LLMs? - Is it multi-cloud or locked to one ecosystem? 3. Security & Compliance - How is data handled? Is it encrypted at rest and in transit? - Does it meet our compliance standards (SOC 2, GDPR, etc)? - Can I audit or delete my data? 4. Integration & Extensibility - Can it connect to my tools (CRM, MAP, CDP)? - Does it expose APIs for other systems to use? - Is there a roadmap for more ecosystem support? 5. UX & Governance - How do users interact with it—chat, UI, workflow? - Are there guardrails for bad outputs or hallucinations? - Who controls permissions, access, and audit trails? 6. Business Impact - What metrics or outcomes has it improved for others? - Can it reduce cost, increase speed, or drive revenue? - Does it scale across teams or stay in a silo? Remember... “AI-first” without infrastructure is just AI branding. If the tech is built on weak systems, the smartest model in the world can’t save it.

Thinking about buying an AI product for your sales team?  Taking a vendor demo is not the first step! You have to set yourself up for success in your evaluation. Here are some of the top areas to consider and ~25 questions to ask yourself before you say yes to that first demo.  Know what you are trying to solve: -Do you know how your team is spending their time today? -Do you know where your metrics are above average vs below average against industry standards? For example, if your phone answer rate is already at 10%, there might not be much room for further improvement. -Do you know where you want to see more efficiency gains or higher results? -Do you have the same pain across all teams, or only certain ones?  Know your scorecard/requirements: 🛠️ Workflow and Tech Stack: -Are you okay with needing a separate UI for this vendor or should it be in a platform your team already uses?  -What main vendors does it need to integrate with?  -What data does it need to read off of? How messy is that data and can the vendor handle how you currently write it? Do you have any common issues in your salesforce such as duplicate accounts? -Do you want to define your ICP for the vendor, or do you want the vendor to help you define it? How clear is your criteria?  -Do you want to replicate or enable the seller? If you want to enable them, does it train the team to be better or just do the task for them? -Does this vendor support your multichannel strategy? Does it work with phone/ email/LinkedIn?  👩💻 Team motion:  -Map out your current workflow clearly and walk the vendor through each step- which can they replicate and which can they not? -Check every piece of data your sales team accesses and discuss how the vendor can do so. Can they work with 1st and 3rd party data? For example, if you care a lot about previous interactions with the account, you will need a vendor who can leverage 1st party data. -How much does account based information versus prospect/persona information matter? 📈 Ongoing improvement: -How can you train the AI? Is it easy or automated to give it feedback? Does it have clear descriptions that allow you to give specific feedback? -Will you need to train different models separately to account for regional or territory differences? -What ongoing insights will the vendor provide you? 🎉 Know how you will pay for it/prove it is successful:  Generally AI is justified by either A) getting time back/efficiency gains, B) increase in conversion rate or C) a mix of both. -At the end of your trial, what will make you confident that you should purchase? Are those reasonable results to expect? Do you have what you need in place to track? Do you need to take some baseline measurements before you start? -Gut check your test plan with the vendor- will you have enough time? Seats?  What reporting will the vendor provide and what will you need to make? -Who will be responsible for team enablement and adoption? What would you add? #ai #salestech

Every SaaS vendor swears they’ve got “AI inside” and they're "AI first" or "AI native." But to be honest, my guess is that half or more of.the “AI-powered CX platforms” out there aren’t using AI. So what are they using? They’re using marketing. If your vendor can’t explain what kind of model they’re running, how it learns, or what decisions it actually makes, it’s not AI. It’s ML in a trench coat. Yes, technically still AI, so they're not lying to you, but not the whiz-bang stuff everyone wants.. AI changes behavior. AI adapts. AI learns from data you didn’t hand-feed it. AI saves time, money, or sanity in a way you can measure. When you buy a so-called AI platform, you’re supposed to be buying outcomes, so that's what you should ask to see. If the vendor can’t tell you exactly what business metric improved in a real-world deployment, and this excludes their “case study,” an actual deployment, it's time for you to walk away. AI isn’t magic. It’s math. It only works if you’ve cleaned your data, defined your goals, and have people who actually know how to teach a machine to behave. You can’t sprinkle AI dust on a broken process and call it innovation. I get calls and DMs from leaders who bought “AI CX” tools that promised to “automate empathy.” Spoiler alert: that doesn’t exist. What they automated was the same lousy experience, just faster. If you’re evaluating vendors, stop asking “What AI features do you have?” and start asking “What measurable business results have your clients achieved, and how?” If they can’t answer without a slide deck, that’s your red flag. The next era of customer experience isn’t going to be defined by who adopted AI first. It’s going to be defined by who used it well and governed it even better. If you’re tired of AI buzzwords and want to know which platforms are actually delivering value, not vapor, send me a message. I’ll help you separate the signal from the SaaS. #customerexperience #ai #saas #leadership #data

Every SaaS vendor claims to be "AI-first" now. Most aren't. At Software Finder, we evaluate hundreds of vendors claiming AI-native capabilities. The gap between real AI infrastructure and marketing wrappers is massive and most buyers don't know what questions to ask. Here are the red flags CEOs should look for: Ask: "What proprietary data differentiates your model from a general LLM?" No visible RAG pipeline. If they can't explain how they retrieve and embed context, they're using generic prompts. Ask: "Show me your RAG architecture." Generic edge case responses. AI wrappers fail predictably on error handling and hallucination mitigation. Ask: "What happens when your AI produces incorrect output?" "AI-powered" is table stakes in 2026. The vendors worth your investment can explain their infrastructure and prove they're solving real technical problems - not just reskinning ChatGPT. At Software Finder, we help buyers cut through AI marketing to find vendors with real capabilities. Because choosing the wrong "AI-first" platform doesn't just waste budget - it delays the outcomes you're trying to achieve. Vet the infrastructure. Not the pitch.

