Generative AI stands apart from previous technological shifts: it’s fundamentally reinventing how businesses operate at breathtaking speed, according to AWS's research.
In Vietnam, for example, decades of digital transformation and industrial automation have helped manufacturing output grow at close to 10 per cent annually. AI is accomplishing similar changes in months. Already, sixty‑one percent of Vietnamese businesses that have adopted AI report revenue growth, averaging 16 per cent, while 58 per cent expect cost savings of around 20 per cent.
Yet despite billions of USD in investment, most organizations still struggle to move from pilot to production to adoption. In fact, according to Gartner research, in 2024, 60 per cent of GenAI POCs were abandoned upon completion.
The difference between AI experimentation and success isn’t about choosing the right large language model; it’s about much more.
“Through our work with partners and customers at various stages of their AI journey, we’ve observed consistent patterns that separate successful implementations from those that stall,” said Ms. Kirsten Gilbertson, APJ Head of SAP GTM & ASEAN Partner Organization Leader, AWS.
Accordingly, Ms. Gilbertson pointed out that organizations that successfully move from pilot to production focus on four interconnected pillars and critically, they recognize that technology is only one of them.
The first is to build data foundation strategically. Simply having data isn’t enough – how organize, govern, and activate it makes all the difference. Leading organizations implement three specific practices: connect all data together, label and organize it so it’s easy to find, and set controls to ensure only the right people (or agents) have access to sensitive data sets.
Heavily regulated industries like financial services and healthcare often have an advantage here their existing governance frameworks can accelerate AI initiatives. “However, for organizations starting from scratch, rather than attempting to unify your entire data warehouse, start by working backwards from a specific use case,” Ms. Gilbertson said.
For instance, a telco operator might begin by connecting network performance data with customer service tickets and billing records for a single purpose: predicting service degradation before customers experience issues. Once that use case delivers value, it’s possible to determine which additional data connections matter most and scale from there.
The second is to build trust through security and verification. “In enterprise AI, trust isn’t just a nice-to-have - it’s the foundation that determines whether investment moves from pilot to production,” said Ms. Gilbertson. “Organizations face a dual challenge: they need AI systems secure enough to protect sensitive data, yet accurate enough to make consequential decisions.”
For example, consider one healthcare provider with over 700,000 members. Their customers call at their most vulnerable moments, needing either medical advice or information about their coverage. The opportunity AI could provide was enormous - supporting customers faster, 24/7, in any language. But a single hallucination in this context could cause real harm, eroding trust that takes years to build.
Leading organizations are moving beyond “trust but verify” to “verify, then trust.” They’re implementing multiple layers of validation: checking inputs for malicious content, verifying outputs against known facts and policies, and continuously monitoring for drift or unexpected behavior. Emerging techniques like automated reasoning—a mathematical approach used for decades in chip design and security verification can now check AI outputs against defined rules, in some cases reducing hallucinations by 99 per cent. This verification-first approach accelerates innovation rather than slowing it down, empowering teams to experiment more boldly when they know guardrails will catch errors before they reach customers.
The third is to transform culture, not just technology. The biggest inhibitor to AI adoption isn’t the technology - it’s change management. Organizations are structured around complex processes, with employees who manage those processes. Getting individuals to step back and reimagine those processes to be end-to-end automated or handled by agents requires intentional cultural transformation.
Success requires both top-down commitment and bottom-up enablement. Leaders must demonstrate visible commitment beyond words, while employees need the space and support to reimagine their own workflows.
Vietnam Technological and Commercial Joint-Stock Bank (Techcombank) is an example of this approach. Instead of simply rolling out generative AI tools, the bank built a full enablement strategy around them. It began with a pilot of 50 developers using Amazon Q Developer, achieving 80 per cent team satisfaction, before rapidly scaling to 600 IT developers with 100 per cent active engagement. This expansion drove a 40 per cent quarter‑over‑quarter increase in team output.
By using this GenAI-powered assistant, the bank also accelerated development of Techcombank Mobile, its flagship digital banking application, and significantly reduced development time. More than 70 per cent of their developers report saving 5–10 hours each week. These efficiency gains are helping Techcombank deliver enhanced digital banking experiences to its 16.5 million customers across Vietnam.
“AI will automate many tasks while simultaneously creating new opportunities and elevating human potential in others,” Ms. Gilbertson noted. “The most successful organizations are transparent about this transformation and invest in reskilling their workforce to thrive in an AI-augmented environment.”
The final is to work with the right experts. “While some organizations have the resources and expertise to build generative AI capabilities entirely in-house, most find that strategic partnerships accelerate their journey from pilot to production,” said Ms. Gilbertson. “The question isn’t whether you can go it alone - it’s whether that’s the fastest path to realizing value.”
The right partners bring three critical advantages: technical expertise to navigate the rapidly evolving AI landscape, domain knowledge to apply AI to specific industry and regulatory environments, and change management experience to drive adoption at scale.
The data bears this out: organizations working with partners possessing deep AI expertise and proven customer success moved their AI projects into production on average 25 per centfaster than those working without specialized partners. In a landscape where speed to value often determines competitive advantage, that acceleration can be decisive.
In Vietnam, AWS Partners offer ready-made solutions that can help accelerate your business in adopting GenAI in areas such as intelligent operations automation (AIOps), document processing and analytics, and AI-powered customer engagement.
“In general, successful organizations approach generative AI as a business transformation, not just a technology deployment,” Ms. Gilbertson affirmed. “The organizations that will thrive aren’t those with the most advanced models, but those that recognize AI success requires equal investment in technology, people, and processes.”
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