How to Pick the Right AI Tool: What Changes, and What Actually Matters

Webinar recap: Haris Ikram and Cynthia Abbott unpack what AI adoption really means for companies, and why most teams are approaching it the wrong way.

As AI adoption accelerates, many HR, Finance, and operational leaders are facing a familiar pressure: “We need to adopt AI this year.”

But as discussed in our latest CandorIQ webinar, the real challenge isn’t choosing the right tool. It’s understanding how AI fits into the organization in the first place.

The conversation focused less on tooling, and more on structure, decision-making, and how companies can avoid creating more complexity instead of leverage.

Below is my recap of the conversation, but you can catch the webinar HERE (it’s worth the watch!). 

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1. Why AI Feels So Overwhelming Right Now

Across companies, there’s a common pattern - Too many tools. Not enough clarity. Conference season, demos, and vendor pitches have made everything “AI-powered,” but teams are still unclear where AI actually fits.

“The real challenge isn’t choosing software. It’s knowing how to evaluate AI in a way that drives leverage, not chaos.” — Haris Ikram

2. The Most Common Mistake: Starting with Tools

Many teams begin with the tool instead of the problem.

“Don’t start with tools. Start with problems.” — Haris Ikram

Without a clear problem to solve, teams end up experimenting without direction — and adding more complexity than value.

3. AI Readiness Is a Structural Problem

One of the biggest reframes from the conversation:

“AI readiness isn’t technical. It’s structural.” — Haris Ikram

It’s not about which tool you pick.  It’s about how your organization is set up to use it. Ownership, workflows, and accountability matter more than features.

‍4. Tool Sprawl Is Already Here

Inside many companies, different teams are already using different tools. That creates fragmentation — not leverage.

“Model uniformity matters for organizations.” — Cynthia Abbott

Without alignment, you don’t get shared learning or efficiency.

5. Where Adoption Breaks Down

AI adoption doesn’t fail because of bad tools. It fails because teams don’t know how to use them.

“People don’t come to work to fail. They just don’t have enough guidance.” — Cynthia Abbott

Without clear direction, even strong tools won’t stick.

6. Measuring the Wrong Things

A common trap is tracking usage instead of impact.

“Adoption numbers alone don’t matter.” — Cynthia Abbott

What matters instead:

• Consistent use
• Time saved
• Performance improvement
• Real outcomes

7. AI Is Still Being Figured Out

There’s no perfect playbook yet.

“We’re still exploring what AI means for us — professionally and personally.” — Cynthia Abbott

The tools will evolve. The way companies use them will too.

8. The Real Risk

The risk isn’t doing AI wrong. It’s not doing it at all.

“Whether you plan for it or not, it’s happening.” — Cynthia Abbott

The Big Takeaway

AI adoption isn’t a tooling decision.  It’s an operating model shift.

The companies that succeed won’t be the ones experimenting the most or buying the most tools. They’ll be the ones that bring clarity to:

• What problems AI is solving
• How work is changing because of it
• And how decisions are made consistently across the organization

Because AI doesn’t create leverage on its own — it exposes how well (or poorly) a company is already structured to use it.

And that’s where the real advantage will come from.

Watch the full webinar HERE