The complete AI implementation checklist

McKinsey’s 2025 State of AI report reveals a troubling gap: while 88% of organizations now use AI, only 7% have fully scaled it across their enterprises. The average enterprise scrapped 46% of AI pilots before they ever reached production in 2025.

Here’s what no one tells you: the problem is not the AI. The problem is that most companies build an ai vendor evaluation checklist focused on features when they should be evaluating support quality, data infrastructure, and whether their teams are ready for the change.

I’ve watched mid-size companies burn through months and significant budgets chasing the wrong checklist items. They compare model accuracy percentages and API response times while ignoring whether the vendor will answer the phone when things break at 3am.

Why most AI projects never make it to production

The numbers get worse when you dig in. Only 5-20% of AI pilots result in high-impact, enterprise-wide deployments with measurable value. Nearly two-thirds of organizations remain stuck in pilot stage.

The failure patterns are predictable. PwC’s 2026 survey found 46% of tech leaders cite AI skill gaps as a major obstacle, with 38% identifying skill gaps as among the top three barriers to scaling AI agents specifically.

But these aren’t technology problems. These are preparation problems.

A company will spend weeks evaluating which AI vendor has the best feature set, then discover their data is scattered across 15 different systems in incompatible formats. By the time they realize this, they’ve already signed the contract.

The ai vendor evaluation checklist should start with your infrastructure and team readiness, not the vendor’s capabilities. Vendors love selling features. What you actually need is someone who’ll help you prepare your organization.

What to evaluate before you write a check

When you’re building your ai vendor evaluation checklist, here’s what actually matters for mid-size companies.

Data infrastructure first. Before looking at any vendor, audit your data. Is it accessible? Is it clean? Can you actually feed it to an AI system without months of preparation work? Research shows that data preparation accounts for 60-75% of total project effort in AI initiatives - the most time-consuming and underestimated component.

Not having clean data is like buying a sports car when you don’t have a driver’s license. The car works fine, but you can’t use it.

Support quality over feature lists. This is where most ai vendor evaluation checklists go wrong. Everyone compares features. Almost no one asks: “What happens when this breaks? How fast do you respond? Can you help us implement, or just sell us software?”

Research shows that companies successfully implementing AI treat vendors as partners, not suppliers. They look for dedicated customer success teams and detailed onboarding support.

Ask vendors about their service level agreements for response times. If they’re vague or dismissive, that’s your answer. When 88% of customers are more likely to renew when SLA requirements are met, support quality directly predicts success.

Team capability assessment. Your ai vendor evaluation checklist needs a brutally honest section about your team. Do they understand how AI works? Not at a PhD level - at a “can they actually use this tool effectively” level?

Job postings for emerging AI roles like “AI agent” developers grew nearly 1000% between 2023 and 2024. The talent gap remains the biggest barrier to scaling. If your team is not ready, no amount of vendor evaluation matters.

If your team is not ready, the fanciest AI in the world sits unused. I’ve seen it happen repeatedly - companies buy enterprise AI tools, then watch usage drop to zero within three months because no one knows what to do with them.

Integration complexity. This one kills more projects than companies admit. 76% of AI use cases were deployed via third-party or off-the-shelf solutions in 2025 rather than custom-built models - and this “buy over build” trend is strengthening.

Translation: if connecting the AI to your current software requires six months of custom development, you’ve picked the wrong vendor. Or the wrong time to implement.

Mid-size companies can not afford integration nightmares. You need AI that works with what you already have, not AI that requires rebuilding your entire tech stack.

Setting up technical infrastructure and team readiness

Once you’ve evaluated vendors properly, the real work starts. Most companies think buying the AI is the hard part. Actually using it is harder.

Phased rollout beats big bang every time. McKinsey found that high performers are nearly 3x as likely to have fundamentally redesigned individual workflows. Technology delivers only about 20% of an initiative’s value - the other 80% comes from redesigning work.

