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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

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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

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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