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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

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...