One of the clearest patterns that emerged from my conversations last year was a nuanced one: that enterprise buyers aren’t buying AI. They’re buying certainty. In every deal, the discussion moves quickly past models and features. Accuracy seems surface level and the conversation quickly moves to whether the solution will survive a board meeting, whether legal will approve it without a prolonged fight, and whether it will actually work when connected to a 25-year-old CRM stack with a web of legacy systems—all without putting careers at risk. That’s how enterprise decisions get made. The companies that succeed don’t win because of flashy demos. They win because they reduce risk for the buyer. Winners speak different languages to different stakeholders—vision for the champion, risk for legal and security, economics for finance—and they show a real path from “promising pilot” to implementing “50,000 users across 30 countries.” Proof, predictability, and partnership can't be "nice to haves". They need to be considered part of the product. As we move into 2026, the strongest AI players will win with confidence, trust, and certainty.

Most people “choose an AI tool” the same way they pick a new app. Pretty UI ✅ Cool demo ✅ A friend said it’s amazing ✅ Then 3 weeks later… outputs are inconsistent security is a question mark nobody knows who owns the generated content integration turns into a duct-tape project leadership asks “is this compliant?” and everything stalls AI is everywhere. And every tool claims it’s “enterprise-ready.” But the real risk isn’t picking the wrong tool. It’s picking the right tool… for the wrong reasons. Because “it works in a demo” is not the same as “it works in production.” So here’s the evaluation framework I use (and it’s in the image): 1️⃣ Core functionality + performance Accuracy, reliability, data quality, scalability 2️⃣ Security + data privacy Data handling, prompt/model security, privacy compliance 3️⃣ Usability + integration Ease of use, API/workflow integration, support + training 4️⃣ Ethical + responsible use Bias/fairness, transparency, accountability And the 3 questions people forget: ✅ Cost + licensing ✅ IP + ownership ✅ Compliance standards (SOC 2, ISO, etc.) When you score tools across these buckets, the “best” AI tool usually changes. Sometimes the flashy one drops to the bottom. Sometimes the boring one becomes the obvious winner. If you’re buying, building, or recommending AI in 2026: Stop asking: “What’s the coolest model?” Start asking: “Can we trust, secure, integrate, and govern it?” 👇 Want the template + more frameworks like this? Join my Skool community: https://lnkd.in/gtAExXGv

Most CEOs greenlight AI without knowing if their company can handle it. Then wonder why millions get wasted. Here are the 6 assessments that separate success from expensive failures: 1. Data Quality Evaluation "Analyze our current data infrastructure. For each major data source: what percentage is structured vs unstructured, how often is it updated, what's the error rate, can AI access it easily or does it need transformation, and what data do we need but don't collect? Identify the top 3 data quality issues preventing effective AI implementation." → AI on garbage data = garbage results → 73% of AI failures trace to poor data quality → Data cleaning takes 60% of implementation time 2. Infrastructure Capability Check "Evaluate our technical infrastructure for AI readiness: current cloud vs on-premise capacity, API integration capabilities, security protocols for AI connections, bandwidth for AI workloads, and tech stack compatibility with major AI platforms. List infrastructure gaps ranked by urgency and cost to fix." → Wrong infrastructure = 3x deployment time → Integration failures kill 40% of projects 3. Team Skill Gap Analysis "Assess AI readiness across departments: map current employee AI literacy levels, identify roles where AI skills are critical vs nice-to-have, calculate training hours needed for minimum competency, determine if we need new hires vs upskilling, and identify early adopters who can become champions. Provide a skills roadmap with timeline and costs." → Best tools fail without trained users → Training = 25% of total AI budget 4. Budget Allocation Review "Break down a realistic AI budget: tool costs, training expenses, consulting support, infrastructure upgrades, testing programs, and contingency buffer. Compare to our allocated budget and identify shortfalls. Show 12-month total cost of ownership." → Companies underestimate costs by 60% → Hidden expenses kill momentum 5. Timeline Reality Check "Create a realistic implementation timeline with phases: assessment (weeks 1-4), tool selection (5-8), pilot rollout (9-12), expansion (months 4-6), and scaling (7-12). For each phase identify resources needed, key milestones, common delays, and dependencies. Flag unrealistic expectations." → Rushed rollouts = 85% failure rate 6. Risk Exposure Audit "Conduct an AI risk assessment covering data privacy vulnerabilities, security risks, regulatory compliance gaps, reputational risks, operational risks, and IP concerns. Rate each risk by likelihood and impact. Provide mitigation strategies with costs." → One breach costs more than entire AI budget Week 1: Run all 6 with respective teams Week 2: Compile into readiness report Week 3: Present go/no-go decision Week 4: Address critical gaps first Most executives think readiness assessments slow them down. They're actually the only thing that speeds you up. P.S. Want to learn more about AI? 1. Scroll to the top 2. Click "Visit my website" 3. Sign-up for our free newsletter