Companies that try to deploy AI across everything at once create chaos. Teams do not know what to focus on, support gets overwhelmed, and projects stall.

Pick one problem. Fix it with AI. Prove it works. Then do the next one.

Partner with IT early, not late. This sounds obvious, but 44% of executives report being slowed by lack of in-house expertise. By the time they bring IT into the conversation, they have made architectural decisions that IT now has to fix.

Your IT team knows your infrastructure. They know where the integration nightmares hide. Involve them from the start, not when things break.

Build training into the timeline. Here is a hard truth: technical setup takes weeks. Getting humans to change how they work takes months.

Research shows that costs typically stabilize after 18-24 months with proper planning - year one focuses on implementation and training, subsequent years shift toward optimization. You need a training plan that extends well beyond launch day.

Most ai vendor evaluation checklists skip training entirely. They assume if you buy the software, people will figure it out. They will not. Not without help.

Launching AI in a way that humans can actually use

Launch day is when your ai vendor evaluation checklist either validates itself or exposes what you missed.

Balance automation with human oversight. Research found that 52% of successful AI implementations pair automation with personal follow-ups. Pure automation feels impersonal and breaks trust. Humans checking the AI’s work builds confidence.

This is especially true for mid-size companies where relationships matter. Your team needs to trust the AI, and trust takes time.

Create feedback loops immediately. Launch is just the beginning. Companies that succeed schedule quarterly reviews to evaluate performance and adjust based on patterns and insights.

Without feedback mechanisms, you won’t know what’s working. Your team will struggle in silence, usage will drop, and you’ll wonder why the AI failed.

Set up regular check-ins. Ask what’s confusing. Fix it. Ask what’s useful. Do more of that.

Expect the learning curve. Most enterprise budgets underestimate true AI total cost of ownership by 40-60%. A $100,000 vendor quote typically translates to $140,000-$160,000 in actual Year 1 costs when hidden factors are included. Plan for this.

Your AI implementation will be messy at first. That is normal. Companies that accept the learning curve and iterate succeed. Companies that expect perfection on day one get frustrated and give up.

Measuring what actually predicts success

The final piece of your ai vendor evaluation checklist should be measurement. Not vanity metrics - actual business impact.

Track adoption before ROI. If no one uses the AI, it doesn’t matter how good it is. Monitor usage patterns, engagement levels, and which teams are actually logging in.

Low adoption signals problems: unclear value, inadequate training, or a solution looking for a problem. Fix adoption first, then worry about ROI.

Measure time to value, not just features deployed. How long before users see real benefit? McKinsey found that a small group of high performers - just 6% of respondents - capture disproportionate value. They are 3x more likely to have senior leaders demonstrating ownership of AI initiatives.

If it takes six months before anyone sees value, your implementation strategy needs work.

Monitor support interactions. How often are teams reaching out for help? What questions keep coming up? This reveals training gaps and usability issues faster than any survey.

When the same questions appear repeatedly, that’s a signal. Either your training is missing something, or the AI tool is confusing.

The checklist most companies skip

Here’s what a complete ai vendor evaluation checklist should include - the parts most companies ignore:

Before vendor evaluation: data audit, team skill assessment, infrastructure review.

During vendor evaluation: support quality testing, integration complexity analysis, partnership approach verification, actual customer reference calls with companies your size.

After vendor selection: phased rollout plan, thorough training program, IT partnership agreement, feedback loop design, measurement framework.

Post-launch: regular performance reviews, continuous training updates, optimization based on usage patterns, support response tracking.

Most ai vendor evaluation checklists focus on features and pricing. The successful ones focus on readiness and support.

Mid-size companies don’t have room for failed AI experiments. Get the checklist right before you start. Evaluate support quality as carefully as technical capabilities. Prepare your team and infrastructure before launch day.

The vendors selling you AI want to talk about their latest features. What you actually need is a partner who’ll help you succeed with AI you bought last year.