How to Pick the Right AI Tool for Your Company

AI adoption has gone from “Should we use AI?” to “Okay… but what should we actually use it for?”

In our latest webinar, Haris Ikram, Co-Founder & CEO of CandorIQ, sat down with Cynthia Abbott, Founder & CEO of WellPay.AI, to unpack how companies should evaluate, buy, and implement AI without simply adding another tool to the tech stack.

Cynthia brought the practitioner perspective from her experience as a former CHRO and now AI founder, while Haris shared what he’s seeing as companies bring AI into compensation and workforce planning.

Below is a recap of the conversation, but you can watch the full webinar HERE.

Start with the problem, not the AI tool

Every vendor is becoming “AI-powered,” but that doesn’t mean every company needs another AI tool.

Cynthia’s starting point is simple:

“What are we trying to solve for?”

From there, companies should think about their real budget, prioritize the problems that matter most, and then find technology that can actually help.

Haris added that AI can always give you an answer—but without the right business context, data, and direction, it may not be the answer you actually need.

The goal shouldn’t be to use more AI. It should be to solve the right problems with it.

Shadow AI might actually be a good thing

Employees experimenting with AI tools outside formal company programs can sound like a governance nightmare.

But Cynthia offered a different perspective:

“I actually think it’s a good thing, shadow AI usage.”

People learn by experimenting. They prompt, play, discover use cases, and sometimes bring those ideas back into their work.

The problem starts when that experimentation involves sensitive company information or violates security and governance policies.

Instead of shutting experimentation down completely, companies can create safer spaces for employees to learn together while establishing clear guardrails around what is—and isn’t—okay.

Don’t measure AI success by adoption alone

Getting 500 employees to log into an AI tool doesn’t necessarily mean the investment is working.

Cynthia suggested looking beyond adoption to questions like:

• Is the tool being used consistently?
• Is it solving the use case we bought it for?
• How much time is it saving?
• Is performance improving?
• Is burnout decreasing?

AI ROI should ultimately connect back to a business outcome.

As Haris pointed out, whether you’re automating job matching, creating offers, or improving workforce planning, the important question is: what actually changed because of the tool?

Build vs. buy still comes down to ROI

AI has made building internal tools easier, which can make “we’ll just build it ourselves” tempting.

Cynthia’s advice was to look at the opportunity cost.

If building an internal tool means pulling engineers away from the company's core product, is that really the best use of their time?

Haris added another consideration: HR technology often involves sensitive employee data, integrations, permissions, compliance, and accuracy. Building the interface may be easy. Building everything underneath it safely is much harder.

The question isn’t simply “Can we build it?”

It’s “Should we?”

AI implementation needs more than an HR owner

Buying the right tool is only half the job.

Cynthia emphasized the importance of bringing champions from across the organization into implementation—HR, IT, Finance, Operations, Revenue, and other teams that will ultimately interact with the technology.

Haris referenced the RACI framework as one way to make ownership clear: who is responsible, accountable, consulted, and informed?

The broader point: AI adoption shouldn’t happen in a silo.

People are much more likely to use a tool when they understand why it was chosen, what it's supposed to solve, and how it fits into their work.

Create a safe place to experiment

When employees resist AI, Cynthia’s first question isn’t “Why won’t they use it?”

It’s simply: “Why?”

Often, someone tried the technology and it failed, gave them the wrong answer, or didn’t fit their workflow.

Haris added that fear of getting something wrong can also stop people from experimenting.

Instead of forcing adoption, leaders can create an environment where people are allowed to test, fail, learn, and share what works.

As Cynthia put it, companies need to create “a place where you can play.”

Governance can’t be an afterthought

More experimentation also means companies need clearer guardrails.

Cynthia recommended involving CTOs, security leaders, and other relevant executives early to determine how AI should operate within the organization.

Haris added another important principle: garbage in, garbage out.

AI can only be as useful as the information underneath it. Clean, structured, accurate data—and clear rules around how that data can be accessed—become even more important as AI moves deeper into company workflows.

The goal isn’t to stop experimentation. It’s to make experimentation safe and scalable.

Final Takeaway

There isn’t one perfect AI stack, framework, or implementation playbook yet.

And that’s okay.

As Cynthia said, we’re still exploring what AI can mean for us professionally and personally. The tools companies use today may not be the tools they use tomorrow.

But doing nothing isn’t much of a strategy either.

Start with the problem. Set clear outcomes. Bring the right people into the process. Create room to experiment. Put governance around it. And measure whether AI is actually making work better.

Because whether companies have an AI plan or not, AI adoption is already happening.

As Cynthia put it:

“Whether you plan for it or not, it’s

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