(2:12 Q&A Video) “Build an AI-Native Culture That Accelerates Business Performance”

In this 2:12 video, Nick Malone explains why becoming AI-native begins with a culture willing to enter uncharted territory and experiment. He describes how executive mandates are pushing companies to discover practical ways AI can improve operational performance, reduce costs, and increase productivity. Nick also shows how AI enables organizations to accomplish more with existing resources while accelerating speed to market.

Becoming AI-native starts with a cultural decision before it becomes a technology decision. Organizations need to be willing to test unfamiliar approaches, learn quickly, and connect experimentation with measurable improvements in business performance.

Nick Malone, Chief Revenue Officer at XTM. Nick sees growing pressure from boards for executive teams to invest in AI, even when those organizations have not completely defined what the final outcome should look like. The opportunity is to turn that broad mandate into focused experimentation that improves productivity, lowers costs, increases speed to market, and helps organizations accomplish more with their existing resources.

“Being an AI-native company, you must go and dig deep and experiment and understand in what ways you can use the AI capabilities today to improve operational performance, possibly reduce costs, definitely expand productivity.” – Nick Malone

To learn more, watch the 2:12 video or read the article below.

To catch the full interview with Nick at “How to Become a Successful AI-Native Company.” CLICK HERE.

Article: "Build an AI-Native Culture That Accelerates Business Performance”

This article is based on an interview with Nick Malone, Chief Revenue Officer at XTM

Build a Culture Willing to Experiment

An AI-native organization is defined as much by its mindset as by its technology. “A company’s got to be willing to go into uncharted territories, to be willing to experiment,” Nick explains. This willingness matters because AI capabilities are developing quickly, making it difficult for organizations to know every valuable application before they begin. Instead of waiting until every question has been answered, leaders can create opportunities to experiment with specific business problems, evaluate what AI can accomplish, and use those lessons to determine where further investment can generate stronger results.

The pressure to explore those opportunities is increasingly coming from senior leadership. Nick says, “A lot of the companies we work with around the world have boards giving mandates to executive leadership teams to invest in AI.” Those mandates may not always include a perfectly defined outcome, but they create momentum for action. The leadership challenge is turning that broad direction into purposeful experimentation. Organizations can identify practical opportunities, establish the outcomes they want to improve, test AI against those objectives, and use the results to guide future adoption and investment.

Connect AI to Business Performance

Experimentation needs a measurable purpose to produce lasting business value. Nick says companies must “dig deep, experiment, and understand how you can use AI capabilities today to improve operational performance, possibly remove costs, and definitely expand productivity.” These objectives provide executives with a straightforward framework for evaluating AI opportunities. Leaders can ask whether an application improves an existing process, removes unnecessary effort, or allows employees to create more output. Those questions keep AI initiatives connected to business performance, rather than letting experimentation become an isolated technology exercise.

Nick also describes AI as “helping companies do more with the same and more with less.” That opportunity is particularly relevant for B2B organizations balancing growth expectations with pressure to operate efficiently. AI can potentially increase operating leverage by allowing existing teams to accomplish more without requiring resources to expand at the same rate. The strongest AI experiments therefore do not have to be the most technologically ambitious. Their value comes from producing measurable improvements that matter to the business and creating evidence leaders can use when deciding what to scale.

Accelerate Productivity and Speed to Market

XTM’s globalization business demonstrates how AI can reshape an established workflow. Traditionally, organizations relied on professional linguists to translate content for international audiences while protecting accuracy, brand standards, and tone of voice. Technology gradually began automating parts of that process. Nick explains, “Machine translation technologies have come on, which essentially did it, automated it, and did it quickly.” AI marks another stage of that evolution by helping companies improve productivity and find more efficient ways to bring content and customer experiences to markets around the world.

The business impact extends beyond automation. Nick says XTM has been deploying AI to customers, creating “a huge cost saving and ability to drive performance and speed to market.” For organizations pursuing global growth, reaching audiences faster while maintaining their brand and tone can create meaningful strategic value. XTM is also applying AI internally so the company can operate more leanly and productively. By using the technology internally as well as delivering it to customers, the organization reinforces the same performance and productivity principles behind its external value proposition.

Conclusion

Companies do not need to know every future AI application before they begin. They need a disciplined way to experiment, measure results, and learn where the technology creates the greatest value. Nick’s framework keeps that process connected to business fundamentals, including operational performance, cost efficiency, productivity, and speed to market.

The competitive advantage is not simply having access to AI. Many organizations will have access to similar technology. The greater advantage comes from building a culture that can repeatedly discover, prove, and scale applications that improve performance. AI-native companies turn experimentation into an ongoing business capability rather than a one-time technology initiative.

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Steven MacDonald

Steven MacDonald is the founder of Content Strategies, CEO of a MarTech SaaS Company, fractional CMO consulting with leading B2B companies and former Director of Strategy and Client Service at top ten digital marketing agencies.

Follow Steve on LinkedIn.

Steven MacDonald

Follow Steve on LinkedIn.