{"id":89249,"date":"2026-06-23T20:37:40","date_gmt":"2026-06-23T20:37:40","guid":{"rendered":"https:\/\/degreed.com\/experience\/?p=89249"},"modified":"2026-06-23T20:37:41","modified_gmt":"2026-06-23T20:37:41","slug":"checklist-for-selecting-the-right-ai-learning-vendor","status":"publish","type":"post","link":"https:\/\/degreed.com\/experience\/pt-br\/blog\/checklist-for-selecting-the-right-ai-learning-vendor\/","title":{"rendered":"A Checklist for Selecting the Right AI Learning Vendor: Top Selection Criteria"},"content":{"rendered":"\n

The pressure to deploy AI solutions and support AI adoption is relentless for learning and development teams. For many, it even means adding a new vendor to the tech stack. But that intense push to “just do something with AI” (often coming from leadership) can derail even well-intentioned vendor selection and buying processes, turning a strategic investment into a costly regret.<\/p>\n\n\n\n

Getting started with a new platform or tool can be exciting. Possibilities feel endless, especially when you\u2019re trying to solve a significant business challenge that\u2019s been a pressure point for a long time. But anyone who has gone through the tech vendor procurement process knows that it\u2019s also arduous work and mistakes can have a high cost.<\/p>\n\n\n\n

As more companies adopt AI solutions, scrutiny of the selection process is growing. It\u2019s more important than ever to set up a structured evaluation process that can hold up under examination from HR, IT, finance, and the C-suite. For L&D leaders managing competing stakeholder priorities, it\u2019s not unusual to miss critical information during the selection and demo process. <\/p>\n\n\n\n

At best, failing to align to stakeholder criteria could slow down the process. At worst, it causes significant execution failures after go-live. According to Capterra, 60% of software buyers have buyer’s remorse for recent tech purchases.<\/a> Most of the time, that regret traces back to misaligned priorities, skipped evaluation steps, or moving too fast without the right criteria in place.<\/p>\n\n\n\n

Knowing that, the pressure is at an all-time high if you\u2019re an L&D leader trying to select your ideal AI-powered learning vendor. Those vendors are evolving at lightspeed, as are workforce capability needs. Before you book a single demo, the most important thing you can do is define what you actually need.<\/p>\n\n\n\n

Don’t Let Flashy Features Drive the Decision<\/h2>\n\n\n\n

The AI learning vendor landscape is noisy and changing constantly. You’ll encounter platforms that can do impressive things. Think dynamic content generation, real-time coaching simulations, and AI-powered skill assessments, for starters. <\/p>\n\n\n\n

It’s easy to become dazzled by what’s possible. But the question is what your organization actually needs, not just what a platform can do. The most important step in vendor evaluation happens before you ever meet a vendor: defining your criteria for success.<\/p>\n\n\n\n

A vendor selection checklist forces discipline. It translates your organization’s specific goals (e.g., closing skill gaps, building AI capabilities, enabling internal mobility, linking learning to business outcomes) into concrete, measurable requirements. It also creates a shared language across stakeholders who would otherwise assess the same vendor demo through completely different lenses.<\/p>\n\n\n\n

What to Evaluate: The Five Areas That Matter<\/h2>\n\n\n\n

When evaluating AI learning solutions, there are five domains where you need clear criteria before you start the conversation.<\/p>\n\n\n\n

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  1. Core Functionality<\/strong><\/li>\n<\/ol>\n\n\n\n

    This is where most evaluations begin, and rightfully so. Although your needs could vary, there are a few must-have functions every serious AI learning vendor should be able to demonstrate:<\/p>\n\n\n\n