Skip to main content

What Does It Mean to Build a Company with AI in Mind?

James Field
LogicMonitor

AI is no longer a niche buzzword. Today, every industry uses AI, from healthcare and finance to retail and transportation — everything. Some companies are just starting to dip their toes into developing AI capabilities, while (few) others can claim they have built a truly AI-first product. Regardless of where a company is on the AI journey, leaders must understand what it means to build every aspect of their product with AI in mind.

First: What Does Building with AI in Mind Entail?

Building AI capabilities is not just about implementing machine learning algorithms or adding a ChatGPT extension. It's about turning information and data into actionable insights and, eventually, automated actions. By prioritizing end users' needs, companies can create innovative products that move the tech world further.

However, in order to do so, companies must understand the data at their fingertips. Low-quality and biased data can lead to flawed AI models, so starting with high-quality input is essential. Even small tasks, like writing instruction manuals or FAQs for customers, should be done with AI in mind, so it is easily readable for bots in the future. This critical mindset sets companies up for success and helps AI become a trusted advisor that enables users to make better decisions and automate actions.

Tactical Tips on How to Do It

Education comes first for both employees and customers. All employees should understand how AI is being used, or how they are utilizing it, to improve products and services and the impact it can have on their jobs. Companies are also obligated, or should be, to inform customers about what AI is and how it will be used to benefit them.

Secondly, companies should have a governance policy for adopting, deploying, and using AI, clearly explaining the privacy and data sources used for AI solutions. This will help ensure that the company uses AI ethically, which is key to building customer trust.

Lastly, companies should use customer data wisely. For example, companies can watch how customers are interacting with a particular tool and track where their pain points are. Product teams make use of AI and this data to look for ways to automate things customers didn't even know they needed.

Always, Always Keep the Customer in Mind

Understanding the end user's needs is the most important aspect of building an AI product. Make sure every decision, big or small, is focused on creating a better product for customers. Ensure reasoning is provided so they understand how AI is making the decisions and it is — never "closed box." Much like being asked to show your work in an exam — it demonstrates your thought process and understanding of the customer's needs.

AI will continue to grow over the next days, months and decades, and sooner than we realize, it will be built into every product we use. To be successful, product leaders must prioritize transparency and education when building these products. Companies should not be afraid to experiment with AI but should also be mindful of its potential risks. By keeping goals and the end users in mind, companies can stay ahead of the curve and provide customers with innovative and helpful products.

James Field is Sr. Director of Product Strategy and Operations at LogicMonitor

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

What Does It Mean to Build a Company with AI in Mind?

James Field
LogicMonitor

AI is no longer a niche buzzword. Today, every industry uses AI, from healthcare and finance to retail and transportation — everything. Some companies are just starting to dip their toes into developing AI capabilities, while (few) others can claim they have built a truly AI-first product. Regardless of where a company is on the AI journey, leaders must understand what it means to build every aspect of their product with AI in mind.

First: What Does Building with AI in Mind Entail?

Building AI capabilities is not just about implementing machine learning algorithms or adding a ChatGPT extension. It's about turning information and data into actionable insights and, eventually, automated actions. By prioritizing end users' needs, companies can create innovative products that move the tech world further.

However, in order to do so, companies must understand the data at their fingertips. Low-quality and biased data can lead to flawed AI models, so starting with high-quality input is essential. Even small tasks, like writing instruction manuals or FAQs for customers, should be done with AI in mind, so it is easily readable for bots in the future. This critical mindset sets companies up for success and helps AI become a trusted advisor that enables users to make better decisions and automate actions.

Tactical Tips on How to Do It

Education comes first for both employees and customers. All employees should understand how AI is being used, or how they are utilizing it, to improve products and services and the impact it can have on their jobs. Companies are also obligated, or should be, to inform customers about what AI is and how it will be used to benefit them.

Secondly, companies should have a governance policy for adopting, deploying, and using AI, clearly explaining the privacy and data sources used for AI solutions. This will help ensure that the company uses AI ethically, which is key to building customer trust.

Lastly, companies should use customer data wisely. For example, companies can watch how customers are interacting with a particular tool and track where their pain points are. Product teams make use of AI and this data to look for ways to automate things customers didn't even know they needed.

Always, Always Keep the Customer in Mind

Understanding the end user's needs is the most important aspect of building an AI product. Make sure every decision, big or small, is focused on creating a better product for customers. Ensure reasoning is provided so they understand how AI is making the decisions and it is — never "closed box." Much like being asked to show your work in an exam — it demonstrates your thought process and understanding of the customer's needs.

AI will continue to grow over the next days, months and decades, and sooner than we realize, it will be built into every product we use. To be successful, product leaders must prioritize transparency and education when building these products. Companies should not be afraid to experiment with AI but should also be mindful of its potential risks. By keeping goals and the end users in mind, companies can stay ahead of the curve and provide customers with innovative and helpful products.

James Field is Sr. Director of Product Strategy and Operations at LogicMonitor

Hot Topics

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...