The reality of responsible AI appreciation: Eight ground-level perspectives  from industry leaders

Luca Spinelli, Managing Director (Singapore), SAS Institute

1. Trust must underpin implementation
“Value-driven decision-making stems from a foundation of data and analytics. As organizations digitally transform, the ones that embrace analytics in their decision processes will gain a distinct advantage. With AI advancing faster than regulation, and innovation surpassing understanding — trust and responsible innovation must take center stage.

During this AI era, it is crucial to have the right AI building blocks powering trusted, scalable systems where reliability, compliance and results matter most. This is when, regardless of industry and the circumstances of vast amounts of data, decisions are the drivers of workforce and not the data in itself.

Within the agents added to the workflows, businesses need to decide the optimal level of AI autonomy and human involvement based on the complexity of task, risk and business goals.
Prioritize trust, traceability, and measurable outcomes. Especially in financial services, government and other regulated industries: these qualities must be foundational and engineered for auditability, fairness, and scalability.
Let us appreciate the invisible engines of accountability: the systems that support high-stakes decisions across borders, and the teams who design AI not just to move fast, but to stand up under scrutiny.”

Guna Chellappan, General Manager for Singapore, Red Hat

2. Ensure unrelenting security and governance
“From predictive analytics in telecommunications to personalized healthcare, the impact of AI is profound, reshaping conventional practices and setting new standards for the future.  Democratizing AI can make advanced technologies accessible to a broader range of organizations while ensuring it integrates seamlessly into their existing IT environments through open, flexible, and secure solutions. 

As AI signals a strategic shift in how organizations view and integrate it into their business and IT workflows, it needs to be deployed where it makes the most sense: whether on-premises for data sovereignty and control, in public clouds for scalability, or at the edge for real-time execution and insights.

As we witness the rise of Agentic AI, it becomes more apparent that innovation must be balanced with strong security and governance. It is poised to play an increasingly important role in shaping industries giving enterprises the power to automate their entire business processes.”

Parvinder Walia, President of the Asia Pacific Region, ESET

3. Secure AI-driven workflows proactively
“Workers across all industries are expected to use AI tools to boost productivity and increase competitiveness. But more tools mean more attack surfaces. Many workers may blur personal and professional lines, using unsecured AI apps without realizing the implications. These scenarios may create new opportunities for malicious actors.

Cybercriminals are using AI too, and doing so at speed. Staying safe in the AI era means evolving how we apply digital hygiene: verify sources, avoid oversharing, be alert to ‘learning’ features that harvest data, and question unexpected communications designed to provoke fear or urgency.

Most critically, workers need AI-powered cybersecurity that anticipates rather than reacts to threats, and scales with how we live and work today.”

Han-Tiong Law, Regional CTO (ASEAN and Greater China), Rimini Street

4. Adoption must serve strategy, not speed
“AI has reached a point where it is no longer just a technology discussion — it is a business one. The real opportunity is not in simply deploying AI, but in knowing when and where it creates tangible value. For enterprise software environments, that means aligning AI initiatives with operational excellence, agility, and long-term strategy.

Many organizations managing mature, mission critical and complex digital environments are weighing these decisions carefully. Embarking on platform migrations purely to stay ‘current’ often brings significant cost, disruption, and uncertain returns. Conversely, we are seeing growing interest in how AI can optimize what is already in place for better finance and supply chain management, improved visibility across the value chain, and hyper-automation.

However, these initiatives can only succeed with a strong foundation in place. Innovation happens when businesses can modernize at their own pace, keeping what works while exploring what is next. The question is no longer ‘How fast can we adopt AI?’ but rather, ‘How do we adopt and deploy AI in a way that actually serves the business?’

For many, the answer lies in staying agile, informed, and intentional about their business mission and roadmap.”

Jeremy Ung, Chief Technology Officer, Blackline

5. Augmentation over automation

“AI is not just transforming industries, but fundamentally reshaping how we work and create value. We have witnessed firsthand the profound impact of AI, particularly in the finance sector, where precision and trust are paramount. The evolution of AI is enabling us to generate more human-like reasoning and outputs. This is not about replacing human intelligence, but augmenting it.

Our focus must be set on empowering finance professionals by automating manual workflows and freeing them to concentrate on strategic, high-value activities. We have seen significant gains in efficiency, with customers saving countless hours on tasks such as document summarization and transaction matching, allowing them to redirect their expertise to critical analysis and decision-making.

A core principle must be on user control and trust. In finance, accuracy is non-negotiable. AI powers true digital transformation, driving us beyond fragmented data silos. With data as a central asset, AI can fuel intelligent workflows that elevate finance to new heights.”

Simon Ma, Managing Director (Asia), Freshworks

6. Keep AI purposeful and practical

“The most valuable AI today is not necessarily the most complex: it is the kind that is easy to adopt; fits into existing workflows; and delivers tangible results without adding friction. So, focus on making work simpler for people. By automating repetitive tasks and surfacing intelligent recommendations, employees can be liberated to concentrate on high-value interactions that require empathy, creativity, and human judgment.

There is a clear shift in how businesses view AI. It is no longer a futuristic add-on, but a core component of daily operations. This shift has been enabled by advances in generative AI and, more importantly, by a growing emphasis on usability: tools that can self-learn, require minimal tuning, and work across systems is what ultimately delivers sustainable value.

Rather than replacing jobs, this technology can help people do their jobs better. The aim is not to overwhelm users with complex features, but to create tools that are intuitive, fast to implement, and flexible enough to meet the needs of businesses both large and small.

The path forward lies in keeping AI human-centric: practical in design, purposeful in use, and accessible to all.”

Nick Magnuson, Head of AI, Qlik

7. Make AI amplify, not replace us!
“We believe the future of AI goes beyond automation, it is about empowerment. The true strength of AI lies in elevating our collective intelligence to address unmet challenges and opportunities; but for AI to deliver real-world value, it needs more than algorithms. It requires context, quality data, strong governance and a clear sense of purpose.

From fraud detection to customer experience, we are seeing early signs of a digital ecosystem where AI agents can operate with autonomy and deliver measurable outcomes… but AI is here to amplify us — not replace us. With the right information, at the right moment, AI can help us work smarter, faster and more impactfully.”

Suvig Sharma, Regional Head of Asia, Confluent

8. Making headlines does not guarantee responsible outcomes

“Real-time data, flowing continuously from transactions, user activity, and more, is what allows AI to stay contextually aware and responsive. This actionable flow of  information enables organizations to continuously process data into insights as it moves from the edge through the pipeline, turning them into assets that are discoverable and contextualized for smarter decision-making.

However, fueling AI is not enough. To drive transformation, AI needs to be operationalized — moving from data warehouses to live, scalable, reliable environments that drive action.

While AI models may grab today’s headlines, it is data in motion that drives real outcomes for tomorrow’s AI future.”