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

GC: "What are the top terms we should revisit when a SaaS vendor adds AI?" When I was in-house at Netflix and Spotify, nobody handed me a list. Now I'm on the other side of the table, I'm watching buyers walk into vendor conversations without it. If you're a GC or Legal Ops leader right now, I know what your week looks like. Three vendor demos. Two renewal conversations. A security team asking questions you're trying to translate back to product. And somewhere in all of it, a vendor just told you they've added AI and you have about forty minutes to figure out what that means before the next call. Nobody has time to build the diligence framework from scratch in that window. I didn't either. So here it is. Ten things to pressure-test every time a vendor tells you they've added AI: 1. Data use How is customer data being used? You want a clear statement that it's not used for model training, reuse or any secondary purpose without explicit approval. 2. Retention and deletion Can retention be controlled, including prompts? Look for configurable policies, not fixed or undefined timelines. 3. Accuracy and validation How is accuracy established and verified? Vendors should explain their method and tie outputs back to source data so your team can validate results directly. 4. Security of the AI layer Does it meet enterprise security standards? You should expect the same or higher standards as the core product with evidence it was designed that way from the start, not bolted on. 5. Audit logs and monitoring Is system activity visible? Look for detailed audit logs, including AI-specific actions and the ability to feed into your SIEM or internal monitoring tools. 6. Data segregation Is customer data isolated? There should be no cross-customer data mixing especially in shared model environments. 7. Third-party models and flexibility Which third parties are involved? Vendors should disclose model providers and clarify whether components can be swapped for internal models if required with no functionality loss. 8. Ownership of inputs and outputs Who owns the data and what's generated from it? Ownership of both should remain with the customer. 9. Indemnification Are there AI-specific carve-outs? Check whether standard protections still apply when AI is involved. 10. Control and opt-out Can AI features be limited or disabled? You should have control over where and how AI is applied. --- Every era of enterprise software has its diligence list. NDAs, cloud, SOC 2, Data residency and so on. AI is the current one. Right now, the gap isn't about whether a vendor has AI (because almost all of them claim they do), it's about whether the system was built to handle it. And the buyers who build the list now are the ones who won't get burned later. Save this for your next vendor call.

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.

Your AI strategy could be destroying your valuation. Here's how to tell. When we went through due diligence for our PE exit, I expected the usual questions: Revenue growth. Customer retention. Tech debt. What I didn't expect was how much AI posture mattered. Not whether we 𝘩𝘢𝘥 AI, but whether our AI was a liability or an asset. 𝗟𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘀𝗶𝗴𝗻𝗮𝗹𝘀: 🔶 AI initiatives with no line of sight to P&L 🔶 "Innovation theater", pilots that never scale, demos that never ship 🔶 No governance framework, no one can explain who owns AI risk 🔶 Data lineage gaps, can't trace where data came from, access control 🔶 Hidden open source exposure from AI tooling That last one caught me off guard. With AI coding assistants everywhere now, buyers are asking: do you actually know what open source is in your stack? What's your license exposure? If your answer is "I think so" instead of "yes, here's the SBOM", that's a red flag. 𝗔𝘀𝘀𝗲𝘁 𝘀𝗶𝗴𝗻𝗮𝗹𝘀: 🔶 AI tied directly to revenue or margin improvement 🔶 Clear governance with documented policies and owners 🔶 Data infrastructure that's audit-ready 🔶 Proactive risk management, you found the problems before they did The gap between these two positions is where valuation discounts happen. Buyers are anti-uncertainty. They want to see you've thought through the risks and can defend your position. If you're 18-24 months from a potential exit, the time to clean this up is now. Not during diligence. In my next post, I'll share the 7-area AI due diligence checklist PE firms are starting to use. What's the one area you'd be least confident about if diligence started tomorrow?

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