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The 3 Questions Every Product Leader Should Ask When Evaluating a New AI Tool

Ranjan Goel
VP of Product
LogicMonitor

All eyes are on the value AI can provide to enterprises. Whether it's simplifying the lives of developers, more accurately forecasting business decisions, or empowering teams to do more with less, AI has already become deeply integrated into businesses. However, it's still early to evaluate its impact using traditional methods. Here's how engineering and IT leaders can make educated decisions despite the ambiguity.

1. Does my current team have the technical ability to implement this?

Even the most advanced technology won't deliver its full potential if it isn't implemented and maintained properly. Leaders must ask:

Can my existing team do this? Can we train them to do an AI implementation in a timely manner?

Or will we need to hire additional staff?

None of the answers to the above questions spell disaster for implementing AI, they do help create a clearer picture of what's possible for your specific team. Given how quickly AI is evolving, upskilling or reskilling is likely required for most organizations. Whether through training or hiring, implementation needs to be feasible.

2. Am I willing to implement this at its current stage?

AI is full of promises — some near-term, some further off. When evaluating AI vendors, it's important to recognize that the technology's current capabilities may continue to evolve rapidly. If the current proof of concept meets most of your needs, great!

Decision makers should evaluate whether the AI tool provider they're entertaining is open to working closely to iterate the tool. Most AI tools are not yet mature enough for all potential use cases to be available already.

3. So you want to move forward. How do you justify the investment?

Think of the ROI of AI as falling into two categories: business benefits and financial benefits.

Most AI tools today offer value in terms of business benefits, such as improved customer experience, enhanced employee productivity, and faster rollouts of new features or products. Businesses using AI can differentiate better from competitors as more innovative in their products and service offerings.

The other category is financial benefits, which, in addition to the above, will undoubtedly catch the attention of the C-suite and board of directors. These include factors like improved top-line growth or improving margins. Quantifying solid financial benefits from AI tools is starting to make its way, especially for domain-specific AI applications like IT operations, medical or retail. This is an area where a partnership with the AI tool vendor and decision-maker can greatly improve the quality of ROI calculation to account for key use cases.

It's rarely one person's responsibility to ask and answer all these questions. These considerations should involve the broader team and be viewed holistically. Some tools that are still in their infancy may be worth the risk if they check many of the other boxes. A more significant monetary investment could be the right choice if the technology addresses a critical need for your team that otherwise couldn't be met. Ask these questions, and reevaluate often.

Ranjan Goel is VP of Product at LogicMonitor

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The 3 Questions Every Product Leader Should Ask When Evaluating a New AI Tool

Ranjan Goel
VP of Product
LogicMonitor

All eyes are on the value AI can provide to enterprises. Whether it's simplifying the lives of developers, more accurately forecasting business decisions, or empowering teams to do more with less, AI has already become deeply integrated into businesses. However, it's still early to evaluate its impact using traditional methods. Here's how engineering and IT leaders can make educated decisions despite the ambiguity.

1. Does my current team have the technical ability to implement this?

Even the most advanced technology won't deliver its full potential if it isn't implemented and maintained properly. Leaders must ask:

Can my existing team do this? Can we train them to do an AI implementation in a timely manner?

Or will we need to hire additional staff?

None of the answers to the above questions spell disaster for implementing AI, they do help create a clearer picture of what's possible for your specific team. Given how quickly AI is evolving, upskilling or reskilling is likely required for most organizations. Whether through training or hiring, implementation needs to be feasible.

2. Am I willing to implement this at its current stage?

AI is full of promises — some near-term, some further off. When evaluating AI vendors, it's important to recognize that the technology's current capabilities may continue to evolve rapidly. If the current proof of concept meets most of your needs, great!

Decision makers should evaluate whether the AI tool provider they're entertaining is open to working closely to iterate the tool. Most AI tools are not yet mature enough for all potential use cases to be available already.

3. So you want to move forward. How do you justify the investment?

Think of the ROI of AI as falling into two categories: business benefits and financial benefits.

Most AI tools today offer value in terms of business benefits, such as improved customer experience, enhanced employee productivity, and faster rollouts of new features or products. Businesses using AI can differentiate better from competitors as more innovative in their products and service offerings.

The other category is financial benefits, which, in addition to the above, will undoubtedly catch the attention of the C-suite and board of directors. These include factors like improved top-line growth or improving margins. Quantifying solid financial benefits from AI tools is starting to make its way, especially for domain-specific AI applications like IT operations, medical or retail. This is an area where a partnership with the AI tool vendor and decision-maker can greatly improve the quality of ROI calculation to account for key use cases.

It's rarely one person's responsibility to ask and answer all these questions. These considerations should involve the broader team and be viewed holistically. Some tools that are still in their infancy may be worth the risk if they check many of the other boxes. A more significant monetary investment could be the right choice if the technology addresses a critical need for your team that otherwise couldn't be met. Ask these questions, and reevaluate often.

Ranjan Goel is VP of Product at LogicMonitor

Hot Topics

The Latest

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...