As artificial intelligence (AI) continues to transform enterprises worldwide, the journey from small-scale pilots to full-scale operationalisation remains a critical challenge. While many organisations experiment with AI to automate processes or explore efficiencies, only a few successfully embed it as a core business driver.
In conversation with iTNews Asia, Mitch Young, Senior Vice President, APAC at Zendesk, shared insights on the critical success factors for operationalising AI including aligning technology, talent, and culture, avoiding common scaling pitfalls, and measuring outcomes that drive both customer loyalty and revenue growth.
Young said the first step in scaling AI is to create a robust technological infrastructure. Effective data management, seamless integration with legacy systems, and scalable infrastructure are essential to unlock AI’s long-term potential.
“Organisations that invest in a solid tech foundation are much more likely to achieve lasting AI impact,” he added.
However, technology alone is insufficient. Organisations must also cultivate human readiness. Cross-functional alignment, AI training, and closing skills gaps enable teams to understand, trust, and maximise AI capabilities.
Firms that neglect this human dimension often see AI remain confined to isolated experiments rather than integrated operational workflows, he explained.
Common pitfalls in scaling AI
Despite widespread interest, many organisations struggle for full-scale deployment due to a combination of skill, integration, and strategic challenges.
Young said scaling AI requires teams to develop new capabilities, embrace continuous learning, and adopt fresh ways of working, yet many teams are not adequately prepared. Integration issues also pose a major hurdle, as AI solutions are only as effective as the workflows and knowledge structures they augment.
Young explained that overconfidence in custom-built AI solutions often compounds the problem, with organisations attempting to develop bespoke platforms after early successes, only to encounter steep operational, compliance, and maintenance challenges.
Additionally, deploying AI without thoughtfully architecting systems and processes can undermine both scalability and ROI.
He pointed out innovations such as no-code platforms and tools that unify knowledge sources across systems are helping businesses avoid these pitfalls while accelerating adoption.
From cost efficiency to revenue generation
According to Young, early AI initiatives were largely focused on automating routine tasks and cutting costs. Today, businesses are using AI to drive revenue growth and customer value.
“The shift is clear: AI is no longer just a cost-saver, it’s a revenue generator,” said Young. Companies leveraging AI for service are seeing improvements in customer satisfaction, loyalty, and even cross-sell opportunities, turning customer experience into a tangible business advantage.
For enterprises implementing AI at scale, Young said success is increasingly measured by outcomes rather than raw activity.
“Our recommended objective for AI at scale is to target 80 percent automation, with AI autonomously handling most routine and mid-complexity issues across channels. This approach frees up human agents, supported by AI copilots, to focus on the remaining 20 percent of high-value or high-risk interactions where empathy, judgment, or creative problem-solving are essential,” he added.
Young further explained that evaluating AI impact requires a focus on outcome-based metrics including first-contact resolution, the percentage of issues fully solved by AI, automated resolution rates, execution reliability across complex workflows, and agent productivity and assist quality across multiple languages.
“Put simply: when AI-powered resolutions drive loyalty and growth, everyone wins,” he added.
Turning pilots into powerhouses
While AI adoption is a global trend, Young noted that the APAC region presents unique challenges and opportunities. Diverse languages, cultures, and digital adoption rates require
organisations to adopt iterative, evidence-driven approaches.
Many APAC companies are partnering with experienced solution providers to bridge talent gaps and accelerate operationalisation.
Progress is rarely linear. Companies that embed a mindset of adaptability and continuous learning are the ones setting the pace for successful AI implementation.
- Mitch Young, Senior Vice President, APAC at Zendesk
For businesses hesitant to scale AI due to legacy systems or complexity, he suggests not to wait for perfect conditions. “Begin with a single high-value use case, launch a pilot, and use the results to build momentum and confidence,” Young said.
He recommends choosing purpose-built AI solutions for faster results than building in-house, as these platforms integrate smoothly with existing infrastructure and reduce operational risk.
As organisations continue to integrate AI, Young said governance, compliance, and risk management must be embedded from day one. Responsible and transparent AI practices are essential to build trust with both customers and internal stakeholders.
The journey from AI pilot to enterprise powerhouse is challenging but achievable. For companies ready to embrace AI at scale, Young said, “the message is clear: start with strong foundations, focus on measurable outcomes, and scale iteratively - success will follow."
