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Avoiding Null and Undefined with NonNullable<T> in TypeScript

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

Use Cases

Use Case 1: Adding Two Numbers

Scenario: A function that adds two numbers and returns their sum. But if one of the numbers is undefined or null, it returns the other number as-is.

function addNumbers(a: number, b?: number | null): NonNullable<number> {
  return a + (b ?? 0);
}

const sum1 = addNumbers(2, 3); // Returns 5
const sum2 = addNumbers(2, null); // Returns 2
const sum3 = addNumbers(2, undefined); // Returns 2

In this code snippet, the addNumbers() function takes two parameters, a and b. a is a required parameter of type number, while b is an optional parameter of type number or null. The function uses the NonNullable<T> conditional type to ensure that the return value is not null or undefined. If b is null or undefined, the function returns the value of a. Otherwise, it adds a and b together and returns the sum. To handle the case where b is null or undefined, the code uses the nullish coalescing operator, ??, which returns the value on its left-hand side if it is not null or undefined, and the value on its right-hand side otherwise.

Use Case 2: Checking Contact Information

Scenario: A class representing a person, but with optional email and phone properties. The contact() function logs the email and phone numbers if they are defined and not null. Otherwise, it logs a message saying that no contact information is available.

class Person {
  name: string;
  email?: string | null;
  phone?: string | null;

  constructor(name: string, email?: string | null, phone?: string | null) {
    this.name = name;
    this.email = email ?? null;
    this.phone = phone ?? null;
  }

  contact() {
    if(this.email !== undefined && this.email !== null && this.phone !== undefined && this.phone !== null) {
      console.log(`${this.name} can be reached at ${this.email} or ${this.phone}`);
    } else {
      console.log(`${this.name} has no contact information available`);
    }
  }
}

const john = new Person('John Doe', 'john.doe@example.com', '(123) 456-7890');
const jane = new Person('Jane Smith', null, '987-654-3210');

john.contact(); // logs 'John Doe can be reached at john.doe@example.com or (123) 456-7890'
jane.contact(); // logs 'Jane Smith has no contact information available'

Explanation: In this code snippet, the Person class has a name property and optional email and phone properties. The contact() function checks if both email and phone are not undefined and not null before logging the details. Otherwise, it logs a message saying that no contact information is available. To initialize the properties with null, the constructor uses the nullish coalescing operator, ??. When creating a new Person, you can pass null or undefined as arguments, and the class will interpret them as null.

Conclusion

Understanding and correctly implementing conditional types like NonNullable<T> in TypeScript is crucial to reduce potential code pitfalls. By reviewing examples of numerical operations and contact information handling, we've seen how this conditional type helps reinforce our code against null or undefined values. This highlights the utility of TypeScript's conditional types not only for enhancing code stability, but also for easing our coding journey. So keep experimenting and finding the best ways to deploy these tools for creating robust, secure, and efficient programs!

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Svelte 4: Unveiled Speed Enhancements and Developer-Centric Features cover image

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Making AI Deliver: From Pilots to Measurable Business Impact cover image

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A lot of organizations have experimented with AI, but far fewer are seeing real business results. At the Leadership Exchange, this panel focused on what it actually takes to move beyond experimentation and turn AI into measurable ROI. Over the past few years, many organizations have experimented with AI, but the challenge today is translating experimentation into measurable business value. Moderated by Tracy Lee, CEO at This Dot Labs, panelists featured Dorren Schmitt, Vice President IT Strategy & Innovation at Allen Media Group, Greg Geodakyan, CTO at Client Command, and Elliott Fouts, CAIO & CTO at This Dot Labs. Panelists discussed how companies are moving from early AI experiments to initiatives that deliver real results. They began by examining how experimentation has evolved over the past year. While many organizations did not fully utilize AI experimentation budgets in 2025, 2026 is showing a shift toward more intentional investment. Structured budgets and clearly defined frameworks are enabling companies to explore AI strategically and identify initiatives with high potential impact. The conversation then turned to alignment and ROI. Panelists highlighted the importance of connecting AI projects to corporate strategy and leadership priorities. Ensuring that AI initiatives translate into operational efficiency, productivity gains, and measurable business impact is essential. Companies that successfully align AI efforts with organizational goals are better equipped to demonstrate tangible outcomes from their investments. Moving from pilots and proofs of concept to production was another major focus. Governance, prioritization, and workflow integration were cited as essential for scaling AI initiatives. One panelist shared that out of nine proofs of concept, eight successfully launched, resulting in improvements in quality and operational efficiency. Panelists also explored the future of AI within organizations, including the potential for agentic workflows and reduced human-in-the-loop processes. New capabilities are emerging that extend beyond coding tasks, reshaping how teams collaborate and how work is structured across departments. Key Takeaways - Structured experimentation and defined budgets allow organizations to explore AI strategically and safely. - Alignment with business priorities is essential for translating AI capabilities into measurable outcomes. - Governance and workflow integration are critical to moving AI initiatives from pilot stages to production deployment. Successfully leveraging AI requires a balance between experimentation, strategic alignment, and operational discipline. Organizations that approach AI as a structured, measurable initiative can capture meaningful results and unlock new opportunities for innovation. Curious how your organization can move from AI experimentation to real impact? Let’s talk. Reach out to continue the conversation or join us at an upcoming Leadership Exchange. Tracy can be reached at tlee@thisdot.co....

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